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- from numbers import Number
- import torch
- from torch.distributions import constraints
- from torch.distributions.distribution import Distribution
- 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 Geometric(Distribution):
- r"""
- Creates a Geometric distribution parameterized by :attr:`probs`,
- where :attr:`probs` is the probability of success of Bernoulli trials.
- It represents the probability that in :math:`k + 1` Bernoulli trials, the
- first :math:`k` trials failed, before seeing a success.
- Samples are non-negative integers [0, :math:`\inf`).
- Example::
- >>> m = Geometric(torch.tensor([0.3]))
- >>> m.sample() # underlying Bernoulli has 30% chance 1; 70% chance 0
- tensor([ 2.])
- Args:
- probs (Number, Tensor): the probability of sampling `1`. Must be in range (0, 1]
- logits (Number, Tensor): the log-odds of sampling `1`.
- """
- arg_constraints = {'probs': constraints.unit_interval,
- 'logits': constraints.real}
- support = constraints.nonnegative_integer
- 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:
- self.probs, = broadcast_all(probs)
- else:
- self.logits, = broadcast_all(logits)
- probs_or_logits = probs if probs is not None else logits
- if isinstance(probs_or_logits, Number):
- batch_shape = torch.Size()
- else:
- batch_shape = probs_or_logits.size()
- super(Geometric, self).__init__(batch_shape, validate_args=validate_args)
- if self._validate_args and probs is not None:
- # Add an extra check beyond unit_interval
- value = self.probs
- valid = value > 0
- if not valid.all():
- invalid_value = value.data[~valid]
- raise ValueError(
- "Expected parameter probs "
- f"({type(value).__name__} of shape {tuple(value.shape)}) "
- f"of distribution {repr(self)} "
- f"to be positive but found invalid values:\n{invalid_value}"
- )
- def expand(self, batch_shape, _instance=None):
- new = self._get_checked_instance(Geometric, _instance)
- batch_shape = torch.Size(batch_shape)
- if 'probs' in self.__dict__:
- new.probs = self.probs.expand(batch_shape)
- if 'logits' in self.__dict__:
- new.logits = self.logits.expand(batch_shape)
- super(Geometric, new).__init__(batch_shape, validate_args=False)
- new._validate_args = self._validate_args
- return new
- @property
- def mean(self):
- return 1. / self.probs - 1.
- @property
- def mode(self):
- return torch.zeros_like(self.probs)
- @property
- def variance(self):
- return (1. / 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)
- def sample(self, sample_shape=torch.Size()):
- shape = self._extended_shape(sample_shape)
- tiny = torch.finfo(self.probs.dtype).tiny
- with torch.no_grad():
- if torch._C._get_tracing_state():
- # [JIT WORKAROUND] lack of support for .uniform_()
- u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
- u = u.clamp(min=tiny)
- else:
- u = self.probs.new(shape).uniform_(tiny, 1)
- return (u.log() / (-self.probs).log1p()).floor()
- def log_prob(self, value):
- if self._validate_args:
- self._validate_sample(value)
- value, probs = broadcast_all(value, self.probs)
- probs = probs.clone(memory_format=torch.contiguous_format)
- probs[(probs == 1) & (value == 0)] = 0
- return value * (-probs).log1p() + self.probs.log()
- def entropy(self):
- return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none') / self.probs
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