Shortcuts

Source code for torch.distributions.weibull

import torch
from torch.distributions import constraints
from torch.distributions.exponential import Exponential
from torch.distributions.gumbel import euler_constant
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, PowerTransform
from torch.distributions.utils import broadcast_all

__all__ = ["Weibull"]


[docs]class Weibull(TransformedDistribution): r""" Samples from a two-parameter Weibull distribution. Example: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = Weibull(torch.tensor([1.0]), torch.tensor([1.0])) >>> m.sample() # sample from a Weibull distribution with scale=1, concentration=1 tensor([ 0.4784]) Args: scale (float or Tensor): Scale parameter of distribution (lambda). concentration (float or Tensor): Concentration parameter of distribution (k/shape). """ arg_constraints = { "scale": constraints.positive, "concentration": constraints.positive, } support = constraints.positive def __init__(self, scale, concentration, validate_args=None): self.scale, self.concentration = broadcast_all(scale, concentration) self.concentration_reciprocal = self.concentration.reciprocal() base_dist = Exponential( torch.ones_like(self.scale), validate_args=validate_args ) transforms = [ PowerTransform(exponent=self.concentration_reciprocal), AffineTransform(loc=0, scale=self.scale), ] super().__init__(base_dist, transforms, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Weibull, _instance) new.scale = self.scale.expand(batch_shape) new.concentration = self.concentration.expand(batch_shape) new.concentration_reciprocal = new.concentration.reciprocal() base_dist = self.base_dist.expand(batch_shape) transforms = [ PowerTransform(exponent=new.concentration_reciprocal), AffineTransform(loc=0, scale=new.scale), ] super(Weibull, new).__init__(base_dist, transforms, validate_args=False) new._validate_args = self._validate_args return new
@property def mean(self): return self.scale * torch.exp(torch.lgamma(1 + self.concentration_reciprocal)) @property def mode(self): return ( self.scale * ((self.concentration - 1) / self.concentration) ** self.concentration.reciprocal() ) @property def variance(self): return self.scale.pow(2) * ( torch.exp(torch.lgamma(1 + 2 * self.concentration_reciprocal)) - torch.exp(2 * torch.lgamma(1 + self.concentration_reciprocal)) )
[docs] def entropy(self): return ( euler_constant * (1 - self.concentration_reciprocal) + torch.log(self.scale * self.concentration_reciprocal) + 1 )

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources