de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class KMeansHamerly.Parameterizer<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<V>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer<V>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly.Parameterizer<V>

All Implemented Interfaces:

Parameterizer

Enclosing class:

KMeansHamerly<V extends NumberVector>

public static class KMeansHamerly.Parameterizer<V extends NumberVector>
extends AbstractKMeans.Parameterizer<V>

Parameterization class.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	varstat Compute the final variance statisic.
static OptionID	VARSTAT_ID Flag to compute the final clustering variance statistic.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer

initializer, k, maxiter

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer

distanceFunction

Constructor Summary

Constructors

Constructor and Description
KMeansHamerly.Parameterizer()

Method Summary

Methods

Modifier and Type	Method and Description
protected Logging	getLogger() Get class logger.
protected void	getParameterDistanceFunction(Parameterization config) Get the distance function parameter.
protected KMeansHamerly<V>	makeInstance() Make an instance after successful configuration.
protected void	makeOptions(Parameterization config) Add all options.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer

getParameterInitialization, getParameterK, getParameterMaxIter

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer

configure, make

Methods inherited from class java.lang.Object

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

Field Detail

VARSTAT_ID

public static final OptionID VARSTAT_ID

Flag to compute the final clustering variance statistic.

varstat

protected boolean varstat

Compute the final variance statisic.

Constructor Detail

KMeansHamerly.Parameterizer

public KMeansHamerly.Parameterizer()

Method Detail

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans.Parameterizer

Get class logger.

Specified by:

getLogger in class AbstractKMeans.Parameterizer<V extends NumberVector>

Returns:

Logger

getParameterDistanceFunction

protected void getParameterDistanceFunction(Parameterization config)

Description copied from class: AbstractKMeans.Parameterizer

Get the distance function parameter.

Overrides:

getParameterDistanceFunction in class AbstractKMeans.Parameterizer<V extends NumberVector>

Parameters:

config - Parameterization

makeOptions

protected void makeOptions(Parameterization config)

Description copied from class: AbstractParameterizer

Add all options. ALWAYS call super.makeOptions(config), unless you have a strong reason to do otherwise!

Overrides:

makeOptions in class AbstractKMeans.Parameterizer<V extends NumberVector>

Parameters:

config - Parameterization to add options to.

makeInstance

protected KMeansHamerly<V> makeInstance()

Description copied from class: AbstractParameterizer

Make an instance after successful configuration.

Specified by:

makeInstance in class AbstractKMeans.Parameterizer<V extends NumberVector>

Returns:

instance