def test_gamma_prior_batch_log_prob(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
concentration = torch.tensor([1.0, 2.0], device=device)
rate = torch.tensor([1.0, 2.0], device=device)
prior = GammaPrior(concentration, rate)
dist = Gamma(concentration, rate)
t = torch.ones(2, device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
t = torch.ones(2, 2, device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
with self.assertRaises(RuntimeError):
prior.log_prob(torch.ones(3, device=device))
mean = torch.tensor([[1.0, 2.0], [0.5, 3.0]], device=device)
variance = torch.tensor([[1.0, 2.0], [0.5, 1.0]], device=device)
prior = GammaPrior(mean, variance)
dist = Gamma(mean, variance)
t = torch.ones(2, device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
t = torch.ones(2, 2, device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
with self.assertRaises(RuntimeError):
prior.log_prob(torch.ones(3, device=device))
with self.assertRaises(RuntimeError):
prior.log_prob(torch.ones(2, 3, device=device))
def test_gamma_prior_log_prob_log_transform(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
concentration = torch.tensor(1.0, device=device)
rate = torch.tensor(1.0, device=device)
prior = GammaPrior(concentration, rate, transform=torch.exp)
dist = Gamma(concentration, rate)
t = torch.tensor(0.0, device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t.exp())))
t = torch.tensor([-1, 0.5], device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t.exp())))
t = torch.tensor([[-1, 0.5], [0.1, -2.0]], device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t.exp())))
def test_gamma_prior_log_prob(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
concentration = torch.tensor(1.0, device=device)
rate = torch.tensor(1.0, device=device)
prior = GammaPrior(concentration, rate)
dist = Gamma(concentration, rate)
t = torch.tensor(1.0, device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
t = torch.tensor([1.5, 0.5], device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
t = torch.tensor([[1.0, 0.5], [3.0, 0.25]], device=device)
self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
def test_scalar_gamma_prior(self):
prior = GammaPrior(1, 1) # this is an exponential w/ rate 1
self.assertFalse(prior.log_transform)
self.assertTrue(prior.is_in_support(prior.rate.new([1])))
self.assertFalse(prior.is_in_support(prior.rate.new([-1])))
self.assertEqual(prior.shape, torch.Size([1]))
self.assertEqual(prior.concentration.item(), 1.0)
self.assertEqual(prior.rate.item(), 1.0)
self.assertAlmostEqual(prior.log_prob(prior.rate.new([1.0])).item(),
-1.0,
places=5)
def test_vector_gamma_prior_size(self):
prior = GammaPrior(1, 1, size=2)
self.assertFalse(prior.log_transform)
self.assertTrue(prior.is_in_support(prior.rate.new_ones(2)))
self.assertFalse(prior.is_in_support(prior.rate.new_zeros(2)))
self.assertEqual(prior.shape, torch.Size([2]))
self.assertTrue(
torch.equal(prior.concentration, prior.rate.new([1.0, 1.0])))
self.assertTrue(torch.equal(prior.rate, prior.rate.new([1.0, 1.0])))
parameter = prior.rate.new([1.0, 2.0])
self.assertAlmostEqual(prior.log_prob(parameter).item(),
-3.0,
places=5)
def test_vector_gamma_prior(self):
prior = GammaPrior(torch.tensor([1.0, 2.0]), torch.tensor([0.5, 2.0]))
self.assertFalse(prior.log_transform)
self.assertTrue(prior.is_in_support(torch.rand(1)))
self.assertEqual(prior.shape, torch.Size([2]))
self.assertTrue(
torch.equal(prior.concentration, torch.tensor([1.0, 2.0])))
self.assertTrue(torch.equal(prior.rate, torch.tensor([0.5, 2.0])))
parameter = torch.tensor([1.0, math.exp(1)])
expected_log_prob = torch.tensor(
[math.log(0.5) - 0.5,
2 * math.log(2) + 1 - 2 * math.exp(1)]).sum().item()
self.assertAlmostEqual(prior.log_prob(torch.tensor(parameter)).item(),
expected_log_prob,
places=5)
def test_scalar_gamma_prior_log_transform(self):
prior = GammaPrior(1, 1, log_transform=True)
self.assertTrue(prior.log_transform)
self.assertAlmostEqual(prior.log_prob(prior.rate.new([0.0])).item(),
-1.0,
places=5)
def test_scalar_gamma_prior_log_transform(self):
prior = GammaPrior(1, 1, log_transform=True)
self.assertTrue(prior.log_transform)
self.assertAlmostEqual(prior.log_prob(torch.tensor(0.0)).item(),
-1.0,
places=5)