function prediction = predict(model,Xt,n, simfct, args)% Iterative prediction of a trained LS-SVM NARX model (in recurrent mode)% % >> Yp = predict({Xw,Yw,type,gam,sig2}, Xt, nb)% >> Yp = predict(model,                 Xt, nb)% % The model needs to be trained using Xw, Yw which is the result of% windowize or windowizeNARX. The number of time lags for the model% is determined by the dimension of the input, or if not% appropriate, by the number of given starting values.% % By default, the model is evaluated on the past points using% simlssvm. However, if one wants to use this procedure for other% models, this default can be overwritten by your favorite training% function. This function (denoted by simfct) has to follow the following syntax:% % >> simfct(model,inputs,arguments)% % thus:% % >> Yp = predict(model, Xt, nb, simfct)% >> Yp = predict(model, Xt, nb, simfct, arguments)% %% Full syntax% %     1. Using the functional interface for the LS-SVMs:% % >> Yp = predict({Xw,Yw,type,gam,sig2,kernel,preprocess}, Xt, nb)% %       Outputs    %         Yp            : nb x m matrix with the predictions%       Inputs    %         Xw            : N x d matrix with the inputs of the training data%         Yw            : N x w matrix 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' (by default)%         Xt            : nb*d matrix of the starting points for the prediction%         nb(*)         : Number of outputs to predict% %%     2. Using the object oriented interface with LS-SVMs:% % >> Yp = predict(model, Xt, nb)% %       Outputs    %         Yp    : nb x m matrix with the predictions%       Inputs    %         Xt    : nb x d matrix of the starting points for the prediction%         nb(*) : Number of outputs to predict% %%     3. Using another model:% % >> Yp = predict(model, Xt, nb, simfct, arguments)% %       Outputs    %         Yp           : nb x m matrix with the predictions%       Inputs    %         Xt           : nb x d matrix of the starting points for the prediction%         nb           : Number of outputs to predict%         simfct       : Function used to evaluate a test point%         arguments(*) : Cell with the extra arguments passed to simfct% % See also:%   windowize, trainlssvm, simlssvm.% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabif size(Xt,2)~=1 & size(Xt,1)~=1,  error('prediction only implemented for the one-dimensional autonomous case');endeval('model = initlssvm(model{:});',' ');eval('xdim = model.x_dim;','xdim = max(size(Xt)); ');eval('n; alle=0;','n=length(Xt)-xdim; alle=1;');Xt = Xt(1:xdim); Xt = reshape(Xt,length(Xt),1);eval('simfct;','simfct=''simlssvm'';');eval('model = trainlssvm(model);');xdelays = length(Xt);prediction = zeros(xdelays+n,1);prediction(1:xdelays) = Xt;% closed loopeval('for i=1:n,  prediction(xdelays+i) = feval(simfct,model, prediction(i-1+(1:xdelays))'',args); end',...     'for i=1:n,  prediction(xdelays+i) = feval(simfct,model,prediction(i-1+(1:xdelays))''); end');if ~alle,  prediction = prediction(xdelays+1:end);end