org.apache.spark.mllib.clustering

Class LocalKMeans

java.lang.Object

org.apache.spark.mllib.clustering.LocalKMeans

public class LocalKMeans
extends java.lang.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 and Description
LocalKMeans()

Method Summary

Methods

Modifier and Type	Method and Description
protected static void	initializeLogIfNecessary(boolean isInterpreter)
protected static boolean	isTraceEnabled()
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.
protected static org.slf4j.Logger	log()
protected static void	logDebug(scala.Function0<java.lang.String> msg)
protected static void	logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logError(scala.Function0<java.lang.String> msg)
protected static void	logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logInfo(scala.Function0<java.lang.String> msg)
protected static void	logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static java.lang.String	logName()
protected static void	logTrace(scala.Function0<java.lang.String> msg)
protected static void	logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logWarning(scala.Function0<java.lang.String> msg)
protected static void	logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)

Methods inherited from class java.lang.Object

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

Constructor Detail

LocalKMeans

public LocalKMeans()

Method Detail

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)

logName

protected static java.lang.String logName()

log

protected static org.slf4j.Logger log()

logInfo

protected static void logInfo(scala.Function0<java.lang.String> msg)

logDebug

protected static void logDebug(scala.Function0<java.lang.String> msg)

logTrace

protected static void logTrace(scala.Function0<java.lang.String> msg)

logWarning

protected static void logWarning(scala.Function0<java.lang.String> msg)

logError

protected static void logError(scala.Function0<java.lang.String> msg)

logInfo

protected static void logInfo(scala.Function0<java.lang.String> msg,
           java.lang.Throwable throwable)

logDebug

protected static void logDebug(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

logTrace

protected static void logTrace(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

logWarning

protected static void logWarning(scala.Function0<java.lang.String> msg,
              java.lang.Throwable throwable)

logError

protected static void logError(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

isTraceEnabled

protected static boolean isTraceEnabled()

initializeLogIfNecessary

protected static void initializeLogIfNecessary(boolean isInterpreter)