msmbuilder.clustering.HybridKMedoids.init¶

HybridKMedoids.__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.

msmbuilder.clustering.HybridKMedoids.__init__¶

msmbuilder.clustering.HybridKMedoids.init¶