def __init__(self,
distribution,
bijector=None,
batch_shape=None,
event_shape=None,
validate_args=False,
name=None):
"""Construct a Transformed Distribution.
Args:
distribution: The base distribution instance to transform. Typically an
instance of `Distribution`.
bijector: The object responsible for calculating the transformation.
Typically an instance of `Bijector`. `None` means `Identity()`.
batch_shape: `integer` vector `Tensor` which overrides `distribution`
`batch_shape`; valid only if `distribution.is_scalar_batch` and
`distribution.is_scalar_event`.
event_shape: `integer` vector `Tensor` which overrides `distribution`
`event_shape`; valid only if `distribution.is_scalar_batch` and
`distribution.is_scalar_event`
validate_args: Python `Boolean`. Whether to validate input with asserts.
If `validate_args` is `False`, and the inputs are invalid,
correct behavior is not guaranteed.
name: The name for the distribution. Default:
`bijector.name + distribution.name`.
"""
parameters = locals()
parameters.pop("self")
if bijector is None:
bijector = bijectors.Identity(validate_args=validate_args)
name = name or bijector.name + distribution.name
with ops.name_scope(name, values=[event_shape, batch_shape]):
if batch_shape is not None or event_shape is not None:
is_scalar_batch_and_scalar_event = _logical_and(
distribution.is_scalar_batch, distribution.is_scalar_event)
if batch_shape is not None:
batch_shape = self._maybe_validate_shape_override(
ops.convert_to_tensor(batch_shape, name="batch_shape"),
is_scalar_batch_and_scalar_event, validate_args)
self._override_batch_shape = batch_shape
if event_shape is not None:
event_shape = self._maybe_validate_shape_override(
ops.convert_to_tensor(event_shape, name="event_shape"),
is_scalar_batch_and_scalar_event, validate_args)
event_ndims = (event_shape.get_shape().ndims
if event_shape.get_shape().ndims is not None
else array_ops.rank(event_shape, "event_ndims"))
self._reduce_event_indices = math_ops.range(-event_ndims, 0)
self._override_event_shape = event_shape
self._distribution = distribution
self._bijector = bijector
super(TransformedDistribution, self).__init__(
dtype=self._distribution.dtype,
is_continuous=self._distribution.is_continuous,
is_reparameterized=self._distribution.is_reparameterized,
validate_args=validate_args,
allow_nan_stats=self._distribution.allow_nan_stats,
parameters=parameters,
# We let TransformedDistribution access _graph_parents since this class
# is more like a baseclass than derived.
graph_parents=(
distribution._graph_parents + # pylint: disable=protected-access
bijector.graph_parents),
name=name)
def __init__(self,
distribution,
bijector=None,
batch_shape=None,
event_shape=None,
validate_args=False,
name=None):
"""Construct a Transformed Distribution.
Args:
distribution: The base distribution instance to transform. Typically an
instance of `Distribution`.
bijector: The object responsible for calculating the transformation.
Typically an instance of `Bijector`. `None` means `Identity()`.
batch_shape: `integer` vector `Tensor` which overrides `distribution`
`batch_shape`; valid only if `distribution.is_scalar_batch()`.
event_shape: `integer` vector `Tensor` which overrides `distribution`
`event_shape`; valid only if `distribution.is_scalar_event()`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
name: Python `str` name prefixed to Ops created by this class. Default:
`bijector.name + distribution.name`.
"""
parameters = locals()
name = name or (("" if bijector is None else bijector.name) +
distribution.name)
with ops.name_scope(name, values=[event_shape, batch_shape]):
# For convenience we define some handy constants.
self._zero = constant_op.constant(0,
dtype=dtypes.int32,
name="zero")
self._empty = constant_op.constant([],
dtype=dtypes.int32,
name="empty")
if bijector is None:
bijector = bijectors.Identity(validate_args=validate_args)
# We will keep track of a static and dynamic version of
# self._is_{batch,event}_override. This way we can do more prior to graph
# execution, including possibly raising Python exceptions.
self._override_batch_shape = self._maybe_validate_shape_override(
batch_shape, distribution.is_scalar_batch(), validate_args,
"batch_shape")
self._is_batch_override = _logical_not(
_logical_equal(_ndims_from_shape(self._override_batch_shape),
self._zero))
self._is_maybe_batch_override = bool(
tensor_util.constant_value(self._override_batch_shape) is None
or tensor_util.constant_value(
self._override_batch_shape).size != 0)
self._override_event_shape = self._maybe_validate_shape_override(
event_shape, distribution.is_scalar_event(), validate_args,
"event_shape")
self._is_event_override = _logical_not(
_logical_equal(_ndims_from_shape(self._override_event_shape),
self._zero))
self._is_maybe_event_override = bool(
tensor_util.constant_value(self._override_event_shape) is None
or tensor_util.constant_value(
self._override_event_shape).size != 0)
