function spd_eig¶
- deeptime.numeric.spd_eig(W, epsilon=1e-10, method='QR', canonical_signs=False, check_sym: bool = False)¶
Rank-reduced eigenvalue decomposition of symmetric positive definite matrix.
Removes all negligible eigenvalues
- Parameters:
W (ndarray((n, n), 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 (bool, default = False) – Fix signs in V, such that the largest element in every column of V is positive.
check_sym (bool, default = False) – Check whether the input matrix is (almost) symmetric.
- Returns:
s (ndarray(k)) – k non-negligible eigenvalues, sorted by descending norms
V (ndarray(n, k)) – k leading eigenvectors