# Source code for pyro.distributions.conjugate

```# Copyright (c) 2017-2019 Uber Technologies, Inc.

import numbers

import torch
from torch.distributions import constraints

from pyro.distributions.torch import Beta, Binomial, Dirichlet, Gamma, Multinomial, Poisson
from pyro.distributions.torch_distribution import TorchDistribution
from pyro.ops.special import log_beta, log_binomial

def _log_beta_1(alpha, value, is_sparse):
if is_sparse:
result = torch.zeros_like(value)
result[mask] = torch.lgamma(1 + value) + torch.lgamma(alpha) - torch.lgamma(value + alpha)
return result
else:

class BetaBinomial(TorchDistribution):
r"""
Compound distribution comprising of a beta-binomial pair. The probability of
success (``probs`` for the :class:`~pyro.distributions.Binomial` distribution)
is unknown and randomly drawn from a :class:`~pyro.distributions.Beta` distribution
prior to a certain number of Bernoulli trials given by ``total_count``.

:param concentration1: 1st concentration parameter (alpha) for the
Beta distribution.
:type concentration1: float or torch.Tensor
:param concentration0: 2nd concentration parameter (beta) for the
Beta distribution.
:type concentration0: float or torch.Tensor
:param total_count: Number of Bernoulli trials.
:type total_count: float or torch.Tensor
"""
arg_constraints = {'concentration1': constraints.positive, 'concentration0': constraints.positive,
'total_count': constraints.nonnegative_integer}
has_enumerate_support = True
support = Binomial.support

# EXPERIMENTAL If set to a positive value, the .log_prob() method will use
# a shifted Sterling's approximation to the Beta function, reducing
# computational cost from 9 lgamma() evaluations to 12 log() evaluations
# plus arithmetic. Recommended values are between 0.1 and 0.01.
approx_log_prob_tol = 0.

def __init__(self, concentration1, concentration0, total_count=1, validate_args=None):
concentration1, concentration0, total_count)
self._beta = Beta(concentration1, concentration0)
self.total_count = total_count
super().__init__(self._beta._batch_shape, validate_args=validate_args)

@property
def concentration1(self):
return self._beta.concentration1

@property
def concentration0(self):
return self._beta.concentration0

def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(BetaBinomial, _instance)
batch_shape = torch.Size(batch_shape)
new._beta = self._beta.expand(batch_shape)
new.total_count = self.total_count.expand_as(new._beta.concentration0)
super(BetaBinomial, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new

def sample(self, sample_shape=()):
probs = self._beta.sample(sample_shape)
return Binomial(self.total_count, probs, validate_args=False).sample()

def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)

n = self.total_count
k = value
a = self.concentration1
b = self.concentration0
tol = self.approx_log_prob_tol
return log_binomial(n, k, tol) + log_beta(k + a, n - k + b, tol) - log_beta(a, b, tol)

@property
def mean(self):
return self._beta.mean * self.total_count

@property
def variance(self):
return self._beta.variance * self.total_count * (self.concentration0 + self.concentration1 + self.total_count)

def enumerate_support(self, expand=True):
total_count = int(self.total_count.max())
if not self.total_count.min() == total_count:
raise NotImplementedError("Inhomogeneous total count not supported by `enumerate_support`.")
values = torch.arange(1 + total_count, dtype=self.concentration1.dtype, device=self.concentration1.device)
values = values.view((-1,) + (1,) * len(self._batch_shape))
if expand:
values = values.expand((-1,) + self._batch_shape)
return values

class DirichletMultinomial(TorchDistribution):
r"""
Compound distribution comprising of a dirichlet-multinomial pair. The probability of
classes (``probs`` for the :class:`~pyro.distributions.Multinomial` distribution)
is unknown and randomly drawn from a :class:`~pyro.distributions.Dirichlet`
distribution prior to a certain number of Categorical trials given by
``total_count``.

:param float or torch.Tensor concentration: concentration parameter (alpha) for the
Dirichlet distribution.
:param int or torch.Tensor total_count: number of Categorical trials.
