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94ff31f54d
* add exponential distribution * add exponential tests * fix default val of sample_shape * lambd->rate * updates per review * remove notes, keep failure_rate same in exponential test
35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
from numbers import Number
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import torch
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from torch.distributions.distribution import Distribution
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from torch.distributions.utils import broadcast_all
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class Exponential(Distribution):
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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 or Variable): rate = 1 / scale of the distribution
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"""
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has_rsample = True
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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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