function [model,H] = lssvmMATLAB(model) % Only for intern LS-SVMlab use;%% MATLAB implementation of the LS-SVM algorithm. This is slower% than the C-mex implementation, but it is more reliable and flexible;%%% This implementation is quite straightforward, based on MATLAB's% backslash matrix division (or PCG if available) and total kernel% matrix construction. It has some extensions towards advanced% techniques, especially applicable on small datasets (weighed% LS-SVM, gamma-per-datapoint)% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab%fprintf('~');%% is it weighted LS-SVM ?%weighted = (length(model.gam)>model.y_dim);if and(weighted,length(model.gam)~=model.nb_data),  warning('not enough gamma''s for Weighted LS-SVMs, simple LS-SVM applied');  weighted=0;end% --------------% classification% --------------%% no blockdiag. as described in papers. (MATLAB '\' ! ),  for% multi-class tasks, the algorithm is multiple times executed. the% kernel-matrix is just calculated once.if (model.type(1)=='c'),   % computation omega and H   H = kernel_matrix(model.xtrain(model.selector, 1:model.x_dim), ...			model.kernel_type, model.kernel_pars);  % initiate alpha and b  model.b = zeros(1,model.y_dim);  model.alpha = zeros(model.nb_data,model.y_dim);    for i=1:model.y_dim,      for t=1:model.nb_data,for s=1:t-1,	H(s,t) = H(s,t)*(model.ytrain(model.selector(s),i)*model.ytrain(model.selector(t),i)');	H(t,s) = H(s,t);    end; end        if size(model.gam,2)==model.nb_data,       invgam = model.gam(i,:).^-1;            for t=1:model.nb_data, H(t,t) = H(t,t)+invgam(t); end    else      invgam = model.gam(i,1)^-1;      for t=1:model.nb_data, H(t,t) = H(t,t)+invgam; end    end        nuv = H\[model.ytrain(model.selector,i) ones(model.nb_data,1)];    %eval('nuv = pcg(H,[model.ytrain(model.selector,i) ones(model.nb_data,1)],100*eps,model.nb_data);','nuv = H\[model.ytrain(model.selector,i) ones(model.nb_data,1)];');       nu(:,i) = nuv(:,1);    v(:,i) = nuv(:,2);        s(i) = model.ytrain(model.selector,i)'*nu(:,i);    model.b(i) = (nu(:,i)'*ones(model.nb_data,1))/s(i);    model.alpha(:,i) = v(:,i)-(nu(:,1)*model.b(i));  end    return else% ---------------------%  function estimation  % ---------------------  % computation omega and H   omega = kernel_matrix(model.xtrain(model.selector, 1:model.x_dim), ...			model.kernel_type, model.kernel_pars);    % initiate alpha and b  model.b = zeros(1,model.y_dim);  model.alpha = zeros(model.nb_data,model.y_dim);  for i=1:model.y_dim,    H = omega;    % computation matrix omega = K(x_i,x_j)*1/gamma    if size(model.gam,2)==model.nb_data,       eval('invgam = model.gam(i,:).^-1;','invgam = model.gam(1,:).^-1;');      for t=1:model.nb_data, H(t,t) = H(t,t)+invgam(t); end    else      eval('invgam = model.gam(i,1).^-1;','invgam = model.gam(1,1).^-1;');      for t=1:model.nb_data, H(t,t) = H(t,t)+invgam; end    end         v = H\model.ytrain(model.selector,i);     %eval('v  = pcg(H,model.ytrain(model.selector,i), 100*eps,model.nb_data);','v = H\model.ytrain(model.selector, i);');    nu = H\ones(model.nb_data,1);    %eval('nu = pcg(H,ones(model.nb_data,i), 100*eps,model.nb_data);','nu = H\ones(model.nb_data,i);');    s = ones(1,model.nb_data)*nu(:,1);    model.b(i) = (nu(:,1)'*model.ytrain(model.selector,i))./s;      model.alpha(1:model.nb_data,i) = v(:,1)-(nu(:,1)*model.b(i));  end  return   end