function [Y,Yl,model] = simlssvm(model,Xt,A3,A4,A5)% Evaluate the LS-SVM at the given points% % >> Yt = simlssvm({X,Y,type,gam,sig2,kernel}, {alpha,b}, Xt)% >> Yt = simlssvm({X,Y,type,gam,sig2,kernel}, Xt)% >> Yt = simlssvm(model, Xt)% % The matrix Xt represents the points one wants to predict. The% first cell contains all arguments needed for defining the LS-SVM% (see also trainlssvm, initlssvm). The second cell contains the% results of training this LS-SVM model. The cell syntax allows for% flexible and consistent default handling.%% As in training, three different implementations are included% (simclssvm.mex*, simFILE.x, simlssvm.m). The cmex algorithm is% called, except when specified otherwise. After a simulation call,% a '.' is displayed.%%% Full syntax% %     1. Using the functional interface:% % >> [Yt, Zt] = simlssvm({X,Y,type,gam,sig2}, Xt)% >> [Yt, Zt] = simlssvm({X,Y,type,gam,sig2,kernel}, Xt)% >> [Yt, Zt] = simlssvm({X,Y,type,gam,sig2,kernel,preprocess}, Xt)% >> [Yt, Zt] = simlssvm({X,Y,type,gam,sig2,kernel,preprocess}, {alpha,b}, Xt)% %       Outputs    %         Yt            : Nt x m matrix with predicted output of test data%         Zt(*)         : Nt x m matrix with predicted latent variables of a classifier%       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          : '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'%         alpha(*)      : Support values obtained from training%         b(*)          : Bias term obtained from training%         Xt            : Nt x d inputs of the test data% %%     2. Using the object oriented interface:% % >> [Yt, Zt, model] = simlssvm(model, Xt)% %       Outputs    %         Yt       : Nt x m matrix with predicted output of test data%         Zt(*)    : Nt x m matrix with predicted latent variables of a classifier%         model(*) : 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, initlssvm, plotlssvm, code, changelssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab%% control inputs%if iscell(model),   iscell_model = 1;  model = initlssvm(model{:});  if iscell(Xt),    model.alpha = Xt{1};    model.b = Xt{2};    model.status = 'trained';    eval('Xt = A3;',' ');  end  eval('nb_to_sim = A4;','nb_to_sim = size(Xt,1)-model.x_delays;');  Yt = [];else  iscell_model = 0;  if nargin>3,    Yt = A3;    eval('nb_to_sim = A4;','nb_to_sim = size(Xt,1)-model.x_delays;');  else    eval('nb_to_sim = A3;','nb_to_sim = size(Xt,1)-model.x_delays;');    Yt =[];  endendeval('Xt;','error(''Test data Xtest undefined...'');');%% check dimensions%if size(Xt,2)~=model.x_dim,  error('dimensions of new datapoints Xt not equal to trainingsset...');endif ~isempty(Yt) & size(Yt,2)~=model.y_dim,  error('dimensions of new targetpoints Yt not equal to trainingsset...');end%% preprocessing ...%if model.preprocess(1)=='p',  [Xt,Yt] = prelssvm(model,Xt,Yt);end%% if dimension of output >1%if model.y_dim>1,  if length(model.kernel_type)>1 | size(model.kernel_pars,2)>1 | size(model.gam,2)==model.y_dim,    %disp('multi dimensional output...');    fprintf('m');    [Y Yl] = simmultidimoutput(model,Xt,Yt,nb_to_sim);    if iscell_model, model = Yl; end    return  endend%% train if status is not  'trained'%if model.status(1)~='t', % not 'trained'  model = trainlssvm(model);end%% set parameters: how much points to evaluate and to simulate%if (model.type(1)=='c'),  nb_sim = nb_to_sim;    Yt=[];elseif (model.type(1)=='f'),  nb_sim = nb_to_sim;  Yt=[];end%% simulate the model,%   using simlssvm.mex*, simlssvmFILE.m or the MATLAB implementations;%if strcmpi(model.implementation,'CMEX'),  eval(['Y = simclssvm(Xt'',Yt, nb_sim, nb_to_sim, model.alpha,model.b,'...	'    model.xtrain'', model.x_dim,  model.ytrain, model.y_dim,'... 	'    model.nb_data, model.type, model.gam, model.cga_eps, model.cga_fi_bound, '...	'    model.cga_max_itr, model.kernel_type, model.kernel_pars, '...	'    model.cga_show, [model.steps, model.x_delays, model.y_delays]); '],...        'model = changelssvm(model,''implementation'',''CFILE''); disp(''converting implementation to CFILE...'');');endif strcmpi(model.implementation,'CFILE'),  eval('Y = simFILE(model,''buffer.mc'',Xt,Yt);',...       ['model = changelssvm(model,''implementation'',''MATLAB'');' ...	'fprintf(''converting now to MATLAB implementation...'');']); endif strcmpi(model.implementation, 'MATLAB'),  if (model.type(1)=='c'), Y=simClass(model,Xt);     % classification  elseif (model.type(1)=='f'), Y=simFct(model,Xt); % function estimation  end  end%% for classification%Yl = Y;if model.type(1)=='c' & strcmp(model.latent,'no'),  Y = 2*(Y>0)-1;end%% postprocessing...%if model.preprocess(1)=='p' & ~(model.type(1)=='c' & strcmp(model.latent,'yes')),  [X,Y] = postlssvm(model,[],Y);end%% decode if multiclass%if model.type(1)=='c' & ~strcmpi(model.codetype,'none' ) & ~strcmpi(model.code,'original'),  Y = codelssvm(model,Y);end    %% Function Estimation%function Y = simFct(model,X)%%%kx = kernel_matrix(model.xtrain(model.selector, 1:model.x_dim), model.kernel_type, model.kernel_pars,X);    Y = kx'*model.alpha(:,1:model.y_dim)+ones(size(kx,2),1)*model.b(:,1:model.y_dim);%% Classification%function Y = simClass(model,X)%%%kx = kernel_matrix(model.xtrain(model.selector, 1:model.x_dim), ...		   model.kernel_type, model.kernel_pars,X);Y = kx'*(model.alpha(model.selector,1:model.y_dim).*model.ytrain(model.selector,1:model.y_dim))+ones(size(kx,2),1)*model.b(:,1:model.y_dim);%%%function [Yt,Yl] = simmultidimoutput(model, Xt, Y,n)%% what to do if output multimensional?%   Yt = []; Yl = [];  for d=1:model.y_dim,    eval('gam = model.gam(:,d);','gam = model.gam;');    eval('sig2 = model.kernel_pars(:,d);','sig2 = model.kernel_pars;');    eval('kernel = model.kernel_typeggamyom;','kernel=model.kernel_type;');    % not yet timeseries nor NARX    [Ytn Yln] = simlssvm({model.xtrain, model.ytrain(:,d),model.type,gam,sig2,kernel,'original'},{model.alpha(:,d),model.b(d)},Xt);        Yt = [Yt Ytn]; Yl = [Yl Yln];   end    % postprocessing...  if model.preprocess(1)=='p' & ~(model.type(1)=='c' & strcmp(model.latent,'yes')),    [X,Yt] = postlssvm(model,[],Yt);  end  % decode if multiclass  if model.type(1)=='c' & ~strcmpi(model.codetype,'none' ) & ~strcmpi(model.code,'original'),    Yt = codelssvm(model,Yt);  end