Add cost function for regularized logistic regression
This commit is contained in:
parent
224a17e4d3
commit
f391ac661e
1 changed files with 4 additions and 6 deletions
|
@ -2,12 +2,12 @@ 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.
|
||||
% 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
|
||||
% You need to return the following variables correctly
|
||||
J = 0;
|
||||
grad = zeros(size(theta));
|
||||
|
||||
|
@ -17,10 +17,8 @@ grad = zeros(size(theta));
|
|||
% 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);
|
||||
|
||||
% =============================================================
|
||||
|
||||
|
|
Reference in a new issue