msmbuilder.clustering._kcenters

msmbuilder.clustering._kcenters(metric, ptraj, k=None, distance_cutoff=None, seed=0, verbose=True)

Run kcenters clustering algorithm.

Terminates either when k clusters have been identified, or when every data is clustered better than distance_cutoff.

Parameters:

metric : msmbuilder.metrics.AbstractDistanceMetric

A metric capable of handling ptraj

ptraj : prepared trajectory

ptraj return by the action of the preceding metric on a msmbuilder trajectory

k : {int, None}

number of desired clusters, or None

distance_cutoff : {float, None}

Stop identifying new clusters once the distance of every data to its cluster center falls below this value. Supply either this or k

seed : int, optional

index of the frame to use as the first cluster center

verbose : bool, optional

print as each new generator is found

Returns:

generator_indices : ndarray

indices (with respect to ptraj) of the frames to be considered cluster centers

assignments : ndarray

the cluster center to which each frame is assigned to (1D)

distances : ndarray

distance from each of the frames to the cluster center it was assigned to

See also

KCenters

wrapper around this implementation that provides more convenience

Notes

the assignments are numbered with respect to the position in ptraj of the generator, not the position in generator_indices. That is, assignments[10] = 1020 means that the 10th simulation frame is assigned to the 1020th simulation frame, not to the 1020th generator.

References

[R9]	Beauchamp, MSMBuilder2