Package org.apache.spark.mllib.clustering

Class LocalKMeans

Object

org.apache.spark.mllib.clustering.LocalKMeans

public class LocalKMeans
extends Object

An utility object to run K-means locally. This is private to the ML package because it's used in the initialization of KMeans but not meant to be publicly exposed.

Constructor Summary

Constructors

Constructor	Description
LocalKMeans()

Method Summary

Modifier and Type	Method	Description
static org.apache.spark.mllib.clustering.VectorWithNorm[]	kMeansPlusPlus(int seed, org.apache.spark.mllib.clustering.VectorWithNorm[] points, double[] weights, int k, int maxIterations)	Run K-means++ on the weighted point set points.
static org.apache.spark.internal.Logging.LogStringContext	LogStringContext(scala.StringContext sc)
static org.slf4j.Logger	org$apache$spark$internal$Logging$$log_()
static void	org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)

Methods inherited from class java.lang.Object

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

Constructor Details

LocalKMeans

public LocalKMeans()

Method Details

kMeansPlusPlus

public static org.apache.spark.mllib.clustering.VectorWithNorm[] kMeansPlusPlus(int seed,
 org.apache.spark.mllib.clustering.VectorWithNorm[] points,
 double[] weights,
 int k,
 int maxIterations)

Run K-means++ on the weighted point set points. This first does the K-means++ initialization procedure and then rounds of Lloyd's algorithm.

Parameters:

seed - (undocumented)

points - (undocumented)

weights - (undocumented)

k - (undocumented)

maxIterations - (undocumented)

Returns:

(undocumented)

org$apache$spark$internal$Logging$$log_

public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()

org$apache$spark$internal$Logging$$log__$eq

public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)

LogStringContext

public static org.apache.spark.internal.Logging.LogStringContext LogStringContext(scala.StringContext sc)