org.openimaj.ml.clustering.random

Class RandomSetShortClusterer

java.lang.Object

org.openimaj.ml.clustering.random.RandomShortClusterer

org.openimaj.ml.clustering.random.RandomSetShortClusterer

All Implemented Interfaces:

Clusterer<short[][]>, SpatialClusterer<ShortCentroidsResult,short[]>

public class RandomSetShortClusterer
extends RandomShortClusterer

A similar strategy to RandomSetShortClusterer 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.RandomShortClusterer

K, M, random, seed

Constructor Summary

Constructors

Constructor and Description
RandomSetShortClusterer(int M) Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetShortClusterer(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
ShortCentroidsResult	cluster(DataSource<short[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
ShortCentroidsResult	cluster(short[][] data) Selects K elements from the provided data as the centroids of the clusters.

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

performClustering, setSeed

Methods inherited from class java.lang.Object

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

Constructor Detail

RandomSetShortClusterer

public RandomSetShortClusterer(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

RandomSetShortClusterer

public RandomSetShortClusterer(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 ShortCentroidsResult cluster(short[][] 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<ShortCentroidsResult,short[]>

Overrides:

cluster in class RandomShortClusterer

Parameters:

data - the data.

Returns:

the selected centroids

cluster

public ShortCentroidsResult cluster(DataSource<short[]> 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<ShortCentroidsResult,short[]>

Overrides:

cluster in class RandomShortClusterer

Parameters:

data - the data.

Returns:

the selected centroids