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 test_none_loc_static_scale(self):
loc = None
scale = linear_operator_diag.LinearOperatorDiag(np.ones((5, 1, 3)))
batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
loc, scale)
self.assertEqual(tensor_shape.TensorShape([5, 1]), batch_shape)
self.assertEqual(tensor_shape.TensorShape([3]), event_shape)
def test_none_loc_dynamic_scale(self):
loc = None
diag = array_ops.placeholder(dtypes.float64)
scale = linear_operator_diag.LinearOperatorDiag(diag)
with self.cached_session() as sess:
batch_shape, event_shape = sess.run(
distribution_util.shapes_from_loc_and_scale(loc, scale),
feed_dict={diag: np.ones((5, 1, 3))})
self.assertAllEqual([5, 1], batch_shape)
self.assertAllEqual([3], event_shape)
def test_dynamic_loc_static_scale(self):
loc = array_ops.placeholder(dtypes.float64)
diag = constant_op.constant(np.ones((5, 2, 3)))
scale = linear_operator_diag.LinearOperatorDiag(diag)
with self.cached_session():
batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
loc, scale)
# batch_shape depends on both args, and so is dynamic. Since loc did not
# have static shape, we inferred event shape entirely from scale, and this
# is available statically.
self.assertAllEqual(
[5, 2], batch_shape.eval(feed_dict={loc: np.zeros((2, 3))}))
self.assertAllEqual([3], event_shape)
def test_static_loc_static_scale_non_matching_event_size_raises(self):
loc = constant_op.constant(np.zeros((2, 4)))
scale = linear_operator_diag.LinearOperatorDiag(np.ones((5, 1, 3)))
with self.assertRaisesRegexp(ValueError, "could not be broadcast"):
distribution_util.shapes_from_loc_and_scale(loc, scale)
def __init__(self,
df,
loc=None,
scale_identity_multiplier=None,
scale_diag=None,
scale_tril=None,
scale_perturb_factor=None,
scale_perturb_diag=None,
validate_args=False,
allow_nan_stats=True,
name="VectorStudentT"):
"""Instantiates the vector Student's t-distributions on `R^k`.
The `batch_shape` is the broadcast between `df.batch_shape` and
`Affine.batch_shape` where `Affine` is constructed from `loc` and
`scale_*` arguments.
The `event_shape` is the event shape of `Affine.event_shape`.
Args:
df: Floating-point `Tensor`. The degrees of freedom of the
distribution(s). `df` must contain only positive values. Must be
scalar if `loc`, `scale_*` imply non-scalar batch_shape or must have the
same `batch_shape` implied by `loc`, `scale_*`.
loc: Floating-point `Tensor`. If this is set to `None`, no `loc` is
applied.
scale_identity_multiplier: floating point rank 0 `Tensor` representing a
scaling done to the identity matrix. When `scale_identity_multiplier =
scale_diag=scale_tril = None` then `scale += IdentityMatrix`. Otherwise
no scaled-identity-matrix is added to `scale`.
scale_diag: Floating-point `Tensor` representing the diagonal matrix.
`scale_diag` has shape [N1, N2, ..., k], which represents a k x k
diagonal matrix. When `None` no diagonal term is added to `scale`.
scale_tril: Floating-point `Tensor` representing the diagonal matrix.
`scale_diag` has shape [N1, N2, ..., k, k], which represents a k x k
lower triangular matrix. When `None` no `scale_tril` term is added to
`scale`. The upper triangular elements above the diagonal are ignored.
scale_perturb_factor: Floating-point `Tensor` representing factor matrix
with last two dimensions of shape `(k, r)`. When `None`, no rank-r
update is added to `scale`.
scale_perturb_diag: Floating-point `Tensor` representing the diagonal
matrix. `scale_perturb_diag` has shape [N1, N2, ..., r], which
represents an r x r Diagonal matrix. When `None` low rank updates will
take the form `scale_perturb_factor * scale_perturb_factor.T`.
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.
"""
parameters = dict(locals())
graph_parents = [
df, loc, scale_identity_multiplier, scale_diag, scale_tril,
scale_perturb_factor, scale_perturb_diag
]
with ops.name_scope(name) as name:
with ops.name_scope("init", values=graph_parents):
