def test_factored_joint_mvn_diag_full(self):
batch_shape = [3, 2]
mvn1 = tfd.MultivariateNormalDiag(loc=tf.zeros(batch_shape + [3]),
scale_diag=tf.ones(batch_shape +
[3]))
mvn2 = tfd.MultivariateNormalFullCovariance(
loc=tf.ones(batch_shape + [2]),
covariance_matrix=(tf.ones(batch_shape + [2, 2]) *
[[5., -2], [-2, 3.1]]))
joint = sts_util.factored_joint_mvn([mvn1, mvn2])
self.assertEqual(
self.evaluate(joint.event_shape_tensor()),
self.evaluate(mvn1.event_shape_tensor() +
mvn2.event_shape_tensor()))
joint_mean_ = self.evaluate(joint.mean())
self.assertAllEqual(joint_mean_[..., :3], self.evaluate(mvn1.mean()))
self.assertAllEqual(joint_mean_[..., 3:], self.evaluate(mvn2.mean()))
joint_cov_ = self.evaluate(joint.covariance())
self.assertAllEqual(joint_cov_[..., :3, :3],
self.evaluate(mvn1.covariance()))
self.assertAllEqual(joint_cov_[..., 3:, 3:],
self.evaluate(mvn2.covariance()))
def test_factored_joint_mvn_diag_full(self):
batch_shape = [3, 2]
mvn1 = tfd.MultivariateNormalDiag(
loc=tf.zeros(batch_shape + [3]),
scale_diag=tf.ones(batch_shape + [3]))
mvn2 = tfd.MultivariateNormalFullCovariance(
loc=tf.ones(batch_shape + [2]),
covariance_matrix=(tf.ones(batch_shape + [2, 2]) *
[[5., -2], [-2, 3.1]]))
joint = sts_util.factored_joint_mvn([mvn1, mvn2])
self.assertEqual(self.evaluate(joint.event_shape_tensor()),
self.evaluate(mvn1.event_shape_tensor() +
mvn2.event_shape_tensor()))
joint_mean_ = self.evaluate(joint.mean())
self.assertAllEqual(joint_mean_[..., :3], self.evaluate(mvn1.mean()))
self.assertAllEqual(joint_mean_[..., 3:], self.evaluate(mvn2.mean()))
joint_cov_ = self.evaluate(joint.covariance())
self.assertAllEqual(joint_cov_[..., :3, :3],
self.evaluate(mvn1.covariance()))
self.assertAllEqual(joint_cov_[..., 3:, 3:],
self.evaluate(mvn2.covariance()))
def test_factored_joint_mvn_broadcast_batch_shape(self):
# Test that combining MVNs with different but broadcast-compatible
# batch shapes yields an MVN with the correct broadcast batch shape.
random_with_shape = (
lambda shape: np.random.standard_normal(shape).astype(np.float32))
event_shape = [3]
# mvn with batch shape [2]
mvn1 = tfd.MultivariateNormalDiag(
loc=random_with_shape([2] + event_shape),
scale_diag=tf.exp(random_with_shape([2] + event_shape)))
# mvn with batch shape [3, 2]
mvn2 = tfd.MultivariateNormalDiag(
loc=random_with_shape([3, 2] + event_shape),
scale_diag=tf.exp(random_with_shape([1, 2] + event_shape)))
# mvn with batch shape [1, 2]
mvn3 = tfd.MultivariateNormalDiag(
loc=random_with_shape([1, 2] + event_shape),
scale_diag=tf.exp(random_with_shape([2] + event_shape)))
joint = sts_util.factored_joint_mvn([mvn1, mvn2, mvn3])
self.assertAllEqual(self.evaluate(joint.batch_shape_tensor()), [3, 2])
joint_mean_ = self.evaluate(joint.mean())
broadcast_means = tf.ones_like(joint.mean()[..., 0:1])
self.assertAllEqual(joint_mean_[..., :3],
self.evaluate(broadcast_means * mvn1.mean()))
self.assertAllEqual(joint_mean_[..., 3:6],
self.evaluate(broadcast_means * mvn2.mean()))
self.assertAllEqual(joint_mean_[..., 6:9],
self.evaluate(broadcast_means * mvn3.mean()))
joint_cov_ = self.evaluate(joint.covariance())
broadcast_covs = tf.ones_like(joint.covariance()[..., :1, :1])
self.assertAllEqual(joint_cov_[..., :3, :3],
self.evaluate(broadcast_covs * mvn1.covariance()))
self.assertAllEqual(joint_cov_[..., 3:6, 3:6],
self.evaluate(broadcast_covs * mvn2.covariance()))
self.assertAllEqual(joint_cov_[..., 6:9, 6:9],
self.evaluate(broadcast_covs * mvn3.covariance()))
def test_factored_joint_mvn_broadcast_batch_shape(self):
