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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, probs_to_logits, logits_to_probs, lazy_property
from torch.nn.functional import binary_cross_entropy_with_logits
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::
>>> 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(Bernoulli, self).__init__(batch_shape, validate_args=validate_args)
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 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()
def sample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
with torch.no_grad():
return torch.bernoulli(self.probs.expand(shape))
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')
def entropy(self):
return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none')
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.log(self.probs / (1 - self.probs)), )
def _log_normalizer(self, x):
return torch.log(1 + torch.exp(x))
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