function [cost,costs,output] = leaveoneout(model, X,Y, estfct,combinefct, corrected,trainfct,simfct)% Estimate the performance of a trained model with leave-one-out crossvalidation% % >> leaveoneout({X,Y,type,gam,sig2}, Xval, Yval)% >> leaveoneout(model, Xval, Yval)% % In each iteration, one leaves one point, and fits a model on the% other data points. The performance of the model is estimated% based on the point left out. This procedure is repeated for each% data point. Finally, all the different estimates of the% performance are combined (default by computing the mean). The% assumption is made that the input data is distributed independent% and identically over the input space. A statistical bias% reduction technique can be applied.% % 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, costs, el] = leaveoneout({X,Y,type,gam,sig2,kernel,preprocess}, Xval, Yval)% >> [cost, costs, el] = leaveoneout({X,Y,type,gam,sig2,kernel,preprocess}, Xval, Yval, estfct)% >> [cost, costs, el] = leaveoneout({X,Y,type,gam,sig2,kernel,preprocess}, Xval, Yval, estfct, combinefct)% >> [cost, costs, el] = leaveoneout({X,Y,type,gam,sig2,kernel,preprocess}, Xval, Yval, estfct, combinefct, correction)% %       Outputs    %         cost          : Cost estimated by leave-one-out crossvalidation%         costs(*)      : N x 1 vector with the costs of the N folds%         el(*)         : N x 1 vector with the leave-one-out residuals%       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'%         Xval          : N x d matrix with the inputs of the data used for leave-one-out cross-validation%         Yval          : N x m matrix with the outputs of the data used for leave-one-out cross-validation%         estfct(*)     : Function estimating the cost based on the residuals (by default mse)%         combinefct(*) : Function combining the estimated costs on the different folds (by default mean)%         correction(*) : 'original'(*) or 'corrected'% %%     2. Using the object oriented interface for the LS-SVMs:% % >> [cost, costs, el] = leaveoneout(model, Xval, Yval)% >> [cost, costs, el] = leaveoneout(model, Xval, Yval, estfct)% >> [cost, costs, el] = leaveoneout(model, Xval, Yval, estfct, combinefct)% >> [cost, costs, el] = leaveoneout(model, Xval, Yval, estfct, combinefct, correction)% %       Outputs    %         cost          : Cost estimated by leave-one-out crossvalidation%         costs(*)      : N x 1 vector with costs estimated on the N different folds%         el(*)         : 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%         estfct(*)     : Function estimating the cost based on the residuals (by default mse)%         combinefct(*) : Function combining the estimated costs on the different folds (by default mean)%         correction(*) : 'original'(*) or 'corrected'% %%     3. Using other modeling techniques:% % >> [cost, costs, el] = leaveoneout(model, Xval, Yval, estfct, combinefct, correction, trainfct, simfct)% %       Outputs    %         cost          : Cost estimated by leave-one-out crossvalidation%         costs(*)      : N x 1 vector with costs estimated on the N different folds%         el(*)         : 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%         estfct(*)     : Function estimating the cost based on the residuals (by default mse)%         combinefct(*) : Function combining the estimated costs on the different folds (by default mean)%         correction(*) : 'original'(*) or 'corrected'%         trainfct      : Function used to train the model%         simfct        : Function used to simulate test data with the model% % See also:%   leaveoneout_lssvm, validate, crossvalidate, trainlssvm, simlssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab%% initialisation and defaults%eval('estfct;','estfct=''mse'';');eval('combinefct;','combinefct=''mean'';');eval('trainfct;','trainfct=''trainlssvm'';');eval('simfct;','simfct=''simlssvm'';');eval('corrected;','corrected=''original'';');[cost,costs,output] = crossvalidate(model,X,Y,size(X,1),estfct,combinefct,corrected,trainfct,simfct);