www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/Perceptron_FM.m
function [D, a] = Perceptron_FM(train_features, train_targets, params, region) % Classify using the Perceptron algorithm but at each iteration updating the worst-classified sample % Inputs: % features - Train features % targets - Train targets % params - [Maximum number of iterations, slack] % region - Decision region vector: [-x x -y y number_of_points] % % Outputs % D - Decision sufrace [max_iter, slack] = process_params(params); rate = 0.1; [c, r] = size(train_features); xi = ones(1,r)/r*slack; train_features = [train_features ; ones(1,r)]; train_zero = find(train_targets == 0); %Preprocessing y = train_features; y(:,train_zero)= -y(:,train_zero); %Initial weights a = sum(y')'; n = length(train_targets); iter = 0; while ((sum(sign(a'*train_features.*(2*train_targets-1))<0)>0) & (iter < max_iter)) iter = iter + 1; %Find worst-classified sample A = a'*train_features.*(2*train_targets-1)+xi; [m, indice] = min(A); if (a' * y(:,indice) <= 0) a = a + y(:,indice); end %Calculate the new slack vector xi(indice) = xi(indice) + rate; xi = xi / sum(xi) * slack; end if (iter == max_iter), 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 = (a(1).*x + a(2).*y + a(c+1)> 0); a = a';