function [g, gdata, gprior] = gpla_g(w, gp, x, y, varargin)
%GPLA_G   Evaluate gradient of Laplace approximation's marginal
%         log posterior estimate (GPLA_E)
%
%  Description
%    G = GPLA_G(W, GP, X, Y, OPTIONS) takes a full GP parameter
%    vector W, structure GP a matrix X of input vectors and a
%    matrix Y of target vectors, and evaluates the gradient G of
%    EP's marginal log posterior estimate. Each row of X
%    corresponds to one input vector and each row of Y corresponds
%    to one target vector.
%
%    [G, GDATA, GPRIOR] = GPLA_G(W, GP, X, Y, OPTIONS) also returns
%    the data and prior contributions to the gradient.
%
%    OPTIONS is optional parameter-value pair
%      z - optional observed quantity in triplet (x_i,y_i,z_i)
%          Some likelihoods may use this. For example, in case of
%          Poisson likelihood we have z_i=E_i, that is, expected
%          value for ith case.
%
%  See also
%    GP_SET, GP_G, GPLA_E, GPLA_PRED
%
% Copyright (c) 2007-2010 Jarno Vanhatalo
% Copyright (c) 2010 Aki Vehtari

% This software is distributed under the GNU General Public
% License (version 3 or later); please refer to the file
% License.txt, included with the software, for details.

ip=inputParser;
ip.FunctionName = 'GPLA_G';
ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
ip.addRequired('gp',@isstruct);
ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
ip.parse(w, gp, x, y, varargin{:});
z=ip.Results.z;

gp = gp_unpak(gp, w);       % unpak the parameters
[tmp,tmp,hier]=gp_pak(gp);   % Get the hierarchy of the parameters
ncf = length(gp.cf);
n=size(x,1);

g = [];
gdata = [];
gprior = [];

if isfield(gp, 'savememory') && gp.savememory
    savememory=1;
else
    savememory=0;
end

% First Evaluate the data contribution to the error
switch gp.type
    case 'FULL'
        % ============================================================
        % FULL
        % ============================================================

        if ~isfield(gp.lik, 'nondiagW')
            % Likelihoos with diagonal Hessian

            % Calculate covariance matrix and the site parameters
            K = gp_trcov(gp,x);
            if isfield(gp,'meanf')
                [H,b_m,B_m]=mean_prep(gp,x,[]);
                K=K+H'*B_m*H;
            end

            %[e, edata, eprior, f, L, a, W, p] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
            [e, edata, eprior, p] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
            [f, L, a, W, p] = deal(p.f, p.L, p.a, p.La2, p.p);
            if isnan(e)
                g=NaN; gdata=NaN; gprior=NaN;
                return;
            end

            if W >= 0              % This is the usual case where likelihood is log concave
                % for example, Poisson and probit
                if issparse(K)                               % use sparse matrix routines

                    % permute
                    y = y(p);
                    x = x(p,:);
                    K = K(p,p);
                    if ~isempty(z)
                        z = z(p,:);
                    end

                    sqrtW = sqrt(W);

                    R = sqrtW*spinv(L,1)*sqrtW;
                    sqrtWK = sqrtW*K;
                    C = ldlsolve(L,sqrtWK);
                    C2 = diag(K) - sum(sqrtWK.*C,1)';
                    s2 = 0.5*C2.*gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
                else                                         % evaluate with full matrices
                    sqrtW = diag(sqrt(W));
                    c = L\sqrtW;
                    R = c'*c;
                    C2 = diag(K) - sum((c*K).^2,1)' ;
                    s2 = 0.5*C2.*gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
                end
            else                         % We might end up here if the likelihood is not log-concave
                % For example Student-t likelihood.
                C = L;
                V = L*diag(W);
                R = diag(W) - V'*V;
                C2 = sum(C.^2,1)';
                s2 = 0.5*C2.*gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
            end

            % =================================================================
            % Gradient with respect to covariance function parameters
            if ~isempty(strfind(gp.infer_params, 'covariance'))
                % Evaluate the gradients from covariance functions
                i1=0;

                if isfield(gp,'deriv') && gp.deriv  % derivative observations in use
                    % initialize few variables related to derivative observations
                    [n,m] = size(x);
                    ind_Ddim = x(:,gp.deriv);
                    ind_Ddim_derivs = ind_Ddim(ind_Ddim>0);
                    uDdim = unique(ind_Ddim_derivs);
                    x1 = x(:,setdiff(1:m,gp.deriv));   % Take only the non-index columns
                end

                for i=1:ncf
                    %           if ~isempty(gprior)
                    %             i1 = length(gprior);
                    %           end

                    gpcf = gp.cf{i};

                    if ~(isfield(gp,'deriv') && gp.deriv)
                        if savememory
                            % If savememory option is used, just get the number of
                            % hyperparameters and calculate gradients later
                            np=gpcf.fh.cfg(gpcf,[],[],[],0);
                        else
                            DKffc = gpcf.fh.cfg(gpcf, x);
                            np=length(DKffc);
                        end
                        gprior_cf = -gpcf.fh.lpg(gpcf);
                    else
                        n=size(x);
                        gprior_cf = -gpcf.fh.lpg(gpcf);
                        if (~isfield(gpcf, 'selectedVariables') || any(ismember(gpcf.selectedVariables,uDdim)))
                            % !!! Note. The check whether to calculate derivative
                            % matrices for a covariance function could/should be made
                            % nicer
                            DKffa = gpcf.fh.cfg(gpcf, x1(ind_Ddim==0,:));
                            np=length(DKffa);
                            for inp=1:np
                                % the block of covariance matrix
                                DKffc{inp}(ind_Ddim==0,ind_Ddim==0) = DKffa{inp};
                            end
                            for u1 = 1:length(uDdim)
                                % the blocks on the left side, below Kff
                                Kdf = gpcf.fh.cfdg(gpcf, x1(ind_Ddim==uDdim(u1),:), x1(ind_Ddim==0,:), uDdim(u1));
                                D = gpcf.fh.cfdg2(gpcf, x1(ind_Ddim==uDdim(u1),:), x1(ind_Ddim==uDdim(u1),:), uDdim(u1), uDdim(u1));
                                for inp=1:np
                                    DKffc{inp}(ind_Ddim==uDdim(u1),ind_Ddim==0) = Kdf{inp};
                                    DKffc{inp}(ind_Ddim==0,ind_Ddim==uDdim(u1)) = Kdf{inp}';
                                    DKffc{inp}(ind_Ddim==uDdim(u1),ind_Ddim==uDdim(u1)) = D{inp};
                                end

                                uDdim2 = uDdim(u1+1:end);
                                for u2=1:length(uDdim2)
                                    Kdf2 = gpcf.fh.cfdg2(gpcf, x1(ind_Ddim==uDdim(u1),:) ,x1(ind_Ddim==uDdim2(u2),:), uDdim(u1), uDdim2(u2));
                                    for inp=1:np
                                        DKffc{inp}(ind_Ddim==uDdim(u1),ind_Ddim==uDdim2(u2)) = Kdf2{inp};
                                        DKffc{inp}(ind_Ddim==uDdim2(u2),ind_Ddim==uDdim(u1)) = Kdf2{inp}';
                                    end
                                end

                            end

                            %DKffc{inp} = Ktemp;

                        else
                            % A covariance function that does not use any
                            % of the input dimensions with respect to which
                            % derivatives are observed
                            warning('not tested yet')
                            DKffa = gpcf.fh.cfg(gpcf, x(ind_Ddim==0,:));
                            np=length(DKffa);
                            for inp=1:np
                                Ktemp = sparse(n,n);
                                Ktemp(ind_Ddim==0,ind_Ddim==0) = DKffa{inp};
                                DKffc{inp} = Ktemp;
                            end
                        end

                    end

                    g1 = gp.lik.fh.llg(gp.lik, y, f, 'latent', z);
                    for i2 = 1:np
                        if savememory
                            DKff=gpcf.fh.cfg(gpcf,x,[],[],i2);
                        else
                            DKff=DKffc{i2};
                        end
                        i1 = i1+1;
                        if ~isfield(gp,'meanf')
                            s1 = 0.5 * a'*DKff*a - 0.5*sum(sum(R.*DKff));
                        else
                            s1 = 0.5 * (a-K\(H'*b_m))'*DKff*(a-K\(H'*b_m)) - 0.5*sum(sum(R.*DKff));
                        end
                        b = DKff * g1;
                        if issparse(K)
                            s3 = b - K*(sqrtW*ldlsolve(L,sqrtW*b));
                        else
                            s3 = b - K*(R*b);
                            %s3 = (1./W).*(R*b);
                        end
                        gdata(i1) = -(s1 + s2'*s3);
                        %             gprior(i1) = gprior_cf(i2);
                    end

                    gprior=[gprior gprior_cf];
                    %           % Set the gradients of hyperparameter
                    %           if length(gprior_cf) > np
                    %             for i2=np+1:length(gprior_cf)
                    %               i1 = i1+1;
                    %               gdata(i1) = 0;
                    %               gprior(i1) = gprior_cf(i2);
                    %             end
                    %           end
                end
                %         if length(gprior) > length(gdata)
                %           tmp=gdata;
                %           gdata=zeros(size(gprior));
                %           gdata(hier(1:length(gprior))==1) = tmp;
                %           i1 = length(gdata);
                %         end

            end

            % =================================================================
            % Gradient with respect to likelihood function parameters
            if ~isempty(strfind(gp.infer_params, 'likelihood')) ...
                    && ~isempty(gp.lik.fh.pak(gp.lik))

                gdata_lik = 0;
                lik = gp.lik;

                g_logPrior = -lik.fh.lpg(lik);
                if ~isempty(g_logPrior)

                    DW_sigma = lik.fh.llg3(lik, y, f, 'latent2+param', z);
                    DL_sigma = lik.fh.llg(lik, y, f, 'param', z);
                    b = K * lik.fh.llg2(lik, y, f, 'latent+param', z);
                    s3 = b - K*(R*b);
                    nl= size(DW_sigma,2);

                    gdata_lik = - DL_sigma - 0.5.*sum(repmat(C2,1,nl).*DW_sigma) - s2'*s3;

                    % set the gradients into vectors that will be returned
                    gdata = [gdata gdata_lik];
                    gprior = [gprior g_logPrior];
                    i1 = length(g_logPrior);
                    i2 = length(gdata_lik);
                    if i1  > i2
                        gdata = [gdata zeros(1,i1-i2)];
                    end
                end
            end

            %       g = gdata + gprior;

        else
            % Likelihoods with non-diagonal Hessian

            [n,nout]=size(y);
            if isfield(gp, 'comp_cf')  % own covariance for each ouput component
                multicf = true;
                if length(gp.comp_cf) ~= nout && nout > 1
                    error('GPLA_ND_G: the number of component vectors in gp.comp_cf must be the same as number of outputs.')
                end
            else
                multicf = false;
            end

            % Get help parameters
            %[e, edata, eprior, f, L, a, E, M] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
            [e, edata, eprior, p] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
            [f, L, a, E, M] = deal(p.f, p.L, p.a, p.La2, p.p);
            if isnan(e)
                return
            end

            switch gp.lik.type

                case {'LGP', 'LGPC'}

                    nl=n;
                    nlp=length(nl); % number of latent processes

                    if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1
                        % Use Kronecker product kron(Ka,Kb) instead of K
                        gptmp=gp; gptmp.jitterSigma2=0;
                        ls=gptmp.cf{1}.lengthScale;
                        if numel(ls)>1
                            gptmp.cf{1}.lengthScale=ls(1);
                        end
                        Ka = gp_trcov(gptmp, unique(x(:,1)));
                        % fix the magnitude sigma to 1 for Kb matrix
                        wtmp=gp_pak(gptmp); wtmp(1)=0; gptmp=gp_unpak(gptmp,wtmp);
                        if numel(ls)>1
                            gptmp.cf{1}.lengthScale=ls(2);
                        end
                        Kb = gp_trcov(gptmp, unique(x(:,2)));
                        clear gptmp
                        n1=size(Ka,1);
                        n2=size(Kb,1);
                    else
                        K = gp_trcov(gp,x);
                    end

                    if isfield(gp,'meanf')
                        [H,b_m,B_m]=mean_prep(gp,x,[]);
                        Hb_m=H'*b_m;
                        if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1
                            % only zero mean function implemented for Kronecker
                            % approximation
                            iKHb_m=zeros(n,1);
                        else
                            K=K+H'*B_m*H;
                            iKHb_m=K\Hb_m;
                        end
                    end

                    g2 = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z);
                    ny=sum(y);

                    g3=gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);

                    if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1

                        [Va,Da]=eig(Ka); [Vb,Db]=eig(Kb);
                        % eigenvalues of K matrix
                        Dtmp=kron(diag(Da),diag(Db));
                        [sDtmp,istmp]=sort(Dtmp,'descend');

