def test_MultivariateNormalQMCEngineShapiroInvTransform(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# test the standard case
mean = torch.zeros(2, device=device, dtype=dtype)
cov = torch.eye(2, device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
seed=12345,
inv_transform=True)
samples = engine.draw(n=250)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2))
self.assertTrue(
torch.all(torch.abs(samples.std(dim=0) - 1) < 1e-2))
# perform Shapiro-Wilk test for normality
samples = samples.cpu().numpy()
for i in (0, 1):
_, pval = shapiro(samples[:, i])
self.assertGreater(pval, 0.9)
# make sure samples are uncorrelated
cov = np.cov(samples.transpose())
self.assertLess(np.abs(cov[0, 1]), 1e-2)
# test the correlated, non-zero mean case
mean = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
cov = torch.tensor([[1.5, 0.5], [0.5, 1.5]],
device=device,
dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
seed=12345,
inv_transform=True)
samples = engine.draw(n=250)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertTrue(
torch.all(torch.abs(samples.mean(dim=0) - mean) < 1e-2))
self.assertTrue(
torch.all(
torch.abs(samples.std(dim=0) - math.sqrt(1.5)) < 1e-2))
# perform Shapiro-Wilk test for normality
samples = samples.cpu().numpy()
for i in (0, 1):
_, pval = shapiro(samples[:, i])
self.assertGreater(pval, 0.9)
# check covariance
cov = np.cov(samples.transpose())
self.assertLess(np.abs(cov[0, 1] - 0.5), 1e-2)
def test_MultivariateNormalQMCEngineDegenerate(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# X, Y iid standard Normal and Z = X + Y, random vector (X, Y, Z)
mean = torch.zeros(3, device=device, dtype=dtype)
cov = torch.tensor(
[[1, 0, 1], [0, 1, 1], [1, 1, 2]], device=device, dtype=dtype
)
engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, seed=12345)
samples = engine.draw(n=2000)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2))
self.assertTrue(torch.abs(torch.std(samples[:, 0]) - 1) < 1e-2)
self.assertTrue(torch.abs(torch.std(samples[:, 1]) - 1) < 1e-2)
self.assertTrue(torch.abs(torch.std(samples[:, 2]) - math.sqrt(2)) < 1e-2)
for i in (0, 1, 2):
_, pval = shapiro(samples[:, i].cpu().numpy())
self.assertGreater(pval, 0.9)
cov = np.cov(samples.cpu().numpy().transpose())
self.assertLess(np.abs(cov[0, 1]), 1e-2)
self.assertLess(np.abs(cov[0, 2] - 1), 1e-2)
# check to see if X + Y = Z almost exactly
self.assertTrue(
torch.all(
torch.abs(samples[:, 0] + samples[:, 1] - samples[:, 2]) < 1e-5
)
)
def test_MultivariateNormalQMCEngineDegenerate(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# X, Y iid standard Normal and Z = X + Y, random vector (X, Y, Z)
mean = torch.zeros(3, device=device, dtype=dtype)
cov = torch.tensor([[1, 0, 1], [0, 1, 1], [1, 1, 2]],
device=device,
dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
seed=12345)
samples = engine.draw(n=2000)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2))
self.assertTrue(torch.abs(torch.std(samples[:, 0]) - 1) < 1e-2)
self.assertTrue(torch.abs(torch.std(samples[:, 1]) - 1) < 1e-2)
self.assertTrue(
torch.abs(torch.std(samples[:, 2]) - math.sqrt(2)) < 1e-2)
for i in (0, 1, 2):
_, pval = shapiro(samples[:, i].cpu().numpy())
self.assertGreater(pval, 0.9)
cov = np.cov(samples.cpu().numpy().transpose())
self.assertLess(np.abs(cov[0, 1]), 1e-2)
self.assertLess(np.abs(cov[0, 2] - 1), 1e-2)
# check to see if X + Y = Z almost exactly
self.assertTrue(
torch.all(
torch.abs(samples[:, 0] + samples[:, 1] -
samples[:, 2]) < 1e-5))
def test_MultivariateNormalQMCEngineSeededInvTransform(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# test even dimension
with manual_seed(54321):
a = torch.randn(2, 2)
cov = a @ a.transpose(-1, -2) + torch.rand(2).diag()
mean = torch.zeros(2, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
seed=12345,
inv_transform=True)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[[-0.560064316, 0.629113674], [-1.292604208, -0.048077226]],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
# test odd dimension
with manual_seed(54321):
a = torch.randn(3, 3)
cov = a @ a.transpose(-1, -2) + torch.rand(3).diag()
mean = torch.zeros(3, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
seed=12345,
inv_transform=True)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[
[-2.388370037, 3.071142435, -0.319439292],
[-0.282978594, -4.350236893, -1.085214734],
],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
def test_MultivariateNormalQMCEngineSeededInvTransform(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# test even dimension
with manual_seed(54321):
a = torch.randn(2, 2)
cov = a @ a.transpose(-1, -2) + torch.rand(2).diag()
mean = torch.zeros(2, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(
mean=mean, cov=cov, seed=12345, inv_transform=True
)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[[-0.560064316, 0.629113674], [-1.292604208, -0.048077226]],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
# test odd dimension
with manual_seed(54321):
a = torch.randn(3, 3)
cov = a @ a.transpose(-1, -2) + torch.rand(3).diag()
mean = torch.zeros(3, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(
mean=mean, cov=cov, seed=12345, inv_transform=True
)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[
[-2.388370037, 3.071142435, -0.319439292],
[-0.282978594, -4.350236893, -1.085214734],
],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
def test_MultivariateNormalQMCEngineShapiroInvTransform(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# test the standard case
mean = torch.zeros(2, device=device, dtype=dtype)
