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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
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