# cellrank.tl.estimators.GPCCA.compute_schur¶

GPCCA.compute_schur(n_components=10, initial_distribution=None, method='krylov', which='LR', alpha=1)

Compute the Schur decomposition.

Parameters
• n_components (int) – Number of vectors to compute.

• initial_distribution (Optional[ndarray]) – Input probability distribution over all cells. If None, uniform is chosen.

• method (str) – Method for calculating the Schur vectors. Valid options are: ‘krylov’ or ‘brandts’. For benefits of each method, see msmtools.analysis.dense.gpcca.GPCCA. The former is an iterative procedure that computes a partial, sorted Schur decomposition for large, sparse matrices whereas the latter computes a full sorted Schur decomposition of a dense matrix.

• 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.

Returns

Nothing, but updates the following fields:

Return type

None