def make_diag_scale(loc, scale_diag, scale_identity_multiplier,
validate_args, assert_positive, name=None):
"""Creates a LinOp from `scale_diag`, `scale_identity_multiplier` kwargs."""
def _convert_to_tensor(x, name):
return None if x is None else ops.convert_to_tensor(x, name=name)
def _maybe_attach_assertion(x):
if not validate_args:
return x
if assert_positive:
return control_flow_ops.with_dependencies([
check_ops.assert_positive(
x, message="diagonal part must be positive"),
], x)
return control_flow_ops.with_dependencies([
check_ops.assert_none_equal(
x,
array_ops.zeros([], x.dtype),
message="diagonal part must be non-zero")], x)
with ops.name_scope(name, "make_diag_scale",
values=[loc, scale_diag, scale_identity_multiplier]):
loc = _convert_to_tensor(loc, name="loc")
scale_diag = _convert_to_tensor(scale_diag, name="scale_diag")
scale_identity_multiplier = _convert_to_tensor(
scale_identity_multiplier,
name="scale_identity_multiplier")
if scale_diag is not None:
if scale_identity_multiplier is not None:
scale_diag += scale_identity_multiplier[..., array_ops.newaxis]
return linalg.LinearOperatorDiag(
diag=_maybe_attach_assertion(scale_diag),
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=assert_positive)
# TODO(b/35290280): Consider inferring shape from scale_perturb_factor.
if loc is None:
raise ValueError(
"Cannot infer `event_shape` unless `loc` is specified.")
num_rows = util.dimension_size(loc, -1)
if scale_identity_multiplier is None:
return linalg.LinearOperatorIdentity(
num_rows=num_rows,
dtype=loc.dtype.base_dtype,
is_self_adjoint=True,
is_positive_definite=True,
assert_proper_shapes=validate_args)
return linalg.LinearOperatorScaledIdentity(
num_rows=num_rows,
multiplier=_maybe_attach_assertion(scale_identity_multiplier),
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=assert_positive,
assert_proper_shapes=validate_args)
def __init__(self,
loc=None,
scale_tril=None,
validate_args=False,
allow_nan_stats=True,
name="MultivariateNormalTriL"):
"""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 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`. A (non-batch) `scale` matrix is:
```none
scale = scale_tril
```
where `scale_tril` is lower-triangular `k x k` matrix with non-zero
diagonal, i.e., `tf.diag_part(scale_tril) != 0`.
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_tril: Floating-point, lower-triangular `Tensor` with non-zero
diagonal elements. `scale_tril` has shape `[B1, ..., Bb, k, k]` where
`b >= 0` and `k` is the event size.
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.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: if neither `loc` nor `scale_tril` are specified.
"""
parameters = locals()
def _convert_to_tensor(x, name):
return None if x is None else ops.convert_to_tensor(x, name=name)
if loc is None and scale_tril is None:
raise ValueError(
"Must specify one or both of `loc`, `scale_tril`.")
with ops.name_scope(name):
with ops.name_scope("init", values=[loc, scale_tril]):
loc = _convert_to_tensor(loc, name="loc")
scale_tril = _convert_to_tensor(scale_tril, name="scale_tril")
if scale_tril is None:
scale = linalg.LinearOperatorIdentity(
num_rows=distribution_util.dimension_size(loc, -1),
dtype=loc.dtype,
is_self_adjoint=True,
is_positive_definite=True,
assert_proper_shapes=validate_args)
else:
# No need to validate that scale_tril is non-singular.
# LinearOperatorLowerTriangular has an assert_non_singular
# method that is called by the Bijector.
scale = linalg.LinearOperatorLowerTriangular(
scale_tril,
is_non_singular=True,
is_self_adjoint=False,
is_positive_definite=False)
super(MultivariateNormalTriL,
self).__init__(loc=loc,
scale=scale,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
self._parameters = parameters
def __init__(self,
loc=None,
scale_tril=None,
validate_args=False,
allow_nan_stats=True,
name="MultivariateNormalTriL"):
"""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 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`. A (non-batch) `scale` matrix is:
```none
scale = scale_tril
```
where `scale_tril` is lower-triangular `k x k` matrix with non-zero
diagonal, i.e., `tf.diag_part(scale_tril) != 0`.
