/usr/local/lib64/python3.6/site-packages/torch/distributions
Edit: /usr/local/lib64/python3.6/site-packages/torch/distributions/fishersnedecor.py (3152B)
from numbers import Number
import torch
from torch._six import nan
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.gamma import Gamma
from torch.distributions.utils import broadcast_all
class FisherSnedecor(Distribution):
r"""
Creates a Fisher-Snedecor distribution parameterized by :attr:`df1` and :attr:`df2`.
Example::
>>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
>>> m.sample() # Fisher-Snedecor-distributed with df1=1 and df2=2
tensor([ 0.2453])
Args:
df1 (float or Tensor): degrees of freedom parameter 1
df2 (float or Tensor): degrees of freedom parameter 2
"""
arg_constraints = {'df1': constraints.positive, 'df2': constraints.positive}
support = constraints.positive
has_rsample = True
def __init__(self, df1, df2, validate_args=None):
self.df1, self.df2 = broadcast_all(df1, df2)
self._gamma1 = Gamma(self.df1 * 0.5, self.df1)
self._gamma2 = Gamma(self.df2 * 0.5, self.df2)
if isinstance(df1, Number) and isinstance(df2, Number):
batch_shape = torch.Size()
else:
batch_shape = self.df1.size()
super(FisherSnedecor, self).__init__(batch_shape, validate_args=validate_args)
def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(FisherSnedecor, _instance)
batch_shape = torch.Size(batch_shape)
new.df1 = self.df1.expand(batch_shape)
new.df2 = self.df2.expand(batch_shape)
new._gamma1 = self._gamma1.expand(batch_shape)
new._gamma2 = self._gamma2.expand(batch_shape)
super(FisherSnedecor, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new
@property
def mean(self):
df2 = self.df2.clone(memory_format=torch.contiguous_format)
df2[df2 <= 2] = nan
return df2 / (df2 - 2)
@property
def variance(self):
df2 = self.df2.clone(memory_format=torch.contiguous_format)
df2[df2 <= 4] = nan
return 2 * df2.pow(2) * (self.df1 + df2 - 2) / (self.df1 * (df2 - 2).pow(2) * (df2 - 4))
def rsample(self, sample_shape=torch.Size(())):
shape = self._extended_shape(sample_shape)
# X1 ~ Gamma(df1 / 2, 1 / df1), X2 ~ Gamma(df2 / 2, 1 / df2)
# Y = df2 * df1 * X1 / (df1 * df2 * X2) = X1 / X2 ~ F(df1, df2)
X1 = self._gamma1.rsample(sample_shape).view(shape)
X2 = self._gamma2.rsample(sample_shape).view(shape)
tiny = torch.finfo(X2.dtype).tiny
X2.clamp_(min=tiny)
Y = X1 / X2
Y.clamp_(min=tiny)
return Y
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
ct1 = self.df1 * 0.5
ct2 = self.df2 * 0.5
ct3 = self.df1 / self.df2
t1 = (ct1 + ct2).lgamma() - ct1.lgamma() - ct2.lgamma()
t2 = ct1 * ct3.log() + (ct1 - 1) * torch.log(value)
t3 = (ct1 + ct2) * torch.log1p(ct3 * value)
return t1 + t2 - t3