org.deeplearning4j.clustering

Class KMeansClustering

java.lang.Object

org.deeplearning4j.clustering.KMeansClustering

All Implemented Interfaces:

Serializable

public class KMeansClustering
extends Object
implements Serializable

Shamelessly based on: https://github.com/pmerienne/trident-ml/blob/master/src/main/java/com/github/pmerienne/trident/ml/clustering/KMeans.java adapted to ndarray matrices

Author:

Adam Gibson

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
KMeansClustering(Integer nbCluster)
KMeansClustering(Integer nbCluster, Class<? extends org.nd4j.linalg.distancefunction.DistanceFunction> clazz)

Method Summary

Modifier and Type	Method and Description
Integer	classify(org.nd4j.linalg.api.ndarray.INDArray features)
protected org.nd4j.linalg.api.ndarray.INDArray	computeDxs()
org.nd4j.linalg.api.ndarray.INDArray	distribution(org.nd4j.linalg.api.ndarray.INDArray features)
org.nd4j.linalg.api.ndarray.INDArray	getCentroids()
protected void	initCentroids() Init clusters using the k-means++ algorithm.
protected void	initIfPossible(org.nd4j.linalg.api.ndarray.INDArray features)
protected boolean	isReady()
protected Integer	nearestCentroid(org.nd4j.linalg.api.ndarray.INDArray features)
void	reset()
Integer	update(org.nd4j.linalg.api.ndarray.INDArray features)

Methods inherited from class java.lang.Object

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

Constructor Detail

KMeansClustering

public KMeansClustering(Integer nbCluster,
                        Class<? extends org.nd4j.linalg.distancefunction.DistanceFunction> clazz)

KMeansClustering

public KMeansClustering(Integer nbCluster)

Method Detail

classify

public Integer classify(org.nd4j.linalg.api.ndarray.INDArray features)

update

public Integer update(org.nd4j.linalg.api.ndarray.INDArray features)

distribution

public org.nd4j.linalg.api.ndarray.INDArray distribution(org.nd4j.linalg.api.ndarray.INDArray features)

getCentroids

public org.nd4j.linalg.api.ndarray.INDArray getCentroids()

nearestCentroid

protected Integer nearestCentroid(org.nd4j.linalg.api.ndarray.INDArray features)

isReady

protected boolean isReady()

initIfPossible

protected void initIfPossible(org.nd4j.linalg.api.ndarray.INDArray features)

initCentroids

protected void initCentroids()

Init clusters using the k-means++ algorithm. (Arthur, D. and Vassilvitskii, S. (2007). "k-means++: the advantages of careful seeding".

computeDxs

protected org.nd4j.linalg.api.ndarray.INDArray computeDxs()

reset

public void reset()