def test_update(self):
P = 10
Q = 5
hyper = vbfa.Hyper(P, Q)
q_lambda = vbfa.Lambda(P, Q)
q_nu = vbfa.Nu(Q)
q_nu.update(hyper, q_lambda)
self.assertTrue(np.all(q_nu.b > 0))
def test_init(self):
P = 10
Q = 5
q_lambda = vbfa.Lambda(P, Q)
self.assertEqual(q_lambda.mean.shape, (P, Q))
self.assertEqual(len(q_lambda.cov), P)
for p in range(P):
self.assertEqual(q_lambda.cov[p].shape, (Q, Q))
self.assertTrue(len(q_lambda.__str__()) > 0)
def test_update(self):
np.random.seed(0)
P = 10
Q = 5
N = 100
q_x = vbfa.X(Q, N)
hyper = vbfa.Hyper(P, Q)
y = np.random.rand(P, N)
q_lambda = vbfa.Lambda(P, Q)
q_mu = vbfa.Mu(P)
q_x.update(hyper, q_lambda, q_mu, y)
def test_update(self):
np.random.seed(0)
P = 10
Q = 5
hyper = vbfa.Hyper(P, Q)
q_lambda = vbfa.Lambda(P, Q)
N = 100
y = np.random.rand(P, N)
q_mu = vbfa.Mu(P)
q_nu = vbfa.Nu(Q)
q_x = vbfa.X(Q, N)
q_lambda.update(hyper, q_mu, q_nu, q_x, y)
for p in range(P):
self.assertTrue(np.all(np.linalg.eigvals(q_lambda.cov[p]) > 0))
def test_permute(self):
P = 10
Q = 4
q_lambda = vbfa.Lambda(P, Q)
mean_before = q_lambda.mean.copy()
cov_before = q_lambda.cov.copy()
order = [2, 3, 1, 0]
q_lambda.permute(order)
self.assertEqual(q_lambda.mean.shape, (P, Q))
for q in range(Q):
npt.assert_equal(q_lambda.mean[:, q], mean_before[:, order[q]])
for p in range(P):
for i in range(Q):
for j in range(Q):
self.assertEqual(q_lambda.cov[p, q, q],
cov_before[p, order[q], order[q]])