| | from numbers import Number |
| |
|
| | import torch |
| | from torch._six 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). |
| | """ |
| | |
| | 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(Uniform, self).__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 |
| |
|
| | @constraints.dependent_property(is_discrete=False, event_dim=0) |
| | 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) |
| |
|
