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Gradient descent for one variable

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neingeist 2014-10-02 21:32:24 +02:00
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ex1/gradientDescent.m
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@ -1,6 +1,6 @@
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters) function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta %GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by % theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha % taking num_iters gradient steps with learning rate alpha
% Initialize some useful values % Initialize some useful values
@ -11,21 +11,16 @@ for iter = 1:num_iters
% ====================== YOUR CODE HERE ====================== % ====================== YOUR CODE HERE ======================
% Instructions: Perform a single gradient step on the parameter vector % Instructions: Perform a single gradient step on the parameter vector
% theta. % theta.
% %
% Hint: While debugging, it can be useful to print out the values % Hint: While debugging, it can be useful to print out the values
% of the cost function (computeCost) and gradient here. % of the cost function (computeCost) and gradient here.
% %
theta = theta - (alpha/m) * X' * (X*theta - y);
% ============================================================ % ============================================================
% Save the cost J in every iteration % Save the cost J in every iteration
J_history(iter) = computeCost(X, y, theta); J_history(iter) = computeCost(X, y, theta);
end end