% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabclc;disp('A simple example shows how to start using the toolbox for a');disp('classification task. We start with constructing a simple example');disp('dataset according to the right formatting. Data are represented ');disp('as matrices where each row contains one datapoint: ');disp(' ');disp('press <ENTER> key'); pausedisp(' ');disp(' >> X = 2.*rand(30,2)-1;');X = 2.*rand(30,2)-1;disp(' >> Y = sign(sin(X(:,1))+X(:,2));');Y = sign(sin(X(:,1))+X(:,2));disp(' >> X');Xdisp(' >> Y');Ydisp(' ');disp('press <ENTER> key'); pausedisp(' ');disp('In order to make an LS-SVM model, we need 2 extra parameters: gamma');disp('(gam) is the regularization parameter, determining the trade-off');disp('between the fitting error minimization and smoothness. In the');disp('common case of the RBF kernel, sigma^2 (sig2) is the bandwidth:');disp(' ');disp(' >> gam = 10;');gam = 10;disp(' >> sig2 = 0.2;');sig2 = 0.2;disp(' >> type = ''classification'';');type = 'classification';disp(' >> [alpha,b] = trainlssvm({X,Y,type,gam,sig2,''RBF_kernel''});');[alpha,b] = trainlssvm({X,Y,type,gam,sig2,'RBF_kernel'});disp(' ');disp('press <ENTER> key'); pausedisp(' ');disp('The parameters and the variables relevant for the LS-SVM are');disp('passed as one cell. This cell allows for consistent default');disp('handling of LS-SVM parameters and syntactical grouping of related');disp('arguments. This definition should be used consistently throughout');disp('the use of that specific LS-SVM model.');disp('The corresponding object oriented interface');disp('to LS-SVMlab leads to shorter function calls (see demomodel). ');disp('By default, the data are preprocessed by application of the function');disp('prelssvm to the raw data and the function postlssvm on the');disp('predictions of the model. This option can explicitly be switched off in');disp('the call: ');disp(' ');disp(' >> [alpha,b] = trainlssvm({X,Y,type,gam,sig2,''RBF_kernel'',''original''});');[alpha,b] = trainlssvm({X,Y,type,gam,sig2,'RBF_kernel','original'});disp(' ');disp('or be switched on (by default):');disp(' ');disp(' >> [alpha,b] = trainlssvm({X,Y,type,gam,sig2,''RBF_kernel'',''preprocess''});');[alpha,b] = trainlssvm({X,Y,type,gam,sig2,'RBF_kernel','preprocess'});disp(' ');disp('press <ENTER> key'); pausedisp(' ');%disp('Remember to consistently use the same option in all successive calls');disp('To evaluate new points for this model, the function');disp('simlssvm is used:');disp(' ');disp(' >> Xt = 2.*rand(10,2)-1;');Xt = 2.*rand(10,2)-1;disp(' >> Ytest = simlssvm({X,Y,type,gam,sig2,''RBF_kernel'',''preprocess''},{alpha,b},Xt);');Ytest = simlssvm({X,Y,type,gam,sig2,'RBF_kernel','preprocess'},{alpha,b},Xt);disp(' ');disp('The LS-SVM result can be displayed if the dimension of the input');disp('data is 2. ');disp(' ');disp('press <ENTER> key'); pausedisp(' ');disp(' ');disp(' >> plotlssvm({X,Y,type,gam,sig2,''RBF_kernel'',''preprocess''},{alpha,b});');figure; plotlssvm({X,Y,type,gam,sig2,'RBF_kernel','preprocess'},{alpha,b});disp(' ');disp('All plotting is done with this simple command. It looks for the');disp('best way of displaying the result. ');disp(' ');disp(' This concludes the demo');