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 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_empirical_resample(self):
dist_means_correct = [2]
dist_stddevs_correct = [5]
dist = Normal(dist_means_correct, dist_stddevs_correct)
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_empirical = dist_empirical.resample(int(empirical_samples/2))
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
util.debug('dist_means_empirical', 'dist_means_correct', 'dist_stddevs_empirical', 'dist_stddevs_correct')
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.25))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.25))
def test_dist_empirical_combine_uniform_weights(self):
dist1_mean_correct = 1
dist1_stddev_correct = 3
dist2_mean_correct = 5
dist2_stddev_correct = 2
dist3_mean_correct = -2.5
dist3_stddev_correct = 1.2
dist_combined_mean_correct = 1.16667
dist_combined_stddev_correct = 3.76858
dist1 = Normal(dist1_mean_correct, dist1_stddev_correct)
dist1_empirical = Empirical([dist1.sample() for i in range(empirical_samples)])
dist1_empirical_mean = float(dist1_empirical.mean)
dist1_empirical_stddev = float(dist1_empirical.stddev)
dist2 = Normal(dist2_mean_correct, dist2_stddev_correct)
dist2_empirical = Empirical([dist2.sample() for i in range(empirical_samples)])
dist2_empirical_mean = float(dist2_empirical.mean)
dist2_empirical_stddev = float(dist2_empirical.stddev)
dist3 = Normal(dist3_mean_correct, dist3_stddev_correct)
dist3_empirical = Empirical([dist3.sample() for i in range(empirical_samples)])
dist3_empirical_mean = float(dist3_empirical.mean)
dist3_empirical_stddev = float(dist3_empirical.stddev)
dist_combined_empirical = Empirical.combine([dist1_empirical, dist2_empirical, dist3_empirical])
dist_combined_empirical_mean = float(dist_combined_empirical.mean)
dist_combined_empirical_stddev = float(dist_combined_empirical.stddev)
util.debug('dist1_empirical_mean', 'dist1_empirical_stddev', 'dist1_mean_correct', 'dist1_stddev_correct', 'dist2_empirical_mean', 'dist2_empirical_stddev', 'dist2_mean_correct', 'dist2_stddev_correct', 'dist3_empirical_mean', 'dist3_empirical_stddev', 'dist3_mean_correct', 'dist3_stddev_correct', 'dist_combined_empirical_mean', 'dist_combined_empirical_stddev', 'dist_combined_mean_correct', 'dist_combined_stddev_correct')
self.assertAlmostEqual(dist1_empirical_mean, dist1_mean_correct, places=1)
self.assertAlmostEqual(dist1_empirical_stddev, dist1_stddev_correct, places=1)
self.assertAlmostEqual(dist2_empirical_mean, dist2_mean_correct, places=1)
self.assertAlmostEqual(dist2_empirical_stddev, dist2_stddev_correct, places=1)
self.assertAlmostEqual(dist3_empirical_mean, dist3_mean_correct, places=1)
self.assertAlmostEqual(dist3_empirical_stddev, dist3_stddev_correct, places=1)
self.assertAlmostEqual(dist_combined_empirical_mean, dist_combined_mean_correct, places=1)
self.assertAlmostEqual(dist_combined_empirical_stddev, dist_combined_stddev_correct, places=1)
