--- a
+++ b/Ensemble Learning/AdaBoost/app1.m
@@ -0,0 +1,43 @@
+close all; clear; clc;
+
+load dataDWT.mat;
+%load dataCSP.mat;
+%load dataAR.mat;
+%load dataPSD.mat;
+
+Y(Y==2) = -1;
+
+N=size(X,1);
+trnX = X(1:N, :);
+trnY = Y(1:N);
+
+iter = 30;
+abClassifier = initAdaBoost(iter);
+
+N = size(trnX, 1); % Number of training samples
+sampleWeight = repmat(1/N, N, 1);
+
+for t = 1:iter
+    weakClassifier = buildStump(trnX, trnY, sampleWeight);
+
+    abClassifier.WeakClas{t} = weakClassifier;
+    abClassifier.nWC = t;
+    % Compute the weight of this classifier
+    abClassifier.Weight(t) = 0.5*log((1-weakClassifier.error)/weakClassifier.error);
+    weakClassifier.error
+    % Update sample weight
+    label = predStump(trnX, weakClassifier);
+    tmpSampleWeight = -1*abClassifier.Weight(t)*(trnY.*label); % N x 1
+    tmpSampleWeight = sampleWeight.*exp(tmpSampleWeight); % N x 1
+    
+    sampleWeight = tmpSampleWeight./sum(tmpSampleWeight); % Normalized
+    
+    % Predict on training data
+    [ttt, abClassifier.trnErr(t)] = predAdaBoost(abClassifier, trnX, trnY);
+    
+    fprintf('\tIteration %d, Training error %f\n', t, abClassifier.trnErr(t));
+end
+
+trnError = abClassifier.trnErr;
+plot(1:iter, trnError);
+