pytorch
62 строки · 2.2 Кб
1# mypy: allow-untyped-defs
2from torch.distributions import constraints3from torch.distributions.exponential import Exponential4from torch.distributions.transformed_distribution import TransformedDistribution5from torch.distributions.transforms import AffineTransform, ExpTransform6from torch.distributions.utils import broadcast_all7
8
9__all__ = ["Pareto"]10
11
12class Pareto(TransformedDistribution):13r"""14Samples from a Pareto Type 1 distribution.
15
16Example::
17
18>>> # xdoctest: +IGNORE_WANT("non-deterministic")
19>>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
20>>> m.sample() # sample from a Pareto distribution with scale=1 and alpha=1
21tensor([ 1.5623])
22
23Args:
24scale (float or Tensor): Scale parameter of the distribution
25alpha (float or Tensor): Shape parameter of the distribution
26"""
27arg_constraints = {"alpha": constraints.positive, "scale": constraints.positive}28
29def __init__(self, scale, alpha, validate_args=None):30self.scale, self.alpha = broadcast_all(scale, alpha)31base_dist = Exponential(self.alpha, validate_args=validate_args)32transforms = [ExpTransform(), AffineTransform(loc=0, scale=self.scale)]33super().__init__(base_dist, transforms, validate_args=validate_args)34
35def expand(self, batch_shape, _instance=None):36new = self._get_checked_instance(Pareto, _instance)37new.scale = self.scale.expand(batch_shape)38new.alpha = self.alpha.expand(batch_shape)39return super().expand(batch_shape, _instance=new)40
41@property42def mean(self):43# mean is inf for alpha <= 144a = self.alpha.clamp(min=1)45return a * self.scale / (a - 1)46
47@property48def mode(self):49return self.scale50
51@property52def variance(self):53# var is inf for alpha <= 254a = self.alpha.clamp(min=2)55return self.scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))56
57@constraints.dependent_property(is_discrete=False, event_dim=0)58def support(self):59return constraints.greater_than_eq(self.scale)60
61def entropy(self):62return (self.scale / self.alpha).log() + (1 + self.alpha.reciprocal())63