def dist(
cls,
dist_params,
*,
shape: Optional[Shape] = None,
size: Optional[Size] = None,
**kwargs,
) -> TensorVariable:
"""Creates a TensorVariable corresponding to the `cls` symbolic distribution.
Parameters
----------
dist_params : array-like
The inputs to the `RandomVariable` `Op`.
shape : int, tuple, Variable, optional
A tuple of sizes for each dimension of the new RV.
An Ellipsis (...) may be inserted in the last position to short-hand refer to
all the dimensions that the RV would get if no shape/size/dims were passed at all.
size : int, tuple, Variable, optional
For creating the RV like in Aesara/NumPy.
Returns
-------
var : TensorVariable
"""
if "testval" in kwargs:
kwargs.pop("testval")
warnings.warn(
"The `.dist(testval=...)` argument is deprecated and has no effect. "
"Initial values for sampling/optimization can be specified with `initval` in a modelcontext. "
"For using Aesara's test value features, you must assign the `.tag.test_value` yourself.",
FutureWarning,
stacklevel=2,
)
if "initval" in kwargs:
raise TypeError(
"Unexpected keyword argument `initval`. "
"This argument is not available for the `.dist()` API.")
if "dims" in kwargs:
raise NotImplementedError(
"The use of a `.dist(dims=...)` API is not supported.")
if shape is not None and size is not None:
raise ValueError(
f"Passing both `shape` ({shape}) and `size` ({size}) is not supported!"
)
shape = convert_shape(shape)
size = convert_size(size)
create_size, ndim_expected, ndim_batch, ndim_supp = find_size(
shape=shape, size=size, ndim_supp=cls.ndim_supp(*dist_params))
# Create the RV with a `size` right away.
# This is not necessarily the final result.
graph = cls.rv_op(*dist_params, size=create_size, **kwargs)
# Replicate dimensions may be prepended via a shape with Ellipsis as the last element:
if shape is not None and Ellipsis in shape:
replicate_shape = cast(StrongShape, shape[:-1])
graph = cls.change_size(rv=graph,
new_size=replicate_shape,
expand=True)
# TODO: Create new attr error stating that these are not available for DerivedDistribution
# rv_out.logp = _make_nice_attr_error("rv.logp(x)", "pm.logp(rv, x)")
# rv_out.logcdf = _make_nice_attr_error("rv.logcdf(x)", "pm.logcdf(rv, x)")
# rv_out.random = _make_nice_attr_error("rv.random()", "rv.eval()")
return graph
def dist(
cls,
dist_params,
*,
shape: Optional[Shape] = None,
size: Optional[Size] = None,
**kwargs,
) -> RandomVariable:
"""Creates a RandomVariable corresponding to the `cls` distribution.
Parameters
----------
dist_params : array-like
The inputs to the `RandomVariable` `Op`.
shape : int, tuple, Variable, optional
A tuple of sizes for each dimension of the new RV.
An Ellipsis (...) may be inserted in the last position to short-hand refer to
all the dimensions that the RV would get if no shape/size/dims were passed at all.
size : int, tuple, Variable, optional
For creating the RV like in Aesara/NumPy.
Returns
-------
rv : RandomVariable
The created RV.
"""
if "testval" in kwargs:
kwargs.pop("testval")
warnings.warn(
"The `.dist(testval=...)` argument is deprecated and has no effect. "
"Initial values for sampling/optimization can be specified with `initval` in a modelcontext. "
"For using Aesara's test value features, you must assign the `.tag.test_value` yourself.",
DeprecationWarning,
stacklevel=2,
)
if "initval" in kwargs:
raise TypeError(
"Unexpected keyword argument `initval`. "
"This argument is not available for the `.dist()` API.")
if "dims" in kwargs:
raise NotImplementedError(
"The use of a `.dist(dims=...)` API is not supported.")
if shape is not None and size is not None:
raise ValueError(
f"Passing both `shape` ({shape}) and `size` ({size}) is not supported!"
)
shape = convert_shape(shape)
size = convert_size(size)
create_size, ndim_expected, ndim_batch, ndim_supp = find_size(
shape=shape, size=size, ndim_supp=cls.rv_op.ndim_supp)
# Create the RV with a `size` right away.
# This is not necessarily the final result.
rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)
rv_out = maybe_resize(
rv_out,
cls.rv_op,
dist_params,
ndim_expected,
ndim_batch,
ndim_supp,
shape,
size,
**kwargs,
)
rng = kwargs.pop("rng", None)
if (rv_out.owner and isinstance(rv_out.owner.op, RandomVariable)
and isinstance(rng, RandomStateSharedVariable)
and not getattr(rng, "default_update", None)):
# This tells `aesara.function` that the shared RNG variable
# is mutable, which--in turn--tells the `FunctionGraph`
# `Supervisor` feature to allow in-place updates on the variable.
# Without it, the `RandomVariable`s could not be optimized to allow
# in-place RNG updates, forcing all sample results from compiled
# functions to be the same on repeated evaluations.
new_rng = rv_out.owner.outputs[0]
rv_out.update = (rng, new_rng)
rng.default_update = new_rng
rv_out.logp = _make_nice_attr_error("rv.logp(x)", "pm.logp(rv, x)")
rv_out.logcdf = _make_nice_attr_error("rv.logcdf(x)",
"pm.logcdf(rv, x)")
rv_out.random = _make_nice_attr_error("rv.random()", "rv.eval()")
return rv_out