--- a
+++ b/Ensemble Learning/Bagging/app1.m
@@ -0,0 +1,47 @@
+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);
+
+