# To convert a scalar distribution into a multivariate distribution we
# will draw dims from the sample dims, which are otherwise iid. This is
# easy to do except in the case that the base distribution has batch dims
# and we're overriding event shape. When that case happens the event dims
# will incorrectly be to the left of the batch dims. In this case we'll
# cyclically permute left the new dims.
self._needs_rotation = _logical_and(
self._is_event_override, _logical_not(self._is_batch_override),
_logical_not(distribution.is_scalar_batch()))
override_event_ndims = _ndims_from_shape(
self._override_event_shape)
self._rotate_ndims = _pick_scalar_condition(
self._needs_rotation, override_event_ndims, 0)
# We'll be reducing the head dims (if at all), i.e., this will be []
# if we don't need to reduce.
self._reduce_event_indices = math_ops.range(
self._rotate_ndims - override_event_ndims, self._rotate_ndims)
self._distribution = distribution
self._bijector = bijector
super(TransformedDistribution, self).__init__(
dtype=self._distribution.dtype,
is_continuous=self._distribution.is_continuous,
reparameterization_type=self._distribution.reparameterization_type,
validate_args=validate_args,
allow_nan_stats=self._distribution.allow_nan_stats,
parameters=parameters,
# We let TransformedDistribution access _graph_parents since this class
# is more like a baseclass than derived.
graph_parents=(
distribution._graph_parents + # pylint: disable=protected-access
bijector.graph_parents),
name=name)
def __init__(self,
distribution,
bijector=None,
batch_shape=None,
event_shape=None,
validate_args=False,
name=None):
"""Construct a Transformed Distribution.
Args:
distribution: The base distribution instance to transform. Typically an
instance of `Distribution`.
bijector: The object responsible for calculating the transformation.
Typically an instance of `Bijector`. `None` means `Identity()`.
batch_shape: `integer` vector `Tensor` which overrides `distribution`
`batch_shape`; valid only if `distribution.is_scalar_batch`.
event_shape: `integer` vector `Tensor` which overrides `distribution`
`event_shape`; valid only if `distribution.is_scalar_event`.
validate_args: Python Boolean. Whether to validate input with asserts.
If `validate_args` is `False`, and the inputs are invalid,
correct behavior is not guaranteed.
name: The name for the distribution. Default:
`bijector.name + distribution.name`.
"""
parameters = locals()
parameters.pop("self")
if bijector is None:
bijector = bijectors.Identity(validate_args=validate_args)
name = name or bijector.name + distribution.name
with ops.name_scope(name, values=[event_shape, batch_shape]):
if batch_shape is not None:
batch_shape = self._maybe_validate_shape_override(
ops.convert_to_tensor(batch_shape, name="batch_shape"),
distribution.is_scalar_batch, validate_args)
self._override_batch_shape = batch_shape
if event_shape is not None:
event_shape = self._maybe_validate_shape_override(
ops.convert_to_tensor(event_shape, name="event_shape"),
distribution.is_scalar_event, validate_args)
self._override_event_ndims = (
event_shape.get_shape().ndims
if event_shape.get_shape().ndims is not None
else array_ops.rank(event_shape, name="event_ndims"))
else:
self._override_event_ndims = 0
self._override_event_shape = event_shape
# To convert a scalar distribution into a multivariate distribution we
# will draw dims from the sample dims, which are otherwise iid. This is
# easy to do except in the case that:
# batch_shape is None and
# event_shape is not None and
# not distribution.is_scalar_batch.
# When that case happens the event dims will incorrectly be to the left of
# the batch dims. In this case we'll cyclically permute left the new dims.
if batch_shape is None and event_shape is not None:
self._needs_rotation = ops.convert_to_tensor(
_logical_not(distribution.is_scalar_batch), name="needs_rotation")
n = _pick_scalar_condition(self._needs_rotation,
self._override_event_ndims, 0)
# We'll be reducing the head dims (if at all), i.e., this will be []
# if we don't need to reduce.
self._reduce_event_indices = math_ops.range(
n - self._override_event_ndims, n)
else:
self._needs_rotation = ops.convert_to_tensor(False,
name="needs_rotation")
# We'll be reducing the tail dims (if at all), i.e., this will be []
# if we don't need to reduce.
self._reduce_event_indices = (
math_ops.range(-self._override_event_ndims, 0)
if event_shape is not None else [])
self._distribution = distribution
self._bijector = bijector
super(TransformedDistribution, self).__init__(
dtype=self._distribution.dtype,
is_continuous=self._distribution.is_continuous,
is_reparameterized=self._distribution.is_reparameterized,
validate_args=validate_args,
allow_nan_stats=self._distribution.allow_nan_stats,
parameters=parameters,
# We let TransformedDistribution access _graph_parents since this class
# is more like a baseclass than derived.
graph_parents=(distribution._graph_parents + # pylint: disable=protected-access
bijector.graph_parents),
name=name)