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