def __new__(cls, name, *args, **kwargs):
if name is _Unpickling:
return object.__new__(cls) # for pickle
try:
model = Model.get_context()
except TypeError:
raise TypeError(
"No model on context stack, which is needed to "
"instantiate distributions. Add variable inside "
"a 'with model:' block, or use the '.dist' syntax "
"for a standalone distribution."
)
if not isinstance(name, string_types):
raise TypeError(f"Name needs to be a string but got: {name}")
data = kwargs.pop("observed", None)
cls.data = data
if isinstance(data, ObservedRV) or isinstance(data, FreeRV):
raise TypeError("observed needs to be data but got: {}".format(type(data)))
total_size = kwargs.pop("total_size", None)
dims = kwargs.pop("dims", None)
has_shape = "shape" in kwargs
shape = kwargs.pop("shape", None)
if dims is not None:
if shape is not None:
raise ValueError("Specify only one of 'dims' or 'shape'")
if isinstance(dims, string_types):
dims = (dims,)
shape = model.shape_from_dims(dims)
# failsafe against 0-shapes
if shape is not None and any(np.atleast_1d(shape) <= 0):
raise ValueError(
f"Distribution initialized with invalid shape {shape}. This is not allowed."
)
# Some distributions do not accept shape=None
if has_shape or shape is not None:
dist = cls.dist(*args, **kwargs, shape=shape)
else:
dist = cls.dist(*args, **kwargs)
return model.Var(name, dist, data, total_size, dims=dims)
def __new__(
cls,
name: str,
*args,
rng=None,
dims: Optional[Dims] = None,
initval=None,
observed=None,
total_size=None,
transform=UNSET,
**kwargs,
) -> RandomVariable:
"""Adds a RandomVariable corresponding to a PyMC3 distribution to the current model.
Note that all remaining kwargs must be compatible with ``.dist()``
Parameters
----------
cls : type
A PyMC3 distribution.
name : str
Name for the new model variable.
rng : optional
Random number generator to use with the RandomVariable.
dims : tuple, optional
A tuple of dimension names known to the model.
initval : optional
Test value to be attached to the output RV.
Must match its shape exactly.
observed : optional
Observed data to be passed when registering the random variable in the model.
See ``Model.register_rv``.
total_size : float, optional
See ``Model.register_rv``.
transform : optional
See ``Model.register_rv``.
**kwargs
Keyword arguments that will be forwarded to ``.dist()``.
Most prominently: ``shape`` and ``size``
Returns
-------
rv : RandomVariable
The created RV, registered in the Model.
"""
try:
from pymc3.model import Model
model = Model.get_context()
except TypeError:
raise TypeError("No model on context stack, which is needed to "
"instantiate distributions. Add variable inside "
"a 'with model:' block, or use the '.dist' syntax "
"for a standalone distribution.")
if "testval" in kwargs:
initval = kwargs.pop("testval")
warnings.warn(
"The `testval` argument is deprecated; use `initval`.",
DeprecationWarning,
stacklevel=2,
)
if not isinstance(name, string_types):
raise TypeError(f"Name needs to be a string but got: {name}")
if rng is None:
rng = model.next_rng()
if dims is not None and "shape" in kwargs:
raise ValueError(
f"Passing both `dims` ({dims}) and `shape` ({kwargs['shape']}) is not supported!"
)
if dims is not None and "size" in kwargs:
raise ValueError(
f"Passing both `dims` ({dims}) and `size` ({kwargs['size']}) is not supported!"
)
dims = convert_dims(dims)
# Create the RV without specifying initval, because the initval may have a shape
# that only matches after replicating with a size implied by dims (see below).
rv_out = cls.dist(*args, rng=rng, initval=None, **kwargs)
ndim_actual = rv_out.ndim
resize_shape = None
# `dims` are only available with this API, because `.dist()` can be used
# without a modelcontext and dims are not tracked at the Aesara level.
if dims is not None:
ndim_resize, resize_shape, dims = resize_from_dims(
dims, ndim_actual, model)
elif observed is not None:
ndim_resize, resize_shape, observed = resize_from_observed(
observed, ndim_actual)
if resize_shape:
# A batch size was specified through `dims`, or implied by `observed`.
rv_out = change_rv_size(rv_var=rv_out,
new_size=resize_shape,
expand=True)
if initval is not None:
# Assigning the testval earlier causes trouble because the RV may not be created with the final shape already.
rv_out.tag.test_value = initval
return model.register_rv(rv_out,
name,
observed,
total_size,
dims=dims,
transform=transform)
