梯度下降线性回归，八度

如何解决梯度下降线性回归，八度

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,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