from torch.distributions import constraints
from torch.distributions.gamma import Gamma
[docs]class Chi2(Gamma):
r"""
Creates a Chi2 distribution parameterized by shape parameter `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
0.1046
[torch.FloatTensor of size 1]
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)
@property
def df(self):
return self.concentration * 2