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