📄 pocket.m
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function [D, w_pocket] = Pocket(train_features, train_targets, alg_param, region)% Classify using the pocket algorithm (an improvement on the perceptron)% Inputs:% features - Train features% targets - Train targets% alg_param - Either: Number of iterations, weights vector or [weights, number of iterations]% region - Decision region vector: [-x x -y y number_of_points]%% Outputs% D - Decision sufrace% w - Decision surface parameters[c, r] = size(train_features);%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);endtrain_features = [train_features ; ones(1,r)];train_one = find(train_targets == 1);train_zero = find(train_targets == 0);%Preprocessingprocessed_features = train_features;processed_features(:,train_zero) = -processed_features(:,train_zero);%Initial weightsw_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_features) < 0.9*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_features(:,indice) <= 0) w_percept = w_percept + p(indice) * processed_features(:,indice); end end %Find if it is neccessary to change weights: if (longest_run(w_percept, processed_features) > longest_run(w_pocket, processed_features)), w_pocket = w_percept; endendif (iter == max_iter)&(length(alg_param)~= r + 1), disp(['Maximum iteration (' num2str(max_iter) ') reached']);end%Find decision regionN = region(5);x = ones(N,1) * linspace (region(1),region(2),N);y = linspace (region(3),region(4),N)' * ones(1,N);D = (w_pocket(1).*x + w_pocket(2).*y + w_pocket(c+1)> 0);w_pocket = w_pocket';
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