function spd_inv_split¶
- deeptime.numeric.spd_inv_split(W, epsilon=1e-10, method='QR', canonical_signs=False)¶
Compute \(W^{-1} = L L^T\) of the symmetric positive-definite matrix \(W\).
by first reducing W to a low-rank approximation that is truly spd.
- Parameters:
W (ndarray((m,m), dtype=float)) – Symmetric positive-definite (spd) matrix.
epsilon (float) – Truncation parameter. Eigenvalues with norms smaller than this cutoff will be removed.
method (str) –
Method to perform the decomposition of \(W\) before inverting. Options are:
’QR’: QR-based robust eigenvalue decomposition of W
’schur’: Schur decomposition of W
canonical_signs (boolean, default = False) – Fix signs in L, s. t. the largest element of in every row of L is positive.
- Returns:
L – Matrix \(L\) from the decomposition \(W^{-1} = L L^T\).
- Return type:
ndarray((n, r))