算法小白的第一次嘗試---Kmeans (適用於任何維度數據)

import org.apache.log4j.{Level, Logger}
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import scala.collection.mutable.ArrayBuffer
import scala.util.Random
/**
  * @author XiaoTangBao
  * @date 2019/3/17 23:02
  * @version 1.0
  */
object Kmeans2 {
  def main(args: Array[String]): Unit = {
    //屏蔽登錄日誌
    Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)
    val sparkSession = SparkSession.builder().master("local[4]").appName("Kmeans").getOrCreate()
    val sc = sparkSession.sparkContext
    //測試數據爲西瓜數據集4.0
    val data = sc.textFile("G:\\mldata\\watermelon 4.0.txt").map(line => line.split(',')).map(arr => (Row(arr(0).toInt,arr(1).toDouble,arr(2).toDouble)))
    val schema = StructType(List(StructField("Label",IntegerType,true),StructField("density",DoubleType,true),StructField("sugarContent",DoubleType,true)))
    val featuresArr = Array("density","sugarContent")

    //測試數據集爲鳶尾花數據集  UCI連接：http://archive.ics.uci.edu/ml/datasets/Iris
//    val data = sc.textFile("G:\\mldata\\iris.txt").map(line => line.split('|')).map(arr => (Row(arr(4),arr(0).toDouble,arr(1).toDouble,arr(2).toDouble,arr(3).toDouble)))
//    val schema = StructType(List(StructField("Label",StringType,true),
//      StructField("sepal_len",DoubleType,true), StructField("sepal_width",DoubleType,true),
//      StructField("petal_len",DoubleType,true),StructField("petal_width",DoubleType,true)))
//    val featuresArr = Array("sepal_len","sepal_width","petal_len","petal_width")

    //轉化爲DataFrame(label,featuresCol)
    val df = sparkSession.sqlContext.createDataFrame(data,schema)
    //定義轉換器
    val vectorAssmeb = new VectorAssembler().setInputCols(featuresArr).setOutputCol("features")
    //轉化數據
    val newdf = vectorAssmeb.transform(df).select("Label","features")
    //定義初始簇數
    val K_number = 3
    //定義最大迭代次數
    val iteratorMax = 10000
    val result = k(newdf,K_number,iteratorMax)
    //輸出最終的結果
    for(i<-0 until K_number){
      println("第" + i +"簇聚類結果：" )
      result(i).foreach(println(_))
    }
  }

  //計算每個樣本所屬的簇
  def cal(data:Vector[Double],meanVectors:Array[Vector[Double]]):(Double,Int) ={
    //disArr存儲數據爲：(dis,label)
    val disArr = ArrayBuffer[(Double,Int)]()
    //計算每個點與簇均值向量的距離
    for(i<- 0 until meanVectors.length){
      var dis = 0.0
      for(j<-0 until data.length){
        dis += math.pow((data(j) - meanVectors(i)(j)),2)
      }
      disArr.append((dis,i))
    }
    //leastdis代表最小距離
    val leastdis = disArr.sortBy(tuple => tuple._1).take(1)(0)
    leastdis
  }

  //更新均值向量
  def reflash(Ck:ArrayBuffer[Vector[Double]],meanVector:Vector[Double],dimentions:Int)={
    //每個維度都需要更新
    val dimentionsArr = ArrayBuffer[Double]()
    //初始化緯度數組
    for(i<-0 until dimentions) dimentionsArr.append(0.0)
    for(i<-0 until meanVector.length) {
      for(j<-0 until Ck.length){
        dimentionsArr(i) += Ck(j)(i)
      }
    }
    for(i<-0 until dimentionsArr.length) dimentionsArr(i) = dimentionsArr(i) / Ck.length

    val newmeanVector = dimentionsArr.toVector
    if(newmeanVector == meanVector) (meanVector,false) else (newmeanVector,true)
  }

