function [pmodel,nb,errest] = sparselssvm(model,tradeoff, step)% Remove iteratively the least relevant support vectors in order to obtain sparsity% % >> selector = sparselssvm({X,Y,type,gam, sig2}, tradeoff, step) % >> model    = sparselssvm(model,                tradeoff, step)% % In each iteration step (by default 5%) of the support values% are set to zero until the performance becomes less than tradeoff% procent (default 75%) of the original performance.% % %  Full syntax% %     1. Using the functional interface:% % >> [selector, errest] = sparselssvm({X,Y,type,gam, sig2,kernel,preprocess})% >> [selector, errest] = sparselssvm({X,Y,type,gam, sig2,kernel,preprocess}, cutoff)% >> [selector, errest] = sparselssvm({X,Y,type,gam, sig2,kernel,preprocess}, cutoff, step)% %       Outputs    %         selector N x 1 vector of index of chosen support vectors%         errest(*) Estimated cost on all training data after pruning%       Inputs    %         X             : N x d matrix with the inputs of the training data%         Y             : N x 1 vector with the outputs of the training data%         type          : 'function estimation' ('f') or 'classifier' ('c')%         gam           : Regularization parameter%         sig2          : Kernel parameter (bandwidth in the case of the 'RBF_kernel')%         kernel(*)     : Kernel type (by default 'RBF_kernel')%         preprocess(*) : 'preprocess'(*) or 'original'%         cutoff(*)     : Cutoff between the validation of the original to the pruned LS-SVM%         step(*)       : Number of the pruned support vectors in each iteration step% %     2. Using the object oriented interface:% % >> [model, errest] = sparselssvm(model)% >> [model, errest] = sparselssvm(model, cutoff)% >> [model, errest] = sparselssvm(model, cutoff, step)% %       Outputs    %         model     : Pruned object oriented representation of the LS-SVM model%         errest(*) : Estimated cost on all training data after pruning%       Inputs    %         model     : Object oriented representation of the LS-SVM model%         cutoff(*) : Cutoff between the validation of the original to the pruned LS-SVM%         step(*)   : Number of the pruned support vectors in each iteration step% % See also:%   trainlssvm, tunelssvm, robustlssvm, crossvalidate		    % Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab% % default%eval('tradeoff;','tradeoff=.75;')eval('step;','step=.05;')eval('estfct;','estfct=''validate'';');errest(1)=0;t=1;if iscell(model), functional=1; else functional=0; end%% initial cost%pmodel = trainlssvm(model);if pmodel.y_dim>1, error('Only possible for one dimensional output models'); end[Xt,Yt] = postlssvm(model,model.xtrain, model.ytrain);ierrest = validate(pmodel,Xt,Yt,Xt,Yt);selector = 1:pmodel.nb_data;%% loop%while and(errest(t)*tradeoff<=ierrest,pmodel.nb_data>5),     %  % prune  %   [spectr,spectri] = sort(abs(pmodel.alpha));  spectri = spectri(max(3,floor(length(spectri)*step)):end);  pmodel = changelssvm(pmodel,'selector',pmodel.selector(spectri));     %  % re-estimate performance  %  pmodel.alpha =[];  %pmodel = trainlssvm(pmodel);  [errest(t+1),pmodel] = validate(pmodel,Xt(pmodel.selector,:),Yt(pmodel.selector,:),Xt,Yt);  t= t+1;end%plot(errest)if functional  pmodel = pmodel.selector;end