K-means算法(Spark Demo)，k-meansspark,import java.

K-means算法(Spark Demo)，k-meansspark,import java.

import java.util.Randomimport spark.SparkContextimport spark.SparkContext._import spark.examples.Vector._object SparkKMeans {    /**     * line -> vector     */def parseVector (line: String) : Vector = {        return new Vector (line.split (' ').map (_.toDouble) )    }    /**     * 计算该节点的最近中心节点     */def closestCenter (p: Vector, centers: Array[Vector]) : Int = {        var bestIndex = 0        var bestDist = p.squaredDist (centers (0) ) //差平方之和        for (i < - 1 until centers.length) {            val dist = p.squaredDist (centers (i) )            if (dist < bestDist) {                bestDist = dist                bestIndex = i            }        }        return bestIndex    }def main (args: Array[String]) {        if (args.length < 3) {            System.err.println ("Usage: SparkKMeans <master> <file> <dimensions> <k> <iters>")            System.exit (1)        }        val sc = new SparkContext (args (0), "SparkKMeans")        val lines = sc.textFile (args (1), args (5).toInt)                    val points = lines.map (parseVector (_) ).cache() //文本中每行为一个节点，再将每个节点转换成Vector                                 val dimensions = args (2).toInt //节点的维度                                         val k = args (3).toInt //聚类个数                                                 val iterations = args (4).toInt //迭代次数                                                         // 随机初始化k个中心节点                                                         val rand = new Random (42)        var centers = new Array[Vector] (k)        for (i < - 0 until k)            centers (i) = Vector (dimensions, _ => 2 * rand.nextDouble - 1)                          println ("Initial centers: " + centers.mkString (", ") )                          val time1 = System.currentTimeMillis()            for (i < - 1 to iterations) {                println ("On iteration " + i)                // Map each point to the index of its closest center and a (point, 1) pair                // that we will use to compute an average later                val mappedPoints = points.map { p => (closestCenter (p, centers), (p, 1) ) }                val newCenters = mappedPoints.reduceByKey {                case ( (sum1, count1), (sum2, count2) ) => (sum1 + sum2, count1 + count2) //(向量相加, 计数器相加)                    } .map {                case (id, (sum, count) ) => (id, sum / count) //根据前面的聚类，重新计算中心节点的位置                    } .collect                // 更新中心节点                for ( (id, value) < - newCenters) {                    centers (id) = value                }            }                       val time2 = System.currentTimeMillis()                                   println ("Final centers: " + centers.mkString (", ") + ", time: " + (time2 - time1) )    }}