You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
34 lines
904 B
Matlab
34 lines
904 B
Matlab
10 years ago
|
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
|
||
|
|
%GRADIENTDESCENT Performs gradient descent to learn theta
|
||
|
|
% theta = GRADIENTDESENT(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.
|
||
|
|
%
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
% ============================================================
|
||
|
|
|
||
|
|
% Save the cost J in every iteration
|
||
|
|
J_history(iter) = computeCost(X, y, theta);
|
||
|
|
|
||
|
|
end
|
||
|
|
|
||
|
|
end
|