function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
h = X * theta;
for iter = 1:num_iters
temp0 = theta(1) - alpha * (1/m) * sum(h - y);
temp1 = theta(2) - alpha * (1/m) * sum(h - y).*X(:,2);
theta(1) = temp0;
theta(2) = temp1;
J_history(iter) = computeCost(X, y, theta);
end
我得到了两个相同的答案。有人能告诉我我的代码有什么问题
您的预测h
需要在循环内进行更改。目前,您正在调整theta,但不会使用新的theta值重新计算预测。所以你的theta值不能收敛。此外,循环内的总和超过了整个乘法运算:
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
for iter = 1:num_iters
h = X * theta
temp0 = theta(1) - alpha * (1/m) * sum(h - y);
temp1 = theta(2) - alpha * (1/m) * sum((h - y).*X(:,2));
theta(1) = temp0;
theta(2) = temp1;
J_history(iter) = computeCost(X, y, theta);
end