def test_lnprob_and_grad_scalar(self):
prior = HorseshoePrior(scale=1, rng=np.random.RandomState(1))
# Legal scalar
self.assertEqual(prior.lnprob(-1), 1.1450937952919953)
self.assertEqual(prior.gradient(-1), -0.6089187456211098)
# Boundary
self.assertTrue(np.isinf(prior._lnprob(0)))
self.assertTrue(np.isinf(prior._gradient(0)))
def test_gradient(self):
for scale in (0.1, 0.5, 1., 2.):
prior = HorseshoePrior(scale=scale, rng=np.random.RandomState(1))
# The function appears to be unstable above 15
for theta in range(-20, 15):
if theta == 0:
continue
error = scipy.optimize.check_grad(
lambda x: prior.lnprob(x[0]),
lambda x: prior.gradient(x[0]),
np.array([theta]),
epsilon=1e-5,
)
self.assertAlmostEqual(error, 0, delta=5)