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70 lines
1.8 KiB
Python
70 lines
1.8 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.exp_family import ExponentialFamily
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from torch.distributions.utils import broadcast_all
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class Exponential(ExponentialFamily):
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r"""
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Creates a Exponential distribution parameterized by `rate`.
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Example::
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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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0.1046
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[torch.FloatTensor of size 1]
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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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params = {'rate': constraints.positive}
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support = constraints.positive
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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 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):
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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(Exponential, self).__init__(batch_shape)
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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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self._validate_log_prob_arg(value)
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return self.rate.log() - self.rate * value
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def cdf(self, value):
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self._validate_log_prob_arg(value)
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return 1 - torch.exp(-self.rate * value)
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def icdf(self, value):
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self._validate_log_prob_arg(value)
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return -torch.log(1 - 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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