# Test that combining MVNs with different but broadcast-compatible
# batch shapes yields an MVN with the correct broadcast batch shape.
random_with_shape = (
lambda shape: np.random.standard_normal(shape).astype(np.float32))
event_shape = [3]
# mvn with batch shape [2]
mvn1 = tfd.MultivariateNormalDiag(
loc=random_with_shape([2] + event_shape),
scale_diag=tf.exp(random_with_shape([2] + event_shape)))
# mvn with batch shape [3, 2]
mvn2 = tfd.MultivariateNormalDiag(
loc=random_with_shape([3, 2] + event_shape),
scale_diag=tf.exp(random_with_shape([1, 2] + event_shape)))
# mvn with batch shape [1, 2]
mvn3 = tfd.MultivariateNormalDiag(
loc=random_with_shape([1, 2] + event_shape),
scale_diag=tf.exp(random_with_shape([2] + event_shape)))
joint = sts_util.factored_joint_mvn([mvn1, mvn2, mvn3])
self.assertAllEqual(self.evaluate(joint.batch_shape_tensor()), [3, 2])
joint_mean_ = self.evaluate(joint.mean())
broadcast_means = tf.ones_like(joint.mean()[..., 0:1])
self.assertAllEqual(joint_mean_[..., :3],
self.evaluate(broadcast_means * mvn1.mean()))
self.assertAllEqual(joint_mean_[..., 3:6],
self.evaluate(broadcast_means * mvn2.mean()))
self.assertAllEqual(joint_mean_[..., 6:9],
self.evaluate(broadcast_means * mvn3.mean()))
joint_cov_ = self.evaluate(joint.covariance())
broadcast_covs = tf.ones_like(joint.covariance()[..., :1, :1])
self.assertAllEqual(joint_cov_[..., :3, :3],
self.evaluate(broadcast_covs * mvn1.covariance()))
self.assertAllEqual(joint_cov_[..., 3:6, 3:6],
self.evaluate(broadcast_covs * mvn2.covariance()))
self.assertAllEqual(joint_cov_[..., 6:9, 6:9],
self.evaluate(broadcast_covs * mvn3.covariance()))
def transition_noise_fn(t):
return sts_util.factored_joint_mvn([
ssm.get_transition_noise_for_timestep(t)
for ssm in component_ssms
])
def __init__(self,
component_ssms,
constant_offset=0.,
observation_noise_scale=None,
initial_state_prior=None,
initial_step=0,
validate_args=False,
name=None,
**linear_gaussian_ssm_kwargs):
"""Build a state space model representing the sum of component models.
Args:
component_ssms: Python `list` containing one or more
`tfd.LinearGaussianStateSpaceModel` instances. The components
will in general implement different time-series models, with possibly
different `latent_size`, but they must have the same `dtype`, event
shape (`num_timesteps` and `observation_size`), and their batch shapes
must broadcast to a compatible batch shape.
constant_offset: `float` `Tensor` of shape broadcasting to
`concat([batch_shape, [num_timesteps]]`) specifying a constant value
added to the sum of outputs from the component models. This allows the
components to model the shifted series
`observed_time_series - constant_offset`.
Default value: `0.`
observation_noise_scale: Optional scalar `float` `Tensor` indicating the
standard deviation of the observation noise. May contain additional
batch dimensions, which must broadcast with the batch shape of elements
in `component_ssms`. If `observation_noise_scale` is specified for the
`AdditiveStateSpaceModel`, the observation noise scales of component
models are ignored. If `None`, the observation noise scale is derived
by summing the noise variances of the component models, i.e.,
`observation_noise_scale = sqrt(sum(
[ssm.observation_noise_scale**2 for ssm in component_ssms]))`.
initial_state_prior: Optional instance of `tfd.MultivariateNormal`
representing a prior distribution on the latent state at time
`initial_step`. If `None`, defaults to the independent priors from
component models, i.e.,
`[component.initial_state_prior for component in component_ssms]`.
