85 lines
2.4 KiB
Python
85 lines
2.4 KiB
Python
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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.exp_family import ExponentialFamily
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from torch.distributions.utils import broadcast_all
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__all__ = ["Exponential"]
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class Exponential(ExponentialFamily):
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r"""
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Creates a Exponential distribution parameterized by :attr:`rate`.
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> m = Exponential(torch.tensor([1.0]))
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>>> m.sample() # Exponential distributed with rate=1
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tensor([ 0.1046])
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Args:
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rate (float or Tensor): rate = 1 / scale of the distribution
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"""
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arg_constraints = {"rate": constraints.positive}
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support = constraints.nonnegative
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has_rsample = True
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_mean_carrier_measure = 0
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@property
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def mean(self):
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return self.rate.reciprocal()
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@property
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def mode(self):
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return torch.zeros_like(self.rate)
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@property
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def stddev(self):
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return self.rate.reciprocal()
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@property
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def variance(self):
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return self.rate.pow(-2)
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def __init__(self, rate, validate_args=None):
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(self.rate,) = broadcast_all(rate)
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batch_shape = torch.Size() if isinstance(rate, Number) else self.rate.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(Exponential, _instance)
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batch_shape = torch.Size(batch_shape)
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new.rate = self.rate.expand(batch_shape)
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super(Exponential, 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 rsample(self, sample_shape=torch.Size()):
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shape = self._extended_shape(sample_shape)
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return self.rate.new(shape).exponential_() / self.rate
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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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return self.rate.log() - self.rate * value
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def cdf(self, value):
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if self._validate_args:
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self._validate_sample(value)
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return 1 - torch.exp(-self.rate * value)
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def icdf(self, value):
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return -torch.log1p(-value) / self.rate
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def entropy(self):
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return 1.0 - torch.log(self.rate)
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@property
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def _natural_params(self):
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return (-self.rate,)
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def _log_normalizer(self, x):
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return -torch.log(-x)
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