costFunctionReg.m

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 = X*theta;
predi = sigmoid(h);
for k = 2:size(theta), 
  thetaSq = theta.^2;
endfor
reg = ((lambda/2)*sum(thetaSq));
J = (1/m)*((sum((-y.*log(predi))-((1-y).*log(1-predi))))+reg); 
grad(1) = (1/m)*sum(predi-y);
for i= 2:size(theta),
  grad(i) = (1/m)*((sum((predi-y).*X(:,i)))+(lambda*theta(i))); 
endfor






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

end