%% DEMONSTRATION OF ADABOOST_tr and ADABOOST_te%% Just type "demo" to run the demo.%% Using adaboost with linear threshold classifier % for a two class classification problem.%% Bug Reporting: Please contact the author for bug reporting and comments.%% Cuneyt Mertayak% email: cuneyt.mertayak@gmail.com% version: 1.0% date: 21/05/2007% Creating the training and testing sets%tr_n = 500;te_n = 500;weak_learner_n = 50;tr_set = abs(rand(tr_n,2))*100;te_set = abs(rand(te_n,2))*100;tr_labels = (tr_set(:,1)-tr_set(:,2) > 0) + 1;te_labels = (te_set(:,1)-te_set(:,2) > 0) + 1;% Displaying the training and testing setsfigure;subplot(2,2,1);hold on; axis square;indices = tr_labels==1;plot(tr_set(indices,1),tr_set(indices,2),'b*');indices = ~indices;plot(tr_set(indices,1),tr_set(indices,2),'r*');title('Training set');subplot(2,2,2);hold on; axis square;indices = te_labels==1;plot(te_set(indices,1),te_set(indices,2),'b*');indices = ~indices;plot(te_set(indices,1),te_set(indices,2),'r*');title('Testing set');% Training and testing error ratestr_error = zeros(1,weak_learner_n);te_error = zeros(1,weak_learner_n);for i=1:weak_learner_n	adaboost_model = ADABOOST_tr(@threshold_tr,@threshold_te,tr_set,tr_labels,i);	[L_tr,hits_tr] = ADABOOST_te(adaboost_model,@threshold_te,tr_set,tr_labels);	tr_error(i) = (tr_n-hits_tr)/tr_n;	[L_te,hits_te] = ADABOOST_te(adaboost_model,@threshold_te,te_set,te_labels);	te_error(i) = (te_n-hits_te)/te_n;endsubplot(2,2,3); plot(1:weak_learner_n,tr_error);axis([1,weak_learner_n,0,1]);title('Training Error');xlabel('weak classifier number');ylabel('error rate');grid on;subplot(2,2,4); axis square;plot(1:weak_learner_n,te_error);axis([1,weak_learner_n,0,1]);title('Testing Error');xlabel('weak classifier number');ylabel('error rate');grid on;