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Source code for torch.distributions.binomial

# mypy: allow-untyped-defs
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import (
lazy_property,
logits_to_probs,
probs_to_logits,
)

__all__ = ["Binomial"]

def _clamp_by_zero(x):
# works like clamp(x, min=0) but has grad at 0 is 0.5
return (x.clamp(min=0) + x - x.clamp(max=0)) / 2

[docs]class Binomial(Distribution):
r"""
Creates a Binomial distribution parameterized by :attr:total_count and
either :attr:probs or :attr:logits (but not both). :attr:total_count must be
broadcastable with :attr:probs/:attr:logits.

Example::

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))
>>> x = m.sample()
tensor([   0.,   22.,   71.,  100.])

>>> m = Binomial(torch.tensor([[5.], [10.]]), torch.tensor([0.5, 0.8]))
>>> x = m.sample()
tensor([[ 4.,  5.],
[ 7.,  6.]])

Args:
total_count (int or Tensor): number of Bernoulli trials
probs (Tensor): Event probabilities
logits (Tensor): Event log-odds
"""
arg_constraints = {
"total_count": constraints.nonnegative_integer,
"probs": constraints.unit_interval,
"logits": constraints.real,
}
has_enumerate_support = True

def __init__(self, total_count=1, probs=None, logits=None, validate_args=None):
if (probs is None) == (logits is None):
raise ValueError(
"Either probs or logits must be specified, but not both."
)
if probs is not None:
(
self.total_count,
self.probs,
self.total_count = self.total_count.type_as(self.probs)
else:
(
self.total_count,
self.logits,
self.total_count = self.total_count.type_as(self.logits)

self._param = self.probs if probs is not None else self.logits
batch_shape = self._param.size()
super().__init__(batch_shape, validate_args=validate_args)

[docs]    def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(Binomial, _instance)
batch_shape = torch.Size(batch_shape)
new.total_count = self.total_count.expand(batch_shape)
if "probs" in self.__dict__:
new.probs = self.probs.expand(batch_shape)
new._param = new.probs
if "logits" in self.__dict__:
new.logits = self.logits.expand(batch_shape)
new._param = new.logits
super(Binomial, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new

def _new(self, *args, **kwargs):
return self._param.new(*args, **kwargs)

@constraints.dependent_property(is_discrete=True, event_dim=0)
def support(self):
return constraints.integer_interval(0, self.total_count)

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

@property
def mode(self):
return ((self.total_count + 1) * self.probs).floor().clamp(max=self.total_count)

@property
def variance(self):
return self.total_count * self.probs * (1 - self.probs)

@lazy_property
def logits(self):
return probs_to_logits(self.probs, is_binary=True)

@lazy_property
def probs(self):
return logits_to_probs(self.logits, is_binary=True)

@property
def param_shape(self):
return self._param.size()

[docs]    def sample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
self.total_count.expand(shape), self.probs.expand(shape)
)

[docs]    def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
log_factorial_n = torch.lgamma(self.total_count + 1)
log_factorial_k = torch.lgamma(value + 1)
log_factorial_nmk = torch.lgamma(self.total_count - value + 1)
# k * log(p) + (n - k) * log(1 - p) = k * (log(p) - log(1 - p)) + n * log(1 - p)
#     (case logit < 0)              = k * logit - n * log1p(e^logit)
#     (case logit > 0)              = k * logit - n * (log(p) - log(1 - p)) + n * log(p)
#                                   = k * logit - n * logit - n * log1p(e^-logit)
#     (merge two cases)             = k * logit - n * max(logit, 0) - n * log1p(e^-|logit|)
normalize_term = (
self.total_count * _clamp_by_zero(self.logits)
+ self.total_count * torch.log1p(torch.exp(-torch.abs(self.logits)))
- log_factorial_n
)
return (
value * self.logits - log_factorial_k - log_factorial_nmk - normalize_term
)

[docs]    def entropy(self):
total_count = int(self.total_count.max())
if not self.total_count.min() == total_count:
raise NotImplementedError(
"Inhomogeneous total count not supported by entropy."
)

log_prob = self.log_prob(self.enumerate_support(False))
return -(torch.exp(log_prob) * log_prob).sum(0)

[docs]    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._param.dtype, device=self._param.device
)
values = values.view((-1,) + (1,) * len(self._batch_shape))
if expand:
values = values.expand((-1,) + self._batch_shape)
return values


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