                        % Form the low-rank approximation. Exclude eigenvalues
                        % smaller than gp.latent_opt.eig_tol or take
                        % gp.latent_opt.eig_prct*n eigenvalues at most.
                        nlr=min([sum(sDtmp>gp.latent_opt.eig_tol) round(gp.latent_opt.eig_prct*n)]);
                        sDtmp=sDtmp+gp.jitterSigma2;

                        itmp1=meshgrid(1:n1,1:n2);
                        itmp2=meshgrid(1:n2,1:n1)';
                        ind=[itmp1(:) itmp2(:)];

                        % included eigenvalues
                        Dlr=sDtmp(1:nlr);
                        % included eigenvectors
                        Vlr=zeros(n,nlr);
                        for i1=1:nlr
                            Vlr(:,i1)=kron(Va(:,ind(istmp(i1),1)),Vb(:,ind(istmp(i1),2)));
                        end

                        Lb=gp_trvar(gp,x)-sum(bsxfun(@times,Vlr.*Vlr,Dlr'),2);

                        if isfield(gp,'meanf')
                            Dt=[Dlr; diag(B_m)];
                            Vt=[Vlr H'];
                        else
                            Dt=Dlr;
                            Vt=Vlr;
                        end

                        Lbt=ny*(g2)+1./Lb;

                        St=[diag(1./Dt)+Vt'*bsxfun(@times,1./Lb,Vt) zeros(size(Dt,1),1); ...
                            zeros(1,size(Dt,1)) 1];
                        Pt=[bsxfun(@times,1./Lb,Vt) sqrt(ny)*g2];
                        Ptt=bsxfun(@times,1./sqrt(Lbt),Pt);

                        StL=chol(St-Ptt'*Ptt,'lower');
                        iStL=StL\(bsxfun(@times,Pt',1./Lbt'));

                        dA=(1./Lbt+sum(iStL.*iStL)');
                        iStLg3=iStL*g3;
                        const1=( 0.5*ny*(sum( dA.*g3))-ny*(g3'*(g3./Lbt)+iStLg3'*iStLg3) );
                        const2=(g3./Lbt)+iStL'*iStLg3;

                        s2=const1.*g3 - 0.5*ny*dA.*g3 + ny*const2.*g3;

                    else
                        if strcmpi(gp.lik.type,'LGPC')
                            n1=gp.lik.gridn(1); n2=gp.lik.gridn(2);
                            ny2=sum(reshape(y,fliplr(gp.lik.gridn)));
                            g2sq=sqrt(g2);

                            R=zeros(n);
                            RR=zeros(n,n2);
                            for k1=1:n1
                                R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)=sqrt(ny2(k1))*(diag(g2sq((1:n2)+(k1-1)*n2))-g2((1:n2)+(k1-1)*n2)*g2sq((1:n2)+(k1-1)*n2)');
                                RR((1:n2)+(k1-1)*n2,:)=R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)*R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)';
                            end
                            KW=K;
                            for k1=1:n1
                                KW(:,(1:n2)+(k1-1)*n2)=KW(:,(1:n2)+(k1-1)*n2)*RR((1:n2)+(k1-1)*n2,:);
                            end
                            %KW=K*(R*R');

                            KW(1:(n+1):end)=KW(1:(n+1):end)+1;
                            iKW=KW\eye(n);
                            A=iKW*K;

                            s2=zeros(n,1);
                            for k1=1:n1
                                if ny2(k1)~=0
                                    g3tmp=g3((1:n2)+(k1-1)*n2);
                                    Atmp=A((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2);
                                    for ind2=1:n2
                                        g3dtmp=-g3tmp*g3tmp(ind2);
                                        g3dtmp(ind2)=g3dtmp(ind2)+g3tmp(ind2);
                                        s2( ind2+(k1-1)*n2 ) = -ny2(k1)*0.5*sum(diag(Atmp).*g3dtmp) ...
                                            + ny2(k1)*sum(sum(Atmp.*(bsxfun(@times,g3tmp,g3dtmp'))));
                                    end
                                end
                            end

                        else
                            KW=-(K*(sqrt(ny)*g2))*(sqrt(ny)*g2)'- bsxfun(@times, K, (-ny*g2)');

                            KW(1:(n+1):end)=KW(1:(n+1):end)+1;
                            iKW=KW\eye(n);
                            A=iKW*K;

                            const1=( 0.5*ny*(sum(A(1:(n+1):end).*g3'))-ny*sum(sum(A.*(g3*g3'))) );
                            const2=sum(bsxfun(@times,A,g3));
                            s2=const1.*g3 - 0.5*ny*diag(A).*g3 + ny*const2'.*g3;
                        end
                    end

                    % =================================================================
                    % Gradient with respect to covariance function parameters
                    if ~isempty(strfind(gp.infer_params, 'covariance'))
                        % Evaluate the gradients from covariance functions
                        for i=1:ncf

                            i1=0;
                            if ~isempty(gprior)
                                i1 = length(gprior);
                            end

                            gpcf = gp.cf{i};
                            if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1

                                gptmp=gp; gptmp.jitterSigma2=0;
                                ls=gptmp.cf{1}.lengthScale;
                                if numel(ls)>1
                                    gptmp.cf{1}.lengthScale=ls(1);
                                end
                                DKa = gpcf.fh.cfg(gptmp.cf{1}, unique(x(:,1)));
                                wtmp=gp_pak(gptmp); wtmp(1)=0; gptmp=gp_unpak(gptmp,wtmp);
                                if numel(ls)>1
                                    gptmp.cf{1}.lengthScale=ls(2);
                                end
                                DKb = gpcf.fh.cfg(gptmp.cf{1}, unique(x(:,2)));
                                clear gptmp

                                for j1=1:length(DKa)
                                    [DVa{j1},DDa{j1}]=eig(DKa{j1});
                                end
                                for j1=1:length(DKb)
                                    [DVb{j1},DDb{j1}]=eig(DKb{j1});
                                end

                                % low-rank approximation of the derivative matrix w.r.t.
                                % magnitude hyperparameter, low-rank + diagonal correction
                                Datmp=kron(diag(DDa{1}),diag(Db));
                                [sDatmp,isatmp]=sort(Datmp,'descend');
                                nlr=min([sum(sDtmp>gp.latent_opt.eig_tol) round(gp.latent_opt.eig_prct*n)]);
                                Dmslr=sDatmp(1:nlr);
                                Vmslr=zeros(n,nlr);
                                for j1=1:nlr
                                    Vmslr(:,j1)=kron(DVa{1}(:,ind(isatmp(j1),1)),Vb(:,ind(isatmp(j1),2)));
                                end
                                % diagonal correction
                                dc=gpcf.fh.cfg(gpcf, x(1,:));
                                Lms=dc{1}*ones(n,1)-sum(bsxfun(@times,Vmslr.^2,Dmslr'),2);

                                % low-rank approximation of the derivative matrix w.r.t.
                                % lengthscale hyperparameter, low-rank1 + lowr-rank2 + diagonal correction
                                %
                                % first low-rank part
                                Datmp=kron(diag(DDa{2}),diag(Db));
                                [sDatmp,isatmp]=sort(abs(Datmp),'descend');
                                nlr=min([sum(sDtmp>gp.latent_opt.eig_tol) round(gp.latent_opt.eig_prct*n)]);
                                sDatmp=Datmp(isatmp);
                                Dlslr1=sDatmp(1:nlr);
                                Vlslr1=zeros(n,nlr);
                                for j1=1:nlr
                                    Vlslr1(:,j1)=kron(DVa{2}(:,ind(isatmp(j1),1)),Vb(:,ind(isatmp(j1),2)));
                                end
                                % second low-rank part
                                Dbtmp=kron(diag(Da),diag(DDb{2}));
                                [sDbtmp,isbtmp]=sort(abs(Dbtmp),'descend');
                                nlr=min([sum(sDtmp>gp.latent_opt.eig_tol) round(gp.latent_opt.eig_prct*n)]);
                                sDbtmp=Dbtmp(isbtmp);
                                Dlslr2=sDbtmp(1:nlr);
                                Vlslr2=zeros(n,nlr);
                                for j1=1:nlr
                                    Vlslr2(:,j1)=kron(Va(:,ind(isbtmp(j1),1)),DVb{2}(:,ind(isbtmp(j1),2)));
                                end
                                % diagonal correction
                                Lls=dc{2}*ones(n,1)-sum(bsxfun(@times,Vlslr1.^2,Dlslr1'),2)-sum(bsxfun(@times,Vlslr2.^2,Dlslr2'),2);

                            else
                                if savememory
                                    % If savememory option is used, just get the number of
                                    % hyperparameters and calculate gradients later
                                    np=gpcf.fh.cfg(gpcf,[],[],[],0);
                                else
                                    DKffc = gpcf.fh.cfg(gpcf, x);
                                    np=length(DKffc);
                                end
                            end

                            gprior_cf = -gpcf.fh.lpg(gpcf);
                            g1 = gp.lik.fh.llg(gp.lik, y, f, 'latent', z);

                            if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1

                                for i2 = 1:length(DDa)
                                    i1 = i1+1;

                                    if ~isfield(gp,'meanf')
                                        if i2==1
                                            % derivative wrt magnitude hyperparameter
                                            Vmsa = Vmslr'*a;
                                            s1a = 0.5*(Vmsa'*(bsxfun(@times,Dmslr,Vmsa)) + a'*(Lms.*a));
                                        elseif i2==2
                                            % derivative wrt lengthscale hyperparameter
                                            Vls1 = Vlslr1'*a;
                                            Vls2 = Vlslr2'*a;
                                            s1a = 0.5*( Vls1'*bsxfun(@times,Dlslr1,Vls1) + Vls2'*bsxfun(@times,Dlslr2,Vls2) + a'*(Lls.*a));
                                        end
                                    else
                                        if i2==1
                                            % derivative wrt magnitude hyperparameter
                                            Vmsa = Vmslr'*(a-iKHb_m);
                                            s1a = 0.5*(Vmsa'*(bsxfun(@times,Dmslr,Vmsa)) + (a-iKHb_m)'*(Lms.*(a-iKHb_m)));
                                        elseif i2==2
                                            % derivative wrt lengthscale hyperparameter
                                            Vls1 = Vlslr1'*(a-iKHb_m);
                                            Vls2 = Vlslr2'*(a-iKHb_m);
                                            s1a = 0.5*( Vls1'*bsxfun(@times,Dlslr1,Vls1) + Vls2'*bsxfun(@times,Dlslr2,Vls2) + (a-iKHb_m)'*(Lls.*(a-iKHb_m)));
                                        end
                                    end

                                    % DKg2=DKff{i2}*g2;
                                    if i2==1
                                        DKg2 = Vmslr*bsxfun(@times,Dmslr,Vmslr'*g2) + Lms.*g2;
                                    elseif i2==2
                                        DKg2 =  Vlslr1*bsxfun(@times,Dlslr1,Vlslr1'*g2) + Vlslr2*bsxfun(@times,Dlslr2,Vlslr2'*g2) + Lls.*g2;
                                    end

                                    WDKg2=ny*(g2.*DKg2-(g2*(g2'*DKg2)));
                                    s1b = -0.5*(ny)*( ( - (DKg2-((WDKg2./Lbt)+(iStL'*(iStL*WDKg2)))))'*(g2) );

                                    if i2==1 % magnitude hyperparameter

                                        % low-rank
                                        WDVa=ny*( bsxfun(@times,g2,Vmslr)-g2*(g2'*Vmslr) );
                                        stmp=Vmslr-(bsxfun(@times,(1./Lbt),WDVa)+(iStL'*(iStL*WDVa)));
                                        s1clr = 0.5*sum( (sum(bsxfun(@times,stmp,Dmslr').*Vmslr,2))' .*(-ny*g2)' );