Default value: `None`.
initial_step: Optional scalar `int` `Tensor` specifying the starting
timestep.
Default value: 0.
validate_args: Python `bool`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
Default value: `False`.
name: Python `str` name prefixed to ops created by this class.
Default value: "AdditiveStateSpaceModel".
**linear_gaussian_ssm_kwargs: Optional additional keyword arguments to
to the base `tfd.LinearGaussianStateSpaceModel` constructor.
Raises:
ValueError: if components have different `num_timesteps`.
"""
parameters = dict(locals())
parameters.update(linear_gaussian_ssm_kwargs)
del parameters['linear_gaussian_ssm_kwargs']
with tf.name_scope(name or 'AdditiveStateSpaceModel') as name:
# Check that all components have the same dtype
dtype = tf.debugging.assert_same_float_dtype(component_ssms)
# Convert scalar offsets to canonical shape `[..., num_timesteps]`.
constant_offset = (tf.convert_to_tensor(
value=constant_offset, name='constant_offset', dtype=dtype) *
tf.ones([1], dtype=dtype))
offset_length = prefer_static.shape(constant_offset)[-1]
assertions = []
# Construct an initial state prior as a block-diagonal combination
# of the component state priors.
if initial_state_prior is None:
initial_state_prior = sts_util.factored_joint_mvn(
[ssm.initial_state_prior for ssm in component_ssms])
dtype = initial_state_prior.dtype
static_num_timesteps = [
tf.get_static_value(ssm.num_timesteps)
for ssm in component_ssms
if tf.get_static_value(ssm.num_timesteps) is not None
]
# If any components have a static value for `num_timesteps`, use that
# value for the additive model. (and check that all other static values
# match it).
if static_num_timesteps:
num_timesteps = static_num_timesteps[0]
if not all([
component_timesteps == num_timesteps
for component_timesteps in static_num_timesteps
]):
raise ValueError(
'Additive model components must all have the same '
'number of timesteps '
'(saw: {})'.format(static_num_timesteps))
else:
num_timesteps = component_ssms[0].num_timesteps
if validate_args and len(static_num_timesteps) != len(
component_ssms):
assertions += [
tf.debugging.assert_equal( # pylint: disable=g-complex-comprehension
num_timesteps,
ssm.num_timesteps,
message='Additive model components must all have '
'the same number of timesteps.')
for ssm in component_ssms
]
# Define the transition and observation models for the additive SSM.
# See the "mathematical details" section of the class docstring for
# further information. Note that we define these as callables to
# handle the fully general case in which some components have time-
# varying dynamics.
def transition_matrix_fn(t):
return tfl.LinearOperatorBlockDiag([
ssm.get_transition_matrix_for_timestep(t)
for ssm in component_ssms
])
def transition_noise_fn(t):
return sts_util.factored_joint_mvn([
ssm.get_transition_noise_for_timestep(t)
for ssm in component_ssms
])
# Build the observation matrix, concatenating (broadcast) observation
# matrices from components. We also take this as an opportunity to enforce
# any dynamic assertions we may have generated above.
broadcast_batch_shape = tf.convert_to_tensor(
value=sts_util.broadcast_batch_shape([
ssm.get_observation_matrix_for_timestep(initial_step)
for ssm in component_ssms
]),
dtype=tf.int32)
broadcast_obs_matrix = tf.ones(tf.concat(
[broadcast_batch_shape, [1, 1]], axis=0),
dtype=dtype)
if assertions:
with tf.control_dependencies(assertions):
broadcast_obs_matrix = tf.identity(broadcast_obs_matrix)
def observation_matrix_fn(t):
return tfl.LinearOperatorFullMatrix(
tf.concat([
ssm.get_observation_matrix_for_timestep(t).to_dense() *
broadcast_obs_matrix for ssm in component_ssms
],
axis=-1))