cov = torch.eye(2, device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(
mean=mean, cov=cov, seed=12345, inv_transform=True
)
samples = engine.draw(n=250)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2))
self.assertTrue(torch.all(torch.abs(samples.std(dim=0) - 1) < 1e-2))
# perform Shapiro-Wilk test for normality
samples = samples.cpu().numpy()
for i in (0, 1):
_, pval = shapiro(samples[:, i])
self.assertGreater(pval, 0.9)
# make sure samples are uncorrelated
cov = np.cov(samples.transpose())
self.assertLess(np.abs(cov[0, 1]), 1e-2)
# test the correlated, non-zero mean case
mean = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
cov = torch.tensor([[1.5, 0.5], [0.5, 1.5]], device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(
mean=mean, cov=cov, seed=12345, inv_transform=True
)
samples = engine.draw(n=250)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertTrue(torch.all(torch.abs(samples.mean(dim=0) - mean) < 1e-2))
self.assertTrue(
torch.all(torch.abs(samples.std(dim=0) - math.sqrt(1.5)) < 1e-2)
)
# perform Shapiro-Wilk test for normality
samples = samples.cpu().numpy()
for i in (0, 1):
_, pval = shapiro(samples[:, i])
self.assertGreater(pval, 0.9)
# check covariance
cov = np.cov(samples.transpose())
self.assertLess(np.abs(cov[0, 1] - 0.5), 1e-2)
def test_MultivariateNormalQMCEngineSeeded(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# test even dimension
with manual_seed(54321):
a = torch.randn(2, 2)
cov = a @ a.transpose(-1, -2) + torch.rand(2).diag()
mean = torch.zeros(2, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
seed=12345)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[[-0.849047422, -0.713852942], [0.398635030, 1.350660801]],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
# test odd dimension
with manual_seed(54321):
a = torch.randn(3, 3)
cov = a @ a.transpose(-1, -2) + torch.rand(3).diag()
mean = torch.zeros(3, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean, cov, seed=12345)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[
[3.113158941, -3.262257099, -0.819938779],
[0.621987879, 2.352285624, -1.992680788],
],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
def test_MultivariateNormalQMCEngineSeeded(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# test even dimension
with manual_seed(54321):
a = torch.randn(2, 2)
cov = a @ a.transpose(-1, -2) + torch.rand(2).diag()
mean = torch.zeros(2, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, seed=12345)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[[-0.849047422, -0.713852942], [0.398635030, 1.350660801]],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
# test odd dimension
with manual_seed(54321):
a = torch.randn(3, 3)
cov = a @ a.transpose(-1, -2) + torch.rand(3).diag()
mean = torch.zeros(3, device=device, dtype=dtype)
cov = cov.to(device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean, cov, seed=12345)
samples = engine.draw(n=2)
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
samples_expected = torch.tensor(
[
[3.113158941, -3.262257099, -0.819938779],
[0.621987879, 2.352285624, -1.992680788],
],
device=device,
dtype=dtype,
)
self.assertTrue(torch.allclose(samples, samples_expected))
def test_MultivariateNormalQMCEngineInvTransform(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# d = 1 scalar
mean = torch.tensor([0], device=device, dtype=dtype)
cov = torch.tensor([[5]], device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
inv_transform=True)
samples = engine.draw()
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.shape, torch.Size([1, 1]))
samples = engine.draw(n=5)
self.assertEqual(samples.shape, torch.Size([5, 1]))
# d = 2 list
mean = torch.tensor([0, 1], device=device, dtype=dtype)
cov = torch.eye(2, device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
inv_transform=True)
samples = engine.draw()
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.shape, torch.Size([1, 2]))
samples = engine.draw(n=5)
self.assertEqual(samples.shape, torch.Size([5, 2]))
# d = 3 Tensor
mean = torch.tensor([0, 1, 2], device=device, dtype=dtype)
cov = torch.eye(3, device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean,
cov=cov,
inv_transform=True)
samples = engine.draw()
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.shape, torch.Size([1, 3]))
samples = engine.draw(n=5)
self.assertEqual(samples.shape, torch.Size([5, 3]))
def test_MultivariateNormalQMCEngineInvTransform(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# d = 1 scalar
mean = torch.tensor([0], device=device, dtype=dtype)
cov = torch.tensor([[5]], device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, inv_transform=True)
samples = engine.draw()
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.shape, torch.Size([1, 1]))
samples = engine.draw(n=5)
self.assertEqual(samples.shape, torch.Size([5, 1]))
# d = 2 list
mean = torch.tensor([0, 1], device=device, dtype=dtype)
cov = torch.eye(2, device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, inv_transform=True)
samples = engine.draw()
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.shape, torch.Size([1, 2]))
samples = engine.draw(n=5)
self.assertEqual(samples.shape, torch.Size([5, 2]))
# d = 3 Tensor
mean = torch.tensor([0, 1, 2], device=device, dtype=dtype)
cov = torch.eye(3, device=device, dtype=dtype)
engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, inv_transform=True)
samples = engine.draw()
self.assertEqual(samples.dtype, dtype)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.shape, torch.Size([1, 3]))
samples = engine.draw(n=5)
self.assertEqual(samples.shape, torch.Size([5, 3]))