Package elki.clustering.kmeans.initialization

Class SphericalKMeansPlusPlus.Instance

java.lang.Object

elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance

Enclosing class:

SphericalKMeansPlusPlus<O>

protected static class SphericalKMeansPlusPlus.Instance
extends java.lang.Object

Abstract instance implementing the weight handling.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
protected double	alpha	Parameter to balance distance vs. uniform sampling.
protected long	diststat	Count the number of distance computations.
protected java.util.Random	random	Random generator
protected Relation<? extends NumberVector>	relation	Data relation.
protected WritableDoubleDataStore	weights	Weights

Constructor Summary

Constructors

Constructor	Description
Instance(Relation<? extends NumberVector> relation, double alpha, RandomFactory rnd)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected void	chooseRemaining(int k, java.util.List<NumberVector> means, double weightsum)	Choose remaining means, weighted by distance.
protected double	initialWeights(NumberVector first)	Initialize the weight list.
protected double	nextDouble(double weightsum)
double[][]	run(int k)	Run k-means++ initialization for number vectors.
protected double	similarity(NumberVector a, DBIDRef b)	Compute the distance of two objects.
protected double	updateWeights(NumberVector latest)	Update the weight list.

Methods inherited from class java.lang.Object

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

Field Detail

alpha

protected double alpha

Parameter to balance distance vs. uniform sampling.

relation

protected Relation<? extends NumberVector> relation

Data relation.

weights

protected WritableDoubleDataStore weights

Weights

diststat

protected long diststat

Count the number of distance computations.

random

protected java.util.Random random

Random generator

Constructor Detail

Instance

public Instance(Relation<? extends NumberVector> relation,
                double alpha,
                RandomFactory rnd)

Constructor.

Parameters:

relation - Data relation

alpha - Alpha parameter

rnd - Random generator

Method Detail

run

public double[][] run(int k)

Run k-means++ initialization for number vectors.

Parameters:

k - K

Returns:

Vectors

similarity

protected double similarity(NumberVector a,
                            DBIDRef b)

Compute the distance of two objects.

Parameters:

a - First object

b - Second object

Returns:

Distance

chooseRemaining

protected void chooseRemaining(int k,
                               java.util.List<NumberVector> means,
                               double weightsum)

Choose remaining means, weighted by distance.

Parameters:

k - Number of means to choose

means - Means storage

weightsum - Sum of weights

initialWeights

protected double initialWeights(NumberVector first)

Initialize the weight list.

Parameters:

first - Added ID

Returns:

Weight sum

updateWeights

protected double updateWeights(NumberVector latest)

Update the weight list.

Parameters:

latest - Added ID

Returns:

Weight sum

nextDouble

protected double nextDouble(double weightsum)