                                        % diagonal correction
                                        s1cdtmp = Lms - ( ny*( (g2.*Lms)./Lbt  - (g2./Lbt).*(g2'.*Lms')' ) + ...
                                            sum(iStL.* (ny*( bsxfun(@times,iStL,(g2.*Lms)') - (iStL*g2)*(g2'.*Lms') )) )' );
                                        s1cd=0.5*sum( s1cdtmp' .*(-ny*g2)' );
                                        s1c=s1clr+s1cd;
                                        DKg = Vmslr*bsxfun(@times,Dmslr,Vmslr'*g1) + Lms.*g1;

                                    elseif i2==2 % lengthscale hyperparameter

                                        % low-rank 1
                                        WDVa=ny*( bsxfun(@times,g2,Vlslr1)-g2*(g2'*Vlslr1) );
                                        stmp=Vlslr1-(bsxfun(@times,(1./Lbt),WDVa)+(iStL'*(iStL*WDVa)));
                                        s1clr1 = 0.5*sum( (sum(bsxfun(@times,stmp,Dlslr1').*Vlslr1,2))' .*(-ny*g2)' );

                                        % low-rank 2
                                        WDVb=ny*( bsxfun(@times,g2,Vlslr2)-g2*(g2'*Vlslr2) );
                                        stmp=Vlslr2-(bsxfun(@times,(1./Lbt),WDVb)+(iStL'*(iStL*WDVb)));
                                        s1clr2 = 0.5*sum( (sum(bsxfun(@times,stmp,Dlslr2').*Vlslr2,2))' .*(-ny*g2)' );

                                        % diagonal correction
                                        s1cdtmp = Lls - ( ny*( (g2.*Lls)./Lbt  - (g2./Lbt).*(g2'.*Lls')' ) + ...
                                            sum(iStL.* (ny*( bsxfun(@times,iStL,(g2.*Lls)') - (iStL*g2)*(g2'.*Lls') )) )' );
                                        s1cd=0.5*sum( s1cdtmp' .*(-ny*g2)' );

                                        s1c=s1clr1+s1clr2+s1cd;

                                        DKg = Vlslr1*bsxfun(@times,Dlslr1,Vlslr1'*g1) + Vlslr2*bsxfun(@times,Dlslr2,Vlslr2'*g1) + Lls.*g1;

                                    end

                                    s1=s1a+s1b+s1c;

                                    %DKg=DKff{i2}*g1;
                                    WDKg=ny*(g2.*DKg-(g2*(g2'*DKg)));
                                    s3=DKg-((WDKg./Lbt)+(iStL'*(iStL*WDKg)));

                                    gdata(i1) = -(s1 + s2'*s3);
                                    gprior(i1) = gprior_cf(i2);
                                end

                            else

                                for i2 = 1:np
                                    i1 = i1+1;
                                    if savememory
                                        DKff=gpcf.fh.cfg(gpcf,x,[],[],i2);
                                    else
                                        DKff=DKffc{i2};
                                    end
                                    if ~isfield(gp,'meanf')
                                        if strcmpi(gp.lik.type,'LGPC')
                                            %s1 = 0.5 * a'*DKff{i2}*a - 0.5*((-iKW*(DKff{i2}*(sqrt(ny)*g2)))'*(sqrt(ny)*g2)) + 0.5*sum(sum(iKW'.*DKff{i2}).*(-ny*g2)');
                                            s1 = 0.5 * a'*DKff*a - 0.5*sum(diag( R*(R'*(iKW*DKff))));
                                        else
                                            s1 = 0.5 * a'*DKff*a - 0.5*((-iKW*(DKff*(sqrt(ny)*g2)))'*(sqrt(ny)*g2)) + 0.5*sum(sum(iKW'.*DKff).*(-ny*g2)');
                                        end
                                    else
                                        if strcmpi(gp.lik.type,'LGPC')
                                            s1 = 0.5 * (a-iKHb_m)'*DKff*(a-iKHb_m) - 0.5*sum(diag( R*(R'*(iKW*DKff))));
                                        else
                                            s1 = 0.5 * (a-iKHb_m)'*DKff*(a-iKHb_m) - 0.5*((-iKW*(DKff*(sqrt(ny)*g2)))'*(sqrt(ny)*g2)) + 0.5*sum(sum(iKW'.*DKff).*(-ny*g2)');
                                        end
                                    end
                                    %b = DKff{i2} * g1;
                                    if issparse(K)
                                        s3 = b - K*(sqrtW*ldlsolve(L,sqrtW*b));
                                    else
                                        s3=iKW*(DKff*g1);
                                    end

                                    gdata(i1) = -(s1 + s2'*s3);
                                    gprior(i1) = gprior_cf(i2);
                                end
                            end
                            %               gprior = [gprior gprior_cf];
                            if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1
                                % Set the gradients of hyperparameter
                                if length(gprior_cf) > length(DKa)
                                    for i2=length(DKa)+1:length(gprior_cf)
                                        i1 = i1+1;
                                        gdata(i1) = 0;
                                        gprior(i1) = gprior_cf(i2);
                                    end
                                end
                            else
                                % Set the gradients of hyperparameter
                                if length(gprior_cf) > np
                                    for i2=np+1:length(gprior_cf)
                                        i1 = i1+1;
                                        gdata(i1) = 0;
                                        gprior(i1) = gprior_cf(i2);
                                    end
                                end
                            end
                        end
                    end

                    % =================================================================
                    % Gradient with respect to likelihood function parameters
                    if ~isempty(strfind(gp.infer_params, 'likelihood')) ...
                            && ~isempty(gp.lik.fh.pak(gp.lik))

                        gdata_lik = 0;
                        lik = gp.lik;

                        g_logPrior = -lik.fh.lpg(lik);
                        if ~isempty(g_logPrior)

                            DW_sigma = lik.fh.llg3(lik, y, f, 'latent2+param', z);
                            DL_sigma = lik.fh.llg(lik, y, f, 'param', z);
                            b = K * lik.fh.llg2(lik, y, f, 'latent+param', z);
                            s3 = iKW*b;

                            gdata_lik = - DL_sigma - 0.5.*sum(sum((A.*DW_sigma))) - s2'*s3;

                            % set the gradients into vectors that will be returned
                            gdata = [gdata gdata_lik];
                            gprior = [gprior g_logPrior];
                            i1 = length(g_logPrior);
                            i2 = length(gdata_lik);
                            if i1  > i2
                                gdata = [gdata zeros(1,i1-i2)];
                            end
                        end
                    end

                    % g = gdata + gprior;

                case {'Softmax', 'Multinom'}

                    K = zeros(n,n,nout);
                    if multicf
                        for i1=1:nout
                            K(:,:,i1) = gp_trcov(gp, x, gp.comp_cf{i1});
                        end
                    else
                        Ktmp=gp_trcov(gp, x);
                        for i1=1:nout
                            K(:,:,i1) = Ktmp;
                        end
                    end

                    % softmax
                    f2=reshape(f,n,nout);

                    llg = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z);
                    [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z);
                    % W = -diag(pi2_vec) + pi2_mat*pi2_mat', where
                    % W_ij = -d^2(log(p(y|f)))/(df_i)(df_j)
                    R = repmat(1./pi2_vec,1,n).*pi2_mat;
                    RE = zeros(n,n*nout);
                    for i1=1:nout
                        RE(:,(1:n)+(i1-1)*n) = R((1:n)+(i1-1)*n,:)'*E(:,:,i1);
                    end

                    inv_iWK=zeros(n,n,nout);

                    % Matrices for computing the derivative of determinant term w.r.t. f
                    A=zeros(nout, nout, n);
                    Minv=M\(M'\eye(n));
                    Minv=(Minv+Minv')./2;
                    for cc1=1:nout
                        EMinv=RE(:,(1:n)+(cc1-1)*n)'*Minv;
                        KEMinv=K(:,:,cc1)*EMinv;
                        for cc2=1:nout
                            if cc2>=cc1
                                if cc1==cc2
                                    EMtmp = - EMinv*RE(:,(1:n)+(cc2-1)*n);
                                    EMtmp = EMtmp + E(:,:,cc1);
                                    inv_iWK(:,:,cc1) = EMtmp;
                                    A(cc1,cc1,:) = diag(K(:,:,cc1))-sum((K(:,:,cc1)*EMtmp).*K(:,:,cc1),2);
                                else
                                    EMtmp = - KEMinv*RE(:,(1:n)+(cc2-1)*n);
                                    A(cc1,cc2,:) = -sum(EMtmp.*K(:,:,cc2),2);
                                    A(cc2,cc1,:) = -sum(EMtmp.*K(:,:,cc2),2);
                                end
                            end
                        end
                    end

                    % Derivative of determinant term w.r.t. f
                    s2=zeros(n*nout,1);
                    dw_mat = gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
                    for cc3=1:nout
                        for ii1=1:n
                            % s2(i)=-0.5*trace(inv(inv(K)+W)*dW/df_i)
                            s2(ii1+(cc3-1)*n) = -0.5*trace(A(:,:,ii1)*dw_mat(:,:,cc3,ii1));
                        end
                    end

                    % Loop over the covariance functions
                    for i=1:ncf
                        DKllg=zeros(size(a));
                        EDKllg=zeros(size(a));
                        DKffba=zeros(n*nout,1);

                        % check in which components the covariance function is present
                        do = false(nout,1);
                        if multicf
                            for z1=1:nout
                                if any(gp.comp_cf{z1}==i)
                                    do(z1) = true;
                                end
                            end
                        else
                            do = true(nout,1);
                        end

                        i1=0;
                        if ~isempty(gprior)
                            i1 = length(gprior);
                        end

                        % Gradients from covariance functions
                        gpcf = gp.cf{i};
                        % DKff{j} = dK(x,x)/dtheta_j
                        if savememory
                            % If savememory option is used, just return number of
                            % hyperparameters and calculate gradients later
                            np=gpcf.fh.cfg(gpcf,[],[],[],0);
                        else
                            DKff = gpcf.fh.cfg(gpcf, x);
                            np=length(DKff);
                        end
                        gprior_cf = -gpcf.fh.lpg(gpcf);

                        for i2 = 1:np
                            i1 = i1+1;
                            if savememory
                                DKffb=gpcf.fh.cfg(gpcf,x,[],[],i2);
                            else
                                DKffb=DKff{i2};
                            end

                            % Derivative of explicit terms
                            trace_sum_tmp=0;
                            for z1=1:nout
                                if do(z1)
                                    DKffba((1:n)+(z1-1)*n)=DKffb*a((1:n)+(z1-1)*n);
                                    trace_sum_tmp = trace_sum_tmp + sum(sum( inv_iWK(:,:,z1) .* DKffb ));
                                end
                            end
                            % s1=0.5*f'*inv(K)*dK/dtheta_j*inv(K)*f - 0.5*trace(inv(inv(W)+K)*dK/dtheta_j)
                            s1 = 0.5 * a'*DKffba - 0.5.*trace_sum_tmp;

                            % Derivative of f w.r.t. theta
                            for z1=1:nout
                                if do(z1)
                                    DKllg((1:n)+(z1-1)*n)=DKffb*llg((1:n)+(z1-1)*n);
                                    EDKllg((1:n)+(z1-1)*n)=E(:,:,z1)*DKllg((1:n)+(z1-1)*n);
                                end
                            end
                            s3 = EDKllg - RE'*(M\(M'\(RE*DKllg)));
                            for z1=1:nout
                                s3((1:n)+(z1-1)*n)=K(:,:,z1)*s3((1:n)+(z1-1)*n);
                            end
                            % s3=inv(I+KW)*dK/dtheta_j*d(log(p(y|f)))/df
                            s3 = DKllg - s3;

                            gdata(i1) = -(s1 + s2'*s3);
                            %               gprior(i1) = gprior_cf(i2);

                        end

                        gprior = [gprior gprior_cf];
                        %             % Set the gradients of hyper-hyperparameter
                        %             if length(gprior_cf) > np
                        %               for i2=np+1:length(gprior_cf)
                        %                 i1 = i1+1;
                        %                 gdata(i1) = 0;
                        %                 gprior(i1) = gprior_cf(i2);
                        %               end
                        %             end
                    end

