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.
29 lines
1.1 KiB
Matlab
29 lines
1.1 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 = 1/m * (-y'*log(sigmoid(X*theta)) - (1-y)'*log(1-sigmoid(X*theta))) ...
|
|
+ lambda/(2*m) * theta(2:end)' * theta(2:end);
|
|
|
|
regularization_term = lambda/m * vertcat([0], theta(2:end));
|
|
grad = 1/m * X' * (sigmoid(X*theta) - y) + regularization_term;
|
|
|
|
% =============================================================
|
|
|
|
end
|