# Broadcast the constant offset across timesteps.
offset_at_step = lambda t: ( # pylint: disable=g-long-lambda
constant_offset if offset_length == 1 else tf.gather(
constant_offset, tf.minimum(t, offset_length - 1), axis=-1)
[..., tf.newaxis])
if observation_noise_scale is not None:
observation_noise_scale = tf.convert_to_tensor(
value=observation_noise_scale,
name='observation_noise_scale',
dtype=dtype)
def observation_noise_fn(t):
return tfd.MultivariateNormalDiag(
loc=(sum([
ssm.get_observation_noise_for_timestep(t).mean()
for ssm in component_ssms
]) + offset_at_step(t)),
scale_diag=observation_noise_scale[..., tf.newaxis])
else:
def observation_noise_fn(t):
offset = offset_at_step(t)
return sts_util.sum_mvns([
tfd.MultivariateNormalDiag(
loc=offset, scale_diag=tf.zeros_like(offset))
] + [
ssm.get_observation_noise_for_timestep(t)
for ssm in component_ssms
])
super(AdditiveStateSpaceModel,
self).__init__(num_timesteps=num_timesteps,
transition_matrix=transition_matrix_fn,
transition_noise=transition_noise_fn,
observation_matrix=observation_matrix_fn,
observation_noise=observation_noise_fn,
initial_state_prior=initial_state_prior,
initial_step=initial_step,
validate_args=validate_args,
name=name,
**linear_gaussian_ssm_kwargs)
self._parameters = parameters
def __init__(self,
component_ssms,
observation_noise_scale=None,
initial_state_prior=None,
initial_step=0,
validate_args=False,
allow_nan_stats=True,
name=None):
"""Build a state space model representing the sum of component models.
Args:
component_ssms: Python `list` containing one or more
`tfd.LinearGaussianStateSpaceModel` instances. The components
will in general implement different time-series models, with possibly
different `latent_size`, but they must have the same `dtype`, event
shape (`num_timesteps` and `observation_size`), and their batch shapes
must broadcast to a compatible batch shape.
observation_noise_scale: Optional scalar `float` `Tensor` indicating the
standard deviation of the observation noise. May contain additional
batch dimensions, which must broadcast with the batch shape of elements
in `component_ssms`. If `observation_noise_scale` is specified for the
`AdditiveStateSpaceModel`, the observation noise scales of component
models are ignored. If `None`, the observation noise scale is derived
by summing the noise variances of the component models, i.e.,
`observation_noise_scale = sqrt(sum(
[ssm.observation_noise_scale**2 for ssm in component_ssms]))`.
initial_state_prior: Optional instance of `tfd.MultivariateNormal`
representing a prior distribution on the latent state at time
`initial_step`. If `None`, defaults to the independent priors from
component models, i.e.,
`[component.initial_state_prior for component in component_ssms]`.
Default value: `None`.
initial_step: Optional scalar `int` `Tensor` specifying the starting
timestep.
Default value: 0.
validate_args: Python `bool`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
Default value: `False`.
allow_nan_stats: Python `bool`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
Default value: `True`.
name: Python `str` name prefixed to ops created by this class.
Default value: "AdditiveStateSpaceModel".
Raises:
ValueError: if components have different `num_timesteps`.
"""
with tf.compat.v1.name_scope(
name,
'AdditiveStateSpaceModel',
values=[observation_noise_scale, initial_step]) as name:
assertions = []
# Check that all components have the same dtype
tf.debugging.assert_same_float_dtype(component_ssms)
# Construct an initial state prior as a block-diagonal combination
# of the component state priors.
if initial_state_prior is None:
initial_state_prior = sts_util.factored_joint_mvn(
[ssm.initial_state_prior for ssm in component_ssms])
dtype = initial_state_prior.dtype
static_num_timesteps = [
tf.get_static_value(ssm.num_timesteps)
for ssm in component_ssms
if tf.get_static_value(ssm.num_timesteps) is not None
]
# If any components have a static value for `num_timesteps`, use that
# value for the additive model. (and check that all other static values
# match it).
if static_num_timesteps:
num_timesteps = static_num_timesteps[0]
if not all([component_timesteps == num_timesteps
for component_timesteps in static_num_timesteps]):
raise ValueError('Additive model components must all have the same '
'number of timesteps '
'(saw: {})'.format(static_num_timesteps))
else:
num_timesteps = component_ssms[0].num_timesteps
if validate_args and len(static_num_timesteps) != len(component_ssms):
assertions += [
tf.compat.v1.assert_equal(
num_timesteps,
ssm.num_timesteps,
message='Additive model components must all have '
'the same number of timesteps.') for ssm in component_ssms
]
