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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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