org.openimaj.ml.clustering.random

Class RandomSetFloatClusterer

java.lang.Object

org.openimaj.ml.clustering.random.RandomFloatClusterer

org.openimaj.ml.clustering.random.RandomSetFloatClusterer

All Implemented Interfaces:

Clusterer<float[][]>, SpatialClusterer<FloatCentroidsResult,float[]>

public class RandomSetFloatClusterer
extends RandomFloatClusterer

A similar strategy to RandomSetFloatClusterer 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.RandomFloatClusterer

K, M, random, seed

Constructor Summary

Constructors

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

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

performClustering, setSeed

Methods inherited from class java.lang.Object

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

Constructor Detail

RandomSetFloatClusterer

public RandomSetFloatClusterer(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

RandomSetFloatClusterer

public RandomSetFloatClusterer(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 FloatCentroidsResult cluster(float[][] 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<FloatCentroidsResult,float[]>

Overrides:

cluster in class RandomFloatClusterer

Parameters:

data - the data.

Returns:

the selected centroids

cluster

public FloatCentroidsResult cluster(DataSource<float[]> 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<FloatCentroidsResult,float[]>

Overrides:

cluster in class RandomFloatClusterer

Parameters:

data - the data.

Returns:

the selected centroids