Package org.apache.spark.ml.clustering

Class KMeansAggregator

Object

org.apache.spark.ml.clustering.KMeansAggregator

All Implemented Interfaces:

Serializable

public class KMeansAggregator
extends Object
implements Serializable

KMeansAggregator computes the distances and updates the centers for blocks in sparse or dense matrix in an online fashion. param: centerMatrix The matrix containing center vectors. param: k The number of clusters. param: numFeatures The number of features. param: distanceMeasure The distance measure. When 'euclidean' is chosen, the instance blocks should contains the squared norms in the labels field; When 'cosine' is chosen, the vectors should be already normalized.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
KMeansAggregator(DenseMatrix centerMatrix, int k, int numFeatures, String distanceMeasure)

Method Summary

Modifier and Type	Method	Description
KMeansAggregator	add(org.apache.spark.ml.feature.InstanceBlock block)
DenseMatrix	centerMatrix()
double	costSum()
long	count()
String	distanceMeasure()
int	k()
int	numFeatures()
DenseMatrix	sumMat()
double	weightSum()
DenseVector	weightSumVec()

Methods inherited from class java.lang.Object

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

Constructor Details

KMeansAggregator

public KMeansAggregator(DenseMatrix centerMatrix,
 int k,
 int numFeatures,
 String distanceMeasure)

Method Details

add

public KMeansAggregator add(org.apache.spark.ml.feature.InstanceBlock block)

centerMatrix

public DenseMatrix centerMatrix()

costSum

public double costSum()

count

public long count()

distanceMeasure

public String distanceMeasure()

k

public int k()

numFeatures

public int numFeatures()

sumMat

public DenseMatrix sumMat()

weightSum

public double weightSum()

weightSumVec

public DenseVector weightSumVec()