org.ddogleg.clustering.kmeans

Class TestStandardKMeans_F64

java.lang.Object

org.ddogleg.clustering.GenericClusterChecks_F64

org.ddogleg.clustering.kmeans.TestStandardKMeans_F64

public class TestStandardKMeans_F64
extends GenericClusterChecks_F64

Can;t use generic checks because he seeds might suck and the sets will be poorly chosen.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	TestStandardKMeans_F64.FixedSeeds

Constructor Summary

Constructors

Constructor and Description
TestStandardKMeans_F64()

Method Summary

Modifier and Type	Method and Description
ComputeClusters<double[]>	createClustersAlg(boolean hint) If hint is true then the first 3 elements are good initial seeds for clustering
static java.util.List<double[]>	createPoints(int DOF, int total, boolean fillRandom)
void	distanceSq()
void	matchPointsToClusters()
void	updateClusterCenters()

Methods inherited from class org.ddogleg.clustering.GenericClusterChecks_F64

computeDistance, simpleCluster

Methods inherited from class java.lang.Object

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

Constructor Detail

TestStandardKMeans_F64

public TestStandardKMeans_F64()

Method Detail

matchPointsToClusters

public void matchPointsToClusters()

updateClusterCenters

public void updateClusterCenters()

distanceSq

public void distanceSq()

createPoints

public static java.util.List<double[]> createPoints(int DOF,
                                                    int total,
                                                    boolean fillRandom)

createClustersAlg

public ComputeClusters<double[]> createClustersAlg(boolean hint)

Description copied from class: GenericClusterChecks_F64

If hint is true then the first 3 elements are good initial seeds for clustering

Specified by:

createClustersAlg in class GenericClusterChecks_F64