msmbuilder.clustering.Clarans¶

class msmbuilder.clustering.Clarans(metric, trajectories=None, prep_trajectories=None, k=None, num_local_minima=10, max_neighbors=20, local_swap=False)[source]¶

__init__(metric, trajectories=None, prep_trajectories=None, k=None, num_local_minima=10, max_neighbors=20, local_swap=False)[source]¶

Run the CLARANS clustering algorithm on the frames in a trajectory

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_local_minima : int number of local minima in the set of all possible clusterings to identify. Execution time will scale linearly with this parameter. The best of these local minima will be returned. max_neighbors : int number of rejected swaps in a row necessary to declare a proposed clustering a local minima local_swap : bool, 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.

See also

_kcenters

implementation

SubsampledClarans

random subsampling version (faster)

Methods

__init__(metric[, trajectories, ...])	Run the CLARANS clustering algorithm on the frames in a trajectory
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