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_tril: Floating-point, lower-triangular `Tensor` with non-zero
diagonal elements. `scale_tril` has shape `[B1, ..., Bb, k, k]` where
`b >= 0` and `k` is the event size.
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.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: if neither `loc` nor `scale_tril` are specified.
"""
parameters = dict(locals())
def _convert_to_tensor(x, name):
return None if x is None else ops.convert_to_tensor(x, name=name)
if loc is None and scale_tril is None:
raise ValueError("Must specify one or both of `loc`, `scale_tril`.")
with ops.name_scope(name) as name:
with ops.name_scope("init", values=[loc, scale_tril]):
loc = _convert_to_tensor(loc, name="loc")
scale_tril = _convert_to_tensor(scale_tril, name="scale_tril")
if scale_tril is None:
scale = linalg.LinearOperatorIdentity(
num_rows=distribution_util.dimension_size(loc, -1),
dtype=loc.dtype,
is_self_adjoint=True,
is_positive_definite=True,
assert_proper_shapes=validate_args)
else:
# No need to validate that scale_tril is non-singular.
# LinearOperatorLowerTriangular has an assert_non_singular
# method that is called by the Bijector.
scale = linalg.LinearOperatorLowerTriangular(
scale_tril,
is_non_singular=True,
is_self_adjoint=False,
is_positive_definite=False)
super(MultivariateNormalTriL, self).__init__(
loc=loc,
scale=scale,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
self._parameters = parameters
def make_diag_scale(loc,
scale_diag,
scale_identity_multiplier,
validate_args,
assert_positive,
name=None):
"""Creates a LinOp from `scale_diag`, `scale_identity_multiplier` kwargs."""
def _convert_to_tensor(x, name):
return None if x is None else ops.convert_to_tensor(x, name=name)
def _maybe_attach_assertion(x):
if not validate_args:
return x
if assert_positive:
return control_flow_ops.with_dependencies([
check_ops.assert_positive(
x, message="diagonal part must be positive"),
], x)
return control_flow_ops.with_dependencies([
check_ops.assert_none_equal(
x,
array_ops.zeros([], x.dtype),
message="diagonal part must be non-zero")
], x)
with ops.name_scope(name,
"make_diag_scale",
values=[loc, scale_diag, scale_identity_multiplier]):
loc = _convert_to_tensor(loc, name="loc")
scale_diag = _convert_to_tensor(scale_diag, name="scale_diag")
scale_identity_multiplier = _convert_to_tensor(
scale_identity_multiplier, name="scale_identity_multiplier")
if scale_diag is not None:
if scale_identity_multiplier is not None:
scale_diag += scale_identity_multiplier[..., array_ops.newaxis]
return linalg.LinearOperatorDiag(
diag=_maybe_attach_assertion(scale_diag),
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=assert_positive)
# TODO(b/35290280): Consider inferring shape from scale_perturb_factor.
if loc is None:
raise ValueError(
"Cannot infer `event_shape` unless `loc` is specified.")
num_rows = util.dimension_size(loc, -1)
if scale_identity_multiplier is None:
return linalg.LinearOperatorIdentity(
num_rows=num_rows,
dtype=loc.dtype.base_dtype,
is_self_adjoint=True,
is_positive_definite=True,
assert_proper_shapes=validate_args)
return linalg.LinearOperatorScaledIdentity(
num_rows=num_rows,
multiplier=_maybe_attach_assertion(scale_identity_multiplier),
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=assert_positive,
assert_proper_shapes=validate_args)
