131 lines
4.1 KiB
Python
131 lines
4.1 KiB
Python
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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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