import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.mllib.clustering.{KMeans, KMeansModel}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.log4j.{ Level, Logger }
import scala.collection.mutable.ArrayBuffer
/*
* 支援五個參數，依次為：
*
* read_file_path 資料讀取目錄
* save_file_path 結果存放目錄
* setk 聚類數 預設為2
* setMaxIterations 最大疊代次數 預設為20
* run 計算并行度 預設為2
* */
object Spark_MLlib_Kmeans {
def main(args: Array[String]) {
if (args.length < 5) {
sys.exit(1)
}
/*關閉INFO 和 WARN 級别的日志*/
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
// 1. 建構spark對象
val conf = new SparkConf().setAppName("K-Means Clustering").setMaster("local")
val sc = new SparkContext(conf)
Logger.getRootLogger.setLevel(Level.WARN)
// 2. 讀取樣本資料，格式為LIBSVM格式，資料及
val rawTranData = sc.textFile(args(0))
val tran = 0.7
val test = 0.3
val dataParts = rawTranData.randomSplit(Array(tran, test), seed = 2)
// 訓練集
val dataParts_TranData = dataParts(0)
// 測試集
val dataParts_TestData = dataParts(1)
// 訓練資料因子化并緩存
val TranData = dataParts_TranData.map(s => Vectors.dense(s.split(' ').map(_.toDouble))).cache()
// 3. 建立kmeans 聚類模型，并訓練
val initMode = "k-means||"
val numClusters = args(2).toInt
val numIterations = args(3).toInt
val runTimes = args(4).toInt
var clusterIndex: Int = 0
val model = new KMeans().setInitializationMode(initMode)
.setK(numClusters)
.setMaxIterations(numIterations)
.run(TranData)
val mutableArr = ArrayBuffer[String]()
// 4. 誤差計算
val WSSSE = model.computeCost(TranData)
mutableArr += "\n"+WSSSE.toString
// 5. 輸出每個類别的中心點
model.clusterCenters.foreach(
x => {
mutableArr += ("\n"+clusterIndex.toString+":"+x.toString)
clusterIndex += 1
})
// 6.測試模型 測試資料因子化并緩存
val TestData = dataParts_TestData.map(s => Vectors.dense(s.split(' ').map(_.toDouble))).cache()
// 7. 輸出測試結果（每條記錄和其所屬的類别）
TestData.collect().foreach(testDataLine => {
val classIndex: Int = model.predict(testDataLine)
mutableArr += ("\n"+testDataLine.toString+"\t"+classIndex.toString)
})
val ws = Array(mutableArr)
val fd= sc.parallelize(ws)
fd.saveAsTextFile(args(1))
println("\nSpark MLlib K-means clustering test finished.\n")
}
}
輸出結果
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
18/12/19 11:54:00 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/12/19 11:54:01 INFO Slf4jLogger: Slf4jLogger started
18/12/19 11:54:01 INFO Remoting: Starting remoting
18/12/19 11:54:01 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://[email protected]:52676]
18/12/19 11:54:03 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
18/12/19 11:54:03 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
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[32445.0,32.3,99.9,43.5,0.0,1.28,0.1,1.07,0.8,0.77] 0
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[75441.0,39.4,100.1,50.3,0.0,0.46,0.1,3.44,1.32,1.19] 3
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[10000.0,25.6,100.71,90.0,0.0,0.49,0.55,0.54,0.53,0.52] 0