                    %         % =================================================================
                    %         % Gradient with respect to likelihood function parameters
                    %         if ~isempty(strfind(gp.infer_params, 'likelihood')) ...
                    %             && ~isempty(gp.lik.fh.pak(gp.lik))
                    %
                    %             gdata_likelih = 0;
                    %             lik = gp.lik;
                    %
                    %             g_logPrior = feval(lik.fh.gprior, lik);
                    %             if ~isempty(g_logPrior)
                    %
                    %                 DW_sigma = feval(lik.fh.llg3, lik, y, f, 'latent2+hyper', z);
                    %                 DL_sigma = feval(lik.fh.llg, lik, y, f, 'hyper', z);
                    %                 b = K * feval(lik.fh.llg2, lik, y, f, 'latent+hyper', z);
                    %                 s3 = b - K*(R*b);
                    %                 nl= size(DW_sigma,2);
                    %
                    %                 gdata_lik = - DL_sigma - 0.5.*sum(repmat(C2,1,nl).*DW_sigma) - s2'*s3;
                    %
                    %                 % set the gradients into vectors that will be returned
                    %                 gdata = [gdata gdata_lik];
                    %                 gprior = [gprior g_logPrior];
                    %                 i1 = length(g_logPrior);
                    %                 i2 = length(gdata_lik);
                    %                 if i1  > i2
                    %                     gdata = [gdata zeros(1,i1-i2)];
                    %                 end
                    %             end
                    %         end

                otherwise

                    if isfield(gp.lik,'xtime')
                        xtime=gp.lik.xtime;
                        if isfield(gp.lik, 'stratificationVariables')
                            ebc_ind=gp.lik.stratificationVariables;
                            ux = unique(x(:,ebc_ind), 'rows');
                            gp.lik.n_u = size(ux,1);
                            for i1=1:size(ux,1)
                                gp.lik.stratind{i1}=(x(:,ebc_ind)==ux(i1));
                            end
                            [xtime1, xtime2] = meshgrid(ux, xtime);
                            xtime = [xtime2(:) xtime1(:)];
                            if isfield(gp.lik, 'removeStratificationVariables') && gp.lik.removeStratificationVariables
                                x(:,ebc_ind)=[];
                            end
                        end
                        ntime = size(xtime,1);
                        nl=[ntime n];

                        % Second derivatives of log-likelihood
                        [llg2vec, llg2mat] = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z);
                        % W = [diag(Wdiag(1:ntime)) Wmat; Wmat' diag(Wdiag(ntime+1:end))]
                        Wdiag=-llg2vec; Wmat=-llg2mat;
                    else
                        nl=repmat(n,1,length(gp.comp_cf));

                        % Second derivatives of log-likelihood
                        Wvec=-gp.lik.fh.llg2(gp.lik, y, f, 'latent',z);
                        % W = [diag(Wvec(1:n,1)) diag(Wvec(1:n,2)); diag(Wvec(n+1:end,1)) diag(Wvec(n+1:end,2))]
                        Wdiag=[Wvec(1:nl(1),1); Wvec(nl(1)+(1:nl(2)),2)];
                    end
                    nlp=length(nl); % Number of latent processes

                    % K is block-diagonal covariance matrix where blocks correspond to
                    % latent processes
                    K = zeros(sum(nl));
                    if isfield(gp.lik,'xtime')
                        K(1:ntime,1:ntime)=gp_trcov(gp, xtime, gp.comp_cf{1});
                        K((1:n)+ntime,(1:n)+ntime) = gp_trcov(gp, x, gp.comp_cf{2});
                    else
                        for i1=1:nlp
                            K((1:n)+(i1-1)*n,(1:n)+(i1-1)*n) = gp_trcov(gp, x, gp.comp_cf{i1});
                        end
                    end

                    if isfield(gp,'meanf')
                        [H,b_m,B_m]=mean_prep(gp,x,[]);
                        Hb_m=H'*b_m;
                        K=K+H'*B_m*H;
                        iKHb_m=K\Hb_m;
                    end

                    KW=zeros(sum(nl));
                    KW(1:nl(1),1:nl(1))=bsxfun(@times, K(1:nl(1),1:nl(1)), Wdiag(1:nl(1))');
                    KW(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))=bsxfun(@times, K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))), Wdiag(nl(1)+(1:nl(2)))');
                    if isfield(gp.lik,'xtime')
                        KW(1:nl(1),nl(1)+(1:nl(2)))=K(1:nl(1),1:nl(1))*Wmat;
                        KW(nl(1)+(1:nl(2)),1:nl(1))=K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))*Wmat';
                    else
                        KW(1:nl(1),nl(1)+(1:nl(2)))=bsxfun(@times, K((1:nl(1)),(1:nl(1))), Wvec((nl(1)+1):2*n,1)');
                        KW(nl(1)+(1:nl(2)),1:nl(1))=bsxfun(@times, K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))), Wvec(1:n,2)');
                    end

                    % B = (I + K*W)
                    B=KW; B(1:(nl(1)+nl(2)+1):end)=B(1:(nl(1)+nl(2)+1):end)+1;

                    iB=B\eye(sum(nl));

                    % A = inv(I+K*W)*K
                    A=iB*K;

                    s2=zeros(sum(nl),1);

                    if isfield(gp.lik,'xtime')
                        A_diag=diag(A);
                        A_mat=A(1:ntime,ntime+(1:n));
                        for i1=1:sum(nl)
                            % Third derivatives
                            [dw_diag,dw_mat]=gp.lik.fh.llg3(gp.lik, y, f, 'latent', z, i1);
                            % s2(i) = -0.5*trace(inv(inv(K)+W)*dW/df_i)
                            s2(i1) = 0.5*(sum(A_diag.*dw_diag)+2*sum(sum(A_mat.*dw_mat)));
                        end
                    else
                        % Third derivatives
                        dw_mat = gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
                        for i1=1:n
                            % s2(i) = -0.5*trace(inv(inv(K)+W)*dW/df_i)
                            s2(i1) = 0.5*trace(A(i1:n:(i1+n),i1:n:(i1+n))*dw_mat(:,:,1,i1));
                            s2(i1+n) = 0.5*trace(A(i1:n:(i1+n),i1:n:(i1+n))*dw_mat(:,:,2,i1));
                        end
                    end

                    % =================================================================
                    % Gradient with respect to covariance function parameters
                    if ~isempty(strfind(gp.infer_params, 'covariance'))
                        % Evaluate the gradients from covariance functions
                        i1 = 0;
                        for i=1:ncf

                            %               i1=0;
                            %               if ~isempty(gprior)
                            %                 i1 = length(gprior);
                            %               end

                            gpcf = gp.cf{i};

                            % check in which components the covariance function is present
                            do = false(nlp,1);
                            for z1=1:nlp
                                if any(gp.comp_cf{z1}==i)
                                    do(z1) = true;
                                end
                            end

                            if isfield(gp.lik,'xtime')
                                if ~isempty(intersect(gp.comp_cf{1},i))
                                    if savememory
                                        % If savememory option is used, just get the number of
                                        % hyperparametrs and calculate gradients later
                                        np=gpcf.fh.cfg(gpcf,[],[],[],0);
                                    else
                                        DKffc = gpcf.fh.cfg(gpcf, xtime);
                                        np=length(DKffc);
                                    end
                                else
                                    if savememory
                                        % If savememory option is used, just get the number of
                                        % hyperparametrs and calculate gradients later
                                        np=gpcf.fh.cfg(gpcf,[],[],[],0);
                                    else
                                        DKffc = gpcf.fh.cfg(gpcf, x);
                                        np=length(DKffc);
                                    end
                                end
                            else
                                if savememory
                                    % If savememory option is used, just get the number of
                                    % hyperparametrs and calculate gradients later
                                    np=gpcf.fh.cfg(gpcf,[],[],[],0);
                                else
                                    DKffc = gpcf.fh.cfg(gpcf, x);
                                    np=length(DKffc);
                                end
                            end
                            gprior_cf = -gpcf.fh.lpg(gpcf);
                            g1 = gp.lik.fh.llg(gp.lik, y, f, 'latent', z);

                            WiB11=bsxfun(@times, Wdiag(1:nl(1)),iB(1:nl(1),1:nl(1)));
                            WiB12=bsxfun(@times, Wdiag(1:nl(1)),iB(1:nl(1),nl(1)+(1:nl(2))));
                            WiB22=bsxfun(@times, Wdiag(nl(1)+(1:nl(2))),iB(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))));
                            if isfield(gp.lik,'xtime')
                                WiB11=WiB11 + Wmat*iB(nl(1)+(1:nl(2)),1:nl(1));
                                WiB12=WiB12 + Wmat*iB(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)));
                                WiB22=WiB22 + Wmat'*iB(1:nl(1),nl(1)+(1:nl(2)));
                            else
                                WiB11=WiB11 + bsxfun(@times,Wvec(1:n,2),iB(nl(1)+(1:nl(2)),1:nl(1)));
                                WiB12=WiB12 + bsxfun(@times,Wvec(1:n,2),iB(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))));
                                WiB22=WiB22 + bsxfun(@times,Wvec(nl(1)+(1:nl(2)),1),iB(1:nl(1),nl(1)+(1:nl(2))));
                            end
                            WiB=[WiB11 WiB12; WiB12' WiB22];
                            % WiB=W*inv(I+KW)

                            for i2 = 1:np
                                i1 = i1+1;
                                if ~isfield(gp,'meanf')
                                    dKnl = zeros(sum(nl));
                                    if isfield(gp.lik,'xtime')
                                        if ~isempty(intersect(gp.comp_cf{1},i)) %do(indnl)
                                            if savememory
                                                DKff=gpcf.fh.cfg(gpcf,xtime,[],[],i2);
                                            else
                                                DKff=DKffc{i2};
                                            end
                                            dKnl(1:ntime,1:ntime) = DKff;
                                            %end
                                        else
                                            if savememory
                                                DKff=gpcf.fh.cfg(gpcf,x,[],[],i2);
                                            else
                                                DKff=DKffc{i2};
                                            end
                                            %if do(indnl)
                                            dKnl(ntime+(1:n),ntime+(1:n)) = DKff;
                                            %end
                                        end
                                    else
                                        if savememory
                                            DKff=gpcf.fh.cfg(gpcf,x,[],[],i2);
                                        else
                                            DKff=DKffc{i2};
                                        end
                                        for indnl=1:nlp
                                            if do(indnl)
                                                dKnl((1:n)+(indnl-1)*n,(1:n)+(indnl-1)*n) = DKff;
                                            end
                                        end
                                    end
                                    % s1 = 0.5*f'*inv(K)*dK/dtheta_j*inv(K)*f -
                                    % 0.5*trace(inv(inv(W)+K)*dK/dtheta_j)
                                    s1 = 0.5 * a'*dKnl*a - 0.5*sum(sum((dKnl.*WiB)));
                                else
                                    %s1 = 0.5 * (a-K\(H'*b_m))'*DKff{i2}*(a-K\(H'*b_m)) - 0.5*sum(sum(R.*DKff{i2}));
                                end
                                %b = DKff{i2} * g1;
                                b = dKnl*g1;
                                % s3 = inv(I+KW)*dK/dtheta_j*d(log(p(y|f)))/df
                                s3=iB*b;

                                gdata(i1) = -(s1 + s2'*s3);
                                %                 gprior(i1) = gprior_cf(i2);
                            end
                            gprior = [gprior gprior_cf];
                        end

                        %             % Set the gradients of hyperparameter
                        %             if length(gprior_cf) > np
                        %               for i2=np+1:length(gprior_cf)
                        %                 i1 = i1+1;
                        %                 gdata(i1) = 0;
                        %                 gprior(i1) = gprior_cf(i2);
                        %               end
                        %             end
                    end

                    % =================================================================
                    % Gradient with respect to likelihood function parameters
                    if ~isempty(strfind(gp.infer_params, 'likelihood')) ...
                            && ~isempty(gp.lik.fh.pak(gp.lik))

                        gdata_lik = 0;
                        lik = gp.lik;

