Spark机器学习(7)：KMenas算法

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KMenas算法比较简单，不详细介绍了，直接上代码。

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.Vectors
import 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 = 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) }}