package main.scala.yang.spark

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.KMeans

object KMeansBeijing {

  def main(args: Array[String]): Unit = {

    // 屏蔽不必要的日志显示在终端上

    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)

    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

    // 设置运行环境

    val conf = new SparkConf().setMaster("local").setAppName("KMeansBeijing")

    val sc = new SparkContext(conf)

    // 装载数据集

    val data = sc.textFile("file:///home/hadoop/yang/USA/AUG_tag.csv", 1)

    val parsedData = data.filter(!isColumnNameLine(_)).map(line => Vectors.dense(line.split(',').map(_.toDouble))).cache()

    //

    // 将数据集聚类，7个类，20次迭代，进行模型训练形成数据模型

    val numClusters = 4

    val numIterations = 800

    val model = KMeans.train(parsedData, numClusters, numIterations)

    // 打印数据模型的中心点

    println("Cluster centers:")

    for (c <- model.clusterCenters) {

      println("  " + c.toString)

    }

    // 使用误差平方之和来评估数据模型

    val cost = model.computeCost(parsedData)

    println("Within Set Sum of Squared Errors = " + cost)

//    // 使用模型测试单点数据

//    println("Vectors 0.2 0.2 0.2 is belongs to clusters:" + model.predict(Vectors.dense("0.2 0.2 0.2".split(' ').map(_.toDouble))))

//    println("Vectors 0.25 0.25 0.25 is belongs to clusters:" + model.predict(Vectors.dense("0.25 0.25 0.25".split(' ').map(_.toDouble))))

//    println("Vectors 8 8 8 is belongs to clusters:" + model.predict(Vectors.dense("8 8 8".split(' ').map(_.toDouble))))

    // 交叉评估1，只返回结果

    val testdata = data.filter(!isColumnNameLine(_)).map(s => Vectors.dense(s.split(',').map(_.toDouble)))

    val result1 = model.predict(testdata)

    result1.saveAsTextFile("file:///home/hadoop/yang/USA/AUG/result1")

    // 交叉评估2，返回数据集和结果

    val result2 = data.filter(!isColumnNameLine(_)).map {

      line =>

        val linevectore = Vectors.dense(line.split(',').map(_.toDouble))

        val prediction = model.predict(linevectore)

        line + " " + prediction

    }.saveAsTextFile("file:///home/hadoop/yang/USA/AUG/result2")

    sc.stop()

  }

  private def isColumnNameLine(line: String): Boolean = {

    if (line != null && line.contains("Electricity")) true

    else false

  }

}