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
`ds.Normal(0., 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 = locals()
with ops.name_scope(name,
values=[
loc, scale_diag, scale_identity_multiplier,
skewness, tailweight
]):
loc = ops.convert_to_tensor(loc,
name="loc") if loc is not None else loc
tailweight = 1. if tailweight is None else tailweight
skewness = 0. if skewness is None else skewness
# Recall, with Z ~ Normal(0, 1),
# Y := loc + C * F(Z),
# F(Z) := Sinh( (Arcsinh(Z) + skewness) * tailweight )
# C := 2 * scale / F(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,
event_ndims=1)
# Make the Affine bijector, Z --> loc + C * Z.
c = 2 * scale_diag_part / f.forward(
ops.convert_to_tensor(2, dtype=dtype))
affine = bijectors.Affine(shift=loc,
scale_diag=c,
validate_args=validate_args,
event_ndims=1)
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
def __init__(self,
loc,
scale,
skewness=None,
tailweight=None,
distribution=None,
validate_args=False,
allow_nan_stats=True,
name="SinhArcsinh"):
"""Construct SinhArcsinh distribution on `(-inf, inf)`.
Arguments `(loc, scale, skewness, tailweight)` must have broadcastable shape
(indexing batch dimensions). They must all have the same `dtype`.
Args:
loc: Floating-point `Tensor`.
scale: `Tensor` of same `dtype` as `loc`.
skewness: Skewness parameter. Default is `0.0` (no skew).
tailweight: Tailweight parameter. Default is `1.0` (unchanged tailweight)
distribution: `tf.Distribution`-like instance. Distribution that is
transformed to produce this distribution.
Default is `tf.distributions.Normal(0., 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 `SinhArcsinh` 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.
"""
parameters = locals()
with ops.name_scope(name, values=[loc, scale, skewness, tailweight]):
loc = ops.convert_to_tensor(loc, name="loc")
dtype = loc.dtype
scale = ops.convert_to_tensor(scale, name="scale", dtype=dtype)
tailweight = 1. if tailweight is None else tailweight
has_default_skewness = skewness is None
skewness = 0. if skewness is None else skewness
tailweight = ops.convert_to_tensor(
tailweight, name="tailweight", dtype=dtype)
skewness = ops.convert_to_tensor(skewness, name="skewness", dtype=dtype)
batch_shape = distribution_util.get_broadcast_shape(
loc, scale, tailweight, 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)
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:
loc = control_flow_ops.with_dependencies(asserts, loc)
# Make the SAS bijector, 'F'.
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 AffineScalar bijector, Z --> loc + scale * Z (2 / F_0(2))
c = 2 * scale / f_noskew.forward(ops.convert_to_tensor(2, dtype=dtype))
affine = bijectors.AffineScalar(
shift=loc,
scale=c,
validate_args=validate_args)
bijector = bijectors.Chain([affine, f])
super(SinhArcsinh, self).__init__(
distribution=distribution,
bijector=bijector,
batch_shape=batch_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters
self._loc = loc
self._scale = scale
self._tailweight = tailweight
self._skewness = skewness
