def test_multivariate_normal_batch_correlated_samples(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=NonLazyTensor(covmat).repeat(2, 1, 1))
base_samples = mvn.get_base_samples(torch.Size((3, 4)))
self.assertTrue(mvn.sample(base_samples=base_samples).shape == torch.Size([3, 4, 2, 3]))
base_samples = mvn.get_base_samples()
self.assertTrue(mvn.sample(base_samples=base_samples).shape == torch.Size([2, 3]))
def test_multivariate_normal_correlated_samples(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=NonLazyTensor(covmat))
base_samples = mvn.get_base_samples(torch.Size([3, 4]))
self.assertTrue(
mvn.sample(
base_samples=base_samples).shape == torch.Size([3, 4, 3]))
base_samples = mvn.get_base_samples()
self.assertTrue(
mvn.sample(base_samples=base_samples).shape == torch.Size([3]))