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| 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"] | |
| 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 | |
| def mean(self): | |
| return (self.high + self.low) / 2 | |
| def mode(self): | |
| return nan * self.high | |
| def stddev(self): | |
| return (self.high - self.low) / 12**0.5 | |
| 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 torch.lt(self.low, self.high).all(): | |
| raise ValueError("Uniform is not defined when low>= high") | |
| 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 | |
| def support(self): | |
| return constraints.interval(self.low, self.high) | |
| 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) | |
| def log_prob(self, value): | |
| if self._validate_args: | |
| self._validate_sample(value) | |
| lb = self.low.le(value).type_as(self.low) | |
| ub = self.high.gt(value).type_as(self.low) | |
| return torch.log(lb.mul(ub)) - torch.log(self.high - self.low) | |
| 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) | |
| def icdf(self, value): | |
| result = value * (self.high - self.low) + self.low | |
| return result | |
| def entropy(self): | |
| return torch.log(self.high - self.low) | |