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

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(P, m)	Initialize self.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().

Attributes

__dict__
__doc__
__module__
__weakref__	list of weak references to the object (if defined)
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