def test_smoothed_box_prior_log_prob_log_transform(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") a, b = torch.zeros(2, device=device), torch.ones(2, device=device) sigma = 0.1 prior = SmoothedBoxPrior(a, b, sigma, transform=torch.exp) t = torch.tensor([0.5, 1.1], device=device).log() self.assertAlmostEqual(prior.log_prob(t).item(), -0.9473, places=4) t = torch.tensor([[0.5, 1.1], [0.1, 0.25]], device=device).log() log_prob_expected = torch.tensor([-0.947347, -0.447347], device=t.device) self.assertTrue(torch.all(approx_equal(prior.log_prob(t), log_prob_expected))) with self.assertRaises(RuntimeError): prior.log_prob(torch.ones(3, device=device))
def test_lkj_covariance_prior_batch_log_prob(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") v = torch.ones(2, 1, device=device) sd_prior = SmoothedBoxPrior(exp(-1) * v, exp(1) * v) prior = LKJCovariancePrior(2, torch.tensor([0.5, 1.5], device=device), sd_prior) corr_dist = LKJCholesky(2, torch.tensor([0.5, 1.5], device=device)) S = torch.eye(2, device=device) dist_log_prob = corr_dist.log_prob(S) + sd_prior.log_prob(S.diag()) self.assertLessEqual((prior.log_prob(S) - dist_log_prob).abs().sum(), 1e-4) S = torch.stack( [S, torch.tensor([[1.0, 0.5], [0.5, 1]], device=S.device)]) S_chol = torch.linalg.cholesky(S) dist_log_prob = corr_dist.log_prob(S_chol) + sd_prior.log_prob( torch.diagonal(S, dim1=-2, dim2=-1)) self.assertLessEqual((prior.log_prob(S) - dist_log_prob).abs().sum(), 1e-4)
def test_lkj_covariance_prior_log_prob_hetsd(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") a = torch.tensor([exp(-1), exp(-2)], device=device) b = torch.tensor([exp(1), exp(2)], device=device) sd_prior = SmoothedBoxPrior(a, b) prior = LKJCovariancePrior(2, torch.tensor(0.5, device=device), sd_prior) corr_dist = LKJCholesky(2, torch.tensor(0.5, device=device)) S = torch.eye(2, device=device) dist_log_prob = corr_dist.log_prob(S) + sd_prior.log_prob( S.diag()).sum() self.assertAlmostEqual(prior.log_prob(S), dist_log_prob, places=4) S = torch.stack( [S, torch.tensor([[1.0, 0.5], [0.5, 1]], device=S.device)]) S_chol = torch.linalg.cholesky(S) dist_log_prob = corr_dist.log_prob(S_chol) + sd_prior.log_prob( torch.diagonal(S, dim1=-2, dim2=-1)) self.assertTrue(approx_equal(prior.log_prob(S), dist_log_prob))