costFunction.m

function [J, grad] = costFunction(theta, X, y)
%COSTFUNCTION Compute cost and gradient for logistic regression
%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
%   parameter for 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
%
% Note: grad should have the same dimensions as theta
%

z=X*theta;
g=e.^(-z);
g=1.+g;
g=1./g;

for i=1:m

  J= J + (y(i)*log(g(i))+(1-y(i))*log(1-g(i)));

endfor
  
J = (-1/m)*J;
  
for i=1:size(theta,1),  
  for j=1:m,
    grad(i)= grad(i)+(g(j)-y(j))*X(j,i);
  endfor
endfor

grad =grad./m;






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

end