                        g_logPrior = -lik.fh.lpg(lik);
                        if ~isempty(g_logPrior)

                            DW_sigma = lik.fh.llg3(lik, y, f, 'latent2+param', z);
                            DL_sigma = lik.fh.llg(lik, y, f, 'param', z);
                            b = K * lik.fh.llg2(lik, y, f, 'latent+param', z);
                            s3 = iB*b;

                            gdata_lik = - DL_sigma - 0.5.*sum(sum((A.*DW_sigma))) - s2'*s3;

                            % set the gradients into vectors that will be returned
                            gdata = [gdata gdata_lik];
                            gprior = [gprior g_logPrior];
                            i1 = length(g_logPrior);
                            i2 = length(gdata_lik);
                            if i1  > i2
                                gdata = [gdata zeros(1,i1-i2)];
                            end
                        end
                    end

            end

            %       g = gdata + gprior;
        end

    case 'FIC'
        % ============================================================
        % FIC
        % ============================================================
        g_ind = zeros(1,numel(gp.X_u));
        gdata_ind = zeros(1,numel(gp.X_u));
        gprior_ind = zeros(1,numel(gp.X_u));

        u = gp.X_u;
        m = size(u,1);

        %[e, edata, eprior, f, L, a, La1] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [e, edata, eprior, p] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [f, a, La1] = deal(p.f, p.a, p.La2);

        K_fu = gp_cov(gp, x, u);         % f x u
        K_uu = gp_trcov(gp, u);          % u x u, noiseles covariance K_uu
        Luu = chol(K_uu);
        B=Luu'\(K_fu');       % u x f
        iKuuKuf = Luu\B;

        W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z);
        sqrtW = sqrt(W);

        % Components for trace( inv(inv(W) + K) * dK) )
        Lah = 1 + sqrtW.*La1.*sqrtW;
        sWKfu = (repmat(sqrtW,1,m).*K_fu);
        B3 = repmat(Lah,1,m).\sWKfu;
        A = K_uu + sWKfu'*B3; A=(A+A')/2;
        L2 = repmat(sqrtW,1,m).*(B3/chol(A));
        iLa2W = sqrtW.*(Lah.\sqrtW);

        LL = sum(L2.*L2,2);
        BB = sum(B.^2)';

        % Evaluate s2
        C1 = L2'*B'*B;
        C2 = L2'.*repmat(La1',m,1);

        s2t = La1 + BB;
        s2t = s2t - (La1.*iLa2W.*La1 - sum(C2.^2)' + sum(B'.*((B*(repmat(iLa2W,1,m).*B'))*B)',2)...
            - sum(C1.^2)' + 2*La1.*iLa2W.*BB - 2*La1.*sum(L2.*C1',2));

        s2 = 0.5*s2t.*gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
        b3 = gp.lik.fh.llg(gp.lik, y, f, 'latent', z);

        % =================================================================
        % Gradient with respect to covariance function parameters
        if ~isempty(strfind(gp.infer_params, 'covariance'))
            i1=0;
            for i=1:ncf
                %           i1=0;
                %           if ~isempty(gprior)
                %             i1 = length(gprior);
                %           end

                % Get the gradients of the covariance matrices
                % and gprior from gpcf_* structures
                gpcf = gp.cf{i};
                if savememory
                    % If savememory option is used, just get the number of
                    % hyperparameters and calculate gradients later
                    np=gpcf.fh.cfg(gpcf,[],[],[],0);
                else
                    DKffc = gpcf.fh.cfg(gpcf, x, [], 1);
                    DKuuc = gpcf.fh.cfg(gpcf, u);
                    DKufc = gpcf.fh.cfg(gpcf, u, x);
                    np=length(DKuuc);
                end
                gprior_cf = -gpcf.fh.lpg(gpcf);

                for i2 = 1:np
                    i1 = i1+1;
                    if savememory
                        DKff=gpcf.fh.cfg(gpcf,x,[],1,i2);
                        DKuu=gpcf.fh.cfg(gpcf,u,[],[],i2);
                        DKuf=gpcf.fh.cfg(gpcf,u,x,[],i2);
                    else
                        DKff=DKffc{i2};
                        DKuu=DKuuc{i2};
                        DKuf=DKufc{i2};
                    end
                    % 0.5* a'*dK*a, where a = K\f
                    KfuiKuuKuu = iKuuKuf'*DKuu;
                    gdata(i1) = -0.5.*((2.*a'*DKuf'-(a'*KfuiKuuKuu))*(iKuuKuf*a) + (a'.*DKff')*a...
                        - (2.*a'.*sum(DKuf'.*iKuuKuf',2)'*a-a'.*sum(KfuiKuuKuu.*iKuuKuf',2)'*a) );

                    % trace( inv(inv(W) + K) * dQ) )
                    gdata(i1) = gdata(i1) - 0.5.*sum(sum(L2'.*(2.*L2'*DKuf'*iKuuKuf - L2'*KfuiKuuKuu*iKuuKuf)));
                    gdata(i1) = gdata(i1) + 0.5.*sum(DKff.*iLa2W - LL.*DKff);
                    gdata(i1) = gdata(i1) + 0.5.*(2.*sum(LL.*sum(DKuf'.*iKuuKuf',2)) - sum(LL.*sum(KfuiKuuKuu.*iKuuKuf',2)));

                    b = (2*DKuf' - KfuiKuuKuu)*(iKuuKuf*b3) + DKff.*b3 - sum((2.*DKuf'- KfuiKuuKuu).*iKuuKuf',2).*b3;
                    bb = sqrtW.*(Lah.\(sqrtW.*b)) - L2*(L2'*b);
                    s3 = b - (La1.*bb + B'*(B*bb));
                    gdata(i1) = gdata(i1) - s2'*s3;

                    %             gprior(i1) = gprior_cf(i2);
                end

                gprior = [gprior gprior_cf];
                %           % Set the gradients of hyperparameter
                %           if length(gprior_cf) > np
                %             for i2=np+1:length(gprior_cf)
                %               i1 = i1+1;
                %               gdata(i1) = 0;
                %               gprior(i1) = gprior_cf(i2);
                %             end
                %           end
            end

        end

        % =================================================================
        % Gradient with respect to likelihood function parameters

        if ~isempty(strfind(gp.infer_params, 'likelihood')) && ~isempty(gp.lik.fh.pak(gp.lik))
            gdata_lik = 0;
            lik = gp.lik;

            DW_sigma = lik.fh.llg3(lik, y, f, 'latent2+param', z);
            DL_sigma = lik.fh.llg(lik, y, f, 'param', z);
            DL_f_sigma = lik.fh.llg2(lik, y, f, 'latent+param', z);
            %         b = La1.*DL_f_sigma + B'*(B*DL_f_sigma);
            %         bb = (iLa2W.*b - L2*(L2'*b));
            %         s3 = b - (La1.*bb + B'*(B*bb));
            b = repmat(La1,1,size(DL_f_sigma,2)).*DL_f_sigma + B'*(B*DL_f_sigma);
            bb = (repmat(iLa2W,1,size(DL_f_sigma,2)).*b - L2*(L2'*b));
            s3 = b - (repmat(La1,1,size(DL_f_sigma,2)).*bb + B'*(B*bb));

            %         gdata_lik = - DL_sigma - 0.5.*sum(s2t.*DW_sigma) - s2'*s3;
            gdata_lik = - DL_sigma - 0.5.*sum(repmat(s2t,1,size(DL_f_sigma,2)).*DW_sigma) - s2'*s3;

            % evaluate prior contribution for the gradient
            if isfield(gp.lik, 'p')
                g_logPrior = -lik.fh.lpg(lik);
            else
                g_logPrior = zeros(size(gdata_lik));
            end
            % set the gradients into vectors that will be returned
            gdata = [gdata gdata_lik];
            gprior = [gprior g_logPrior];
            i1 = length(gdata);
        end

        % =================================================================
        % Gradient with respect to inducing inputs

        if ~isempty(strfind(gp.infer_params, 'inducing'))
            if isfield(gp.p, 'X_u') && ~isempty(gp.p.X_u)
                m = size(gp.X_u,2);
                st=0;
                if ~isempty(gdata)
                    st = length(gdata);
                end

                gdata(st+1:st+length(gp.X_u(:))) = 0;
                i1 = st+1;
                gprior_ind=[];
                if iscell(gp.p.X_u) % Own prior for each inducing input
                    for i = 1:size(gp.X_u,1)
                        pr = gp.p.X_u{i};
                        gprior_ind =[gprior_ind -pr.fh.lpg(gp.X_u(i,:), pr)];
                    end
                else % One prior for all inducing inputs
                    gprior_ind = -gp.p.X_u.fh.lpg(gp.X_u(:)', gp.p.X_u);
                end
                gprior = [gprior gprior_ind];

                for i=1:ncf
                    i1=st;

                    gpcf = gp.cf{i};
                    if savememory
                        % If savememory option is used, just get the number of
                        % covariates in X and calculate gradients later
                        np=gpcf.fh.ginput(gpcf,u,[],0);
                    else
                        np=1;
                        DKuu = gpcf.fh.ginput(gpcf, u);
                        DKuf = gpcf.fh.ginput(gpcf, u, x);
                    end

                    for i3 = 1:np
                        if savememory
                            DKuu=gpcf.fh.ginput(gpcf,u,[],i3);
                            DKuf=gpcf.fh.ginput(gpcf,u,x,i3);
                        end
                        for i2 = 1:length(DKuu)
                            if savememory
                                i1 = st + (i2-1)*np+i3;
                                %i1 = st + 2*i2 - (i3==1);
                            else
                                i1 = i1+1;
                            end

                            % 0.5* a'*dK*a, where a = K\f
                            KfuiKuuKuu = iKuuKuf'*DKuu{i2};
                            gdata(i1) = gdata(i1) -0.5.*((2.*a'*DKuf{i2}'-(a'*KfuiKuuKuu))*(iKuuKuf*a) + ...
                                - (2.*a'.*sum(DKuf{i2}'.*iKuuKuf',2)'*a-a'.*sum(KfuiKuuKuu.*iKuuKuf',2)'*a) );

                            % trace( inv(inv(W) + K) * dQ) )
                            gdata(i1) = gdata(i1) - 0.5.*(sum(sum(L2'.*(2.*L2'*DKuf{i2}'*iKuuKuf - L2'*KfuiKuuKuu*iKuuKuf))));
                            gdata(i1) = gdata(i1) + 0.5.*(2.*sum(LL.*sum(DKuf{i2}'.*iKuuKuf',2)) - sum(LL.*sum(KfuiKuuKuu.*iKuuKuf',2)));

                            % b2*dK*b3
                            b = (2*DKuf{i2}'-KfuiKuuKuu)*(iKuuKuf*b3)  - sum((2.*DKuf{i2}'- KfuiKuuKuu).*iKuuKuf',2).*b3;
                            bb = (iLa2W.*b - L2*(L2'*b));
                            s3 = b - (La1.*bb + B'*(B*bb));
                            gdata(i1) = gdata(i1) - s2'*s3;
                        end
                    end
                end
            end
        end

        %       g = gdata + gprior;

    case {'PIC' 'PIC_BLOCK'}
        % ============================================================
        % PIC
        % ============================================================
        g_ind = zeros(1,numel(gp.X_u));
        gdata_ind = zeros(1,numel(gp.X_u));
        gprior_ind = zeros(1,numel(gp.X_u));

        u = gp.X_u;
        m = size(u,1);
        ind = gp.tr_index;

        %[e, edata, eprior, f, L, a, La1] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [e, edata, eprior, p] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [f, a, La1] = deal(p.f, p.a, p.La2);

        K_fu = gp_cov(gp, x, u);         % f x u
        K_uu = gp_trcov(gp, u);          % u x u, noiseles covariance K_uu
        K_uu = (K_uu+K_uu')./2;          % ensure the symmetry of K_uu
        Luu = chol(K_uu);
        iKuuKuf = Luu\(Luu'\K_fu');
        B=Luu'\(K_fu');       % u x f

        W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z);
        sqrtW = sqrt(W);