# The shape of the _VectorStudentT distribution is governed by the
# relationship between df.batch_shape and affine.batch_shape. In
# pseudocode the basic procedure is:
# if df.batch_shape is scalar:
# if affine.batch_shape is not scalar:
# # broadcast distribution.sample so
# # it has affine.batch_shape.
# self.batch_shape = affine.batch_shape
# else:
# if affine.batch_shape is scalar:
# # let affine broadcasting do its thing.
# self.batch_shape = df.batch_shape
# All of the above magic is actually handled by TransformedDistribution.
# Here we really only need to collect the affine.batch_shape and decide
# what we're going to pass in to TransformedDistribution's
# (override) batch_shape arg.
affine = bijectors.Affine(
shift=loc,
scale_identity_multiplier=scale_identity_multiplier,
scale_diag=scale_diag,
scale_tril=scale_tril,
scale_perturb_factor=scale_perturb_factor,
scale_perturb_diag=scale_perturb_diag,
validate_args=validate_args)
distribution = student_t.StudentT(
df=df,
loc=array_ops.zeros([], dtype=affine.dtype),
scale=array_ops.ones([], dtype=affine.dtype))
batch_shape, override_event_shape = (
distribution_util.shapes_from_loc_and_scale(
affine.shift, affine.scale))
override_batch_shape = distribution_util.pick_vector(
distribution.is_scalar_batch(), batch_shape,
constant_op.constant([], dtype=dtypes.int32))
super(_VectorStudentT,
self).__init__(distribution=distribution,
bijector=affine,
batch_shape=override_batch_shape,
event_shape=override_event_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters
def __init__(self,
loc=None,
scale_diag=None,
scale_identity_multiplier=None,
skewness=None,
tailweight=None,
distribution=None,
validate_args=False,
allow_nan_stats=True,
name="MultivariateNormalLinearOperator"):
"""Construct VectorSinhArcsinhDiag distribution on `R^k`.
The arguments `scale_diag` and `scale_identity_multiplier` combine to
define the diagonal `scale` referred to in this class docstring:
```none
scale = diag(scale_diag + scale_identity_multiplier * ones(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
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_diag: Non-zero, floating-point `Tensor` representing a diagonal
matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
and characterizes `b`-batches of `k x k` diagonal matrices added to
`scale`. When both `scale_identity_multiplier` and `scale_diag` are
`None` then `scale` is the `Identity`.
scale_identity_multiplier: Non-zero, floating-point `Tensor` representing
a scale-identity-matrix added to `scale`. May have shape
`[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scale
`k x k` identity matrices added to `scale`. When both
`scale_identity_multiplier` and `scale_diag` are `None` then `scale`
is the `Identity`.
skewness: Skewness parameter. floating-point `Tensor` with shape
broadcastable with `event_shape`.
tailweight: Tailweight parameter. floating-point `Tensor` with shape
broadcastable with `event_shape`.
distribution: `tf.Distribution`-like instance. Distribution from which `k`
iid samples are used as input to transformation `F`. Default is
`tfp.distributions.Normal(loc=0., scale=1.)`.
Must be a scalar-batch, scalar-event distribution. Typically
`distribution.reparameterization_type = FULLY_REPARAMETERIZED` or it is
a function of non-trainable parameters. WARNING: If you backprop through
a VectorSinhArcsinhDiag sample and `distribution` is not
`FULLY_REPARAMETERIZED` yet is a function of trainable variables, then
the gradient will be incorrect!
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 at most `scale_identity_multiplier` is specified.
"""
parameters = dict(locals())
with ops.name_scope(name,
values=[
loc, scale_diag, scale_identity_multiplier,
skewness, tailweight
]) as name:
loc = ops.convert_to_tensor(loc,
name="loc") if loc is not None else loc
tailweight = 1. if tailweight is None else tailweight
has_default_skewness = skewness is None
skewness = 0. if skewness is None else skewness
# Recall, with Z a random variable,
# Y := loc + C * F(Z),
# F(Z) := Sinh( (Arcsinh(Z) + skewness) * tailweight )
# F_0(Z) := Sinh( Arcsinh(Z) * tailweight )
# C := 2 * scale / F_0(2)
# Construct shapes and 'scale' out of the scale_* and loc kwargs.
# scale_linop is only an intermediary to:
# 1. get shapes from looking at loc and the two scale args.
# 2. combine scale_diag with scale_identity_multiplier, which gives us
# 'scale', which in turn gives us 'C'.
scale_linop = distribution_util.make_diag_scale(
loc=loc,
scale_diag=scale_diag,
scale_identity_multiplier=scale_identity_multiplier,
validate_args=False,
assert_positive=False)
batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
loc, scale_linop)
# scale_linop.diag_part() is efficient since it is a diag type linop.
scale_diag_part = scale_linop.diag_part()
dtype = scale_diag_part.dtype
if distribution is None:
distribution = normal.Normal(loc=array_ops.zeros([],
dtype=dtype),
scale=array_ops.ones([],
dtype=dtype),
allow_nan_stats=allow_nan_stats)
else:
asserts = distribution_util.maybe_check_scalar_distribution(
distribution, dtype, validate_args)
if asserts:
scale_diag_part = control_flow_ops.with_dependencies(
asserts, scale_diag_part)
# Make the SAS bijector, 'F'.
skewness = ops.convert_to_tensor(skewness,
dtype=dtype,
name="skewness")
tailweight = ops.convert_to_tensor(tailweight,
dtype=dtype,
name="tailweight")
f = bijectors.SinhArcsinh(skewness=skewness, tailweight=tailweight)
if has_default_skewness:
f_noskew = f
else:
f_noskew = bijectors.SinhArcsinh(
skewness=skewness.dtype.as_numpy_dtype(0.),
tailweight=tailweight)
# Make the Affine bijector, Z --> loc + C * Z.
c = 2 * scale_diag_part / f_noskew.forward(
ops.convert_to_tensor(2, dtype=dtype))
affine = bijectors.Affine(shift=loc,
scale_diag=c,
validate_args=validate_args)
bijector = bijectors.Chain([affine, f])
super(VectorSinhArcsinhDiag,
self).__init__(distribution=distribution,
bijector=bijector,
batch_shape=batch_shape,
event_shape=event_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters
self._loc = loc
self._scale = scale_linop
self._tailweight = tailweight
self._skewness = skewness