Source code for torch.distributions.gamma

from numbers import Number

import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all

__all__ = ['Gamma']

def _standard_gamma(concentration):
    return torch._standard_gamma(concentration)

[docs]class Gamma(ExponentialFamily): r""" Creates a Gamma distribution parameterized by shape :attr:`concentration` and :attr:`rate`. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0])) >>> m.sample() # Gamma distributed with concentration=1 and rate=1 tensor([ 0.1046]) Args: concentration (float or Tensor): shape parameter of the distribution (often referred to as alpha) rate (float or Tensor): rate = 1 / scale of the distribution (often referred to as beta) """ arg_constraints = {'concentration': constraints.positive, 'rate': constraints.positive} support = constraints.nonnegative has_rsample = True _mean_carrier_measure = 0 @property def mean(self): return self.concentration / self.rate @property def mode(self): return ((self.concentration - 1) / self.rate).clamp(min=0) @property def variance(self): return self.concentration / self.rate.pow(2) def __init__(self, concentration, rate, validate_args=None): self.concentration, self.rate = broadcast_all(concentration, rate) if isinstance(concentration, Number) and isinstance(rate, Number): batch_shape = torch.Size() else: batch_shape = self.concentration.size() super().__init__(batch_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Gamma, _instance) batch_shape = torch.Size(batch_shape) new.concentration = self.concentration.expand(batch_shape) new.rate = self.rate.expand(batch_shape) super(Gamma, new).__init__(batch_shape, validate_args=False) new._validate_args = self._validate_args return new
[docs] def rsample(self, sample_shape=torch.Size()): shape = self._extended_shape(sample_shape) value = _standard_gamma(self.concentration.expand(shape)) / self.rate.expand(shape) value.detach().clamp_(min=torch.finfo(value.dtype).tiny) # do not record in autograd graph return value
[docs] def log_prob(self, value): value = torch.as_tensor(value, dtype=self.rate.dtype, device=self.rate.device) if self._validate_args: self._validate_sample(value) return (torch.xlogy(self.concentration, self.rate) + torch.xlogy(self.concentration - 1, value) - self.rate * value - torch.lgamma(self.concentration))
[docs] def entropy(self): return (self.concentration - torch.log(self.rate) + torch.lgamma(self.concentration) + (1.0 - self.concentration) * torch.digamma(self.concentration))
@property def _natural_params(self): return (self.concentration - 1, -self.rate) def _log_normalizer(self, x, y): return torch.lgamma(x + 1) + (x + 1) * torch.log(-y.reciprocal())
[docs] def cdf(self, value): if self._validate_args: self._validate_sample(value) return torch.special.gammainc(self.concentration, self.rate * value)


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