Source code for torch.distributions.uniform

from numbers import Number

import torch
from torch import nan
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all

__all__ = ["Uniform"]

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


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