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from numbers import Number |
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import torch |
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from torch import nan |
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from torch.distributions import constraints |
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from torch.distributions.exp_family import ExponentialFamily |
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from torch.distributions.utils import ( |
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broadcast_all, |
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lazy_property, |
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logits_to_probs, |
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probs_to_logits, |
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) |
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from torch.nn.functional import binary_cross_entropy_with_logits |
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__all__ = ["Bernoulli"] |
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class Bernoulli(ExponentialFamily): |
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r""" |
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Creates a Bernoulli distribution parameterized by :attr:`probs` |
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or :attr:`logits` (but not both). |
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Samples are binary (0 or 1). They take the value `1` with probability `p` |
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and `0` with probability `1 - p`. |
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Example:: |
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>>> # xdoctest: +IGNORE_WANT("non-deterministic") |
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>>> m = Bernoulli(torch.tensor([0.3])) |
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>>> m.sample() # 30% chance 1; 70% chance 0 |
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tensor([ 0.]) |
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Args: |
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probs (Number, Tensor): the probability of sampling `1` |
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logits (Number, Tensor): the log-odds of sampling `1` |
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""" |
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arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real} |
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support = constraints.boolean |
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has_enumerate_support = True |
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_mean_carrier_measure = 0 |
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def __init__(self, probs=None, logits=None, validate_args=None): |
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if (probs is None) == (logits is None): |
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raise ValueError( |
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"Either `probs` or `logits` must be specified, but not both." |
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) |
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if probs is not None: |
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is_scalar = isinstance(probs, Number) |
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(self.probs,) = broadcast_all(probs) |
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else: |
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is_scalar = isinstance(logits, Number) |
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(self.logits,) = broadcast_all(logits) |
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self._param = self.probs if probs is not None else self.logits |
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if is_scalar: |
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batch_shape = torch.Size() |
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else: |
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batch_shape = self._param.size() |
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super().__init__(batch_shape, validate_args=validate_args) |
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def expand(self, batch_shape, _instance=None): |
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new = self._get_checked_instance(Bernoulli, _instance) |
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batch_shape = torch.Size(batch_shape) |
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if "probs" in self.__dict__: |
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new.probs = self.probs.expand(batch_shape) |
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new._param = new.probs |
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if "logits" in self.__dict__: |
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new.logits = self.logits.expand(batch_shape) |
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new._param = new.logits |
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super(Bernoulli, new).__init__(batch_shape, validate_args=False) |
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new._validate_args = self._validate_args |
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return new |
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def _new(self, *args, **kwargs): |
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return self._param.new(*args, **kwargs) |
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@property |
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def mean(self): |
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return self.probs |
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@property |
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def mode(self): |
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mode = (self.probs >= 0.5).to(self.probs) |
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mode[self.probs == 0.5] = nan |
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return mode |
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@property |
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def variance(self): |
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return self.probs * (1 - self.probs) |
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@lazy_property |
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def logits(self): |
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return probs_to_logits(self.probs, is_binary=True) |
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@lazy_property |
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def probs(self): |
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return logits_to_probs(self.logits, is_binary=True) |
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@property |
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def param_shape(self): |
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return self._param.size() |
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def sample(self, sample_shape=torch.Size()): |
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shape = self._extended_shape(sample_shape) |
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with torch.no_grad(): |
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return torch.bernoulli(self.probs.expand(shape)) |
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def log_prob(self, value): |
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if self._validate_args: |
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self._validate_sample(value) |
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logits, value = broadcast_all(self.logits, value) |
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return -binary_cross_entropy_with_logits(logits, value, reduction="none") |
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def entropy(self): |
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return binary_cross_entropy_with_logits( |
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self.logits, self.probs, reduction="none" |
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) |
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def enumerate_support(self, expand=True): |
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values = torch.arange(2, dtype=self._param.dtype, device=self._param.device) |
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values = values.view((-1,) + (1,) * len(self._batch_shape)) |
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if expand: |
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values = values.expand((-1,) + self._batch_shape) |
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return values |
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@property |
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def _natural_params(self): |
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return (torch.logit(self.probs),) |
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def _log_normalizer(self, x): |
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return torch.log1p(torch.exp(x)) |
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