gradientDescentMulti.m

function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
%   theta = GRADIENTDESCENTMULTI(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 (computeCostMulti) and gradient here.
    %
  error = (X*theta)-y;

  %temp = X * theta; 
  %error = temp - y;
  %novaX = error' * X; 
  %theta = theta - ((alpha/m) * novaX'); 

	for i=1:size(theta,1),
		temp(i) = theta(i) - (alpha/m)*sum(error.*(X(:,i)));
		theta(i,:)=temp(i);
	end;	
    % ============================================================

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

	printf("Compute Cost\n");
	disp(J_history(iter));
end

end