所以我正在尝试解决 Andrew Ng 的 ML Coursera 课程中的第一个编程练习。我在八度音阶中实现线性梯度下降时遇到了一些麻烦。下面的代码显示了我正在尝试实现的内容,根据图片中发布的等式,但我得到的值与预期值不同。我不确定我错过了什么,我希望有人可以解析这个。
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESCENT(X, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha
% Initialize some useful values
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
theta0 = theta(1);
theta1 = theta(2);
temp0 = 0;
temp1 = 0;
errFunc = 0;
for iter = 1:num_iters
h = X * theta;
errFunc = h - y;
temp0 = temp0 + (alpha/m).*sum(errFunc'*X(:, 1));
temp1 = temp1 + (alpha/m).*sum(errFunc'*X(:, 2));
theta0 = theta0 - temp0;
theta1 = theta1 - temp1;
theta = [theta0; theta1];
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCost(X, y, theta);
end
end
code 我的预期结果是 [ -3.6303; 1.1664],但我得到 [-1.361798; 0.931592]。这是我正在使用的等式。 results