| |
| from typing import Optional, Union |
|
|
| from torch import Tensor |
| 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: Union[Tensor, float], |
| validate_args: Optional[bool] = None, |
| ) -> 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) -> Tensor: |
| return self.concentration * 2 |
|
|
