org.openimaj.ml.clustering.random

Class RandomSetIntClusterer

java.lang.Object

org.openimaj.ml.clustering.random.RandomIntClusterer

org.openimaj.ml.clustering.random.RandomSetIntClusterer

All Implemented Interfaces:

Clusterer<int[][]>, SpatialClusterer<IntCentroidsResult,int[]>

public class RandomSetIntClusterer
extends RandomIntClusterer

A similar strategy to RandomSetIntClusterer however it is guaranteed that the same training vector will not be sampled more than once.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk), Sina Samangooei (ss@ecs.soton.ac.uk)

Field Summary

Fields inherited from class org.openimaj.ml.clustering.random.RandomIntClusterer

K, M, random, seed

Constructor Summary

Constructors

Constructor and Description
RandomSetIntClusterer(int M) Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetIntClusterer(int M, int K) Creates a new random byte cluster used to create centroids with data containing M elements.

Method Summary

Modifier and Type	Method and Description
IntCentroidsResult	cluster(DataSource<int[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
IntCentroidsResult	cluster(int[][] data) Selects K elements from the provided data as the centroids of the clusters.

Methods inherited from class org.openimaj.ml.clustering.random.RandomIntClusterer

performClustering, setSeed

Methods inherited from class java.lang.Object

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

Constructor Detail

RandomSetIntClusterer

public RandomSetIntClusterer(int M)

Creates a new random byte cluster used to create K centroids with data containing M elements.

Parameters:

M - number of elements in each data vector

RandomSetIntClusterer

public RandomSetIntClusterer(int M,
                             int K)

Creates a new random byte cluster used to create centroids with data containing M elements. The number of clusters will be equal to the number of data points provided in training.

Parameters:

M - number of elements in each data vector

K - number of centroids to be created

Method Detail

cluster

public IntCentroidsResult cluster(int[][] data)

Selects K elements from the provided data as the centroids of the clusters. If K is -1 all provided data points will be selected. It is guaranteed that the same data point will not be selected many times, though this is not the case if two seperate entries provided are identical.

Specified by:

cluster in interface SpatialClusterer<IntCentroidsResult,int[]>

Overrides:

cluster in class RandomIntClusterer

Parameters:

data - the data.

Returns:

the selected centroids

cluster

public IntCentroidsResult cluster(DataSource<int[]> data)

Selects K elements from the provided DataSource as the centroids of the clusters. If K is -1 all provided data points will be selected. It is guaranteed that the same data point will not be selected many times, though this is not the case if two seperate entries provided are identical.

Specified by:

cluster in interface SpatialClusterer<IntCentroidsResult,int[]>

Overrides:

cluster in class RandomIntClusterer

Parameters:

data - the data.

Returns:

the selected centroids