:param bool is_sparse: Whether to assume value is mostly zero when computing
:meth:`log_prob`, which can speed up computation when data is sparse.
"""
arg_constraints = {'concentration': constraints.positive, 'total_count': constraints.nonnegative_integer}
support = Multinomial.support

def __init__(self, concentration, total_count=1, is_sparse=False, validate_args=None):
if isinstance(total_count, numbers.Number):
total_count = torch.tensor(total_count, dtype=concentration.dtype, device=concentration.device)
total_count_1 = total_count.unsqueeze(-1)
total_count = total_count_1.squeeze(-1)
self._dirichlet = Dirichlet(concentration)
self.total_count = total_count
self.is_sparse = is_sparse
super().__init__(
self._dirichlet._batch_shape, self._dirichlet.event_shape, validate_args=validate_args)

@property
def concentration(self):
return self._dirichlet.concentration

def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(DirichletMultinomial, _instance)
batch_shape = torch.Size(batch_shape)
new._dirichlet = self._dirichlet.expand(batch_shape)
new.total_count = self.total_count.expand(batch_shape)
new.is_sparse = self.is_sparse
super(DirichletMultinomial, new).__init__(
new._dirichlet.batch_shape, new._dirichlet.event_shape, validate_args=False)
new._validate_args = self._validate_args
return new

def sample(self, sample_shape=()):
probs = self._dirichlet.sample(sample_shape)
total_count = int(self.total_count.max())
if not self.total_count.min() == total_count:
raise NotImplementedError("Inhomogeneous total count not supported by `sample`.")
return Multinomial(total_count, probs).sample()

def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
alpha = self.concentration
return (_log_beta_1(alpha.sum(-1), value.sum(-1), self.is_sparse) -
_log_beta_1(alpha, value, self.is_sparse).sum(-1))

@property
def mean(self):
return self._dirichlet.mean * self.total_count.unsqueeze(-1)

@property
def variance(self):
n = self.total_count.unsqueeze(-1)
alpha = self.concentration
alpha_sum = self.concentration.sum(-1, keepdim=True)
alpha_ratio = alpha / alpha_sum
return n * alpha_ratio * (1 - alpha_ratio) * (n + alpha_sum) / (1 + alpha_sum)

class GammaPoisson(TorchDistribution):
r"""
Compound distribution comprising of a gamma-poisson pair, also referred to as
a gamma-poisson mixture. The ``rate`` parameter for the
:class:`~pyro.distributions.Poisson` distribution is unknown and randomly
drawn from a :class:`~pyro.distributions.Gamma` distribution.

.. note:: This can be treated as an alternate parametrization of the
:class:`~pyro.distributions.NegativeBinomial` (``total_count``, ``probs``)
distribution, with `concentration = total_count` and `rate = (1 - probs) / probs`.

:param float or torch.Tensor concentration: shape parameter (alpha) of the Gamma
distribution.
:param float or torch.Tensor rate: rate parameter (beta) for the Gamma
distribution.
"""

arg_constraints = {'concentration': constraints.positive, 'rate': constraints.positive}
support = Poisson.support

def __init__(self, concentration, rate, validate_args=None):
self._gamma = Gamma(concentration, rate)
super().__init__(self._gamma._batch_shape, validate_args=validate_args)

@property
def concentration(self):
return self._gamma.concentration

@property
def rate(self):
return self._gamma.rate

def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(GammaPoisson, _instance)
batch_shape = torch.Size(batch_shape)
new._gamma = self._gamma.expand(batch_shape)
super(GammaPoisson, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new

def sample(self, sample_shape=()):
rate = self._gamma.sample(sample_shape)
return Poisson(rate).sample()

def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
post_value = self.concentration + value
return -log_beta(self.concentration, value + 1) - post_value.log() + \
self.concentration * self.rate.log() - post_value * (1 + self.rate).log()

@property
def mean(self):
return self.concentration / self.rate

@property
def variance(self):
return self.concentration / self.rate.pow(2) * (1 + self.rate)
```