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[40000.0,25.5,100.1,56.3,0.0,5.1,3.9,3.3,4.1,0.7] 0
[12345.0,29.7,101.1,74.4,800.0,0.14,8.23,3.35,3.91,2.5] 0
[12345.0,32.5,101.1,59.7,800.0,5.0,3.27,0.55,2.94,2.5] 0
[12345.0,27.4,101.1,82.4,800.0,5.19,3.52,0.03,2.91,2.5] 0
[12345.0,30.0,101.1,99.0,800.0,2.7,2.26,3.84,2.93,2.5] 0
[12345.0,28.8,101.1,99.0,800.0,4.81,3.33,2.62,3.59,2.5] 0
[12345.0,35.9,101.1,65.6,800.0,2.2,2.45,3.01,2.55,2.5] 0
[62101.0,25.4,99.6,33.0,800.0,2.4,0.1,1.9,1.5,1.3] 3
[20000.0,21.2,99.2,78.7,800.0,2.8,1.4,0.7,1.6,1.4] 0
[60000.0,31.3,98.3,38.0,800.0,2.1,1.8,1.6,1.9,1.3] 3
[152500.0,25.5,101.3,60.5,800.0,1.8,2.6,0.4,1.6,1.3] 3
[10000.0,32.8,100.9,95.0,800.0,4.4,2.9,2.2,3.2,3.0] 0
[50200.0,35.8,101.3,67.6,800.0,2.6,2.5,2.5,2.5,1.1] 0
[65201.0,36.4,101.1,83.9,800.0,1.1,1.1,4.5,2.2,1.6] 3
[53419.0,34.3,101.0,78.6,800.0,3.6,3.5,3.5,3.5,1.5] 0
[46103.0,36.1,101.3,77.0,508.0,2.4,1.0,7.5,3.6,3.0] 0
[70000.0,32.2,100.9,82.6,781.0,0.6,0.6,3.4,1.5,1.1] 3
[87545.0,33.4,101.5,89.4,810.0,0.8,0.8,3.4,1.7,1.5] 3
[30159.0,23.4,100.7,83.4,0.0,0.0,0.0,0.0,0.0,1.5] 0
[72169.0,34.5,100.0,85.2,0.0,1.44,1.26,3.49,2.06,1.5] 3
[61028.0,30.7,99.7,84.8,0.0,11.92,8.7,5.82,8.81,0.8] 3
[82917.0,26.4,100.0,84.8,0.0,1.14,0.0,0.0,0.0,1.4] 3
[94135.0,30.1,100.2,84.8,0.0,0.0,0.0,0.0,0.0,1.5] 3
[20025.0,21.5,101.1,56.2,0.0,1.7,1.7,1.9,1.7,1.6] 0
[10354.0,21.5,101.1,56.2,0.0,1.6,1.9,1.8,1.7,0.7] 0
[25841.0,21.5,101.1,56.2,0.0,0.8,0.9,0.9,0.9,0.7] 0
[30001.0,21.5,101.1,56.2,0.0,1.6,2.1,1.4,1.7,0.8] 0
[69000.0,21.5,101.1,56.2,0.0,1.2,2.1,1.9,1.8,1.3] 3
[10042.0,21.5,101.1,56.2,0.0,2.1,1.7,2.0,1.9,1.2] 0
[52456.0,21.5,101.1,56.2,0.0,1.2,1.3,2.2,1.6,1.4] 0
[50001.0,21.5,101.1,56.2,0.0,1.5,1.4,1.4,1.4,1.3] 0
[65584.0,21.5,101.1,56.2,0.0,1.3,1.4,1.7,1.5,1.3] 3
[70001.0,21.5,101.1,56.2,0.0,1.9,1.7,1.8,1.8,1.7] 3
[70052.0,21.5,101.1,56.2,0.0,1.6,2.1,2.1,1.9,1.3] 3
[26598.0,29.3,109.8,38.2,600.0,1.6,1.1,1.9,1.5,1.3] 0
[85101.0,28.5,110.8,49.0,600.0,4.5,1.1,0.6,2.0,1.7] 3
[31410.0,27.0,101.9,47.0,600.0,1.4,1.5,1.0,1.3,1.3] 0
[92100.0,28.1,109.8,51.4,600.0,0.2,1.0,3.9,1.7,1.7] 3
[152429.0,27.2,109.9,52.9,1062.0,2.1,2.1,1.0,1.7,1.4] 3
[200764.0,26.1,102.0,52.1,707.0,5.2,3.7,2.2,3.7,2.5] 3
[91780.0,25.5,101.9,49.4,829.0,1.6,1.6,1.5,1.6,1.4] 3
[117940.0,27.8,109.9,51.2,751.0,8.6,3.7,4.9,5.7,2.5] 3
[354835.0,28.4,109.9,50.3,532.0,1.8,4.5,1.4,2.6,2.5] 2
[71200.0,24.7,110.0,55.2,0.0,2.6,2.9,3.0,2.9,1.1] 3
[66854.0,27.7,110.0,54.4,538.0,2.6,2.6,2.5,2.6,1.4] 3
[80489.0,27.3,109.9,51.5,0.0,1.9,2.0,2.6,2.1,1.4] 3
[38749.0,27.4,109.9,51.6,821.0,6.3,3.0,1.7,3.6,3.0] 0
[285625.0,27.3,109.9,43.8,713.0,8.3,2.4,0.1,3.6,3.0] 2
[123669.0,28.0,110.0,52.7,0.0,1.4,2.8,1.5,1.9,1.7] 3
轉載于:https://www.cnblogs.com/RHadoop-Hive/p/10142384.html
![9.spark Core 進階2--Cashe[圖]](data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACwAAAAAAQABAAACAkQBADs=)