1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
|
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-deterinistic")
>>> 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
|
