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
Hermitian adjoint.
Transpose this linear operator.
Explicit dense representation of the covariance matrix.
Log of the pseudo-determinant of the covariance matrix.
Number of points in the domain (n).
Explicit dense representation of the precision matrix with shape (n, n).
Rank of the covariance matrix.
Shape of the covariance matrix (n, n).
Subspace size of the covariance matrix.
Methods
Hermitian adjoint.
Perform a colorizing transformation on data.
Matrix-matrix or matrix-vector multiplication.
Representation of a covariance provided via choleksy factorization.
Representation of a covariance provided via diagonal.
Representation of a covariance provided via eigendecomposition.
Return a representation of a covariance from its precision matrix.
Return the diagonal entries of the matrix (variances).
Return the trace of the covariance matrix (sum of diagonal elements).
Matrix-matrix multiplication.
Matrix-vector multiplication.
Adjoint matrix-matrix multiplication.
Adjoint matrix-vector multiplication.
Draw samples from the multivariate normal N(0, A).
Return x = A^{-1} b.
Explicit dense representation of the covariance matrix with shape (n, n).
Transpose this linear operator.
Perform a whitening transformation on data.