cellrank.tl.estimators.GPCCA.compute_schur
- GPCCA.compute_schur(n_components=10, initial_distribution=None, method='krylov', which='LR', alpha=1.0)
Compute Schur decomposition.
- Parameters
n_components (
int
) – Number of Schur vectors to compute.initial_distribution (
Optional
[ndarray
]) – Input distribution over all cells. If None, uniform distribution is used.method (
Literal
[‘krylov’, ‘brandts’]) –Method for calculating the Schur vectors. Valid options are:
’krylov’ - an iterative procedure that computes a partial, sorted Schur decomposition for large, sparse matrices.
’brandts’ - full sorted Schur decomposition of a dense matrix.
For benefits of each method, see
pygpcca.GPCCA
.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.
- Returns
Nothing, just updates the following fields:
schur_vectors
- Real Schur vectors of the transition matrix.schur_matrix
- Schur matrix.eigendecomposition
- Eigendecomposition oftransition_matrix
.