def cross(X, Y, W, b):
    P = evaluateClassifier(X, W, b);
    result = 0;
    i = 0;
    while i < np.shape(Y)[1]:
        result = result - np.log(Y[:,i].transpose().dot(P[:,i]))
        i += 1;

    return result;

def computeCost(X, Y, W, b, l):
    lCross = cross(X, Y, W, b);

    length = np.shape(Y)[1];
    result = 1/length * lCross + l*np.sum(np.power(W,2));
    return result;