【Data Algorithms_Recipes for Scaling up with Hadoop and Spark】Chapter 12. K-Means Clustering

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// scalastyle:off println
package org.apache.spark.examples

import breeze.linalg.{ Vector, DenseVector, squaredDistance }

import org.apache.spark.{ SparkConf, SparkContext }
import org.apache.spark.SparkContext._

/**
* K-means clustering.
*
* This is an example implementation for learning how to use Spark. For more conventional use,
* please refer to org.apache.spark.mllib.clustering.KMeans
*/
object SparkKMeans {

def parseVector(line: String): Vector[Double] = {
DenseVector(line.split(' ').map(_.toDouble))
}

def closestPoint(p: Vector[Double], centers: Array[Vector[Double]]): Int = {
var bestIndex = 0
var closest = Double.PositiveInfinity

for (i <- 0 until centers.length) {
val tempDist = squaredDistance(p, centers(i))
if (tempDist < closest) {
closest = tempDist
bestIndex = i
}
}

bestIndex
}

def showWarning() {
System.err.println(
"""WARN: This is a naive implementation of KMeans Clustering and is given as an example!
|Please use the KMeans method found in org.apache.spark.mllib.clustering
|for more conventional use.
""".stripMargin)
}

def main(args: Array[String]) {

if (args.length < 3) {
System.err.println("Usage: SparkKMeans <file> <k> <convergeDist>")
System.exit(1)
}

showWarning()

val sparkConf = new SparkConf().setAppName("SparkKMeans")
val sc = new SparkContext(sparkConf)
val lines = sc.textFile(args(0))
val data = lines.map(parseVector _).cache()
val K = args(1).toInt
val convergeDist = args(2).toDouble

val kPoints = data.takeSample(withReplacement = false, K, 42).toArray
var tempDist = 1.0

while (tempDist > convergeDist) {
val closest = data.map(p => (closestPoint(p, kPoints), (p, 1)))

val pointStats = closest.reduceByKey { case ((p1, c1), (p2, c2)) => (p1 + p2, c1 + c2) }

val newPoints = pointStats.map { pair =>
(pair._1, pair._2._1 * (1.0 / pair._2._2))
}.collectAsMap()

tempDist = 0.0
for (i <- 0 until K) {
tempDist += squaredDistance(kPoints(i), newPoints(i))
}

for (newP <- newPoints) {
kPoints(newP._1) = newP._2
}
println("Finished iteration (delta = " + tempDist + ")")
}

println("Final centers:")
kPoints.foreach(println)
sc.stop()
}
}
// scalastyle:on println