  /**
    * Kmeans
    * @param  df  DataFrame(Label,FeaturesCol)
    * @param K  the number of clusters
    * @param iteratorMax  the Max number of interations
    * @return  the result of the Kmeans
    */
  def k(df:DataFrame,K:Int,iteratorMax:Int)={
    val trainData = df.select("features").rdd.map(row => row.toString())
      .map(str => str.replace('[',' '))
      .map(str => str.replace(']',' '))
      .map(str => str.trim()).map(str => str.split(','))
      .map(arr => arr.map(str => str.toDouble))
      .collect()
    //特徵維度
    val dimensions = trainData(0).length
    //初始化均值向量
    val meanVectors = ArrayBuffer[Vector[Double]]()
    //確保初始化時，沒有相同的向量
    val rn = ArrayBuffer[Int]()
    var flag_mean = true
    while(flag_mean){
      val index = Random.nextInt(trainData.length-1)
      if(!rn.contains(index)){
        rn.append(index)
        meanVectors.append(trainData(index).toVector)
      }
      if(meanVectors.length == K) flag_mean = false
    }
    val iniCk = new Array[ArrayBuffer[Vector[Double]]](K)
    for(j<-0 until K){
      iniCk(j) = ArrayBuffer(Vector(0.0))
      iniCk(j).clear()
    }
    var iteratornum = -1
    var flag = true
    while(flag){
      //迭代計數器
      iteratornum += 1
      for(i<-0 until trainData.length){
        //將樣本劃分到對應的簇中
        var flag2 = true
        val leastdis = cal(trainData(i).toVector,meanVectors.toArray)
        for(j<-0 until K if flag2){
          if(leastdis._2 == j){
            iniCk(j).append(trainData(i).toVector)
            flag2 = false
          }
        }
      }
      //判斷向量是否更新
      val vectorArr = ArrayBuffer[(Vector[Double],Boolean)]()
      //更新均值向量
      for(i<-0 until meanVectors.length){
        vectorArr.append(reflash(iniCk(i),meanVectors(i),dimensions))
      }
     for(i<-0 until meanVectors.length) meanVectors(i) = vectorArr(i)._1
      val boolArr = ArrayBuffer[Boolean]()
      for (bool<- vectorArr){
        if(!bool._2) boolArr.append(bool._2)
      }
      if(boolArr.length == meanVectors.length || iteratornum > iteratorMax) flag = false else {
        for(ci <- iniCk) ci.clear()
      }
    }
    iniCk
  }
}
-------西瓜數據集結果-----------------------------------------------------------------------------
第0簇聚類結果：
Vector(0.697, 0.46)
Vector(0.774, 0.376)
Vector(0.748, 0.232)
Vector(0.714, 0.346)
Vector(0.751, 0.489)
Vector(0.725, 0.445)
第1簇聚類結果：
Vector(0.634, 0.264)
Vector(0.608, 0.318)
Vector(0.556, 0.215)
Vector(0.666, 0.091)
Vector(0.639, 0.161)
Vector(0.657, 0.198)
Vector(0.593, 0.042)
Vector(0.719, 0.103)
Vector(0.483, 0.312)
Vector(0.478, 0.437)
Vector(0.525, 0.369)
Vector(0.532, 0.472)
Vector(0.473, 0.376)
Vector(0.446, 0.459)
第2簇聚類結果：
Vector(0.403, 0.237)
Vector(0.481, 0.149)
Vector(0.437, 0.211)
Vector(0.243, 0.267)
Vector(0.245, 0.057)
Vector(0.343, 0.099)
Vector(0.36, 0.37)
Vector(0.359, 0.188)
Vector(0.339, 0.241)
Vector(0.282, 0.257)

