def test_cost_lnprob(self):
t = np.arange(0, 1, 0.005)
X = t - 3
Y = t * t - 1. / (t + 1)
like = KurslMethod.cost_lnprob(X, Y)
expected_like, eps = -459.6, 1e-1
self.assertTrue(
abs(like - expected_like) < eps,
"Expected likelihood ({} +- {}), but got {}".format(
expected_like, eps, like))
def test_cost_lnprob_same(self):
X = np.random.random(100)
Y = X.copy()
like = KurslMethod.cost_lnprob(X, Y)
self.assertEqual(like, 0, "Same processes produce 0 lnprob")
def test_cost_lnprob_zeros(self):
(X, Y) = np.zeros((2, 100))
like = KurslMethod.cost_lnprob(X, Y)
self.assertEqual(like, 0)