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function [test_targets, w_pocket] = Pocket(train_patterns, train_targets, test_patterns, alg_param) % Classify using the pocket algorithm (an improvement on the 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 % w - Pocket weights % % NOTE: Works for only two classes. [c, r] = size(train_patterns); %Weighted Pocket 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 = 500; otherwise %Number of iterations given max_iter = alg_param; p = ones(1,r); end train_patterns = [train_patterns ; ones(1,r)]; train_one = find(train_targets == 1); train_zero = find(train_targets == 0); %Preprocessing processed_patterns = train_patterns; processed_patterns(:,train_zero) = -processed_patterns(:,train_zero); %Initial weights w_percept = sum(processed_patterns')'; %w_percept = train_patterns .* (ones(c+1,1) * (2*(train_targets-0.5))); %w_percept = rand(c+1,1); w_pocket = rand(c+1,1); correct_classified = 0; n = length(train_targets); iter = 0; while ((longest_run(w_percept, processed_patterns) < n) & (iter < max_iter)) iter = iter + 1; %Every 10 points, do the pocket switchover for i = 1:10, indice = 1 + floor(rand(1)*n); if (w_percept' * processed_patterns(:,indice) <= 0) w_percept = w_percept + p(indice) * processed_patterns(:,indice); end end %Find if it is neccessary to change weights: if (longest_run(w_percept, processed_patterns) > longest_run(w_pocket, processed_patterns)), w_pocket = w_percept; end end if (iter == max_iter)&(length(alg_param)~= r + 1), disp(['Maximum iteration (' num2str(max_iter) ') reached']); end %Classify test patterns test_targets = w_pocket'*[test_patterns; ones(1, size(test_patterns,2))] > 0; %END function L = longest_run(weights, patterns) %Find the length of the longest run of correctly classified random points n = length(patterns); indices = randperm(n); L = 0; correct = 1; for i = 1:n, if (weights' * patterns(:,indices(i)) <= 0) %Find if it is correctly classified break end L = i; end