function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters) m = length(y); % number of training examples J_history = zeros(num_iters, 1); for iter = 1:num_iters theta = theta - alpha * X‘ * (X * theta - y) / m; iter = iter +1; J_history(iter) = computeCostMulti(X, y, theta); end end
时间: 2024-10-10 00:56:15