function [model,b,X,Y]  = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation% % >> [alpha, b] = trainlssvm({X,Y,type,gam,kernel_par,kernel,preprocess})% >> model      = trainlssvm(model)% % type can be 'classifier' or 'function estimation' (these strings% can be abbreviated into 'c' or 'f', respectively). X and Y are% matrices holding the training input and output data. The i-th% data point is represented by the i-th row X(i,:) and Y(i,:). gam% is the regularization parameter: for gam low minimizing of the% complexity of the model is emphasized, for gam high, good fitting% of the training data points is stressed. kernel_par is the% parameter of the kernel; in the common case of an RBF kernel, a% large sig2 indicates a stronger smoothing. The kernel_type% indicates the function that is called to compute the kernel value% (by default RBF_kernel). Other kernels can be used for example: % % >> [alpha, b] = trainlssvm({X,Y,type,gam,[d p],'poly_kernel'})% >> [alpha, b] = trainlssvm({X,Y,type,gam,[]   ,'lin_kernel'})% % The kernel parameter(s) are passed as a row vector, in the case% no kernel parameter is needed, pass the empty vector! % % The training can either be proceeded by the preprocessing% function ('preprocess') (by default) or not ('original'). The% training calls the preprocessing (prelssvm, postlssvm) and the% encoder (codelssvm) if appropiate. % % In the remainder of the text, the content of the cell determining% the LS-SVM is given by {X,Y, type, gam, sig2}. However, the% additional arguments in this cell can always be added in the% calls. % % If one uses the object oriented interface (see also A.3.14), the training is done by% % >> model = trainlssvm(model)% >> model = trainlssvm(model, X, Y)% % The status of the model checks whether a retraining is% needed. The extra arguments X, Y allow to re-initialize the model% with this new training data as long as its dimensions are the% same as the old initiation. % % Three training implementations are included:% %     * The C-implementation linked with CMEX: this implementation%     is based on the iterative solver Conjugate Gradient algorithm%     (CG) (lssvm.mex*). After this training call, a '-' is%     displayed. This is recommended for use on larger data sets. % %     * The C-implementation called via a buffer file: this is%     based on CG; check if the executable 'lssvmFILE.x' is in the%     current directory; (lssvmFILE.x). After this training call, a%     '-' is displayed. %  %     * The Matlab implementation: a straightforward implementation%     based on the matrix division '\' (lssvmMATLAB.m). After this%     training call, a '~' is  displayed. This is recommended for a%     number of training data points smaller than 500 (depending on%     the computer memory).  % % By default, the cmex implementation is called. If this one fails,% the Matlab implementation is chosen instead. One can specify% explicitly which implementation to use using the object oriented% interface. % % This implementation allows to train a multidimensional output% problem. If each output uses the same kernel type, kernel% parameters and regularization parameter, this is% straightforward. If not so, one can specify the different types% and/or parameters as a row vector in the appropriate% argument. Each dimension will be trained with the corresponding% column in this vector. % % >> [alpha, b] = trainlssvm({X, [Y_1 ... Y_d],type,...%                              [gam_1 ... gam_d], ...%                             [sig2_1 ... sig2_d],...%                           {kernel_1,...,kernel_d}})% % Full syntax% %     1. Using the functional interface:% % >> [alpha, b] = trainlssvm({X,Y,type,gam,sig2})% >> [alpha, b] = trainlssvm({X,Y,type,gam,sig2,kernel})% >> [alpha, b] = trainlssvm({X,Y,type,gam,sig2,kernel,preprocess})% %       Outputs    %         alpha         : N x m matrix with support values of the LS-SVM%         b             : 1 x m vector with bias term(s) of the LS-SVM%       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'% %%     * Using the object oriented interface:% % >> model = trainlssvm(model)% >> model = trainlssvm({X,Y,type,gam,sig2})% >> model = trainlssvm({X,Y,type,gam,sig2,kernel})% >> model = trainlssvm({X,Y,type,gam,sig2,kernel,preprocess})% %       Outputs    %         model          : Trained object oriented representation of the LS-SVM model%       Inputs    %         model          : Object oriented representation of the LS-SVM model%         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'% % See also:%   simlssvm, initlssvm, changelssvm, plotlssvm, prelssvm, codelssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab%% initialise the model 'model'%if (iscell(model)),  model = initlssvm(model{:});end%% given X and Y?