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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
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
import math
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from torch._six import inf, nan
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from numbers import Number
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import torch
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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.utils import broadcast_all
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__all__ = ['Cauchy']
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class Cauchy(Distribution):
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r"""
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Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of
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independent normally distributed random variables with means `0` follows a
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Cauchy distribution.
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = Cauchy(torch.tensor([0.0]), torch.tensor([1.0]))
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>>> m.sample() # sample from a Cauchy distribution with loc=0 and scale=1
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tensor([ 2.3214])
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Args:
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loc (float or Tensor): mode or median of the distribution.
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scale (float or Tensor): half width at half maximum.
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"""
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arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}
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support = constraints.real
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has_rsample = True
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def __init__(self, loc, scale, validate_args=None):
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self.loc, self.scale = broadcast_all(loc, scale)
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if isinstance(loc, Number) and isinstance(scale, Number):
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batch_shape = torch.Size()
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else:
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batch_shape = self.loc.size()
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super(Cauchy, 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(Cauchy, _instance)
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batch_shape = torch.Size(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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super(Cauchy, 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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return torch.full(self._extended_shape(), nan, dtype=self.loc.dtype, device=self.loc.device)
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@property
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def mode(self):
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return self.loc
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@property
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def variance(self):
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return torch.full(self._extended_shape(), inf, dtype=self.loc.dtype, device=self.loc.device)
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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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eps = self.loc.new(shape).cauchy_()
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return self.loc + eps * self.scale
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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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return -math.log(math.pi) - self.scale.log() - (1 + ((value - self.loc) / self.scale)**2).log()
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def cdf(self, value):
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if self._validate_args:
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self._validate_sample(value)
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return torch.atan((value - self.loc) / self.scale) / math.pi + 0.5
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def icdf(self, value):
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return torch.tan(math.pi * (value - 0.5)) * self.scale + self.loc
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def entropy(self):
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return math.log(4 * math.pi) + self.scale.log()
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