www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/Pocket.m
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); end train_features = [train_features ; ones(1,r)]; train_one = find(train_targets == 1); train_zero = find(train_targets == 0); %Preprocessing processed_features = train_features; processed_features(:,train_zero) = -processed_features(:,train_zero); %Initial weights w_percept = sum(processed_features')'; %w_percept = train_features .* (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_features) < 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; end end if (iter == max_iter)&(length(alg_param)~= r + 1), disp(['Maximum iteration (' num2str(max_iter) ') reached']); end %Find decision region N = 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'; function L = longest_run(weights, features) %Find the length of the longest run of correctly classified random points n = length(features); indices = randperm(n); L = 0; correct = 1; for i = 1:n, if (weights' * features(:,indices(i)) <= 0) %Find if it is correctly classified break end L = i; end