100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
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from numbers import Number
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import torch
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from torch import nan
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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
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__all__ = ["Uniform"]
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class Uniform(Distribution):
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r"""
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Generates uniformly distributed random samples from the half-open interval
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``[low, high)``.
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Example::
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>>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
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>>> m.sample() # uniformly distributed in the range [0.0, 5.0)
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>>> # xdoctest: +SKIP
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tensor([ 2.3418])
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Args:
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low (float or Tensor): lower range (inclusive).
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high (float or Tensor): upper range (exclusive).
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"""
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# TODO allow (loc,scale) parameterization to allow independent constraints.
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arg_constraints = {
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"low": constraints.dependent(is_discrete=False, event_dim=0),
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"high": constraints.dependent(is_discrete=False, event_dim=0),
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}
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has_rsample = True
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@property
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def mean(self):
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return (self.high + self.low) / 2
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@property
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def mode(self):
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return nan * self.high
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@property
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def stddev(self):
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return (self.high - self.low) / 12**0.5
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@property
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def variance(self):
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return (self.high - self.low).pow(2) / 12
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def __init__(self, low, high, validate_args=None):
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self.low, self.high = broadcast_all(low, high)
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if isinstance(low, Number) and isinstance(high, Number):
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batch_shape = torch.Size()
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else:
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batch_shape = self.low.size()
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super().__init__(batch_shape, validate_args=validate_args)
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if self._validate_args and not torch.lt(self.low, self.high).all():
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raise ValueError("Uniform is not defined when low>= high")
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(Uniform, _instance)
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batch_shape = torch.Size(batch_shape)
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new.low = self.low.expand(batch_shape)
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new.high = self.high.expand(batch_shape)
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super(Uniform, 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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@constraints.dependent_property(is_discrete=False, event_dim=0)
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def support(self):
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return constraints.interval(self.low, self.high)
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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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rand = torch.rand(shape, dtype=self.low.dtype, device=self.low.device)
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return self.low + rand * (self.high - self.low)
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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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lb = self.low.le(value).type_as(self.low)
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ub = self.high.gt(value).type_as(self.low)
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return torch.log(lb.mul(ub)) - torch.log(self.high - self.low)
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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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result = (value - self.low) / (self.high - self.low)
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return result.clamp(min=0, max=1)
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def icdf(self, value):
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result = value * (self.high - self.low) + self.low
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return result
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def entropy(self):
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return torch.log(self.high - self.low)
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