Source code for pyro.distributions.lkj

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

import warnings

import torch

from pyro.distributions.torch import LKJCholesky, TransformedDistribution
from pyro.distributions.transforms.cholesky import CorrMatrixCholeskyTransform

from . import constraints

[docs]class LKJCorrCholesky(LKJCholesky): # DEPRECATED def __init__(self, d, eta, validate_args=None): warnings.warn( "class LKJCorrCholesky(d, eta, validate_args=None) is deprecated " "in favor of LKJCholesky(dim, concentration, validate_args=None).", FutureWarning, ) super().__init__(d, concentration=eta, validate_args=validate_args)
[docs]class LKJ(TransformedDistribution): r""" LKJ distribution for correlation matrices. The distribution is controlled by ``concentration`` parameter :math:`\eta` to make the probability of the correlation matrix :math:`M` propotional to :math:`\det(M)^{\eta - 1}`. Because of that, when ``concentration == 1``, we have a uniform distribution over correlation matrices. When ``concentration > 1``, the distribution favors samples with large large determinent. This is useful when we know a priori that the underlying variables are not correlated. When ``concentration < 1``, the distribution favors samples with small determinent. This is useful when we know a priori that some underlying variables are correlated. :param int dimension: dimension of the matrices :param ndarray concentration: concentration/shape parameter of the distribution (often referred to as eta) **References** [1] `Generating random correlation matrices based on vines and extended onion method`, Daniel Lewandowski, Dorota Kurowicka, Harry Joe """ arg_constraints = {"concentration": constraints.positive} support = constraints.corr_matrix def __init__(self, dim, concentration=1.0, validate_args=None): base_dist = LKJCholesky(dim, concentration) self.dim, self.concentration = base_dist.dim, base_dist.concentration super(LKJ, self).__init__( base_dist, CorrMatrixCholeskyTransform().inv, validate_args=validate_args )
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(LKJCholesky, _instance) return super(LKJCholesky, self).expand(batch_shape, _instance=new)
@property def mean(self): return torch.eye(self.dim).expand(self.batch_shape + (self.dim, self.dim))