demo.m

%
% 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: [e-mail]
% version: 1.0
% date: 21/05/2007


% Creating the training and testing sets
%
tr_n = 200;
te_n = 200;
weak_learner_n = 20;

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 sets
figure;
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 rates
tr_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;
end

subplot(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;