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import torch |
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from torch import Tensor |
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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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from torch.types import _Number, _size |
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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) -> Tensor: |
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return self.rate.reciprocal() |
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@property |
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def mode(self) -> Tensor: |
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return torch.zeros_like(self.rate) |
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@property |
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def stddev(self) -> Tensor: |
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return self.rate.reciprocal() |
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@property |
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def variance(self) -> Tensor: |
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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: _size = torch.Size()) -> Tensor: |
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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) -> tuple[Tensor]: |
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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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