Gradient Descent for the Machine Learning course at Stanford

gradientDescent.m

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
%   theta = GRADIENTDESENT(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);
theta_len = length(theta);

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.
    %

	theta -= (alpha/m) * (X' * (X*theta - y));
%	temp_theta = theta;
%	for j = 1:theta_len		
%		value = 0;
%
%		for i = 1:m
%			value += (X(i,:) * theta- y(i,:)) * X(i,j);
%		end
%
%		temp_theta(j,:) = temp_theta(j,:) - ((alpha/m)*value);
%	end
%
%	theta = temp_theta;

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

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

end

end

When I try to implement my version, my theta ends up as 0; has anyone else had that issue?