diff --git a/ex2/costFunctionReg.m b/ex2/costFunctionReg.m
index 87b7e8e..deaae32 100644
--- a/ex2/costFunctionReg.m
+++ b/ex2/costFunctionReg.m
@@ -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);
 
 % =============================================================