function model = initlssvm(X,Y,type, gam, sig2, kernel_type, preprocess, implementation,cgashow)% Initiate the object oriented structure representing the LS-SVM model%%   model = initlssvm(X,Y, type, gam, sig2)%   model = initlssvm(X,Y, type, gam, sig2, kernel_type)%   model = initlssvm(X,Y, type, gam, sig2, kernel_type, implementation)%% Full syntax% % >> model = initlssvm(X, Y, type, gam, sig2, kernel, preprocess)% %       Outputs    %         model         : Object oriented representation of the LS-SVM model%       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'%         implementation(*): 'CMEX' (*), 'CFILE' or 'MATLAB'% %% see also:%   trainlssvm, simlssvm, changelssvm, codelssvm, prelssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab% check enough arguments?if nargin<5,  error('Not enough arguments to initialize model..');elseif ~isnumeric(sig2),  error(['Kernel parameter ''sig2'' needs to be a (array of) reals' ...	 ' or the empty matrix..']); end%% CHECK TYPE%if type(1)~='f', if type(1)~='c', if type(1)~='t', if type(1)~='N',       error(['type has to be ''function (estimation)'', ''classification'', ''timeserie'' or ''NARX'''] );end;end;end;end;model.type = type;%% choice of implementation: 'MATLAB', 'MATLAB', 'MATLAB' %eval('model.implementation=implementation;','model.implementation=''CMEX'';');%model.implementation='MATLAB';%model.implementation='CFILE';%% check datapoints%model.x_dim = size(X,2);model.y_dim = size(Y,2);if and(type(1)~='t',and(size(X,1)~=size(Y,1),size(X,2)~=0)), error(['number of datapoints not equal to number of targetpoints...']); end  model.nb_data = size(X,1);%if size(X,1)<size(X,2), warning('less datapoints than dimension of a datapoint ?'); end%if size(Y,1)<size(Y,2), warning('less targetpoints than dimension of a targetpoint ?'); endif isempty(Y), error('empty datapoint vector...'); end%% using preprocessing {'preprocess','original'}%eval('preprocess; model.preprocess=preprocess;','model.preprocess=''preprocess'';');if model.preprocess(1) == 'p',   model.prestatus='changed';else model.prestatus='ok'; end%% initiate datapoint selector%model.xtrain = X;model.ytrain = Y;model.selector=1:model.nb_data;%% regularisation term and kenel parameters%if(gam<=0) error('gam must be larger then 0');endmodel.gam = gam;% kernel type: for MATLAB implementation the function <kernel>.m% must do the job. In MATLAB and MATLAB, the implementation of the% kernels is in c-src/kernels.h and c-src/kernels.c%% initializing kernel type%eval('model.kernel_type = kernel_type;','model.kernel_type = ''RBF_kernel'';');% kernel parameters (i.c. RBF:sigma^2) if sig2<=0,  model.kernel_pars = (model.x_dim);else  model.kernel_pars = sig2;end%% cga options; only used if C implementation is used%model.cga_max_itr = model.nb_data;model.cga_eps = 1e-15;model.cga_fi_bound = 1e-15;eval('model.cga_show = cgashow;','model.cga_show = 0;');%% dynamic models%model.x_delays = 0;model.y_delays = 0;model.steps = 1;% for classification: one is interested in the latent variables or% in the class labelsmodel.latent = 'no';model.duration = 0;% coding type used for classificationmodel.code = 'original';eval('codetype;model.codetype=codetype;',...     'model.codetype =''none'';');% preprocessing stepmodel = prelssvm(model);% to be called after right initialization%model = codelssvm(model);% status of the model: 'changed' or 'trained'model.status = 'changed';