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
h=sigmoid(X*theta);
theta(1,1)=0;
J=sum(-y'*log(h)-(1-y)'*log(1-h))/m+lambda/2/m*sum(power(theta,2));
grad=((h-y)'*X)/m+lambda/m*theta';





% =============================================================

end

loserChen
github-repos/MATLAB/loserChen-Coursera-MachineLearning/Coursera-MachineLearning-ce2360516c36805e8bd4fb3c796d7820f320cc78/machine-learning-ex2/ex2/costFunctionReg.m
Coursera-MachineLearning
github-repos/MATLAB/loserChen-Coursera-MachineLearning
ce2360516c36805e8bd4fb3c796d7820f320cc78