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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
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
from torch.distributions import constraints
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from torch.distributions.transforms import ExpTransform
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from torch.distributions.normal import Normal
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from torch.distributions.transformed_distribution import TransformedDistribution
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__all__ = ['LogNormal']
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class LogNormal(TransformedDistribution):
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r"""
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Creates a log-normal distribution parameterized by
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:attr:`loc` and :attr:`scale` where::
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X ~ Normal(loc, scale)
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Y = exp(X) ~ LogNormal(loc, scale)
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))
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>>> m.sample() # log-normal distributed with mean=0 and stddev=1
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tensor([ 0.1046])
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Args:
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loc (float or Tensor): mean of log of distribution
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scale (float or Tensor): standard deviation of log of the distribution
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"""
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arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}
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support = constraints.positive
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has_rsample = True
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def __init__(self, loc, scale, validate_args=None):
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base_dist = Normal(loc, scale, validate_args=validate_args)
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super(LogNormal, self).__init__(base_dist, ExpTransform(), validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(LogNormal, _instance)
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return super(LogNormal, self).expand(batch_shape, _instance=new)
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@property
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def loc(self):
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return self.base_dist.loc
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@property
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def scale(self):
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return self.base_dist.scale
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@property
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def mean(self):
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return (self.loc + self.scale.pow(2) / 2).exp()
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@property
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def mode(self):
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return (self.loc - self.scale.square()).exp()
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@property
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def variance(self):
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return (self.scale.pow(2).exp() - 1) * (2 * self.loc + self.scale.pow(2)).exp()
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def entropy(self):
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return self.base_dist.entropy() + self.loc
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