gradientDescent.m

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. 
    %
	x=X(:,2);
	h=theta(1)+(theta(2).*x);%row one and row two of theta
	theta_zero=theta(1)-alpha*(1/m)*sum(h-y)%simicolon not there to print the result
	theta_one=theta(2)-alpha*(1/m)*sum((h-y).*x);
	theta=[theta_zero;theta_one];
    % Hint: While debugging, it can be useful to print out the values
    %       of the cost function (computeCost) and gradient here.
 







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

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

end
disp(max(J_history))
end