import org.apache.log4j.{Level, Logger}import org.apache.spark.{SparkConf, SparkContext}import org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.clustering._/**  * Created by Administrator on 2017/7/11.  */object Kmenas {  def main(args:Array[String]): Unit ={    // 设置运行环境    val conf = new SparkConf().setAppName("KMeans Test")      .setMaster("spark://master:7077").setJars(Seq("E:\\Intellij\\Projects\\MachineLearning\\MachineLearning.jar"))    val sc = new SparkContext(conf)    Logger.getRootLogger.setLevel(Level.WARN)    // 读取样本数据并解析    val data = http://www.mamicode.com/sc.textFile("hdfs://master:9000/ml/data/kmeans_data.txt")    val parsedData = data.map(s => Vectors.dense(s.split(‘ ‘).map(_.toDouble))).cache()    // 新建KMeans聚类模型并训练    val initMode = "k-means||"    val numClusters = 2    val numIterations = 500    val model = new KMeans().      setInitializationMode(initMode).      setK(numClusters).      setMaxIterations(numIterations).      run(parsedData)    val centers = model.clusterCenters    println("Centers:")    for (i <- 0 to centers.length - 1) {      println(centers(i)(0) + "\t" + centers(i)(1))    }    // 误差计算    val Error = model.computeCost(parsedData)    println("Errors = " + Error)  }}