Table of Contents
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"]

    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)

        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))
            )

        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

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.