function [theta,hist] = findmin(CF, 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

hist = zeros(num_iters+1, length(theta));
hist(1,:) = 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 (computeCostMulti) and gradient here.
    %


	[J,g] = CF( theta, X, y );

	theta = theta - alpha * g;

	hist(iter+1,:) = theta';

end

end
