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4618371da5
This is a new version of #15648 based on the latest master branch. Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR. In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.) Fixes https://github.com/pytorch/pytorch/issues/71105 @ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797 Approved by: https://github.com/ezyang
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
from numbers import Number
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import torch
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from torch._six import nan
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from torch.distributions import constraints
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from torch.distributions.distribution import Distribution
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from torch.distributions.gamma import Gamma
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from torch.distributions.utils import broadcast_all
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__all__ = ['FisherSnedecor']
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class FisherSnedecor(Distribution):
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r"""
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Creates a Fisher-Snedecor distribution parameterized by :attr:`df1` and :attr:`df2`.
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
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>>> m.sample() # Fisher-Snedecor-distributed with df1=1 and df2=2
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tensor([ 0.2453])
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Args:
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df1 (float or Tensor): degrees of freedom parameter 1
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df2 (float or Tensor): degrees of freedom parameter 2
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"""
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arg_constraints = {'df1': constraints.positive, 'df2': constraints.positive}
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support = constraints.positive
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has_rsample = True
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def __init__(self, df1, df2, validate_args=None):
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self.df1, self.df2 = broadcast_all(df1, df2)
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self._gamma1 = Gamma(self.df1 * 0.5, self.df1)
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self._gamma2 = Gamma(self.df2 * 0.5, self.df2)
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if isinstance(df1, Number) and isinstance(df2, Number):
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batch_shape = torch.Size()
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else:
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batch_shape = self.df1.size()
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super(FisherSnedecor, self).__init__(batch_shape, validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(FisherSnedecor, _instance)
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batch_shape = torch.Size(batch_shape)
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new.df1 = self.df1.expand(batch_shape)
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new.df2 = self.df2.expand(batch_shape)
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new._gamma1 = self._gamma1.expand(batch_shape)
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new._gamma2 = self._gamma2.expand(batch_shape)
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super(FisherSnedecor, new).__init__(batch_shape, validate_args=False)
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new._validate_args = self._validate_args
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return new
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@property
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def mean(self):
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df2 = self.df2.clone(memory_format=torch.contiguous_format)
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df2[df2 <= 2] = nan
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return df2 / (df2 - 2)
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@property
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def mode(self):
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mode = (self.df1 - 2) / self.df1 * self.df2 / (self.df2 + 2)
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mode[self.df1 <= 2] = nan
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return mode
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@property
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def variance(self):
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df2 = self.df2.clone(memory_format=torch.contiguous_format)
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df2[df2 <= 4] = nan
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return 2 * df2.pow(2) * (self.df1 + df2 - 2) / (self.df1 * (df2 - 2).pow(2) * (df2 - 4))
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def rsample(self, sample_shape=torch.Size(())):
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shape = self._extended_shape(sample_shape)
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# X1 ~ Gamma(df1 / 2, 1 / df1), X2 ~ Gamma(df2 / 2, 1 / df2)
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# Y = df2 * df1 * X1 / (df1 * df2 * X2) = X1 / X2 ~ F(df1, df2)
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X1 = self._gamma1.rsample(sample_shape).view(shape)
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X2 = self._gamma2.rsample(sample_shape).view(shape)
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tiny = torch.finfo(X2.dtype).tiny
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X2.clamp_(min=tiny)
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Y = X1 / X2
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Y.clamp_(min=tiny)
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return Y
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def log_prob(self, value):
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if self._validate_args:
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self._validate_sample(value)
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ct1 = self.df1 * 0.5
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ct2 = self.df2 * 0.5
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ct3 = self.df1 / self.df2
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t1 = (ct1 + ct2).lgamma() - ct1.lgamma() - ct2.lgamma()
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t2 = ct1 * ct3.log() + (ct1 - 1) * torch.log(value)
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t3 = (ct1 + ct2) * torch.log1p(ct3 * value)
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return t1 + t2 - t3
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