吴恩达ex1的多变量梯度下降为什么错误？

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%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);

for iter = 1:num_iters

    % ====================== YOUR CODE HERE ======================
    % Instructions: Perform a single gradient step on the parameter vector
    %               theta. 
    %
    % Hint: While debugging, it can be useful to print out the values
    %       of the cost function (computeCost) and gradient here.
    %
    
    sum1 = 0;
    sum2 = 0;
    sum3 = 0;
    for i = 1 : m
        a=theta.';
        x = X([i],:);
        sum1 =sum1 + (a*x.'-y(i));
        sum2 =sum2 + (a*x.'-y(i))* X(i,2);
        sum3 =sum3 + (a*x.'-y(i))* X(i,3);
    end
theta(1) = theta(1) - sum1 * alpha *(1/m);
theta(2) = theta(2) - sum2 * alpha *(1/m);
theta(3) = theta(3) - sum3 * alpha *(1/m);






    % ============================================================

    % Save the cost J in every iteration    
    J_history(iter) = computeCost(X, y, theta);

end

end