Files
machine-learning-ex5/ex5/linearRegCostFunction.m
2017-05-28 20:20:47 +09:00

39 lines
1.1 KiB
Matlab

function [J, grad] = linearRegCostFunction(X, y, theta, lambda)
%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear
%regression with multiple variables
% [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the
% cost of using theta as the parameter for linear regression to fit the
% data points in X and y. Returns the cost in J and the gradient in grad
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
grad = zeros(size(theta));
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost and gradient of regularized linear
% regression for a particular choice of theta.
%
% You should set J to the cost and grad to the gradient.
%
hx = X*theta;
J = sum((hx - y).**2)/(2*m) + lambda*sum((theta(2:end).**2))/(2*m);
grad = (sum((hx-y).*X)/m)' + lambda*[0; theta(2:end)]/m;
% =========================================================================
grad = grad(:);
end