# Define the transition and observation models for the additive SSM.
# See the "mathematical details" section of the class docstring for
# further information. Note that we define these as callables to
# handle the fully general case in which some components have time-
# varying dynamics.
def transition_matrix_fn(t):
return tfl.LinearOperatorBlockDiag(
[ssm.get_transition_matrix_for_timestep(t)
for ssm in component_ssms])
def transition_noise_fn(t):
return sts_util.factored_joint_mvn(
[ssm.get_transition_noise_for_timestep(t)
for ssm in component_ssms])
# Build the observation matrix, concatenating (broadcast) observation
# matrices from components. We also take this as an opportunity to enforce
# any dynamic assertions we may have generated above.
broadcast_batch_shape = tf.convert_to_tensor(
value=sts_util.broadcast_batch_shape(component_ssms), dtype=tf.int32)
broadcast_obs_matrix = tf.ones(
tf.concat([broadcast_batch_shape, [1, 1]], axis=0), dtype=dtype)
if assertions:
with tf.control_dependencies(assertions):
broadcast_obs_matrix = tf.identity(broadcast_obs_matrix)
def observation_matrix_fn(t):
return tfl.LinearOperatorFullMatrix(
tf.concat([ssm.get_observation_matrix_for_timestep(t).to_dense() *
broadcast_obs_matrix for ssm in component_ssms],
axis=-1))
if observation_noise_scale is not None:
observation_noise_scale = tf.convert_to_tensor(
value=observation_noise_scale,
name='observation_noise_scale',
dtype=dtype)
def observation_noise_fn(t):
return tfd.MultivariateNormalDiag(
loc=sum([ssm.get_observation_noise_for_timestep(t).mean()
for ssm in component_ssms]),
scale_diag=observation_noise_scale[..., tf.newaxis])
else:
def observation_noise_fn(t):
return sts_util.sum_mvns(
[ssm.get_observation_noise_for_timestep(t)
for ssm in component_ssms])
super(AdditiveStateSpaceModel, self).__init__(
num_timesteps=num_timesteps,
transition_matrix=transition_matrix_fn,
transition_noise=transition_noise_fn,
observation_matrix=observation_matrix_fn,
observation_noise=observation_noise_fn,
initial_state_prior=initial_state_prior,
initial_step=initial_step,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
def transition_noise_fn(t):
return sts_util.factored_joint_mvn(
[ssm.get_transition_noise_for_timestep(t)
for ssm in component_ssms])
def __init__(self,
component_ssms,
observation_noise_scale=None,
initial_state_prior=None,
initial_step=0,
validate_args=False,
allow_nan_stats=True,
name=None):
"""Build a state space model representing the sum of component models.
Args:
component_ssms: Python `list` containing one or more
`tfd.LinearGaussianStateSpaceModel` instances. The components
will in general implement different time-series models, with possibly
different `latent_size`, but they must have the same `dtype`, event
shape (`num_timesteps` and `observation_size`), and their batch shapes
must broadcast to a compatible batch shape.
observation_noise_scale: Optional scalar `float` `Tensor` indicating the
standard deviation of the observation noise. May contain additional
batch dimensions, which must broadcast with the batch shape of elements
in `component_ssms`. If `observation_noise_scale` is specified for the
`AdditiveStateSpaceModel`, the observation noise scales of component
models are ignored. If `None`, the observation noise scale is derived
by summing the noise variances of the component models, i.e.,
`observation_noise_scale = sqrt(sum(
[ssm.observation_noise_scale**2 for ssm in component_ssms]))`.
initial_state_prior: Optional instance of `tfd.MultivariateNormal`
representing a prior distribution on the latent state at time
`initial_step`. If `None`, defaults to the independent priors from
component models, i.e.,
`[component.initial_state_prior for component in component_ssms]`.
Default value: `None`.
initial_step: Optional scalar `int` `Tensor` specifying the starting
timestep.
