close all; clear;clc;

load dataDWT.mat;
%load dataAR.mat;
%load dataCSP.mat;
%load dataPSD.mat;
Y(Y==2) = -1;


y=Y;
min_parent = 20;
num_iterations=30;
base_algorithm= 'classification_tree';

no_objects = length(y);
fifth = floor(no_objects / 5);
error_train = zeros([num_iterations 1]);
error_test = zeros([num_iterations 1]);

for fold = 1 : 5
    mask_test = zeros([1 no_objects]);
    mask_test((fold - 1) * fifth + 1 : fifth * fold) = 1;
    mask_test = logical(mask_test);
    train_set = X(~mask_test,:);
    train_ans = y(~mask_test);
    test_set = X(mask_test,:);
    test_ans = y(mask_test);
    
    
    model = bagging_train(train_set, train_ans, num_iterations, base_algorithm, ...
        'min_parent', min_parent);
    
    [~, error_train_loop] = bagging_predict(model, train_set, train_ans);
    [~, error_test_loop] = bagging_predict(model, test_set, test_ans);
    error_train = error_train + error_train_loop;
    error_test = error_test + error_test_loop;
end
error_train = error_train / 5;
error_test = error_test / 5;

hold on;
xlabel('Number of iterations');
ylabel('Error');
title('Bagging ');
plot(1 : num_iterations, error_train, 'linewidth', 2);