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| 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 ( | |
| broadcast_all, | |
| lazy_property, | |
| logits_to_probs, | |
| probs_to_logits, | |
| ) | |
| from torch.nn.functional import binary_cross_entropy_with_logits | |
| __all__ = ["Bernoulli"] | |
| 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) | |
| (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) | |
| 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) | |
| def mean(self): | |
| return self.probs | |
| def mode(self): | |
| mode = (self.probs >= 0.5).to(self.probs) | |
| mode[self.probs == 0.5] = nan | |
| return mode | |
| def variance(self): | |
| return self.probs * (1 - self.probs) | |
| def logits(self): | |
| return probs_to_logits(self.probs, is_binary=True) | |
| def probs(self): | |
| return logits_to_probs(self.logits, is_binary=True) | |
| 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 | |
| def _natural_params(self): | |
| return (torch.logit(self.probs),) | |
| def _log_normalizer(self, x): | |
| return torch.log1p(torch.exp(x)) | |