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- from torch.distributions import constraints
- from torch.distributions.exponential import Exponential
- from torch.distributions.transformed_distribution import TransformedDistribution
- from torch.distributions.transforms import AffineTransform, ExpTransform
- from torch.distributions.utils import broadcast_all
- class Pareto(TransformedDistribution):
- r"""
- Samples from a Pareto Type 1 distribution.
- Example::
- >>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
- >>> m.sample() # sample from a Pareto distribution with scale=1 and alpha=1
- tensor([ 1.5623])
- Args:
- scale (float or Tensor): Scale parameter of the distribution
- alpha (float or Tensor): Shape parameter of the distribution
- """
- arg_constraints = {'alpha': constraints.positive, 'scale': constraints.positive}
- def __init__(self, scale, alpha, validate_args=None):
- self.scale, self.alpha = broadcast_all(scale, alpha)
- base_dist = Exponential(self.alpha, validate_args=validate_args)
- transforms = [ExpTransform(), AffineTransform(loc=0, scale=self.scale)]
- super(Pareto, self).__init__(base_dist, transforms, validate_args=validate_args)
- def expand(self, batch_shape, _instance=None):
- new = self._get_checked_instance(Pareto, _instance)
- new.scale = self.scale.expand(batch_shape)
- new.alpha = self.alpha.expand(batch_shape)
- return super(Pareto, self).expand(batch_shape, _instance=new)
- @property
- def mean(self):
- # mean is inf for alpha <= 1
- a = self.alpha.clamp(min=1)
- return a * self.scale / (a - 1)
- @property
- def mode(self):
- return self.scale
- @property
- def variance(self):
- # var is inf for alpha <= 2
- a = self.alpha.clamp(min=2)
- return self.scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))
- @constraints.dependent_property(is_discrete=False, event_dim=0)
- def support(self):
- return constraints.greater_than_eq(self.scale)
- def entropy(self):
- return ((self.scale / self.alpha).log() + (1 + self.alpha.reciprocal()))
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