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.
%
%theta(iter)=theta(iter)-0.01*(1/m)*(((theta(1)+theta(2))*X-y)*X(iter,2))
theta=theta-(alpha*(1/m)*(X*theta-y)*X(iter,2);
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCost(X, y, theta);
end
end
theta=theta-(alpha*(1/m)*(X*theta-y)*X(iter,2);
The parentheses are not balanced as far as I can tell?
You're missing a closing parenthesis )
somewhere, are you not?