98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
from numbers import Number
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import torch
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from torch.distributions import constraints
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from torch.distributions.distribution import Distribution
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from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property
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from torch.nn.functional import binary_cross_entropy_with_logits
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class Geometric(Distribution):
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r"""
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Creates a Geometric distribution parameterized by :attr:`probs`,
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where :attr:`probs` is the probability of success of Bernoulli trials.
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It represents the probability that in :math:`k + 1` Bernoulli trials, the
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first :math:`k` trials failed, before seeing a success.
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Samples are non-negative integers [0, :math:`\inf`).
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Example::
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>>> m = Geometric(torch.tensor([0.3]))
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>>> m.sample() # underlying Bernoulli has 30% chance 1; 70% chance 0
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tensor([ 2.])
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Args:
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probs (Number, Tensor): the probability of sampling `1`. Must be in range (0, 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,
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'logits': constraints.real}
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support = constraints.nonnegative_integer
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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("Either `probs` or `logits` must be specified, but not both.")
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if probs is not None:
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self.probs, = broadcast_all(probs)
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if not self.probs.gt(0).all():
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raise ValueError('All elements of probs must be greater than 0')
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else:
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self.logits, = broadcast_all(logits)
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probs_or_logits = probs if probs is not None else logits
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if isinstance(probs_or_logits, Number):
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batch_shape = torch.Size()
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else:
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batch_shape = probs_or_logits.size()
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super(Geometric, self).__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(Geometric, _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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if 'logits' in self.__dict__:
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new.logits = self.logits.expand(batch_shape)
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super(Geometric, 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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@property
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def mean(self):
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return 1. / self.probs - 1.
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@property
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def variance(self):
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return (1. / 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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def sample(self, sample_shape=torch.Size()):
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shape = self._extended_shape(sample_shape)
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tiny = torch.finfo(self.probs.dtype).tiny
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with torch.no_grad():
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if torch._C._get_tracing_state():
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# [JIT WORKAROUND] lack of support for .uniform_()
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u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
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u = u.clamp(min=tiny)
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else:
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u = self.probs.new(shape).uniform_(tiny, 1)
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return (u.log() / (-self.probs).log1p()).floor()
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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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value, probs = broadcast_all(value, self.probs)
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probs = probs.clone(memory_format=torch.contiguous_format)
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probs[(probs == 1) & (value == 0)] = 0
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return value * (-probs).log1p() + self.probs.log()
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def entropy(self):
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return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none') / self.probs
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