pyemma.msm.PCCA

class pyemma.msm.PCCA(P, m)

PCCA+ spectral clustering method with optimized memberships [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.

References

[1] S. Roeblitz and M. Weber, Fuzzy spectral clustering by PCCA+:

application to Markov state models and data classification. Adv Data Anal Classif 7, 147-179 (2013).

[2] F. Noe, multiset PCCA and HMMs, in preparation. [3] F. Noe, H. Wu, J.-H. Prinz and N. Plattner:

Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules J. Chem. Phys. 139, 184114 (2013)

__init__(P, m)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(P, m)

Initialize self.

Attributes

coarse_grained_stationary_probability

coarse_grained_transition_matrix

memberships

metastable_assignment

Crisp clustering using PCCA.

metastable_sets

Crisp clustering using PCCA.

n_metastable

output_probabilities

stationary_probability

transition_matrix

metastable_assignment

Crisp clustering using PCCA. This is only recommended for visualization purposes. You cannot compute any actual quantity of the coarse-grained kinetics without employing the fuzzy memberships!

Returns

Return type

For each microstate, the metastable state it is located in.

metastable_sets

Crisp clustering using PCCA. This is only recommended for visualization purposes. You cannot compute any actual quantity of the coarse-grained kinetics without employing the fuzzy memberships!

Returns

Return type

A list of length equal to metastable states. Each element is an array with microstate indexes contained in it