34 lines
974 B
Python
34 lines
974 B
Python
|
from torch.distributions import constraints
|
||
|
|
from torch.distributions.gamma import Gamma
|
||
|
|
|
||
|
|
__all__ = ["Chi2"]
|
||
|
|
|
||
|
|
|
||
|
|
class Chi2(Gamma):
|
||
|
|
r"""
|
||
|
|
Creates a Chi-squared distribution parameterized by shape parameter :attr:`df`.
|
||
|
|
This is exactly equivalent to ``Gamma(alpha=0.5*df, beta=0.5)``
|
||
|
|
|
||
|
|
Example::
|
||
|
|
|
||
|
|
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
|
||
|
|
>>> m = Chi2(torch.tensor([1.0]))
|
||
|
|
>>> m.sample() # Chi2 distributed with shape df=1
|
||
|
|
tensor([ 0.1046])
|
||
|
|
|
||
|
|
Args:
|
||
|
|
df (float or Tensor): shape parameter of the distribution
|
||
|
|
"""
|
||
|
|
arg_constraints = {"df": constraints.positive}
|
||
|
|
|
||
|
|
def __init__(self, df, validate_args=None):
|
||
|
|
super().__init__(0.5 * df, 0.5, validate_args=validate_args)
|
||
|
|
|
||
|
|
def expand(self, batch_shape, _instance=None):
|
||
|
|
new = self._get_checked_instance(Chi2, _instance)
|
||
|
|
return super().expand(batch_shape, new)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def df(self):
|
||
|
|
return self.concentration * 2
|