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
%
#disp(X)
#disp (theta)
disp(size(X))
disp(size(theta))
t = (theta' * X')';
h = sigmoid(t);
J = 0

% How to vectorize this?

for i = 1:length(h)
    th = h(i,1)
    ty = y(i,1)
    c = -ty * log(th ) - (1-ty) * log (1-th)
    J=J+c
endfor
J = J/m
% =============================================================
end

h = sigmoid((theta' * X')');

J = sum((-y.*log(h)) - (1-y).*log(1-h)) / m;