Source code for torch.distributions.normal

import math
from numbers import Real
from numbers import Number

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']

[docs]class Normal(ExponentialFamily): r""" Creates a normal (also called Gaussian) distribution parameterized by :attr:`loc` and :attr:`scale`. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = Normal(torch.tensor([0.0]), torch.tensor([1.0])) >>> m.sample() # normally distributed with loc=0 and scale=1 tensor([ 0.1046]) Args: loc (float or Tensor): mean of the distribution (often referred to as mu) scale (float or Tensor): standard deviation of the distribution (often referred to as 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)
[docs] 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
[docs] 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))
[docs] 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
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) # compute the variance var = (self.scale ** 2) log_scale = math.log(self.scale) if isinstance(self.scale, Real) else self.scale.log() return -((value - self.loc) ** 2) / (2 * var) - log_scale - math.log(math.sqrt(2 * math.pi))
[docs] def cdf(self, value): if self._validate_args: self._validate_sample(value) return 0.5 * (1 + torch.erf((value - self.loc) * self.scale.reciprocal() / math.sqrt(2)))
[docs] def icdf(self, value): return self.loc + self.scale * torch.erfinv(2 * value - 1) * math.sqrt(2)
[docs] def entropy(self): return 0.5 + 0.5 * math.log(2 * math.pi) + torch.log(self.scale)
@property def _natural_params(self): return (self.loc / self.scale.pow(2), -0.5 * self.scale.pow(2).reciprocal()) def _log_normalizer(self, x, y): return -0.25 * x.pow(2) / y + 0.5 * torch.log(-math.pi / y)


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