function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Train targets
% test_patterns - Test patterns
% Params - [NumberOfIterations, Weak Learner Type, Learner's parameters]
%
% Outputs
% test_targets - Predicted targets
% E - Errors through the iterations
%
% NOTE: Suitable for only two classes
%
[k_max, weak_learner, alg_param] = process_params(params);
[Ni,M] = size(train_patterns);
W = ones(1,M)/M;
IterDisp = 10;
full_patterns = [train_patterns, test_patterns];
test_targets = zeros(1, size(test_patterns,2));
%Do the AdaBoosting
for k = 1:k_max,
%Train weak learner Ck using the data sampled according to W:
%...so sample the data according to W
randnum = rand(1,M);
cW = cumsum(W);
indices = zeros(1,M);
for i = 1:M,
%Find which bin the random number falls into
loc = max(find(randnum(i) > cW))+1;
if isempty(loc)
indices(i) = 1;
else
indices(i) = loc;
end
end
%...and now train the classifier
Ck = feval(weak_learner, train_patterns(:, indices), train_targets(indices), full_patterns, alg_param);
%Ek <- Training error of Ck
E(k) = sum(W.*(Ck(1:M) ~= train_targets));
if (E(k) == 0),
break
end
%alpha_k <- 1/2*ln(1-Ek)/Ek)
alpha_k = 0.5*log((1-E(k))/E(k));
%W_k+1 = W_k/Z*exp(+/-alpha)
W = W.*exp(alpha_k*(xor(Ck(1:M),train_targets)*2-1));
W = W./sum(W);
%Update the test targets
test_targets = test_targets + alpha_k*(2*Ck(M+1:end)-1);
if (k/IterDisp == floor(k/IterDisp)),
disp(['Completed ' num2str(k) ' boosting iterations'])
end
end
test_targets = test_targets > 0;
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