msmbuilder.clustering.HybridKMedoids¶

class msmbuilder.clustering.HybridKMedoids(metric, trajectories=None, prep_trajectories=None, k=None, distance_cutoff=None, local_num_iters=10, global_num_iters=0, norm_exponent=2.0, too_close_cutoff=0.0001, ignore_max_objective=False)[source]¶

__init__(metric, trajectories=None, prep_trajectories=None, k=None, distance_cutoff=None, local_num_iters=10, global_num_iters=0, norm_exponent=2.0, too_close_cutoff=0.0001, ignore_max_objective=False)[source]¶

Run the hybrid kmedoids clustering algorithm on a set of trajectories

Parameters:

metric : msmbuilder.metrics.AbstractDistanceMetric A metric capable of handling ptraj trajectory : Trajectory or list of msmbuilder.Trajectory data to cluster k : int number of desired clusters num_iters : int number of swaps to attempt per medoid local_swap : boolean, optional If true, proposed swaps will be between a medoid and a data point currently assigned to that medoid. If false, the data point for the proposed swap is selected randomly. norm_exponent : float, optional exponent to use in pnorm of the distance to generate objective function too_close_cutoff : float, optional Summarily reject proposed swaps if the distance of the medoid to the trial medoid is less than thus value ignore_max_objective : boolean, optional Ignore changes to the distance of the worst classified point, and only reject or accept swaps based on changes to the p norm of all the data points.

See also

KCenters

faster, less accurate

Clarans

slightly more clever termination criterion

References

[R4]	Beauchamp, K, et. al. MSMBuilder2

Methods

__init__(metric[, trajectories, ...])	Run the hybrid kmedoids clustering algorithm on a set of trajectories
get_assignments	Assign the trajectories you passed into the constructor based on
get_distances	Extract the distance from each frame to its assigned cluster kcenter
get_generator_indices	Get the generator indices corresponding to frames in self.ptraj.
get_generators_as_traj	Get a trajectory containing the generators