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);