Package elki.clustering.kmeans

Class HamerlyKMeans.Instance

java.lang.Object

elki.clustering.kmeans.AbstractKMeans.Instance

elki.clustering.kmeans.HamerlyKMeans.Instance

Direct Known Subclasses:

AnnulusKMeans.Instance, ExponionKMeans.Instance

Enclosing class:

HamerlyKMeans<V extends NumberVector>

protected static class HamerlyKMeans.Instance
extends AbstractKMeans.Instance

Inner instance, storing state for a single data set.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
(package private) WritableDoubleDataStore	lower	Lower bounds
(package private) double[][]	newmeans	Scratch space for new means.
(package private) double[]	sep	Separation of means / distance moved.
(package private) double[][]	sums	Sum aggregate for the new mean.
(package private) WritableDoubleDataStore	upper	Upper bounds

Fields inherited from class elki.clustering.kmeans.AbstractKMeans.Instance

assignment, clusters, diststat, isSquared, k, key, means, relation, varsum

Constructor Summary

Constructors

Constructor	Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected int	assignToNearestCluster()	Assign each object to the nearest cluster.
protected Logging	getLogger()	Get the class logger.
protected int	initialAssignToNearestCluster()	Perform initial cluster assignment.
protected int	iterate(int iteration)	Main loop function.
protected void	recomputeSeperation(double[] sep)	Recompute the separation of cluster means.
protected void	updateBounds(double[] move)	Update the bounds for k-means.

Methods inherited from class elki.clustering.kmeans.AbstractKMeans.Instance

buildResult, buildResult, computeSquaredSeparation, copyMeans, distance, distance, distance, initialSeperation, meansFromSums, movedDistance, recomputeSeperation, recomputeVariance, run, sqrtdistance, sqrtdistance, sqrtdistance

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

sums

double[][] sums

Sum aggregate for the new mean.

newmeans

double[][] newmeans

Scratch space for new means.

upper

WritableDoubleDataStore upper

Upper bounds

lower

WritableDoubleDataStore lower

Lower bounds

sep

double[] sep

Separation of means / distance moved.

Constructor Detail

Instance

public Instance(Relation<? extends NumberVector> relation,
                NumberVectorDistance<?> df,
                double[][] means)

Constructor.

Parameters:

relation - Relation

df - Distance function

means - Initial means

Method Detail

iterate

protected int iterate(int iteration)

Description copied from class: AbstractKMeans.Instance

Main loop function.

Specified by:

iterate in class AbstractKMeans.Instance

Parameters:

iteration - Iteration number (beginning at 1)

Returns:

Number of reassigned points

initialAssignToNearestCluster

protected int initialAssignToNearestCluster()

Perform initial cluster assignment.

Returns:

Number of changes (i.e., relation size)

assignToNearestCluster

protected int assignToNearestCluster()

Description copied from class: AbstractKMeans.Instance

Assign each object to the nearest cluster.

Overrides:

assignToNearestCluster in class AbstractKMeans.Instance

Returns:

number of objects reassigned

recomputeSeperation

protected void recomputeSeperation(double[] sep)

Recompute the separation of cluster means.

Used by Hamerly.

Parameters:

sep - Output array of separation (half-sqrt scaled)

updateBounds

protected void updateBounds(double[] move)

Update the bounds for k-means.

Parameters:

move - Movement of centers

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans.Instance

Get the class logger.

Specified by:

getLogger in class AbstractKMeans.Instance

Returns:

Logger