def testSampleProbConsistentBroadcastMixNonStandardBase(self):
dims = 4
vdm = tfd.VectorDiffeomixture(
mix_loc=[[0.], [1.]],
temperature=[1.],
distribution=tfd.Normal(1., 1.5),
loc=[
None,
np.float32([2.] * dims),
],
scale=[
tf.linalg.LinearOperatorScaledIdentity(
num_rows=dims,
multiplier=np.float32(1.1),
is_positive_definite=True),
tf.linalg.LinearOperatorDiag(
diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
is_positive_definite=True),
],
quadrature_size=8,
validate_args=True)
# Ball centered at component0's mean.
self.run_test_sample_consistent_log_prob(
self.evaluate,
vdm,
num_samples=int(5e5),
radius=2.,
center=1.,
rtol=0.05)
# Larger ball centered at component1's mean.
self.run_test_sample_consistent_log_prob(
self.evaluate, vdm, radius=4., center=3., rtol=0.025)
def testMeanCovarianceBatch(self):
dims = 3
vdm = tfd.VectorDiffeomixture(
mix_loc=[[0.], [4.]],
temperature=[0.1],
distribution=tfd.Normal(0., 1.),
loc=[
np.float32([[-2.]]),
None,
],
scale=[
tf.linalg.LinearOperatorScaledIdentity(
num_rows=dims,
multiplier=[np.float32(1.5)],
is_positive_definite=True),
tf.linalg.LinearOperatorDiag(
diag=np.stack([
np.linspace(2.5, 3.5, dims, dtype=np.float32),
np.linspace(0.5, 1.5, dims, dtype=np.float32),
]),
is_positive_definite=True),
],
quadrature_size=8,
validate_args=True)
self.run_test_sample_consistent_mean_covariance(
self.evaluate, vdm, rtol=0.02, cov_rtol=0.07)
def testSampleProbConsistentBroadcastMixTwoBatchDims(self):
dims = 4
loc_1 = rng.randn(2, 3, dims).astype(np.float32)
vdm = tfd.VectorDiffeomixture(
mix_loc=(rng.rand(2, 3, 1) - 0.5).astype(np.float32),
temperature=[1.],
distribution=tfd.Normal(0., 1.),
loc=[
None,
loc_1,
],
scale=[
tf.linalg.LinearOperatorScaledIdentity(
num_rows=dims,
multiplier=[np.float32(1.1)],
is_positive_definite=True),
] * 2,
validate_args=True)
# Ball centered at component0's mean.
self.run_test_sample_consistent_log_prob(
self.evaluate,
vdm,
num_samples=int(3e5),
radius=2.,
center=0.,
rtol=0.02)
# Larger ball centered at component1's mean.
self.run_test_sample_consistent_log_prob(
self.evaluate,
vdm,
num_samples=int(3e5),
radius=3.,
center=loc_1,
rtol=0.025)
def testMeanCovarianceNoBatchUncenteredNonStandardBase(self):
dims = 3
vdm = tfd.VectorDiffeomixture(
mix_loc=[[0.], [4.]],
temperature=[0.1],
distribution=tfd.Normal(-1., 1.5),
loc=[
np.float32([-2.]),
np.float32([0.]),
],
scale=[
tf.linalg.LinearOperatorScaledIdentity(
num_rows=dims,
multiplier=np.float32(1.5),
is_positive_definite=True),
tf.linalg.LinearOperatorDiag(diag=np.linspace(
2.5, 3.5, dims, dtype=np.float32),
is_positive_definite=True),
],
quadrature_size=8,
validate_args=True)
self.run_test_sample_consistent_mean_covariance(self.evaluate,
vdm,
num_samples=int(1e6),
rtol=0.01,
cov_atol=0.05)
def testConcentrationLocControlsHowMuchWeightIsOnEachComponent(self):
dims = 1
vdm = tfd.VectorDiffeomixture(
mix_loc=[[-1.], [0.], [1.]],
temperature=[0.5],
distribution=tfd.Normal(0., 1.),
loc=[
np.float32([-2.]),
np.float32([2.]),
],
scale=[
tf.linalg.LinearOperatorScaledIdentity(
num_rows=dims,
multiplier=np.float32(0.5),
is_positive_definite=True),
] * 2, # Use the same scale for each component.
quadrature_size=8,
validate_args=True)
samps = vdm.sample(10000)
self.assertAllEqual((10000, 3, 1), samps.shape)
samps_ = self.evaluate(samps).reshape(10000,
3) # Make scalar event shape.
# One characteristic of putting more weight on a component is that the
# mean is closer to that component's mean.
# Get the mean for each batch member, the names signify the value of
# concentration for that batch member.
mean_neg1, mean_0, mean_1 = samps_.mean(axis=0)
# Since concentration is the concentration for component 0,
# concentration = -1 ==> more weight on component 1, which has mean = 2
# concentration = 0 ==> equal weight
# concentration = 1 ==> more weight on component 0, which has mean = -2
self.assertLess(-2, mean_1)
self.assertLess(mean_1, mean_0)
self.assertLess(mean_0, mean_neg1)
self.assertLess(mean_neg1, 2)
# Run this test as well, just because we can.
self.run_test_sample_consistent_mean_covariance(self.evaluate,
vdm,
rtol=0.02,
cov_rtol=0.08)
def testTemperatureControlsHowMuchThisLooksLikeDiscreteMixture(self):
# As temperature decreases, this should approach a mixture of normals, with
# components at -2, 2.
dims = 1
vdm = tfd.VectorDiffeomixture(
mix_loc=[0.],
temperature=[[2.], [1.], [0.2]],
distribution=tfd.Normal(0., 1.),
loc=[
np.float32([-2.]),
np.float32([2.]),
],
scale=[
tf.linalg.LinearOperatorScaledIdentity(
num_rows=dims,
multiplier=np.float32(0.5),
is_positive_definite=True),
] * 2, # Use the same scale for each component.
quadrature_size=8,
validate_args=True)
samps = vdm.sample(10000)
self.assertAllEqual((10000, 3, 1), samps.shape)
samps_ = self.evaluate(samps).reshape(10000,
3) # Make scalar event shape.
# One characteristic of a discrete mixture (as opposed to a "smear") is
# that more weight is put near the component centers at -2, 2, and thus
# less weight is put near the origin.
prob_of_being_near_origin = (np.abs(samps_) < 1).mean(axis=0)
self.assertGreater(prob_of_being_near_origin[0],
prob_of_being_near_origin[1])
self.assertGreater(prob_of_being_near_origin[1],
prob_of_being_near_origin[2])
# Run this test as well, just because we can.
self.run_test_sample_consistent_mean_covariance(self.evaluate,
vdm,
rtol=0.02,
cov_rtol=0.08)
