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# Source code for torch.distributions.bernoulli

# mypy: allow-untyped-defs
from numbers import Number

import torch
from torch import nan
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import (
lazy_property,
logits_to_probs,
probs_to_logits,
)
from torch.nn.functional import binary_cross_entropy_with_logits

__all__ = ["Bernoulli"]

[docs]class Bernoulli(ExponentialFamily):
r"""
Creates a Bernoulli distribution parameterized by :attr:probs
or :attr:logits (but not both).

Samples are binary (0 or 1). They take the value 1 with probability p
and 0 with probability 1 - p.

Example::

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Bernoulli(torch.tensor([0.3]))
>>> m.sample()  # 30% chance 1; 70% chance 0
tensor([ 0.])

Args:
probs (Number, Tensor): the probability of sampling 1
logits (Number, Tensor): the log-odds of sampling 1
"""
arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
support = constraints.boolean
has_enumerate_support = True
_mean_carrier_measure = 0

def __init__(self, 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:
is_scalar = isinstance(probs, Number)
else:
is_scalar = isinstance(logits, Number)
self._param = self.probs if probs is not None else self.logits
if is_scalar:
batch_shape = torch.Size()
else:
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(Bernoulli, _instance)
batch_shape = torch.Size(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(Bernoulli, 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)

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

@property
def mode(self):
mode = (self.probs >= 0.5).to(self.probs)
mode[self.probs == 0.5] = nan
return mode

@property
def variance(self):
return 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)

[docs]    def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
return -binary_cross_entropy_with_logits(logits, value, reduction="none")

[docs]    def entropy(self):
return binary_cross_entropy_with_logits(
self.logits, self.probs, reduction="none"
)

[docs]    def enumerate_support(self, expand=True):
values = torch.arange(2, 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

@property
def _natural_params(self):
return (torch.logit(self.probs),)

def _log_normalizer(self, x):


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