cellrank.tl.estimators.GPCCA.compute_eigendecomposition

GPCCA.compute_eigendecomposition(k=20, which='LR', alpha=1.0, 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.

Parameters
  • k (int) – Number of eigenvectors or eigenvalues to compute.

  • which (Literal[‘LR’, ‘LM’]) –

    How to sort the eigenvalues. Valid option are:

    • ’LR’ - the largest real part.

    • ’LM’ - the 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) – Whether to compute only eigenvalues.

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

Return type

None

Returns

Nothing, just updates the following field: