org.ddogleg.example

Class ExampleClustering

java.lang.Object

org.ddogleg.example.ExampleClustering

public class ExampleClustering
extends java.lang.Object

Demonstration of how to cluster a N-dimensional points into clusters. The high level interface provides the capability to do hard and soft assignments. Hard assignments select a single best match for a point and soft assignments compute a weight for each cluster, high weights mean better matches. Results are visualized in a window. Points in different clusters are assigned different colors. If you click the inside of the window the algorithm is run again and a new set of clusters is shown.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ExampleClustering.Gui Basic visualization which draws the points in a window.

Field Summary

Fields

Modifier and Type	Field and Description
static boolean	clicked
static boolean	gaussian
static java.util.Random	rand

Constructor Summary

Constructors

Constructor and Description
ExampleClustering()

Method Summary

Modifier and Type	Method and Description
static java.util.List<double[]>	createCluster(double x, double y, double width, int N)
static void	main(java.lang.String[] args)

Methods inherited from class java.lang.Object

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

Field Detail

gaussian

public static boolean gaussian

rand

public static java.util.Random rand

clicked

public static boolean clicked

Constructor Detail

ExampleClustering

public ExampleClustering()

Method Detail

createCluster

public static java.util.List<double[]> createCluster(double x,
                                                     double y,
                                                     double width,
                                                     int N)

main

public static void main(java.lang.String[] args)