def testSampleConsistentLogProb(self):
with self.test_session() as sess:
gm = mixture_same_family_lib.MixtureSameFamily(
mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
components_distribution=mvn_diag_lib.MultivariateNormalDiag(
loc=[[-1., 1], [1, -1]], scale_identity_multiplier=[1., 0.5]))
# Ball centered at component0's mean.
self.run_test_sample_consistent_log_prob(
sess, gm, radius=1., center=[-1., 1], rtol=0.02)
# Larger ball centered at component1's mean.
self.run_test_sample_consistent_log_prob(
sess, gm, radius=1., center=[1., -1], rtol=0.02)
def testSampleAndLogProbBatchMultivariateShapes(self):
with self.test_session():
gm = mixture_same_family_lib.MixtureSameFamily(
mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
components_distribution=mvn_diag_lib.MultivariateNormalDiag(
loc=[[[-1., 1],
[1, -1]],
[[0., 1],
[1, 0]]],
scale_identity_multiplier=[1., 0.5]))
x = gm.sample([4, 5], seed=42)
log_prob_x = gm.log_prob(x)
self.assertEqual([4, 5, 2, 2], x.shape)
self.assertEqual([4, 5, 2], log_prob_x.shape)
def testSampleAndLogProbShapesBroadcastMix(self):
mix_probs = np.float32([.3, .7])
bern_probs = np.float32([[.4, .6], [.25, .75]])
with self.test_session():
bm = mixture_same_family_lib.MixtureSameFamily(
mixture_distribution=categorical_lib.Categorical(probs=mix_probs),
components_distribution=bernoulli_lib.Bernoulli(probs=bern_probs))
x = bm.sample([4, 5], seed=42)
log_prob_x = bm.log_prob(x)
x_ = x.eval()
self.assertEqual([4, 5, 2], x.shape)
self.assertEqual([4, 5, 2], log_prob_x.shape)
self.assertAllEqual(
np.ones_like(x_, dtype=np.bool), np.logical_or(x_ == 0., x_ == 1.))
def test_pad_mixture_dimensions_mixture_same_family(self):
with self.test_session() as sess:
gm = mixture_same_family.MixtureSameFamily(
mixture_distribution=categorical.Categorical(probs=[0.3, 0.7]),
components_distribution=mvn_diag.MultivariateNormalDiag(
loc=[[-1., 1], [1, -1]],
scale_identity_multiplier=[1.0, 0.5]))
x = array_ops.constant([[1.0, 2.0], [3.0, 4.0]])
x_pad = distribution_util.pad_mixture_dimensions(
x, gm, gm.mixture_distribution, gm.event_shape.ndims)
x_out, x_pad_out = sess.run([x, x_pad])
self.assertAllEqual(x_pad_out.shape, [2, 2, 1])
self.assertAllEqual(x_out.reshape([-1]), x_pad_out.reshape([-1]))
def testLogCdf(self):
with self.test_session() as sess:
gm = mixture_same_family_lib.MixtureSameFamily(
mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
components_distribution=normal_lib.Normal(
loc=[-1., 1], scale=[0.1, 0.5]))
x = gm.sample(10, seed=42)
actual_log_cdf = gm.log_cdf(x)
expected_log_cdf = math_ops.reduce_logsumexp(
(gm.mixture_distribution.logits +
gm.components_distribution.log_cdf(x[..., array_ops.newaxis])),
axis=1)
actual_log_cdf_, expected_log_cdf_ = sess.run([
actual_log_cdf, expected_log_cdf])
self.assertAllClose(actual_log_cdf_, expected_log_cdf_,
rtol=1e-6, atol=0.0)
