--- a
+++ b/costFunction.m
@@ -0,0 +1,19 @@
+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
+
+grad = zeros(size(theta));
+
+h = sigmoid(X * theta);
+J = -(1 / m) * sum( (y .* log(h)) + ((1 - y) .* log(1 - h)) );
+
+for i = 1 : size(theta, 1)
+    grad(i) = (1 / m) * sum( (h - y) .* X(:, i) );
+end
+
+end