package cn.ml
 
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.rdd.RDD
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.linalg.Vectors
 
object RandomForstExample {
 def main(args: Array[String]) {
 val sparkConf = new SparkConf().setAppName("RandomForestExample").
          setMaster("spark://sparkmaster:7077")
 val sc = new SparkContext(sparkConf)
 
 val data: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "/data/sample_data.txt")
 
 val numClasses = 2
 val featureSubsetStrategy = "auto"
 val numTrees = 3
 val model: RandomForestModel =RandomForest.trainClassifier(
                    data, Strategy.defaultStrategy("classification"),numTrees, 
 featureSubsetStrategy,new java.util.Random().nextInt())
  
 val input: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "/data/input.txt")
 
 val predictResult = input.map { point =>
 val prediction = model.predict(point.features)
 (point.label, prediction)
}
//打印输出结果，在 spark-shell 上执行时使用
 predictResult.collect()
 //将结果保存到 hdfs //predictResult.saveAsTextFile("/data/predictResult")
 sc.stop()
 
 }
}