% load input training data
trn = load('riply_trn');
inx1 = find(trn.y==1);
inx2 = find(trn.y==2);
% Estimation of class-conditional distributions by EM
bayes_model.Pclass{1} = emgmm(trn.X(:,inx1),struct('ncomp',2));
bayes_model.Pclass{2} = emgmm(trn.X(:,inx2),struct('ncomp',2));
% Estimation of priors
n1 = length(inx1); n2 = length(inx2);
bayes_model.Prior = [n1 n2]/(n1+n2);
% Evaluation on testing data
tst = load('riply_tst');
ypred = bayescls(tst.X,bayes_model);
cerror(ypred,tst.y);

% Visualization
figure; hold on; ppatterns(trn);
bayes_model.fun = 'bayescls';
pboundary(bayes_model);
% Penalization for don’t know decision
reject_model = bayes_model;
reject_model.eps = 0.1;
% Vislualization of rejet-option rule
pboundary(reject_model,struct('line_style','k--'));