| | 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 |
| |
|
| | __all__ = ['FisherSnedecor'] |
| |
|
| | class FisherSnedecor(Distribution): |
| | r""" |
| | Creates a Fisher-Snedecor distribution parameterized by :attr:`df1` and :attr:`df2`. |
| | |
| | Example:: |
| | |
| | >>> # xdoctest: +IGNORE_WANT("non-deterinistic") |
| | >>> 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 mode(self): |
| | mode = (self.df1 - 2) / self.df1 * self.df2 / (self.df2 + 2) |
| | mode[self.df1 <= 2] = nan |
| | return mode |
| |
|
| | @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 = 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 |
| |
|
