GPCCA.compute_eigendecomposition(k=20, which='LR', alpha=1, only_evals=False, ncv=None)

Compute eigendecomposition of transition matrix.

Uses a sparse implementation, if possible, and only computes the top \(k\) eigenvectors to speed up the computation. Computes both left and right eigenvectors.

  • k (int) – Number of eigenvalues/vectors to compute.

  • which (str) – Eigenvalues are in general complex. ‘LR’ - largest real part, ‘LM’ - largest magnitude.

  • alpha (float) – Used to compute the eigengap. alpha is the weight given to the deviation of an eigenvalue from one.

  • only_evals (bool) – Compute only eigenvalues.

  • ncv (Optional[int]) – Number of Lanczos vectors generated.


Nothing, but updates the following field:

Return type