xref: /aosp_15_r20/external/pytorch/torch/distributions/uniform.py (revision da0073e96a02ea20f0ac840b70461e3646d07c45) 
1# mypy: allow-untyped-defs
2from numbers import Number
3
4import torch
5from torch import nan
6from torch.distributions import constraints
7from torch.distributions.distribution import Distribution
8from torch.distributions.utils import broadcast_all
9from torch.types import _size
10
11
12__all__ = ["Uniform"]
13
14
15class Uniform(Distribution):
16    r"""
17    Generates uniformly distributed random samples from the half-open interval
18    ``[low, high)``.
19
20    Example::
21
22        >>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
23        >>> m.sample()  # uniformly distributed in the range [0.0, 5.0)
24        >>> # xdoctest: +SKIP
25        tensor([ 2.3418])
26
27    Args:
28        low (float or Tensor): lower range (inclusive).
29        high (float or Tensor): upper range (exclusive).
30    """
31    # TODO allow (loc,scale) parameterization to allow independent constraints.
32    arg_constraints = {
33        "low": constraints.dependent(is_discrete=False, event_dim=0),
34        "high": constraints.dependent(is_discrete=False, event_dim=0),
35    }
36    has_rsample = True
37
38    @property
39    def mean(self):
40        return (self.high + self.low) / 2
41
42    @property
43    def mode(self):
44        return nan * self.high
45
46    @property
47    def stddev(self):
48        return (self.high - self.low) / 12**0.5
49
50    @property
51    def variance(self):
52        return (self.high - self.low).pow(2) / 12
53
54    def __init__(self, low, high, validate_args=None):
55        self.low, self.high = broadcast_all(low, high)
56
57        if isinstance(low, Number) and isinstance(high, Number):
58            batch_shape = torch.Size()
59        else:
60            batch_shape = self.low.size()
61        super().__init__(batch_shape, validate_args=validate_args)
62
63        if self._validate_args and not torch.lt(self.low, self.high).all():
64            raise ValueError("Uniform is not defined when low>= high")
65
66    def expand(self, batch_shape, _instance=None):
67        new = self._get_checked_instance(Uniform, _instance)
68        batch_shape = torch.Size(batch_shape)
69        new.low = self.low.expand(batch_shape)
70        new.high = self.high.expand(batch_shape)
71        super(Uniform, new).__init__(batch_shape, validate_args=False)
72        new._validate_args = self._validate_args
73        return new
74
75    @constraints.dependent_property(is_discrete=False, event_dim=0)
76    def support(self):
77        return constraints.interval(self.low, self.high)
78
79    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
80        shape = self._extended_shape(sample_shape)
81        rand = torch.rand(shape, dtype=self.low.dtype, device=self.low.device)
82        return self.low + rand * (self.high - self.low)
83
84    def log_prob(self, value):
85        if self._validate_args:
86            self._validate_sample(value)
87        lb = self.low.le(value).type_as(self.low)
88        ub = self.high.gt(value).type_as(self.low)
89        return torch.log(lb.mul(ub)) - torch.log(self.high - self.low)
90
91    def cdf(self, value):
92        if self._validate_args:
93            self._validate_sample(value)
94        result = (value - self.low) / (self.high - self.low)
95        return result.clamp(min=0, max=1)
96
97    def icdf(self, value):
98        result = value * (self.high - self.low) + self.low
99        return result
100
101    def entropy(self):
102        return torch.log(self.high - self.low)
103