def make_node(self, rng, size, dtype, *dist_params):
"""Create a random variable node.
XXX: Unnamed/non-keyword arguments are considered distribution
parameters! If you want to set `size`, `rng`, and/or `name`, use their
keywords.
Parameters
----------
rng: RandomStateType
Existing Aesara `RandomState` object to be used. Creates a
new one, if `None`.
size: int or Sequence
Numpy-like size of the output (i.e. replications).
dtype: str
The dtype of the sampled output. If the value ``"floatX"`` is
given, then ``dtype`` is set to ``aesara.config.floatX``. This
value is only used when `self.dtype` isn't set.
dist_params: list
Distribution parameters.
Results
-------
out: `Apply`
A node with inputs `(rng, size, dtype) + dist_args` and outputs
`(rng_var, out_var)`.
"""
size = normalize_size_param(size)
dist_params = tuple(
as_tensor_variable(p) if not isinstance(p, Variable) else p
for p in dist_params
)
if rng is None:
rng = aesara.shared(np.random.RandomState())
elif not isinstance(rng.type, RandomStateType):
raise TypeError("The type of rng should be an instance of RandomStateType")
bcast = self.compute_bcast(dist_params, size)
dtype = self.dtype or dtype
if dtype == "floatX":
dtype = config.floatX
elif dtype is None or (isinstance(dtype, str) and dtype not in all_dtypes):
raise TypeError("dtype is unspecified")
if isinstance(dtype, str):
dtype_idx = constant(all_dtypes.index(dtype), dtype="int64")
else:
dtype_idx = constant(dtype, dtype="int64")
dtype = all_dtypes[dtype_idx.data]
outtype = TensorType(dtype=dtype, broadcastable=bcast)
out_var = outtype()
inputs = (rng, size, dtype_idx) + dist_params
outputs = (rng.type(), out_var)
return Apply(self, inputs, outputs)
def make_node(self, rng, size, dtype, *dist_params):
"""Create a random variable node.
Parameters
----------
rng: RandomGeneratorType or RandomStateType
Existing Aesara `Generator` or `RandomState` object to be used. Creates a
new one, if `None`.
size: int or Sequence
NumPy-like size parameter.
dtype: str
The dtype of the sampled output. If the value ``"floatX"`` is
given, then `dtype` is set to ``aesara.config.floatX``. This value is
only used when ``self.dtype`` isn't set.
dist_params: list
Distribution parameters.
Results
-------
out: Apply
A node with inputs ``(rng, size, dtype) + dist_args`` and outputs
``(rng_var, out_var)``.
"""
size = normalize_size_param(size)
dist_params = tuple(
as_tensor_variable(p) if not isinstance(p, Variable) else p
for p in dist_params)
if rng is None:
rng = aesara.shared(np.random.default_rng())
elif not isinstance(rng.type, RandomType):
raise TypeError(
"The type of rng should be an instance of either RandomGeneratorType or RandomStateType"
)
shape = self._infer_shape(size, dist_params)
_, bcast = infer_broadcastable(shape)
dtype = self.dtype or dtype
if dtype == "floatX":
dtype = config.floatX
elif dtype is None or (isinstance(dtype, str)
and dtype not in all_dtypes):
raise TypeError("dtype is unspecified")
if isinstance(dtype, str):
dtype_idx = constant(all_dtypes.index(dtype), dtype="int64")
else:
dtype_idx = constant(dtype, dtype="int64")
dtype = all_dtypes[dtype_idx.data]
outtype = TensorType(dtype=dtype, shape=bcast)
out_var = outtype()
inputs = (rng, size, dtype_idx) + dist_params
outputs = (rng.type(), out_var)
return Apply(self, inputs, outputs)