28 lines
1.0 KiB
Matlab
28 lines
1.0 KiB
Matlab
function [J, grad] = costFunctionReg(theta, X, y, lambda)
|
|
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
|
|
% J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
|
|
% theta as the parameter for regularized logistic regression and the
|
|
% gradient of the cost w.r.t. to the parameters.
|
|
|
|
% 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 of a particular choice of theta.
|
|
% You should set J to the cost.
|
|
% Compute the partial derivatives and set grad to the partial
|
|
% derivatives of the cost w.r.t. each parameter in theta
|
|
|
|
[J_, grad_] = costFunction(theta, X, y);
|
|
J = J_ + lambda / (2 * m) * (theta' * theta);
|
|
grad = grad_ + theta * lambda / m;
|
|
grad(1) = grad_(1);
|
|
|
|
% =============================================================
|
|
|
|
end
|