/usr/local/lib64/python3.6/site-packages/torch/distributions
Edit: /usr/local/lib64/python3.6/site-packages/torch/distributions/weibull.py (2854B)
import torch
from torch.distributions import constraints
from torch.distributions.exponential import Exponential
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, PowerTransform
from torch.distributions.utils import broadcast_all
from torch.distributions.gumbel import euler_constant
class Weibull(TransformedDistribution):
r"""
Samples from a two-parameter Weibull distribution.
Example:
>>> 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(Weibull, self).__init__(base_dist,
transforms,
validate_args=validate_args)
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 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)))
def entropy(self):
return euler_constant * (1 - self.concentration_reciprocal) + \
torch.log(self.scale * self.concentration_reciprocal) + 1