www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/Perceptron_BVI.m
function D = Perceptron_BVI(train_features, train_targets, params, region) % Classify using the batch variable increment Perceptron algorithm % Inputs: % features - Train features % targets - Train targets % param - [Num iter, Convergence rate] % region - Decision region vector: [-x x -y y number_of_points] % % Outputs % D - Decision sufrace [c, n] = size(train_features); [theta, eta] = process_params(params); train_features = [train_features ; ones(1,n)]; train_zero = find(train_targets == 0); %Preprocessing y = train_features; y(:,train_zero) = -y(:,train_zero); a = sum(y')'; %Initial weights iter = 0; Yk = [1]; while (~isempty(Yk) & (iter < theta)) iter = iter + 1; %If y_j is misclassified then append y_j to Yk Yk = []; for k = 1:n, if (sign(a'*train_features(:,k).*(2*train_targets(:,k)-1)) < 0), Yk = [Yk k]; end end % a <- a + eta*sum(Yk) a = a + eta * sum(y(:,Yk)')'; end if (iter == theta), disp(['Maximum iteration (' num2str(theta) ') 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);