torch.distributions.normal 的源代码

import math
from numbers import Number, Real

import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import _standard_normal, broadcast_all

__all__ = ["Normal"]


class Normal(ExponentialFamily):
    r"""
    创建一个正态（也称为高斯）分布，由
    :attr:`loc` 和 :attr:`scale` 参数化。

    示例::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Normal(torch.tensor([0.0]), torch.tensor([1.0]))
        >>> m.sample()  # 正态分布，loc=0，scale=1
        tensor([ 0.1046])

    参数:
        loc (float 或 Tensor): 分布的均值（通常称为 mu）
        scale (float 或 Tensor): 分布的标准差
            （通常称为 sigma）
    """
    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
    support = constraints.real
    has_rsample = True
    _mean_carrier_measure = 0

    @property
    def mean(self):
        return self.loc

    @property
    def mode(self):
        return self.loc

    @property
    def stddev(self):
        return self.scale

    @property
    def variance(self):
        return self.stddev.pow(2)

    def __init__(self, loc, scale, validate_args=None):
        self.loc, self.scale = broadcast_all(loc, scale)
        if isinstance(loc, Number) and isinstance(scale, Number):
            batch_shape = torch.Size()
        else:
            batch_shape = self.loc.size()
        super().__init__(batch_shape, validate_args=validate_args)

    def expand(self, batch_shape, _instance=None):
        new = self._get_checked_instance(Normal, _instance)
        batch_shape = torch.Size(batch_shape)
        new.loc = self.loc.expand(batch_shape)
        new.scale = self.scale.expand(batch_shape)
        super(Normal, new).__init__(batch_shape, validate_args=False)
        new._validate_args = self._validate_args
        return new

    def sample(self, sample_shape=torch.Size()):
        shape = self._extended_shape(sample_shape)
        with torch.no_grad():
            return torch.normal(self.loc.expand(shape), self.scale.expand(shape))

    def rsample(self, sample_shape=torch.Size()):
        shape = self._extended_shape(sample_shape)
        eps = _standard_normal(shape, dtype=self.loc.dtype, device=self.loc.device)
        return self.loc + eps * self.scale

    def log_prob(self, value):
        if self._validate_args:
            self._validate_sample(value)
        # 计算方差
        var = self.scale**2
        log_scale = (
            math.log(self.scale) if</