% simple procedure for using svm-s
% detail description of input and output arguments is in the header of each function
% uncomment one of four data sets available


%load l_sep.mat;
%load n_sep.mat;
%load iris.mat;
%load wine_data.mat;


% ker='polynomial';
% param='POLYORDER';
ker='rbf';
param='RBF_SIGMA';



% training the model
model= svmtrain(x_tra,y_tra,'KERNEL_FUNCTION',ker,param,1,'showplot','true');

classes = svmclassify(model,x_tes,'showplot','true');


% calculating the error
err = svcerror(classes,y_tes);