function pcca

deeptime.markov.pcca(P, m, stationary_distribution=None)

PCCA+ spectral clustering method with optimized memberships.

Implementation according to [1]. Clusters the first m eigenvectors of a transition matrix in order to cluster the states. This function does not assume that the transition matrix is fully connected. Disconnected sets will automatically define the first metastable states, with perfect membership assignments.

Parameters:

• P (ndarray (n,n)) – Transition matrix.

• m (int) – Number of clusters to group to.

• stationary_distribution (ndarray(n,), optional, default=None) – Stationary distribution over the full state space, can be given if already computed.

References

[1]

Susanna Röblitz and Marcus Weber. Fuzzy spectral clustering by pcca+: application to markov state models and data classification. Advances in Data Analysis and Classification, 7(2):147–179, 2013.