pytorch

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