covmats.CovViaSparsePrecisionCholesky#

class covmats.CovViaSparsePrecisionCholesky(*args, **kwargs)[source]#

Representation of a covariance via the sparse Cholesky factorization of its sparse inverse (aka the precision matrix).

Notes

Blablabla.

__init__(scf: SparseCholeskyFactor, sparse_precision: sparray | None = None) None[source]#

Initialize the instance.

Parameters:
  • scf (SparseCholeskyFactor) – Lower triangle of the sparse precision matrix Cholesky factorization.

  • sparse_precision (Optional[sp.sparse.sparray], optional) – Sparse precision matrix (inverse of the covariance matrix), by default None

Properties

H

Hermitian adjoint.

T

Transpose this linear operator.

covariance

Explicit dense representation of the covariance matrix.

log_pdet

Log of the pseudo-determinant of the covariance matrix.

n_pts

Number of points in the domain (n).

ndim

precision

Explicit dense representation of the precision matrix with shape (n, n).

rank

Rank of the covariance matrix.

scf

shape

Shape of the covariance matrix (n, n).

subspace_size

Subspace size of the covariance matrix.

Methods

adjoint

Hermitian adjoint.

colorize

Perform a colorizing transformation on data.

dot

Matrix-matrix or matrix-vector multiplication.

from_cholesky

Representation of a covariance provided via choleksy factorization.

from_diagonal

Representation of a covariance provided via diagonal.

from_eigendecomposition

Representation of a covariance provided via eigendecomposition.

from_precision

Return a representation of a covariance from its precision matrix.

get_diagonal

Return the diagonal entries of the matrix (variances).

get_trace

Return the trace of the covariance matrix (sum of diagonal elements).

matmat

Matrix-matrix multiplication.

matvec

Matrix-vector multiplication.

rmatmat

Adjoint matrix-matrix multiplication.

rmatvec

Adjoint matrix-vector multiplication.

sample_mvnormal

Draw samples from the multivariate normal N(0, A).

solve

Return x = A^{-1} b.

todense

Explicit dense representation of the covariance matrix with shape (n, n).

transpose

Transpose this linear operator.

whiten

Perform a whitening transformation on data.