www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/Perceptron.m
function D = Perceptron(train_features, train_targets, alg_param, region) % Classify using the Perceptron algorithm (Fixed increment single-sample 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 [c, r] = size(train_features); %Weighted Perceptron 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 = 5000; otherwise %Number of iterations given max_iter = alg_param; p = ones(1,r); end 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; indice = 1 + floor(rand(1)*n); if (a' * y(:,indice) <= 0) a = a + p(indice)* y(:,indice); 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 = (a(1).*x + a(2).*y + a(c+1)> 0);