        % Components for trace( inv(inv(W) + K) * dK) )
        B2 = (repmat(sqrtW,1,m).*K_fu);
        for i=1:length(ind)
            La2{i} = eye(size(La1{i})) + diag(sqrtW(ind{i}))*La1{i}*diag(sqrtW(ind{i}));
            LLa2{i} = chol(La2{i});
            B3(ind{i},:) = LLa2{i}\(LLa2{i}'\B2(ind{i},:));
        end
        A2 = K_uu + B2'*B3; A2=(A2+A2')/2;
        L2 = repmat(sqrtW,1,m).*B3/chol(A2);
        for i=1:length(ind)
            iLa2W{i} = diag(sqrtW(ind{i}))*(LLa2{i}\(LLa2{i}'\diag(sqrtW(ind{i}))));
        end

        LL = sum(L2.*L2,2);
        BB = sum(B.^2)';

        % Evaluate s2
        C1 = L2'*B'*B;
        s2t = BB;
        for i=1:length(ind)
            C2(:,ind{i}) = L2(ind{i},:)'*La1{i};
            s2t1(ind{i},:) = diag(La1{i}*iLa2W{i}*La1{i});
            s2t2(ind{i},:) = La1{i}*iLa2W{i}*B(:,ind{i})';
            s2t3(ind{i},:) = La1{i}*L2(ind{i},:);
            Bt(ind{i},:) = iLa2W{i}*B(:,ind{i})';
            s2t(ind{i}) = s2t(ind{i}) + diag(La1{i});
        end

        s2t = s2t - (s2t1 - sum(C2.^2)' + sum(B'.*((B*Bt)*B)',2)...
            - sum(C1.^2)' + 2*sum(s2t2.*B',2) - 2*sum(s2t3.*C1',2));

        s2 = 0.5*s2t.*gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
        b3 = gp.lik.fh.llg(gp.lik, y, f, 'latent', z);

        % =================================================================
        % Gradient with respect to covariance function parameters
        if ~isempty(strfind(gp.infer_params, 'covariance'))
            i1=0;
            for i=1:ncf
                %           i1=0;
                %           if ~isempty(gprior)
                %             i1 = length(gprior);
                %           end

                % Get the gradients of the covariance matrices
                % and gprior from gpcf_* structures
                gpcf = gp.cf{i};

                if savememory
                    % If savememory option is used, just get the number of
                    % hyperparameters and calculate gradients later
                    np=gpcf.fh.cfg(gpcf,[],[],[],0);
                else
                    DKuuc = gpcf.fh.cfg(gpcf, u);
                    DKufc = gpcf.fh.cfg(gpcf, u, x);
                    for kk = 1:length(ind)
                        DKffc{kk} = gpcf.fh.cfg(gpcf, x(ind{kk},:));
                    end
                    np=length(DKuuc);
                end
                gprior_cf = -gpcf.fh.lpg(gpcf);

                for i2 = 1:np
                    i1 = i1+1;
                    if savememory
                        DKuu=gpcf.fh.cfg(gpcf,u,[],[],i2);
                        DKuf=gpcf.fh.cfg(gpcf,u,x,[],i2);
                    else
                        DKuu=DKuuc{i2};
                        DKuf=DKufc{i2};
                    end
                    KfuiKuuKuu = iKuuKuf'*DKuu;
                    gdata(i1) = -0.5.*((2.*a'*DKuf'-(a'*KfuiKuuKuu))*(iKuuKuf*a) );
                    gdata(i1) = gdata(i1) - 0.5.*(sum(sum(L2'.*(2.*L2'*DKuf'*iKuuKuf - L2'*KfuiKuuKuu*iKuuKuf))));

                    b = (2*DKuf'-KfuiKuuKuu)*(iKuuKuf*b3);
                    for kk=1:length(ind)
                        if savememory
                            DKff=gpcf.fh.cfg(gpcf, x(ind{kk},:),[],[],i2);
                        else
                            DKff=DKffc{kk}{i2};
                        end
                        gdata(i1) = gdata(i1) -0.5.*(a(ind{kk})'*DKff*a(ind{kk})...
                            - (2.*a(ind{kk})'*DKuf(:,ind{kk})'*iKuuKuf(:,ind{kk})*a(ind{kk})...
                            -a(ind{kk})'*KfuiKuuKuu(ind{kk},:)*iKuuKuf(:,ind{kk})*a(ind{kk})) );

                        % trace( inv(inv(W) + K) * dQ) )
                        gdata(i1) = gdata(i1) + 0.5.*(sum(sum(iLa2W{kk}.*DKff)) - sum(sum(L2(ind{kk},:)'.*(L2(ind{kk},:)'*DKff))));
                        gdata(i1) = gdata(i1) + 0.5.*(2.*sum(sum(L2(ind{kk},:)'.*(L2(ind{kk},:)'*DKuf(:,ind{kk})'*iKuuKuf(:,ind{kk})))) - ...
                            sum(sum(L2(ind{kk},:)'.*((L2(ind{kk},:)'*KfuiKuuKuu(ind{kk},:))*iKuuKuf(:,ind{kk})))));

                        b(ind{kk}) = b(ind{kk}) + DKff*b3(ind{kk})...
                            - (2.*DKuf(:,ind{kk})'- KfuiKuuKuu(ind{kk},:))*iKuuKuf(:,ind{kk})*b3(ind{kk});
                        bbt(ind{kk},:) = iLa2W{kk}*b(ind{kk});
                    end

                    % b2*dK*b3
                    bb = (bbt - L2*(L2'*b));
                    for kk=1:length(ind)
                        s3t(ind{kk},:) = La1{kk}*bb(ind{kk});
                    end
                    s3 = b - (s3t + B'*(B*bb));
                    gdata(i1) = gdata(i1) - s2'*s3;

                    %             gprior(i1) = gprior_cf(i2);
                end

                gprior = [gprior gprior_cf];
                %           % Set the gradients of hyperparameter
                %           if length(gprior_cf) > np
                %             for i2=np+1:length(gprior_cf)
                %               i1 = i1+1;
                %               gdata(i1) = 0;
                %               gprior(i1) = gprior_cf(i2);
                %             end
                %           end
            end

        end

        % =================================================================
        % Gradient with respect to likelihood function parameters

        if ~isempty(strfind(gp.infer_params, 'likelihood')) && ~isempty(gp.lik.fh.pak(gp.lik))
            gdata_lik = 0;
            lik = gp.lik;

            DW_sigma = lik.fh.llg3(lik, y, f, 'latent2+param', z);
            DL_sigma = lik.fh.llg(lik, y, f, 'param', z);
            DL_f_sigma = lik.fh.llg2(lik, y, f, 'latent+param', z);
            b = B'*(B*DL_f_sigma);
            for kk=1:length(ind)
                b(ind{kk}) = b(ind{kk}) + La1{kk}*DL_f_sigma(ind{kk});
                bbt(ind{kk},:) = iLa2W{kk}*b(ind{kk});
            end
            bb = (bbt - L2*(L2'*b));
            for kk=1:length(ind)
                s3t(ind{kk},:) = La1{kk}*bb(ind{kk});
            end
            s3 = b - (s3t + B'*(B*bb));

            gdata_lik = - DL_sigma - 0.5.*sum(s2t.*DW_sigma) - s2'*s3;

            % evaluate prior contribution for the gradient
            if isfield(gp.lik, 'p')
                g_logPrior = -lik.fh.lpg(lik);
            else
                g_logPrior = zeros(size(gdata_lik));
            end
            % set the gradients into vectors that will be returned
            gdata = [gdata gdata_lik];
            gprior = [gprior g_logPrior];
            i1 = length(gdata);
        end

        % =================================================================
        % Gradient with respect to inducing inputs

        if ~isempty(strfind(gp.infer_params, 'inducing'))
            if isfield(gp.p, 'X_u') && ~isempty(gp.p.X_u)
                m = size(gp.X_u,2);

                st=0;
                if ~isempty(gdata)
                    st = length(gdata);
                end
                gdata(st+1:st+length(gp.X_u(:))) = 0;

                i1 = st+1;
                gprior_ind=[];
                if iscell(gp.p.X_u) % Own prior for each inducing input
                    for i = 1:size(gp.X_u,1)
                        pr = gp.p.X_u{i};
                        gprior_ind =[gprior_ind -pr.fh.lpg(gp.X_u(i,:), pr)];
                    end
                else % One prior for all inducing inputs
                    gprior_ind = -gp.p.X_u.fh.lpg(gp.X_u(:)', gp.p.X_u);
                end
                gprior = [gprior gprior_ind];

                % Loop over the  covariance functions
                for i=1:ncf
                    i1=st;
                    gpcf = gp.cf{i};
                    if savememory
                        % If savememory option is used, just get the number of
                        % covariates in X and calculate gradients later
                        np=gpcf.fh.ginput(gpcf,u,[],0);
                    else
                        DKuu = gpcf.fh.ginput(gpcf, u);
                        DKuf = gpcf.fh.ginput(gpcf, u, x);
                        np=1;
                    end

                    for i3 = 1:np
                        if savememory
                            DKuu=gpcf.fh.ginput(gpcf,u,[],i3);
                            DKuf=gpcf.fh.ginput(gpcf,u,x,i3);
                        end
                        for i2 = 1:length(DKuu)
                            if savememory
                                i1 = st + (i2-1)*np+i3;
                                %i1 = st + 2*i2 - (i3==1);
                            else
                                i1 = i1+1;
                            end

                            KfuiKuuKuu = iKuuKuf'*DKuu{i2};
                            gdata(i1) = -0.5.*((2.*a'*DKuf{i2}'-(a'*KfuiKuuKuu))*(iKuuKuf*a) );
                            gdata(i1) = gdata(i1) - 0.5.*(sum(sum(L2'.*(2.*L2'*DKuf{i2}'*iKuuKuf - L2'*KfuiKuuKuu*iKuuKuf))));

                            b = (2*DKuf{i2}'-KfuiKuuKuu)*(iKuuKuf*b3);
                            for kk=1:length(ind)
                                gdata(i1) = gdata(i1) -0.5.*(- (2.*a(ind{kk})'*DKuf{i2}(:,ind{kk})'*iKuuKuf(:,ind{kk})*a(ind{kk})...
                                    -a(ind{kk})'*KfuiKuuKuu(ind{kk},:)*iKuuKuf(:,ind{kk})*a(ind{kk})) );

                                % trace( inv(inv(W) + K) * dQ) )
                                gdata(i1) = gdata(i1) + 0.5.*(2.*sum(sum(L2(ind{kk},:)'.*(L2(ind{kk},:)'*DKuf{i2}(:,ind{kk})'*iKuuKuf(:,ind{kk})))) - ...
                                    sum(sum(L2(ind{kk},:)'.*((L2(ind{kk},:)'*KfuiKuuKuu(ind{kk},:))*iKuuKuf(:,ind{kk})))));

                                b(ind{kk}) = b(ind{kk}) + ...
                                    - (2.*DKuf{i2}(:,ind{kk})'- KfuiKuuKuu(ind{kk},:))*iKuuKuf(:,ind{kk})*b3(ind{kk});
                                bbt(ind{kk},:) = iLa2W{kk}*b(ind{kk});
                            end

                            % b2*dK*b3
                            bb = (bbt - L2*(L2'*b));
                            for kk=1:length(ind)
                                s3t(ind{kk},:) = La1{kk}*bb(ind{kk});
                            end
                            s3 = b - (s3t + B'*(B*bb));
                            gdata(i1) = gdata(i1) - s2'*s3;
                        end
                    end
                end
            end
        end

        %       g = gdata + gprior;

    case 'CS+FIC'
        % ============================================================
        % CS+FIC
        % ============================================================
        u = gp.X_u;
        m = size(u,1);

        %[e, edata, eprior, f, L, a, La1] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [e, edata, eprior, p] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [f, L, a, La1] = deal(p.f, p.L, p.a, p.La2);

        cf_orig = gp.cf;

        cf1 = {};
        cf2 = {};
        j = 1;
        k = 1;
        for i = 1:ncf
            if ~isfield(gp.cf{i},'cs')
                cf1{j} = gp.cf{i};
                j = j + 1;
            else
                cf2{k} = gp.cf{i};
                k = k + 1;
            end
        end
        gp.cf = cf1;

