function [zt,model] = latentlssvm(varargin)% Calculate the latent variables of the LS-SVM classifier at the given test data% % >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, Xt)% >> [Zt, model] = latentlssvm(model, Xt)% % The latent variables of a binary classifier are the continuous% simulated values of the test data which are used to make the% final classifications. The classification of a testpoint depends% on whether the latent value exceeds the model's threshold (b). If% appropriate, the model is trained by the standard procedure (trainlssvm) first.% % As an application example: crossvalidation can be based on the latent variables:% % >> cost = crossvalidate(model, X, Y, 10, 'mse', 'mean', 'original', 'trainlssvm', 'latentlssvm')% %% Full syntax% %     1. Using the functional interface:% % >> Zt = latentlssvm({X,Y,type,gam,sig2,kernel,preprocess}, Xt)% %       Outputs    %         Zt            : Nt x m matrix with predicted latent simulated outputs%       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          : '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'%         Xt            : Nt x d matrix with the inputs of the test data% %%     2. Using the object oriented interface:% % >> [Zt, model] = latentlssvm(model, Xt)% %       Outputs    %         Zt       : Nt x m matrix with continuous latent simulated outputs%         model(*) : Trained object oriented representation of the LS-SVM model%       Inputs    %         model    : Object oriented representation of the LS-SVM model%         Xt       : Nt x d matrix with the inputs of the test data% % See also:%   trainlssvm, simlssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabmodel = varargin{1};if iscell(model),  model = initlssvm(model{:});endif model.type(1)~='c',  error('Only usefull for classification tasks...');end[Y, zt, model] = simlssvm(varargin{:});