costFunctionJ.m

function J =costFunctionJ(X, y, theta)

% X is the "design matrix" containing our training examples.
% y is the class labels

m = size(X,1); 			% number of training examples
predictions = X*theta; 	% predictions of hypothesis on all 'm' examples
sqrErrors = (predictions-y).^2;  % squared errors 

J = 1/(2*m) * sum(sqrErrors);