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- 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);
- predict = theta' .* X;
- deviation = (sum(predict,2) - y);
- 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.
- for i = 1:length(theta)
- theta(i) = theta(i) - (alpha/m) * sum(deviation .* X(:,i));
- % ============================================================
- % Save the cost J in every iteration
- J_history(iter) = computeCost(X, y, theta);
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