def test_multivariate_normal_batch_correlated_sampels(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
mean = torch.tensor([[0, 1], [2, 3]], dtype=torch.float, device=device).repeat(2, 1, 1)
variance = 1 + torch.arange(4, dtype=torch.float, device=device)
covmat = torch.diag(variance).repeat(2, 1, 1)
mtmvn = MultitaskMultivariateNormal(mean=mean, covariance_matrix=covmat)
base_samples = mtmvn.get_base_samples(torch.Size((3, 4)))
self.assertTrue(mtmvn.sample(base_samples=base_samples).shape == torch.Size([3, 4, 2, 2, 2]))
base_samples = mtmvn.get_base_samples()
self.assertTrue(mtmvn.sample(base_samples=base_samples).shape == torch.Size([2, 2, 2]))
def test_multivariate_normal_correlated_sampels(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, 3]], dtype=dtype, device=device)
variance = torch.tensor([[1, 2], [3, 4]],
dtype=dtype,
device=device)
covmat = variance.view(-1).diag()
mtmvn = MultitaskMultivariateNormal(mean=mean,
covariance_matrix=covmat)
base_samples = mtmvn.get_base_samples(torch.Size([3, 4]))
self.assertTrue(
mtmvn.sample(base_samples=base_samples).shape == torch.Size(
[3, 4, 2, 2]))
base_samples = mtmvn.get_base_samples()
self.assertTrue(
mtmvn.sample(
base_samples=base_samples).shape == torch.Size([2, 2]))
def test_multivariate_normal_batch_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, 3], [4, 5]],
dtype=dtype,
device=device).repeat(2, 1, 1)
variance = torch.tensor([[1, 2], [3, 4], [5, 6]],
dtype=dtype,
device=device).repeat(2, 1, 1)
covmat = variance.view(2, 1, -1) * torch.eye(
6, device=device, dtype=dtype)
mtmvn = MultitaskMultivariateNormal(mean=mean,
covariance_matrix=covmat)
base_samples = mtmvn.get_base_samples(torch.Size((3, 4)))
self.assertTrue(
mtmvn.sample(base_samples=base_samples).shape == torch.Size(
[3, 4, 2, 3, 2]))
base_samples = mtmvn.get_base_samples()
self.assertTrue(
mtmvn.sample(
base_samples=base_samples).shape == torch.Size([2, 3, 2]))
