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 _finfo, broadcast_all
euler_constant = 0.57721566490153286060 # Euler Mascheroni Constant
[docs]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
1.0124
[torch.FloatTensor of size 1]
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 = _finfo(self.loc)
if isinstance(loc, Number) and isinstance(scale, Number):
batch_shape = torch.Size()
base_dist = Uniform(finfo.tiny, 1 - finfo.eps)
else:
batch_shape = self.scale.size()
base_dist = Uniform(self.loc.new(self.loc.size()).fill_(finfo.tiny), 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)
@property
def mean(self):
return self.loc + self.scale * euler_constant
@property
def stddev(self):
return (math.pi / math.sqrt(6)) * self.scale
@property
def variance(self):
return self.stddev.pow(2)
[docs] def entropy(self):
return self.scale.log() + (1 + euler_constant)