function [coste, costse, costs] = rcrossvalidate(model, X,Y, L, times, estfct,combinefct, trainfct,simfct)% Estimate the model performance with robust L-fold crossvalidation% % >> cost = rcrossvalidate({X,Y,'function',gam,sig2}, X,Y)% >> cost = rcrossvalidate(model, X,Y)% % Robustness in the L-fold crossvalidation score function is% obtained by using a trimmed mean of the squared residuals in the% individual error estimates and by repeating the crossvalidation% over different partitions of the data. % % This routine is very computational intensive.% % By default, this function will call the training (robustlssvm)% and simulation (simlssvm) algorithms for LS-SVMs. However, one% can use the validation function more generically by specifying% the appropriate training and simulation function. % % Full syntax% %     * Using LS-SVMlab with the functional interface:% % >> [cost, costs, ec] = rcrossvalidate({X,Y,type,gam,sig2,kernel, preprocess},Xval, Yval)% >> [cost, costs, ec] = rcrossvalidate({X,Y,type,gam,sig2,kernel, preprocess},Xval, Yval, L)% >> [cost, costs, ec] = rcrossvalidate({X,Y,type,gam,sig2,kernel, preprocess},Xval, Yval, L, times)% >> [cost, costs, ec] = rcrossvalidate({X,Y,type,gam,sig2,kernel, preprocess},Xval, Yval, L, times, estfct, combinefct)% %       Outputs    %         cost          : Cost estimation of the robust L-fold cross-validation%         costs(*)      : L x 1 vector with costs estimated on the L different folds%         ec(*)         : N x 1 vector with residuals of all data%       Inputs    %         X             : N x d matrix with training input data used for defining the LS-SVM and the preprocessing%         Y             : N x m matrix with training output data used for defining the LS-SVM and the preprocessing%         type          : 'function estimation' ('f') or 'classifier' ('c')%         gam           : Regularization parameter%         sig2          : Kernel parameter(s) (for linear kernel, use |[]|)%         kernel(*)     : Kernel type (by default 'RBF_kernel')%         preprocess(*) : 'preprocess'(*) or 'original'%         Xval          : N x d matrix with the inputs of the data used for cross-validation%         Yval          : N x m matrix with the outputs of the data used for cross-validation%         L(*)          : Number of folds (by default 10)%         times(*)      : Number of times the data is distributed over the L folds%         estfct(*)     : Function estimating the cost based on the residuals (by default trimmedmse)%         combinefct(*) : Function combining the estimated costs on the different folds (by default mean)% %%     * Using the object oriented interface:% % >> [cost, costs, ec] = rcrossvalidate(model, Xval, Yval)% >> [cost, costs, ec] = rcrossvalidate(model, Xval, Yval, L)% >> [cost, costs, ec] = rcrossvalidate(model, Xval, Yval, L, times)% >> [cost, costs, ec] = rcrossvalidate(model, Xval, Yval, L, times, estfct, combinefct)% %       Outputs    %         cost          : Cost estimation of the robust L-fold cross-validation%         costs(*)      : L x 1 vector with costs estimated on the L different folds%         ec(*)         : N x 1 vector with residuals of all data%       Inputs    %         model         : Object oriented representation of the LS-SVM model%         Xval          : Nt x d matrix with the inputs of the validation points used in the procedure%         Yval          : Nt x m matrix with the outputs of the validation points used in the procedure%         L(*)          : Number of folds (by default 10)%         times(*)      : Number of times the data is distributed over the L folds%         estfct(*)     : Function estimating the cost based on the residuals (by default trimmedmse)%         combinefct(*) : Function combining the estimated costs on the different folds (by default mean)% %  %     * Using other modeling techniques:%   % >> [cost, costs, ec] = rcrossvalidate(model, Xval, Yval, L, times, estfct, combinefct, trainfct, simfct)% %       Outputs    %         cost          : Cost estimation of the robust L-fold cross-validation%         costs(*)      : l x 1 vector with costs estimated on the L different folds%         ec(*)         : N x 1 vector with residuals of all data%       Inputs    %         model         : Object oriented representation of the model%         Xval          : Nt x d matrix with the inputs of the validation points used%         Yval          : Nt x m matrix with the outputs of the validation points used in the procedure%         L(*)          : Number of folds (by default 10)%         times(*)      : Number of times the data is distributed over the L folds%         estfct(*)     : Function estimating the cost based on the residuals (by default trimmedmse)%         combinefct(*) : Function combining the estimated costs on the different folds (by default mean)%         trainfct      : Function used to train robustly the model%         simfct        : Function used to simulate test data with the model% % See also:%  trimmedmse, crossvalidate, validate, trainlssvm, robustlssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabeval('times;','times=10;');eval('L;','L=10;');eval('estfct;','estfct=''trimmedmse'';');eval('combinefct;','combinefct=''mean'';');eval('trainfct;','trainfct=''robustlssvm'';');eval('simfct;','simfct=''simlssvm'';');estfct='trimmedmse';for t=1:times,  [costse(t),costs(:,t)] = ...      crossvalidate(model, X,Y, L, estfct,combinefct,0,trainfct,simfct);endcoste = mean(costse);