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)