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Add cost function for regularized logistic regression

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neingeist 2014-10-15 10:10:30 +02:00
parent 224a17e4d3
commit f391ac661e
1 changed files with 4 additions and 6 deletions
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10
ex2/costFunctionReg.m
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@ -2,12 +2,12 @@ function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization %COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
% J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using % J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
% theta as the parameter for regularized logistic regression and the % 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 % Initialize some useful values
m = length(y); % number of training examples 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; J = 0;
grad = zeros(size(theta)); grad = zeros(size(theta));
@ -17,10 +17,8 @@ grad = zeros(size(theta));
% Compute the partial derivatives and set grad to the partial % Compute the partial derivatives and set grad to the partial
% derivatives of the cost w.r.t. each parameter in theta % 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);
% ============================================================= % =============================================================