def __init__(self,
loc=None,
scale=None,
validate_args=False,
allow_nan_stats=True,
name="VectorExponentialLinearOperator"):
"""Construct Vector Exponential distribution supported on a subset of `R^k`.
The `batch_shape` is the broadcast shape between `loc` and `scale`
arguments.
The `event_shape` is given by last dimension of the matrix implied by
`scale`. The last dimension of `loc` (if provided) must broadcast with this.
Recall that `covariance = scale @ scale.T`.
Additional leading dimensions (if any) will index batches.
Args:
loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
`b >= 0` and `k` is the event size.
scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
`[B1, ..., Bb, k, k]`.
validate_args: Python `bool`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
allow_nan_stats: Python `bool`, default `True`. 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.
name: The name to give Ops created by the initializer.
Raises:
ValueError: if `scale` is unspecified.
TypeError: if not `scale.dtype.is_floating`
"""
parameters = dict(locals())
if scale is None:
raise ValueError("Missing required `scale` parameter.")
if not scale.dtype.is_floating:
raise TypeError(
"`scale` parameter must have floating-point dtype.")
with ops.name_scope(name, values=[loc] + scale.graph_parents) as name:
# Since expand_dims doesn't preserve constant-ness, we obtain the
# non-dynamic value if possible.
loc = ops.convert_to_tensor(loc,
name="loc") if loc is not None else loc
batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
loc, scale)
super(VectorExponentialLinearOperator,
self).__init__(distribution=exponential.Exponential(
rate=array_ops.ones([], dtype=scale.dtype),
allow_nan_stats=allow_nan_stats),
bijector=bijectors.AffineLinearOperator(
shift=loc,
scale=scale,
validate_args=validate_args),
batch_shape=batch_shape,
event_shape=event_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters
def __init__(self,
loc=None,
scale=None,
validate_args=False,
allow_nan_stats=True,
name="MultivariateNormalLinearOperator"):
"""Construct Multivariate Normal distribution on `R^k`.
The `batch_shape` is the broadcast shape between `loc` and `scale`
arguments.
The `event_shape` is given by the last dimension of `loc` or the last
dimension of the matrix implied by `scale`.
Recall that `covariance = scale @ scale.T`.
Additional leading dimensions (if any) will index batches.
Args:
loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
`b >= 0` and `k` is the event size.
scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
`[B1, ..., Bb, k, k]`.
validate_args: Python `bool`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
allow_nan_stats: Python `bool`, default `True`. 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.
name: The name to give Ops created by the initializer.
Raises:
ValueError: if `scale` is unspecified.
TypeError: if not `scale.dtype.is_floating`
"""
parameters = locals()
if scale is None:
raise ValueError("Missing required `scale` parameter.")
if not scale.dtype.is_floating:
raise TypeError("`scale` parameter must have floating-point dtype.")
# Since expand_dims doesn't preserve constant-ness, we obtain the
# non-dynamic value if possible.
event_shape = scale.domain_dimension_tensor()
if tensor_util.constant_value(event_shape) is not None:
event_shape = tensor_util.constant_value(event_shape)
event_shape = event_shape[array_ops.newaxis]
super(MultivariateNormalLinearOperator, self).__init__(
distribution=normal.Normal(
loc=array_ops.zeros([], dtype=scale.dtype),
scale=array_ops.ones([], dtype=scale.dtype)),
bijector=bijectors.AffineLinearOperator(
shift=loc, scale=scale, validate_args=validate_args),
batch_shape=scale.batch_shape_tensor(),
event_shape=event_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters
