de.lmu.ifi.dbs.elki.algorithm.statistics

Class HopkinsStatisticClusteringTendency.Parameterizer

java.lang.Object

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

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

de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency.Parameterizer

All Implemented Interfaces:

Parameterizer

Enclosing class:

HopkinsStatisticClusteringTendency

public static class HopkinsStatisticClusteringTendency.Parameterizer
extends AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<NumberVector>

Parameterization class.

Author:

Lisa Reichert

Field Summary

Fields

Modifier and Type	Field and Description
protected int	k Nearest neighbor number.
static OptionID	K_ID Parameter for k.
private double[]	maxima Stores the maximum in each dimension.
static OptionID	MAXIMA_ID Parameter for maximum.
private double[]	minima Stores the minimum in each dimension.
static OptionID	MINIMA_ID Parameter for minimum.
protected RandomFactory	random Random source.
protected int	rep Number of repetitions.
static OptionID	REP_ID Parameter to specify the number of repetitions of computing the hopkins value.
protected int	sampleSize Sample size.
static OptionID	SAMPLESIZE_ID Sample size.
static OptionID	SEED_ID Parameter to specify the random generator seed.

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

distanceFunction

Constructor Summary

Constructors

Constructor and Description
HopkinsStatisticClusteringTendency.Parameterizer()

Method Summary

Methods

Modifier and Type	Method and Description
protected HopkinsStatisticClusteringTendency	makeInstance() Make an instance after successful configuration.
protected void	makeOptions(Parameterization config) Add all options.

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

SAMPLESIZE_ID

public static final OptionID SAMPLESIZE_ID

Sample size.

REP_ID

public static final OptionID REP_ID

Parameter to specify the number of repetitions of computing the hopkins value.

SEED_ID

public static final OptionID SEED_ID

Parameter to specify the random generator seed.

MINIMA_ID

public static final OptionID MINIMA_ID

Parameter for minimum.

MAXIMA_ID

public static final OptionID MAXIMA_ID

Parameter for maximum.

K_ID

public static final OptionID K_ID

Parameter for k.

sampleSize

protected int sampleSize

Sample size.

rep

protected int rep

Number of repetitions.

k

protected int k

Nearest neighbor number.

random

protected RandomFactory random

Random source.

maxima

private double[] maxima

Stores the maximum in each dimension.

minima

private double[] minima

Stores the minimum in each dimension.

Constructor Detail

HopkinsStatisticClusteringTendency.Parameterizer

public HopkinsStatisticClusteringTendency.Parameterizer()

Method Detail

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 AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<NumberVector>

Parameters:

config - Parameterization to add options to.

makeInstance

protected HopkinsStatisticClusteringTendency makeInstance()

Description copied from class: AbstractParameterizer

Make an instance after successful configuration.

Specified by:

makeInstance in class AbstractParameterizer

Returns:

instance