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function [test_targets, a] = Perceptron_BVI(train_patterns, train_targets, test_patterns, params) % Classify using the batch variable increment Perceptron algorithm % Inputs: % train_patterns - Train patterns % train_targets - Train targets % test_patterns - Test patterns % param - [Num iter, Convergence rate] % % Outputs % test_targets - Predicted targets % a - Perceptron weights % % NOTE: Works for only two classes. [c, n] = size(train_patterns); [Max_iter, eta] = process_params(params); train_patterns = [train_patterns ; ones(1,n)]; train_zero = find(train_targets == 0); %Preprocessing y = train_patterns; y(:,train_zero) = -y(:,train_zero); a = sum(y')'; %Initial weights iter = 0; Yk = [1]; while (~isempty(Yk) & (iter < Max_iter)) iter = iter + 1; %If y_j is misclassified then append y_j to Yk Yk = []; for k = 1:n, if (a'*train_patterns(:,k).*(2*train_targets(:,k)-1) < 0), Yk = [Yk k]; end end % a <- a + eta*sum(Yk) a = a + eta * sum(y(:,Yk)')'; end if (iter == Max_iter), disp(['Maximum iteration (' num2str(Max_iter) ') reached']); end %Classify test patterns test_targets = a'*[test_patterns; ones(1, size(test_patterns,2))] > 0;