%%model = codelssvm(model);eval('model = changelssvm(model,''xtrain'',X);',';');eval('model = changelssvm(model,''ytrain'',Y);',';');eval('model = changelssvm(model,''selector'',1:size(X,1));',';');%% no training needed if status = 'trained'%if model.status(1) == 't',  if (nargout>1),    % [alpha,b]    X = model.xtrain;    Y = model.ytrain;    b = model.b;    model = model.alpha;  end  returnend  %% control of the inputs%if ~((strcmp(model.kernel_type,'RBF_kernel') & length(model.kernel_pars)>=1) |...     (strcmp(model.kernel_type,'lin_kernel') & length(model.kernel_pars)>=0) |...     (strcmp(model.kernel_type,'MLP_kernel') & length(model.kernel_pars)>=2) |...     (strcmp(model.kernel_type,'poly_kernel')& length(model.kernel_pars)>=1)),  eval('feval(model.kernel_type,model.xtrain(1,:),model.xtrain(2,:),model.kernel_pars);model.implementation=''MATLAB'';',...       'error(''The kernel type is not valid or to few arguments'');');elseif (model.steps<=0),  error('steps must be larger then 0');elseif (model.gam<=0),  error('gamma must be larger then 0');% elseif (model.kernel_pars<=0),%   error('sig2 must be larger then 0');elseif or(model.x_dim<=0, model.y_dim<=0),  error('dimension of datapoints must be larger than 0');end%% coding if needed%if model.code(1) == 'c', % changed  model = codelssvm(model);end%% preprocess%eval('if model.prestatus(1)==''c'', changed=1; else changed=0;end;','changed=0;');if model.preprocess(1) =='p' & changed,  model = prelssvm(model);elseif model.preprocess(1) =='o' & changed   model = postlssvm(model);end% clocktic;%% set & control input variables and dimensions% if (model.type(1) == 'f'), % function  dyn_pars=[];elseif (model.type(1) == 'c'), % class  dyn_pars=[];  end% only MATLABif size(model.gam,1)>1,   model.implementation='MATLAB'; end%% output dimension > 1...recursive call on each dimension%if model.y_dim>1,  if (length(model.kernel_pars)==model.y_dim | size(model.gam,2)==model.y_dim |prod(size(model.kernel_type,2))==model.y_dim)    disp('multidimensional output...');    model = trainmultidimoutput(model);    %    % wich output is wanted?    %    if (nargout>1),      X = model.xtrain;      Y = model.ytrain;      b = model.b;      model = model.alpha;    else           model.duration = toc;      model.status = 'trained';    end    return    endend%% call lssvmMATLAB.m, lssvm.mex* or lssvmFILE.m%if strcmpi(model.implementation,'CMEX'),  model.cga_startvalues = [];  eval('model.cga_startvalues;','model.cga_startvalues = [];');    eval(['[model.alpha, model.b,model.cga_startvalues] =' ...	'lssvm(model.xtrain(model.selector, 1:model.x_dim)'',model.x_dim,'...	'model.ytrain(model.selector, 1:model.y_dim),model.y_dim,'...	'model.nb_data, model.type, model.gam,' ...	'model.cga_eps, model.cga_fi_bound,model.cga_max_itr,' ...	'model.cga_startvalues,'...	'model.kernel_type,  model.kernel_pars,' ...	'model.cga_show,dyn_pars);'],...        'model.implementation=''CFILE''; disp(''converting now to CFILE implementation'');');% if error in CMEX ...endif strcmpi(model.implementation,'CFILE'),  eval('model.cga_startvalues;','model.cga_startvalues = [];');  eval('model = lssvmFILE(model,''buffer.mc'');',...       ['model.implementation=''MATLAB'';'...	'disp(''make sure lssvmFILE.x (lssvmFILE.exe) is in the' ...	 ' current directory, change now to MATLAB implementation...'');']);   % if error in CFILE ...endif strcmpi(model.implementation(1),'m'),  model = lssvmMATLAB(model);end%% wich output is wanted?%if (nargout>1),  X = model.xtrain;  Y = model.ytrain;  b = model.b;  model = model.alpha;else       model.duration = toc;  model.status = 'trained';end    %%function model = trainmultidimoutput(model)%% %  model.alpha = zeros(model.nb_data, model.y_dim);  model.b = zeros(1,model.y_dim);  model.cga_startvalues = [];  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_typejqwsuyi;','kernel=model.kernel_type;');    [model.alpha(:,d),model.b(d)] = trainlssvm({model.xtrain,model.ytrain(:,d),model.type,gam,sig2,kernel,'original'});  end    %  % wich output is wanted?  %  if (nargout>1),    X = model.xtrain;    Y = model.ytrain;    b = model.b;    model = model.alpha;  else         model.duration = toc;    model.status = 'trained';  end