def test_dist_normal(self): dist_sample_shape_correct = [1] dist_means_correct = [0] dist_stddevs_correct = [1] dist_log_probs_correct = [-0.918939] dist = Normal(dist_means_correct, dist_stddevs_correct) dist_sample_shape = list(dist.sample().size()) dist_empirical = Empirical( [dist.sample() for i in range(empirical_samples)]) dist_means = util.to_numpy(dist.mean) dist_means_empirical = util.to_numpy(dist_empirical.mean) dist_stddevs = util.to_numpy(dist.stddev) dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev) dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct)) util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct') self.assertEqual(dist_sample_shape, dist_sample_shape_correct) self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1)) self.assertTrue( np.allclose(dist_means_empirical, dist_means_correct, atol=0.1)) self.assertTrue( np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1)) self.assertTrue( np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1)) self.assertTrue( np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_normal_multivariate_batched(self): dist_sample_shape_correct = [2, 3] dist_means_correct = [[0, 2, 0], [2, 0, 2]] dist_stddevs_correct = [[1, 3, 1], [3, 1, 3]] dist_log_probs_correct = [[sum([-0.918939, -2.01755, -0.918939])], [sum([-2.01755, -0.918939, -2.01755])]] dist = Normal(dist_means_correct, dist_stddevs_correct) dist_sample_shape = list(dist.sample().size()) dist_empirical = Empirical( [dist.sample() for i in range(empirical_samples)]) dist_means = util.to_numpy(dist.mean) dist_means_empirical = util.to_numpy(dist_empirical.mean) dist_stddevs = util.to_numpy(dist.stddev) dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev) dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct)) util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct') self.assertEqual(dist_sample_shape, dist_sample_shape_correct) self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1)) self.assertTrue( np.allclose(dist_means_empirical, dist_means_correct, atol=0.1)) self.assertTrue( np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1)) self.assertTrue( np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1)) self.assertTrue( np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def forward(self): mu = pyprob.sample(Normal(self.prior_mean, self.prior_stddev)) likelihood = Normal(mu, self.likelihood_stddev) likelihood_func = lambda x: likelihood.log_prob(x) pyprob.factor(log_prob=likelihood_func(8)) pyprob.factor(log_prob=likelihood_func(9)) return mu