Source code for pyro.distributions.transforms.lower_cholesky_affine

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

import torch
from torch.distributions import constraints
from torch.distributions.transforms import Transform

from pyro.distributions.util import copy_docs_from

[docs]@copy_docs_from(Transform) class LowerCholeskyAffine(Transform): """ A bijection of the form, :math:`\\mathbf{y} = \\mathbf{L} \\mathbf{x} + \\mathbf{r}` where `\\mathbf{L}` is a lower triangular matrix and `\\mathbf{r}` is a vector. :param loc: the fixed D-dimensional vector to shift the input by. :type loc: torch.tensor :param scale_tril: the D x D lower triangular matrix used in the transformation. :type scale_tril: torch.tensor """ codomain = constraints.real_vector bijective = True event_dim = 1 volume_preserving = False def __init__(self, loc, scale_tril, cache_size=0): super().__init__(cache_size=cache_size) self.loc = loc self.scale_tril = scale_tril assert loc.size(-1) == scale_tril.size(-1) == scale_tril.size(-2), \ "loc and scale_tril must be of size D and D x D, respectively (instead: {}, {})".format(loc.shape, scale_tril.shape) def _call(self, x): """ :param x: the input into the bijection :type x: torch.Tensor Invokes the bijection x=>y; in the prototypical context of a :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from the base distribution (or the output of a previous transform) """ return torch.matmul(self.scale_tril, x.unsqueeze(-1)).squeeze(-1) + self.loc def _inverse(self, y): """ :param y: the output of the bijection :type y: torch.Tensor Inverts y => x. """ return torch.triangular_solve((y - self.loc).unsqueeze(-1), self.scale_tril, upper=False, transpose=False)[0].squeeze(-1)
[docs] def log_abs_det_jacobian(self, x, y): """ Calculates the elementwise determinant of the log Jacobian, i.e. log(abs(dy/dx)). """ return torch.ones(x.size()[:-1], dtype=x.dtype, layout=x.layout, device=x.device) * \ self.scale_tril.diag().log().sum()
[docs] def with_cache(self, cache_size=1): if self._cache_size == cache_size: return self return LowerCholeskyAffine(self.loc, self.scale_tril, cache_size=cache_size)