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173f224570
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
import math
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import torch
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from torch._six import inf, nan
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from torch.distributions import Chi2, constraints
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from torch.distributions.distribution import Distribution
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from torch.distributions.utils import _standard_normal, broadcast_all
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class StudentT(Distribution):
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r"""
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Creates a Student's t-distribution parameterized by degree of
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freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`.
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Example::
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>>> m = StudentT(torch.tensor([2.0]))
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>>> m.sample() # Student's t-distributed with degrees of freedom=2
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tensor([ 0.1046])
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Args:
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df (float or Tensor): degrees of freedom
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loc (float or Tensor): mean of the distribution
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scale (float or Tensor): scale of the distribution
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"""
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arg_constraints = {'df': constraints.positive, 'loc': constraints.real, 'scale': constraints.positive}
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support = constraints.real
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has_rsample = True
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@property
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def mean(self):
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m = self.loc.clone()
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m[self.df <= 1] = nan
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return m
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@property
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def variance(self):
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m = self.df.clone()
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m[self.df > 2] = self.scale[self.df > 2].pow(2) * self.df[self.df > 2] / (self.df[self.df > 2] - 2)
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m[(self.df <= 2) & (self.df > 1)] = inf
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m[self.df <= 1] = nan
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return m
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def __init__(self, df, loc=0., scale=1., validate_args=None):
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self.df, self.loc, self.scale = broadcast_all(df, loc, scale)
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self._chi2 = Chi2(self.df)
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batch_shape = self.df.size()
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super(StudentT, 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(StudentT, _instance)
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batch_shape = torch.Size(batch_shape)
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new.df = self.df.expand(batch_shape)
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new.loc = self.loc.expand(batch_shape)
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new.scale = self.scale.expand(batch_shape)
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new._chi2 = self._chi2.expand(batch_shape)
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super(StudentT, 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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def rsample(self, sample_shape=torch.Size()):
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# NOTE: This does not agree with scipy implementation as much as other distributions.
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# (see https://github.com/fritzo/notebooks/blob/master/debug-student-t.ipynb). Using DoubleTensor
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# parameters seems to help.
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# X ~ Normal(0, 1)
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# Z ~ Chi2(df)
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# Y = X / sqrt(Z / df) ~ StudentT(df)
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shape = self._extended_shape(sample_shape)
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X = _standard_normal(shape, dtype=self.df.dtype, device=self.df.device)
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Z = self._chi2.rsample(sample_shape)
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Y = X * torch.rsqrt(Z / self.df)
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return self.loc + self.scale * 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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y = (value - self.loc) / self.scale
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Z = (self.scale.log() +
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0.5 * self.df.log() +
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0.5 * math.log(math.pi) +
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torch.lgamma(0.5 * self.df) -
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torch.lgamma(0.5 * (self.df + 1.)))
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return -0.5 * (self.df + 1.) * torch.log1p(y**2. / self.df) - Z
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def entropy(self):
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lbeta = torch.lgamma(0.5 * self.df) + math.lgamma(0.5) - torch.lgamma(0.5 * (self.df + 1))
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return (self.scale.log() +
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0.5 * (self.df + 1) *
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(torch.digamma(0.5 * (self.df + 1)) - torch.digamma(0.5 * self.df)) +
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0.5 * self.df.log() + lbeta)
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