def test_multivariate_normal_batch_non_lazy(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
mean = torch.tensor([0, 1, 2], dtype=torch.float, device=device)
covmat = torch.diag(torch.tensor([1, 0.75, 1.5], device=device))
mvn = MultivariateNormal(mean=mean.repeat(2, 1), covariance_matrix=covmat.repeat(2, 1, 1), validate_args=True)
self.assertTrue(torch.is_tensor(mvn.covariance_matrix))
self.assertIsInstance(mvn.lazy_covariance_matrix, LazyTensor)
self.assertTrue(approx_equal(mvn.variance, covmat.diag().repeat(2, 1)))
self.assertTrue(approx_equal(mvn.scale_tril, torch.diag(covmat.diag().sqrt()).repeat(2, 1, 1)))
mvn_plus1 = mvn + 1
self.assertTrue(torch.equal(mvn_plus1.mean, mvn.mean + 1))
self.assertTrue(torch.equal(mvn_plus1.covariance_matrix, mvn.covariance_matrix))
mvn_times2 = mvn * 2
self.assertTrue(torch.equal(mvn_times2.mean, mvn.mean * 2))
self.assertTrue(torch.equal(mvn_times2.covariance_matrix, mvn.covariance_matrix * 4))
mvn_divby2 = mvn / 2
self.assertTrue(torch.equal(mvn_divby2.mean, mvn.mean / 2))
self.assertTrue(torch.equal(mvn_divby2.covariance_matrix, mvn.covariance_matrix / 4))
self.assertTrue(approx_equal(mvn.entropy(), 4.3157 * torch.ones(2, device=device)))
logprob = mvn.log_prob(torch.zeros(2, 3, device=device))
logprob_expected = -4.8157 * torch.ones(2, device=device)
self.assertTrue(approx_equal(logprob, logprob_expected))
logprob = mvn.log_prob(torch.zeros(2, 2, 3, device=device))
logprob_expected = -4.8157 * torch.ones(2, 2, device=device)
self.assertTrue(approx_equal(logprob, logprob_expected))
conf_lower, conf_upper = mvn.confidence_region()
self.assertTrue(approx_equal(conf_lower, mvn.mean - 2 * mvn.stddev))
self.assertTrue(approx_equal(conf_upper, mvn.mean + 2 * mvn.stddev))
self.assertTrue(mvn.sample().shape == torch.Size([2, 3]))
self.assertTrue(mvn.sample(torch.Size([2])).shape == torch.Size([2, 2, 3]))
self.assertTrue(mvn.sample(torch.Size([2, 4])).shape == torch.Size([2, 4, 2, 3]))
def test_multivariate_normal_non_lazy(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
mean = torch.tensor([0, 1, 2], device=device, dtype=dtype)
covmat = torch.diag(
torch.tensor([1, 0.75, 1.5], device=device, dtype=dtype))
mvn = MultivariateNormal(mean=mean,
covariance_matrix=covmat,
validate_args=True)
self.assertTrue(torch.is_tensor(mvn.covariance_matrix))
self.assertIsInstance(mvn.lazy_covariance_matrix, LazyTensor)
self.assertTrue(torch.allclose(mvn.variance, torch.diag(covmat)))
self.assertTrue(torch.allclose(mvn.scale_tril, covmat.sqrt()))
mvn_plus1 = mvn + 1
self.assertTrue(torch.equal(mvn_plus1.mean, mvn.mean + 1))
self.assertTrue(
torch.equal(mvn_plus1.covariance_matrix,
mvn.covariance_matrix))
mvn_times2 = mvn * 2
self.assertTrue(torch.equal(mvn_times2.mean, mvn.mean * 2))
self.assertTrue(
torch.equal(mvn_times2.covariance_matrix,
mvn.covariance_matrix * 4))
mvn_divby2 = mvn / 2
self.assertTrue(torch.equal(mvn_divby2.mean, mvn.mean / 2))
self.assertTrue(
torch.equal(mvn_divby2.covariance_matrix,
mvn.covariance_matrix / 4))
self.assertAlmostEqual(mvn.entropy().item(), 4.3157, places=4)
self.assertAlmostEqual(mvn.log_prob(
torch.zeros(3, device=device, dtype=dtype)).item(),
-4.8157,
places=4)
logprob = mvn.log_prob(
torch.zeros(2, 3, device=device, dtype=dtype))
logprob_expected = torch.tensor([-4.8157, -4.8157],
device=device,
dtype=dtype)
self.assertTrue(torch.allclose(logprob, logprob_expected))
conf_lower, conf_upper = mvn.confidence_region()
self.assertTrue(
torch.allclose(conf_lower, mvn.mean - 2 * mvn.stddev))
self.assertTrue(
torch.allclose(conf_upper, mvn.mean + 2 * mvn.stddev))
self.assertTrue(mvn.sample().shape == torch.Size([3]))
self.assertTrue(
mvn.sample(torch.Size([2])).shape == torch.Size([2, 3]))
self.assertTrue(
mvn.sample(torch.Size([2, 4])).shape == torch.Size([2, 4, 3]))