        % First evaluate needed covariance matrices
        % v defines that parameter is a vector
        [Kv_ff, Cv_ff] = gp_trvar(gp, x);  % f x 1  vector
        K_fu = gp_cov(gp, x, u);         % f x u
        K_uu = gp_trcov(gp, u);    % u x u, noiseles covariance K_uu
        K_uu = (K_uu+K_uu')./2;     % ensure the symmetry of K_uu
        gp.cf = cf_orig;

        W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z);

        % Find fill reducing permutation and permute all the
        % matrices
        p = analyze(La1);
        if ~isempty(z)
            z = z(p,:);
        end
        f = f(p);
        y = y(p);
        La1 = La1(p,p);
        K_fu = K_fu(p,:);
        L = L(p,:);
        x = x(p,:);
        W = W(p);
        a = a(p);

        Luu = chol(K_uu)';
        B=Luu\(K_fu');       % u x f
        iKuuKuf = Luu'\B;
        sW = sqrt(W);
        sqrtW = sparse(1:n,1:n,sW,n,n);
        Inn = sparse(1:n,1:n,1,n,n);

        % Components for trace( inv(inv(W) + K) * dK) )
        Lah = Inn + sqrtW*La1*sqrtW;
        LD2 = ldlchol(Lah);
        B2 = (repmat(sW,1,m).*K_fu);
        %B3 = Lah\B2;
        B3 = ldlsolve(LD2,B2);
        A2 = K_uu + B2'*B3; A2=(A2+A2')/2;
        L2 = repmat(sW,1,m).*B3/chol(A2);

        siLa2 = spinv(LD2,1);
        dsiLa2 = diag(siLa2);

        LL = sum(L2.*L2,2);
        BB = sum(B.^2)';

        % Evaluate s2
        C1 = L2'*B'*B;
        C2 = L2'*La1;
        C3 = repmat(sW,1,m).*ldlsolve(LD2,repmat(sW,1,m).*B');

        s2t = diag(La1) + BB;
        %diag(La1*sqrtW*ldlsolve(LD2,sqrtW*La1))
        s2t = s2t - (diag(La1) - sum(La1*sqrtW.*siLa2',2)./sW - sum(C2.^2)' + sum(B'.*(B*C3*B)',2)...
            - sum(C1.^2)' + 2*sum((La1*C3).*B',2) - 2*sum(C2'.*C1',2));

        s2 = 0.5*s2t.*gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);

        b3 = gp.lik.fh.llg(gp.lik, y, f, 'latent', z);

        % =================================================================
        % Gradient with respect to covariance function parameters
        if ~isempty(strfind(gp.infer_params, 'covariance'))
            i1=0;
            for i=1:ncf
                %           i1=0;
                %           if ~isempty(gprior)
                %             i1 = length(gprior);
                %           end

                gpcf = gp.cf{i};

                % Evaluate the gradient for FIC covariance functions
                if ~isfield(gpcf,'cs')
                    % Get the gradients of the covariance matrices
                    % and gprior from gpcf_* structures
                    if savememory
                        % If savememory option is used, just get the number of
                        % hyperparameters and calculate gradients later
                        np=gpcf.fh.cfg(gpcf,[],[],[],0);
                    else
                        DKffc = gpcf.fh.cfg(gpcf, x, [], 1);
                        DKuuc = gpcf.fh.cfg(gpcf, u);
                        DKufc = gpcf.fh.cfg(gpcf, u, x);
                        np=length(DKuuc);
                    end
                    gprior_cf = -gpcf.fh.lpg(gpcf);

                    for i2 = 1:np
                        i1 = i1+1;
                        if savememory
                            DKff=gpcf.fh.cfg(gpcf,x,[],1,i2);
                            DKuu=gpcf.fh.cfg(gpcf,u,[],[],i2);
                            DKuf=gpcf.fh.cfg(gpcf,u,x,[],i2);
                        else
                            DKff=DKffc{i2};
                            DKuu=DKuuc{i2};
                            DKuf=DKufc{i2};
                        end
                        % 0.5* a'*dK*a, where a = K\f
                        KfuiKuuKuu = iKuuKuf'*DKuu;
                        gdata(i1) = -0.5.*((2.*a'*DKuf'-(a'*KfuiKuuKuu))*(iKuuKuf*a) + (a'.*DKff')*a...
                            - (2.*a'.*sum(DKuf'.*iKuuKuf',2)'*a-a'.*sum(KfuiKuuKuu.*iKuuKuf',2)'*a) );

                        % trace( inv(inv(W) + K) * dQ) )
                        gdata(i1) = gdata(i1) - 0.5.*(sum(sum(L2'.*(2.*L2'*DKuf'*iKuuKuf - L2'*KfuiKuuKuu*iKuuKuf))));
                        gdata(i1) = gdata(i1) + 0.5.*(sum(DKff.*dsiLa2.*W - LL.*DKff));
                        gdata(i1) = gdata(i1) + 0.5.*(2.*sum(LL.*sum(DKuf'.*iKuuKuf',2)) - sum(LL.*sum(KfuiKuuKuu.*iKuuKuf',2)));

                        gdata(i1) = gdata(i1) + 0.5.*sum(sum(sqrtW*ldlsolve(LD2,repmat(sW,1,m).*(2.*DKuf' - KfuiKuuKuu)).*iKuuKuf',2));
                        gdata(i1) = gdata(i1) - 0.5.*sum(sW.*dsiLa2.*sW.*sum((2.*DKuf' - KfuiKuuKuu).*iKuuKuf',2) );

                        % b2*dK*b3
                        b = (2*DKuf'-KfuiKuuKuu)*(iKuuKuf*b3) + DKff.*b3 - sum((2.*DKuf'- KfuiKuuKuu).*iKuuKuf',2).*b3;
                        bb = (sW.*ldlsolve(LD2,sW.*b) - L2*(L2'*b));
                        s3 = b - (La1*bb + B'*(B*bb));
                        gdata(i1) = gdata(i1) - s2'*s3;

                        %               gprior(i1) = gprior_cf(i2);
                    end

                    % Evaluate the gradient for compact support covariance functions
                else
                    % Get the gradients of the covariance matrices
                    % and gprior from gpcf_* structures
                    if savememory
                        % If savememory option is used, just get the number of
                        % hyperparameters and calculate gradients later
                        np=gpcf.fh.cfg(gpcf,[],[],[],0);
                    else
                        DKffc = gpcf.fh.cfg(gpcf, x);
                        np=length(DKffc);
                    end
                    gprior_cf = -gpcf.fh.lpg(gpcf);

                    for i2 = 1:np
                        i1 = i1+1;
                        if savememory
                            DKff=gpcf.fh.cfg(gpcf,x,[],[],i2);
                        else
                            DKff=DKffc{i2};
                        end

                        % Evaluate the gradient with respect to magnSigma
                        gdata(i1) = 0.5*(sum(sW.*sum(siLa2.*(sqrtW*DKff)',2)) - sum(sum(L2.*(L2'*DKff')')) - a'*DKff*a);
                        b = DKff*b3;
                        bb = (sW.*ldlsolve(LD2,sW.*b) - L2*(L2'*b));
                        s3 = b - (La1*bb + B'*(B*bb));
                        gdata(i1) = gdata(i1) - s2'*s3;
                        %               gprior(i1) = gprior_cf(i2);

                    end
                end

                gprior = [gprior gprior_cf];
                %           % Set the gradients of hyperparameter
                %           if length(gprior_cf) > np
                %             for i2=np+1:length(gprior_cf)
                %               i1 = i1+1;
                %               gdata(i1) = 0;
                %               gprior(i1) = gprior_cf(i2);
                %             end
                %           end
            end

        end

        % =================================================================
        % Gradient with respect to likelihood function parameters

        if ~isempty(strfind(gp.infer_params, 'likelihood')) && ~isempty(gp.lik.fh.pak(gp.lik))
            gdata_lik = 0;
            lik = gp.lik;

            DW_sigma = lik.fh.llg3(lik, y, f, 'latent2+param', z);
            DL_sigma = lik.fh.llg(lik, y, f, 'param', z);
            DL_f_sigma = lik.fh.llg2(lik, y, f, 'latent+param', z);
            b = La1*DL_f_sigma + B'*(B*DL_f_sigma);
            bb = (sW.*ldlsolve(LD2,sW.*b) - L2*(L2'*b));
            s3 = b - (La1*bb + B'*(B*bb));

            gdata_lik = - DL_sigma - 0.5.*sum(s2t.*DW_sigma) - s2'*s3;

            % evaluate prior contribution for the gradient
            if isfield(gp.lik, 'p')
                g_logPrior = -lik.fh.lpg(lik);
            else
                g_logPrior = zeros(size(gdata_lik));
            end
            % set the gradients into vectors that will be returned
            gdata = [gdata gdata_lik];
            gprior = [gprior g_logPrior];
            i1 = length(gdata);
        end

        % =================================================================
        % Gradient with respect to inducing inputs

        if ~isempty(strfind(gp.infer_params, 'inducing'))
            if isfield(gp.p, 'X_u') && ~isempty(gp.p.X_u)
                m = size(gp.X_u,2);
                st=0;
                if ~isempty(gdata)
                    st = length(gdata);
                end

                gdata(st+1:st+length(gp.X_u(:))) = 0;
                i1 = st+1;
                gprior_ind=[];
                if iscell(gp.p.X_u) % Own prior for each inducing input
                    for i = 1:size(gp.X_u,1)
                        pr = gp.p.X_u{i};
                        gprior_ind =[gprior_ind -pr.fh.lpg(gp.X_u(i,:), pr)];
                    end
                else % One prior for all inducing inputs
                    gprior_ind = -gp.p.X_u.fh.lpg(gp.X_u(:)', gp.p.X_u);
                end
                gprior = [gprior gprior_ind];

                for i=1:ncf
                    i1=st;
                    gpcf = gp.cf{i};
                    if ~isfield(gpcf,'cs')
                        if savememory
                            % If savememory option is used, just get the number of
                            % covariates in X and calculate gradients later
                            np=gpcf.fh.ginput(gpcf,u,[],0);
                        else
                            DKuu = gpcf.fh.ginput(gpcf, u);
                            DKuf = gpcf.fh.ginput(gpcf, u, x);
                            np=1;
                        end

                        for i3 = 1:np
                            if savememory
                                DKuu=gpcf.fh.ginput(gpcf,u,[],i3);
                                DKuf=gpcf.fh.ginput(gpcf,u,x,i3);
                            end
                            for i2 = 1:length(DKuu)
                                if savememory
                                    i1 = st + (i2-1)*np+i3;
                                    %i1 = st + 2*i2 - (i3==1);
                                else
                                    i1 = i1+1;
                                end

                                % 0.5* a'*dK*a, where a = K\f
                                KfuiKuuKuu = iKuuKuf'*DKuu{i2};
                                gdata(i1) = -0.5.*((2.*a'*DKuf{i2}'-(a'*KfuiKuuKuu))*(iKuuKuf*a) ...
                                    - (2.*a'.*sum(DKuf{i2}'.*iKuuKuf',2)'*a-a'.*sum(KfuiKuuKuu.*iKuuKuf',2)'*a) );

                                % trace( inv(inv(W) + K) * dQ) )
                                gdata(i1) = gdata(i1) - 0.5.*(sum(sum(L2'.*(2.*L2'*DKuf{i2}'*iKuuKuf - L2'*KfuiKuuKuu*iKuuKuf))));
                                gdata(i1) = gdata(i1) + 0.5.*(2.*sum(LL.*sum(DKuf{i2}'.*iKuuKuf',2)) - sum(LL.*sum(KfuiKuuKuu.*iKuuKuf',2)));

                                gdata(i1) = gdata(i1) + 0.5.*sum(sum(sqrtW*ldlsolve(LD2,repmat(sW,1,size(u,1)).*(2.*DKuf{i2}' - KfuiKuuKuu)).*iKuuKuf',2));
                                gdata(i1) = gdata(i1) - 0.5.*( sum(sW.*dsiLa2.*sW.*sum((2.*DKuf{i2}' - KfuiKuuKuu).*iKuuKuf',2)) );

