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- from torch.distributions import constraints
- from torch.distributions.gamma import Gamma
- 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::
- >>> 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(Chi2, self).__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(Chi2, self).expand(batch_shape, new)
- @property
- def df(self):
- return self.concentration * 2
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