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function [test_targets, a] = Perceptron(train_patterns, train_targets, test_patterns, alg_param) % Classify using the Perceptron algorithm (Fixed increment single-sample perceptron) % Inputs: % train_patterns - Train patterns % train_targets - Train targets % test_patterns - Test patterns % alg_param - Either: Number of iterations, weights vector or [weights, number of iterations] % % Outputs % test_targets - Predicted targets % a - Perceptron weights % % NOTE: Works for only two classes. [c, r] = size(train_patterns); %Weighted Perceptron or not? switch length(alg_param), case r + 1, %Ada boost form p = alg_param(1:end-1); max_iter = alg_param(end); case {r,0}, %No parameter given p = ones(1,r); max_iter = 5000; otherwise %Number of iterations given max_iter = alg_param; p = ones(1,r); end train_patterns = [train_patterns ; ones(1,r)]; train_zero = find(train_targets == 0); %Preprocessing y = train_patterns; y(:,train_zero)= -y(:,train_zero); %Initial weights a = sum(y')'; n = length(train_targets); iter = 0; while ((sum(a'*train_patterns.*(2*train_targets-1)<0)>0) & (iter < max_iter)) iter = iter + 1; indice = 1 + floor(rand(1)*n); if (a' * y(:,indice) <= 0) a = a + p(indice)* y(:,indice); end end if (iter == max_iter)&(length(alg_param)~= r + 1), disp(['Maximum iteration (' num2str(max_iter) ') reached']); end %Classify test patterns test_targets = a'*[test_patterns; ones(1, size(test_patterns,2))] > 0;