Default value: 0.
validate_args: Python `bool`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
Default value: `False`.
allow_nan_stats: Python `bool`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
Default value: `True`.
name: Python `str` name prefixed to ops created by this class.
Default value: "AdditiveStateSpaceModel".
Raises:
ValueError: if components have different `num_timesteps`.
"""
with tf.name_scope(name, 'AdditiveStateSpaceModel',
values=[observation_noise_scale, initial_step]) as name:
assertions = []
# Check that all components have the same dtype
tf.assert_same_float_dtype(component_ssms)
# Construct an initial state prior as a block-diagonal combination
# of the component state priors.
if initial_state_prior is None:
initial_state_prior = sts_util.factored_joint_mvn(
[ssm.initial_state_prior for ssm in component_ssms])
dtype = initial_state_prior.dtype
static_num_timesteps = [
distribution_util.static_value(ssm.num_timesteps)
for ssm in component_ssms
if distribution_util.static_value(ssm.num_timesteps) is not None
]
# If any components have a static value for `num_timesteps`, use that
# value for the additive model. (and check that all other static values
# match it).
if static_num_timesteps:
num_timesteps = static_num_timesteps[0]
if not all([component_timesteps == num_timesteps
for component_timesteps in static_num_timesteps]):
raise ValueError('Additive model components must all have the same '
'number of timesteps '
'(saw: {})'.format(static_num_timesteps))
else:
num_timesteps = component_ssms[0].num_timesteps
if validate_args and len(static_num_timesteps) != len(component_ssms):
assertions += [
tf.assert_equal(num_timesteps,
ssm.num_timesteps,
message='Additive model components must all have '
'the same number of timesteps.')
for ssm in component_ssms]
# Define the transition and observation models for the additive SSM.
# See the "mathematical details" section of the class docstring for
# further information. Note that we define these as callables to
# handle the fully general case in which some components have time-
# varying dynamics.
def transition_matrix_fn(t):
return tfl.LinearOperatorBlockDiag(
[ssm.get_transition_matrix_for_timestep(t)
for ssm in component_ssms])
def transition_noise_fn(t):
return sts_util.factored_joint_mvn(
[ssm.get_transition_noise_for_timestep(t)
for ssm in component_ssms])
# Build the observation matrix, concatenating (broadcast) observation
# matrices from components. We also take this as an opportunity to enforce
# any dynamic assertions we may have generated above.
broadcast_batch_shape = tf.convert_to_tensor(
sts_util.broadcast_batch_shape(component_ssms), dtype=tf.int32)
broadcast_obs_matrix = tf.ones(
tf.concat([broadcast_batch_shape, [1, 1]], axis=0), dtype=dtype)
if assertions:
with tf.control_dependencies(assertions):
broadcast_obs_matrix = tf.identity(broadcast_obs_matrix)
def observation_matrix_fn(t):
return tfl.LinearOperatorFullMatrix(
tf.concat([ssm.get_observation_matrix_for_timestep(t).to_dense() *
broadcast_obs_matrix for ssm in component_ssms],
axis=-1))
if observation_noise_scale is not None:
observation_noise_scale = tf.convert_to_tensor(
observation_noise_scale,
name='observation_noise_scale',
dtype=dtype)
def observation_noise_fn(t):
return tfd.MultivariateNormalDiag(
loc=sum([ssm.get_observation_noise_for_timestep(t).mean()
for ssm in component_ssms]),
scale_diag=observation_noise_scale[..., tf.newaxis])
else:
def observation_noise_fn(t):
return sts_util.sum_mvns(
[ssm.get_observation_noise_for_timestep(t)
for ssm in component_ssms])
super(AdditiveStateSpaceModel, self).__init__(
num_timesteps=num_timesteps,
transition_matrix=transition_matrix_fn,
transition_noise=transition_noise_fn,
observation_matrix=observation_matrix_fn,
observation_noise=observation_noise_fn,
initial_state_prior=initial_state_prior,
initial_step=initial_step,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
def _pad_mvn_with_trailing_zeros(mvn, num_zeros):
zeros = tf.zeros([num_zeros], dtype=mvn.dtype)
return sts_util.factored_joint_mvn(
[mvn,
tfd.MultivariateNormalDiag(loc=zeros, scale_diag=zeros)])