                                % b2*dK*b3
                                b = (2*DKuf{i2}'-KfuiKuuKuu)*(iKuuKuf*b3) - sum((2.*DKuf{i2}'- KfuiKuuKuu).*iKuuKuf',2).*b3;
                                bb = (sW.*ldlsolve(LD2,sW.*b) - L2*(L2'*b));
                                s3 = b - (La1*bb + B'*(B*bb));
                                gdata(i1) = gdata(i1) - s2'*s3;
                            end
                        end
                    end
                end
            end
        end

        %       g = gdata + gprior;

    case {'DTC', 'VAR', 'SOR'}
        % ============================================================
        % DTC, VAR, SOR
        % ============================================================
        g_ind = zeros(1,numel(gp.X_u));
        gdata_ind = zeros(1,numel(gp.X_u));
        gprior_ind = zeros(1,numel(gp.X_u));

        u = gp.X_u;
        m = size(u,1);

        %[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [e, edata, eprior, p] = gpla_e(gp_pak(gp), gp, x, y, 'z', z);
        [f, L, a, La2] = deal(p.f, p.L, p.a, p.La2);

        K_fu = gp_cov(gp, x, u);         % f x u
        K_uu = gp_trcov(gp, u);          % u x u, noiseles covariance K_uu
        Luu = chol(K_uu);
        B=Luu'\(K_fu');       % u x f
        iKuuKuf = Luu\B;

        W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z);
        sqrtW = sqrt(W);

        % Components for trace( inv(inv(W) + K) * dK) )
        sWKfu = bsxfun(@times, sqrtW, K_fu);
        % L = chol(K_uu + sWKfu'*sWKfu)
        L2 = repmat(sqrtW,1,m).*(sWKfu/L);

        LL = sum(L2.*L2,2);
        BB = sum(B.^2)';

        % Evaluate s2
        C1 = L2'*B'*B;
        C2 = zeros(size(L2'));

        s2t = BB;
        s2t = s2t - (sum(C2.^2)' + sum(B'.*((B*(repmat(W,1,m).*B'))*B)',2)...
            - sum(C1.^2)');

        g3 = gp.lik.fh.llg3(gp.lik, y, f, 'latent', z);
        s2 = 0.5*s2t.*g3;
        b3 = gp.lik.fh.llg(gp.lik, y, f, 'latent', z);

        % =================================================================
        % Gradient with respect to covariance function parameters
        if ~isempty(strfind(gp.infer_params, 'covariance'))
            i1=0;
            for i=1:ncf
                %           i1=0;
                %           if ~isempty(gprior)
                %             i1 = length(gprior);
                %           end

                % Get the gradients of the covariance matrices
                % and gprior from gpcf_* structures
                gpcf = gp.cf{i};
                if savememory
                    % If savememory option is used, just get the number of
                    % hyperparameters and calculate gradients later
                    np=gpcf.fh.cfg(gpcf,[],[],[],0);
                else
                    %             DKffc = gpcf.fh.cfg(gpcf, x, [], 1);
                    DKuuc = gpcf.fh.cfg(gpcf, u);
                    DKufc = gpcf.fh.cfg(gpcf, u, x);
                    if isequal(gp.type, 'VAR')
                        DKffc=gpcf.fh.cfg(gpcf,x,[],1);
                    end
                    np=length(DKuuc);
                end
                gprior_cf = -gpcf.fh.lpg(gpcf);

                for i2 = 1:np
                    i1 = i1+1;
                    if savememory
                        DKuu=gpcf.fh.cfg(gpcf,u,[],[],i2);
                        DKuf=gpcf.fh.cfg(gpcf,u,x,[],i2);
                    else
                        DKuu=DKuuc{i2};
                        DKuf=DKufc{i2};
                    end
                    % 0.5* a'*dK*a, where a = K\f
                    KfuiKuuDKuu = iKuuKuf'*DKuu;
                    gdata(i1) = -0.5.*((2.*a'*DKuf'-(a'*KfuiKuuDKuu))*(iKuuKuf*a));

                    % trace( inv(inv(W) + K) * dQ) )
                    gdata(i1) = gdata(i1) - 0.5.*sum(sum(L2'.*(2.*L2'*DKuf'*iKuuKuf - L2'*KfuiKuuDKuu*iKuuKuf)));
                    gdata(i1) = gdata(i1) + 0.5.*(2.*sum(W.*sum(DKuf'.*iKuuKuf',2)) - sum(W.*sum(KfuiKuuDKuu.*iKuuKuf',2)));

                    b = (2*DKuf' - KfuiKuuDKuu)*(iKuuKuf*b3);
                    bb = sqrtW.*sqrtW.*b - L2*(L2'*b);
                    s3 = b - (B'*(B*bb));
                    gdata(i1) = gdata(i1) - s2'*s3;

                    if isequal(gp.type, 'VAR')
                        % Derivative of tr(diag(K-Q).*W) (Titsias, 2009) wrt th
                        % Here we have to also split in explicit and implicit
                        % derivatives
                        if savememory
                            DKff=gpcf.fh.cfg(gpcf,x,[],1,i2);
                        else
                            DKff=DKffc{i2};
                        end
                        gdata(i1) = gdata(i1) + 0.5*sum(DKff.*W) - 0.5.*(2.*sum(W.*sum(DKuf'.*iKuuKuf',2)) - sum(W.*sum(KfuiKuuDKuu.*iKuuKuf',2)));
                        % La2 = diag(K - Q);
                        gdata(i1) = gdata(i1) - 0.5*(La2.*g3)'*s3;
                    end

                    %             gprior(i1) = gprior_cf(i2);
                end

                gprior = [gprior gprior_cf];
                %           % Set the gradients of hyperparameter
                %           if length(gprior_cf) > np
                %             for i2=np+1:length(gprior_cf)
                %               i1 = i1+1;
                %               gdata(i1) = 0;
                %               gprior(i1) = gprior_cf(i2);
                %             end
                %           end
            end

        end

        % =================================================================
        % Gradient with respect to likelihood function parameters

        if ~isempty(strfind(gp.infer_params, 'likelihood')) && ~isempty(gp.lik.fh.pak(gp.lik))
            gdata_lik = 0;
            lik = gp.lik;

            DW_sigma = lik.fh.llg3(lik, y, f, 'latent2+param', z);
            DL_sigma = lik.fh.llg(lik, y, f, 'param', z);
            DL_f_sigma = lik.fh.llg2(lik, y, f, 'latent+param', z);
            %         b = La1.*DL_f_sigma + B'*(B*DL_f_sigma);
            %         bb = (iLa2W.*b - L2*(L2'*b));
            %         s3 = b - (La1.*bb + B'*(B*bb));

            % b = Kfu*inv(Kuu)*Kfu'*dW/dth
            b = B'*(B*DL_f_sigma);
            % bb = Kfu*inv(Kuu+Kfu'*W*Kfu)*Kfu'*W
            bb = repmat(1./W,1,size(DL_f_sigma,2)).*(L2*(L2'*b));
            % s3 = df/dth
            s3 = b - bb;
            %bb = (repmat(W,1,size(DL_f_sigma,2)).*b - L2*(L2'*b));
            %s3 = b + B'*(B*bb);

            %         gdata_lik = - DL_sigma - 0.5.*sum(s2t.*DW_sigma) - s2'*s3;
            gdata_lik = - DL_sigma - 0.5.*sum(repmat(s2t,1,size(DL_f_sigma,2)).*DW_sigma) - s2'*s3;

            if isequal(gp.type, 'VAR')
                % Derivative of the trace term tr(diag(K-Q).*W)
                % Explicit dW/dth
                % La2 = diag(K-Q)
                gdata_lik = gdata_lik - 0.5*sum(La2.*DW_sigma);
                % Implicit dW/df*df/dth
                gdata_lik = gdata_lik + 0.5.*(La2.*g3)'*s3;
            end

            % evaluate prior contribution for the gradient
            if isfield(gp.lik, 'p')
                g_logPrior = -lik.fh.lpg(lik);
            else
                g_logPrior = zeros(size(gdata_lik));
            end
            % set the gradients into vectors that will be returned
            gdata = [gdata gdata_lik];
            gprior = [gprior g_logPrior];
            i1 = length(gdata);
        end

        % =================================================================
        % Gradient with respect to inducing inputs

        if ~isempty(strfind(gp.infer_params, 'inducing'))
            if isfield(gp.p, 'X_u') && ~isempty(gp.p.X_u)
                m = size(gp.X_u,2);
                st=0;
                if ~isempty(gdata)
                    st = length(gdata);
                end

                gdata(st+1:st+length(gp.X_u(:))) = 0;
                i1 = st+1;
                gprior_ind=[];
                if iscell(gp.p.X_u) % Own prior for each inducing input
                    for i = 1:size(gp.X_u,1)
                        pr = gp.p.X_u{i};
                        gprior_ind =[gprior_ind -pr.fh.lpg(gp.X_u(i,:), pr)];
                    end
                else % One prior for all inducing inputs
                    gprior_ind = -gp.p.X_u.fh.lpg(gp.X_u(:)', gp.p.X_u);
                end
                gprior = [gprior gprior_ind];

                for i=1:ncf
                    i1=st;

                    gpcf = gp.cf{i};
                    if savememory
                        % If savememory option is used, just get the number of
                        % covariates in X and calculate gradients later
                        np=gpcf.fh.ginput(gpcf,u,[],0);
                    else
                        np=1;
                        DKuu = gpcf.fh.ginput(gpcf, u);
                        DKuf = gpcf.fh.ginput(gpcf, u, x);
                    end

                    for i3 = 1:np
                        if savememory
                            DKuu=gpcf.fh.ginput(gpcf,u,[],i3);
                            DKuf=gpcf.fh.ginput(gpcf,u,x,i3);
                        end

                        for i2 = 1:length(DKuu)
                            if savememory
                                i1 = st + (i2-1)*np+i3;
                                %i1 = st + 2*i2 - (i3==1);
                            else
                                i1 = i1+1;
                            end

                            % 0.5* a'*dK*a, where a = K\f
                            KfuiKuuDKuu = iKuuKuf'*DKuu{i2};
                            gdata(i1) = -0.5.*((2.*a'*DKuf{i2}'-(a'*KfuiKuuDKuu))*(iKuuKuf*a));

                            % trace( inv(inv(W) + K) * dQ) )
                            gdata(i1) = gdata(i1) - 0.5.*sum(sum(L2'.*(2.*L2'*DKuf{i2}'*iKuuKuf - L2'*KfuiKuuDKuu*iKuuKuf)));
                            tt=0.5.*(2.*sum(W.*sum(DKuf{i2}'.*iKuuKuf',2)) - sum(W.*sum(KfuiKuuDKuu.*iKuuKuf',2)));
                            gdata(i1) = gdata(i1) + tt;

                            b = (2*DKuf{i2}' - KfuiKuuDKuu)*(iKuuKuf*b3);
                            bb = sqrtW.*sqrtW.*b - L2*(L2'*b);
                            s3 = b - (B'*(B*bb));
                            gdata(i1) = gdata(i1) - s2'*s3;

                            if isequal(gp.type, 'VAR')
                                % Derivative of 0.5.*tr(diag(K-Q).*W) (Titsias, 2009) wrt X_u
                                % Here we have to also split in explicit and implicit
                                % derivatives
                                gdata(i1) = gdata(i1) + 0 - tt;
                                % La2 = diag(K - Q);
                                gdata(i1) = gdata(i1) - 0.5*(La2.*g3)'*s3;
                            end
                        end
                    end
                end
            end
        end

        %       g = gdata + gprior;

end

% If ther parameters of the model (covariance function parameters,
% likelihood function parameters, inducing inputs) have additional
% hyperparameters that are not fixed,
% set the gradients in correct order
if length(gprior) > length(gdata)
    %gdata(gdata==0)=[];
    tmp=gdata;
    gdata = zeros(size(gprior));
    % Set the gradients to right place
    if any(hier==0)
        gdata([hier(1:find(hier==0,1)-1)==1 ...  % Covariance function
            hier(find(hier==0,1):find(hier==0,1)+length(g_logPrior)-1)==0 ... % Likelihood function
            hier(find(hier==0,1)+length(g_logPrior):end)==1]) = tmp;  % Inducing inputs
    else
        gdata(hier==1)=tmp;
    end
end
g = gdata + gprior;

assert(isreal(gdata))
assert(isreal(gprior))
end