function [cost,nmodel,output] = validate(model, Xtrain, Ytrain, Xtest, Ytest,estfct, trainfct, simfct) % Validate a trained model on a fixed validation set% % >> cost = validate({X,Y,type,gam,sig2}, Xtrain, Ytrain, Xtest, Ytest)% % In the case of regression, most common is to use the mean squared% error ('mse') as an estimate of the cost of the model. It is known% that the trimmed mean of the squared errors ('trimmedmse') is more% robust estimate when non-Gaussian noise or outliers occur. For% classification, a suitable cost criterion is the rate of% misclassification ('misclass'). % % By default, this function will call the training ('trainlssvm') 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% %     1. Using the functional interface for the LS-SVMs:% % >> cost = validate({X,Y,type,gam,sig2,kernel,preprocess}, Xtrain, Ytrain, Xtest, Ytest)% >> cost = validate({X,Y,type,gam,sig2,kernel,preprocess}, Xtrain, Ytrain, Xtest, Ytest, estfct)% %       Outputs    %         cost          : Cost estimated by validation on test set%       Inputs    %         X             : Training input data used for defining the LS-SVM and the preprocessing%         Y             : 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 (bandwidth in the case of the 'RBF_kernel')%         kernel(*)     : Kernel type (by default 'RBF_kernel')%         preprocess(*) : 'preprocess'(*) or 'original'%         Xtrain        : N x d matrix with the input data used for training%         Ytrain        : N x m matrix with the output data used for training%         Xtest         : N x d matrix with the input data used for testing%         Ytest         : N x m matrix with the output data used for testing%         estfct(*)     : Function estimating the cost based on the residuals (by default 'mse')% %%     2. Using the object oriented interface for the LS-SVMs:% % >> cost = validate(model, Xtrain, Ytrain, Xtest, Ytest)% >> cost = validate(model, Xtrain, Ytrain, Xtest, Ytest, estfct)% %       Outputs    %         cost      : Cost estimated by validation on test set%       Inputs    %         model     : Object oriented representation of the model%         Xtrain    : N x d matrix with the input data used for training%         Ytrain    : N x m matrix with the output data used for training%         Xtest     : N x d matrix with the input data used for testing%         Ytest     : N x m matrix with the output data used for testing%         estfct(*) : Function estimating the cost based on the residuals (by default 'mse')% %%     3. Using other modeling techniques::% % >> cost = validate(model, Xtrain, Ytrain, Xtest, Ytest, estfct, trainfct, simfct)% %       Outputs    %         cost     : Cost estimated by validation on test set%       Inputs    %         model    : Object oriented representation of the model%         Xtrain   : N x d matrix with the input data used for training%         Ytrain   : N x m matrix with the output data used for training%         Xtest    : N x d matrix with the input data used for testing%         Ytest    : N x m matrix with the output data used for testing%         estfct   : Function estimating the cost based on the residuals %         trainfct : Function used for training model%         simfct   : Function used for simulating model%%% See also:%   crossvalidate, leaveoneout % Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab%% defaults%eval('estfct;','estfct=''mse'';');eval('trainfct;','trainfct=''trainlssvm'';');eval('simfct;','simfct=''simlssvm'';');  % LS-SVMlabeval('model = initlssvm(model{:});',' ');model.status = 'changed';nmodel = feval(trainfct,model,Xtrain, Ytrain);output = feval(simfct,nmodel,Xtest);%% try to train and to evaluate the model%%disp(['-> ' num2str(size(Xtest)) ' & ' num2str(size(output)) ' & ' num2str(size(Ytest))]);eval(['cost = ' estfct '(output-Ytest);'],'cost = inf;')if cost==inf,   eval(['cost = ' estfct '(output,Ytest);'],...       'warning(''Error in estimator function ...'');');end%cost = eval('feval(estfct,output-Ytest);','feval(estfct,output,Ytest);');