Source code for torch.distributions.kumaraswamy

import torch
from torch.distributions import constraints
from torch.distributions.uniform import Uniform
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, PowerTransform
from torch.distributions.utils import broadcast_all, euler_constant

def _moments(a, b, n):
    Computes nth moment of Kumaraswamy using using torch.lgamma
    arg1 = 1 + n / a
    log_value = torch.lgamma(arg1) + torch.lgamma(b) - torch.lgamma(arg1 + b)
    return b * torch.exp(log_value)

[docs]class Kumaraswamy(TransformedDistribution): r""" Samples from a Kumaraswamy distribution. Example:: >>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0])) >>> m.sample() # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1 tensor([ 0.1729]) Args: concentration1 (float or Tensor): 1st concentration parameter of the distribution (often referred to as alpha) concentration0 (float or Tensor): 2nd concentration parameter of the distribution (often referred to as beta) """ arg_constraints = {'concentration1': constraints.positive, 'concentration0': constraints.positive} support = constraints.unit_interval has_rsample = True def __init__(self, concentration1, concentration0, validate_args=None): self.concentration1, self.concentration0 = broadcast_all(concentration1, concentration0) finfo = torch.finfo(self.concentration0.dtype) base_dist = Uniform(torch.full_like(self.concentration0, 0), torch.full_like(self.concentration0, 1), validate_args=validate_args) transforms = [PowerTransform(exponent=self.concentration0.reciprocal()), AffineTransform(loc=1., scale=-1.), PowerTransform(exponent=self.concentration1.reciprocal())] super(Kumaraswamy, self).__init__(base_dist, transforms, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Kumaraswamy, _instance) new.concentration1 = self.concentration1.expand(batch_shape) new.concentration0 = self.concentration0.expand(batch_shape) return super(Kumaraswamy, self).expand(batch_shape, _instance=new)
@property def mean(self): return _moments(self.concentration1, self.concentration0, 1) @property def variance(self): return _moments(self.concentration1, self.concentration0, 2) - torch.pow(self.mean, 2)
[docs] def entropy(self): t1 = (1 - self.concentration1.reciprocal()) t0 = (1 - self.concentration0.reciprocal()) H0 = torch.digamma(self.concentration0 + 1) + euler_constant return t0 + t1 * H0 - torch.log(self.concentration1) - torch.log(self.concentration0)


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