82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import math
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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.transformed_distribution import TransformedDistribution
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from torch.distributions.transforms import AffineTransform, ExpTransform
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from torch.distributions.uniform import Uniform
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from torch.distributions.utils import broadcast_all, euler_constant
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__all__ = ["Gumbel"]
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class Gumbel(TransformedDistribution):
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r"""
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Samples from a Gumbel Distribution.
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Examples::
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
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>>> m.sample() # sample from Gumbel distribution with loc=1, scale=2
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tensor([ 1.0124])
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Args:
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loc (float or Tensor): Location parameter of the distribution
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scale (float or Tensor): Scale parameter 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.real
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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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finfo = torch.finfo(self.loc.dtype)
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if isinstance(loc, Number) and isinstance(scale, Number):
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base_dist = Uniform(finfo.tiny, 1 - finfo.eps, validate_args=validate_args)
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else:
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base_dist = Uniform(
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torch.full_like(self.loc, finfo.tiny),
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torch.full_like(self.loc, 1 - finfo.eps),
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validate_args=validate_args,
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)
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transforms = [
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ExpTransform().inv,
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AffineTransform(loc=0, scale=-torch.ones_like(self.scale)),
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ExpTransform().inv,
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AffineTransform(loc=loc, scale=-self.scale),
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]
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super().__init__(base_dist, transforms, validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(Gumbel, _instance)
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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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return super().expand(batch_shape, _instance=new)
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# Explicitly defining the log probability function for Gumbel due to precision issues
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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 = (self.loc - value) / self.scale
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return (y - y.exp()) - self.scale.log()
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@property
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def mean(self):
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return self.loc + self.scale * euler_constant
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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 stddev(self):
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return (math.pi / math.sqrt(6)) * self.scale
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@property
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def variance(self):
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return self.stddev.pow(2)
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def entropy(self):
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return self.scale.log() + (1 + euler_constant)
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