学习进度笔记
学习进度笔记27
Spark学习——Mlib机器学习
import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors
object Kmeans {
def main(args: Array[String]) {
// 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().setAppName("Kmeans").setMaster("local[4]")
val sc = new SparkContext(conf)
// 装载数据集
val data = sc.textFile("/home/hadoop/upload/class8/kmeans_data.txt", 1)
val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble)))
// 将数据集聚类,2个类,20次迭代,进行模型训练形成数据模型
val numClusters = 2
val numIterations = 20
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.map(s => Vectors.dense(s.split(' ').map(_.toDouble)))
val result1 = model.predict(testdata)
result1.saveAsTextFile("/home/hadoop/upload/class8/result_kmeans1")
// 交叉评估2,返回数据集和结果
val result2 = data.map {
line =>
val linevectore = Vectors.dense(line.split(' ').map(_.toDouble))
val prediction = model.predict(linevectore)
line + " " + prediction
}.saveAsTextFile("/home/hadoop/upload/class8/result_kmeans2")
sc.stop()
}
}