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# Source code for torch.distributions.bernoulli

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-deterinistic") >>> 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) (self.probs,) = broadcast_all(probs) else: is_scalar = isinstance(logits, Number) (self.logits,) = broadcast_all(logits) 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) with torch.no_grad(): return torch.bernoulli(self.probs.expand(shape))
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) logits, value = broadcast_all(self.logits, 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): return torch.log1p(torch.exp(x))

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