-------鳶尾花數據集結果---------------------------------------------------------------------------------------------------------------------
第0簇聚類結果：
Vector(4.9, 3.0, 1.4, 0.2)
Vector(4.7, 3.2, 1.3, 0.2)
Vector(4.6, 3.1, 1.5, 0.2)
Vector(4.6, 3.4, 1.4, 0.3)
Vector(4.4, 2.9, 1.4, 0.2)
Vector(4.9, 3.1, 1.5, 0.1)
Vector(4.8, 3.4, 1.6, 0.2)
Vector(4.8, 3.0, 1.4, 0.1)
Vector(4.3, 3.0, 1.1, 0.1)
Vector(4.6, 3.6, 1.0, 0.2)
Vector(4.8, 3.4, 1.9, 0.2)
Vector(5.0, 3.0, 1.6, 0.2)
Vector(4.7, 3.2, 1.6, 0.2)
Vector(4.8, 3.1, 1.6, 0.2)
Vector(4.9, 3.1, 1.5, 0.1)
Vector(5.0, 3.2, 1.2, 0.2)
Vector(4.9, 3.1, 1.5, 0.1)
Vector(4.4, 3.0, 1.3, 0.2)
Vector(4.5, 2.3, 1.3, 0.3)
Vector(4.4, 3.2, 1.3, 0.2)
Vector(4.8, 3.0, 1.4, 0.3)
Vector(4.6, 3.2, 1.4, 0.2)
Vector(5.0, 3.3, 1.4, 0.2)
第1簇聚類結果：
Vector(5.1, 3.5, 1.4, 0.2)
Vector(5.0, 3.6, 1.4, 0.2)
Vector(5.4, 3.9, 1.7, 0.4)
Vector(5.0, 3.4, 1.5, 0.2)
Vector(5.4, 3.7, 1.5, 0.2)
Vector(5.8, 4.0, 1.2, 0.2)
Vector(5.7, 4.4, 1.5, 0.4)
Vector(5.4, 3.9, 1.3, 0.4)
Vector(5.1, 3.5, 1.4, 0.3)
Vector(5.7, 3.8, 1.7, 0.3)
Vector(5.1, 3.8, 1.5, 0.3)
Vector(5.4, 3.4, 1.7, 0.2)
Vector(5.1, 3.7, 1.5, 0.4)
Vector(5.1, 3.3, 1.7, 0.5)
Vector(5.0, 3.4, 1.6, 0.4)
Vector(5.2, 3.5, 1.5, 0.2)
Vector(5.2, 3.4, 1.4, 0.2)
Vector(5.4, 3.4, 1.5, 0.4)
Vector(5.2, 4.1, 1.5, 0.1)
Vector(5.5, 4.2, 1.4, 0.2)
Vector(5.5, 3.5, 1.3, 0.2)
Vector(5.1, 3.4, 1.5, 0.2)
Vector(5.0, 3.5, 1.3, 0.3)
Vector(5.0, 3.5, 1.6, 0.6)
Vector(5.1, 3.8, 1.9, 0.4)
Vector(5.1, 3.8, 1.6, 0.2)
Vector(5.3, 3.7, 1.5, 0.2)
第2簇聚類結果：
Vector(7.0, 3.2, 4.7, 1.4)
Vector(6.4, 3.2, 4.5, 1.5)
Vector(6.9, 3.1, 4.9, 1.5)
Vector(5.5, 2.3, 4.0, 1.3)
Vector(6.5, 2.8, 4.6, 1.5)
Vector(5.7, 2.8, 4.5, 1.3)
Vector(6.3, 3.3, 4.7, 1.6)
Vector(4.9, 2.4, 3.3, 1.0)
Vector(6.6, 2.9, 4.6, 1.3)
Vector(5.2, 2.7, 3.9, 1.4)
Vector(5.0, 2.0, 3.5, 1.0)
Vector(5.9, 3.0, 4.2, 1.5)
Vector(6.0, 2.2, 4.0, 1.0)
Vector(6.1, 2.9, 4.7, 1.4)
Vector(5.6, 2.9, 3.6, 1.3)
Vector(6.7, 3.1, 4.4, 1.4)
Vector(5.6, 3.0, 4.5, 1.5)
Vector(5.8, 2.7, 4.1, 1.0)
Vector(6.2, 2.2, 4.5, 1.5)
Vector(5.6, 2.5, 3.9, 1.1)
Vector(5.9, 3.2, 4.8, 1.8)
Vector(6.1, 2.8, 4.0, 1.3)
Vector(6.3, 2.5, 4.9, 1.5)
Vector(6.1, 2.8, 4.7, 1.2)
Vector(6.4, 2.9, 4.3, 1.3)
Vector(6.6, 3.0, 4.4, 1.4)
Vector(6.8, 2.8, 4.8, 1.4)
Vector(6.7, 3.0, 5.0, 1.7)
Vector(6.0, 2.9, 4.5, 1.5)
Vector(5.7, 2.6, 3.5, 1.0)
Vector(5.5, 2.4, 3.8, 1.1)
Vector(5.5, 2.4, 3.7, 1.0)
Vector(5.8, 2.7, 3.9, 1.2)
Vector(6.0, 2.7, 5.1, 1.6)
Vector(5.4, 3.0, 4.5, 1.5)
Vector(6.0, 3.4, 4.5, 1.6)
Vector(6.7, 3.1, 4.7, 1.5)
Vector(6.3, 2.3, 4.4, 1.3)
Vector(5.6, 3.0, 4.1, 1.3)
Vector(5.5, 2.5, 4.0, 1.3)
Vector(5.5, 2.6, 4.4, 1.2)
Vector(6.1, 3.0, 4.6, 1.4)
Vector(5.8, 2.6, 4.0, 1.2)
Vector(5.0, 2.3, 3.3, 1.0)
Vector(5.6, 2.7, 4.2, 1.3)
Vector(5.7, 3.0, 4.2, 1.2)
Vector(5.7, 2.9, 4.2, 1.3)
Vector(6.2, 2.9, 4.3, 1.3)
Vector(5.1, 2.5, 3.0, 1.1)
Vector(5.7, 2.8, 4.1, 1.3)

-------由於鳶尾花數據集特徵列爲4維，不方便作圖，特選擇西瓜數據集作圖-------------------------------------------------
![西瓜數據集4.0](https://img-blog.csdnimg.cn/20190318215307642.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0phdmFfTWFuX0NoaW5h,size_16,color_FFFFFF,t_70)