Source code for pyro.distributions.delta

from __future__ import absolute_import, division, print_function

import numbers

import torch
from torch.distributions import constraints

from pyro.distributions.torch_distribution import TorchDistribution
from pyro.distributions.util import sum_rightmost


[docs]class Delta(TorchDistribution): """ Degenerate discrete distribution (a single point). Discrete distribution that assigns probability one to the single element in its support. Delta distribution parameterized by a random choice should not be used with MCMC based inference, as doing so produces incorrect results. :param torch.Tensor v: The single support element. :param torch.Tensor log_density: An optional density for this Delta. This is useful to keep the class of :class:`Delta` distributions closed under differentiable transformation. :param int event_dim: Optional event dimension, defaults to zero. """ has_rsample = True arg_constraints = {'v': constraints.real, 'log_density': constraints.real} support = constraints.real def __init__(self, v, log_density=0.0, event_dim=0, validate_args=None): if event_dim > v.dim(): raise ValueError('Expected event_dim <= v.dim(), actual {} vs {}'.format(event_dim, v.dim())) batch_dim = v.dim() - event_dim batch_shape = v.shape[:batch_dim] event_shape = v.shape[batch_dim:] if isinstance(log_density, numbers.Number): log_density = v.new_empty(batch_shape).fill_(log_density) elif log_density.shape != batch_shape: raise ValueError('Expected log_density.shape = {}, actual {}'.format( log_density.shape, batch_shape)) self.v = v self.log_density = log_density super(Delta, self).__init__(batch_shape, event_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape): validate_args = self.__dict__.get('validate_args') v = self.v.expand(batch_shape + self.event_shape) log_density = self.log_density.expand(batch_shape) return Delta(v, log_density, self.event_dim, validate_args=validate_args)
[docs] def rsample(self, sample_shape=torch.Size()): shape = sample_shape + self.v.shape return self.v.expand(shape)
[docs] def log_prob(self, x): v = self.v.expand(self.shape()) log_prob = x.new_tensor(x == v).log() log_prob = sum_rightmost(log_prob, self.event_dim) return log_prob + self.log_density
@property def mean(self): return self.v @property def variance(self): return torch.zeros_like(self.v)