西瓜书中决策树树桩的实现，即只分类一次，是弱分类器，效果极差。
主要目的是作为adaboost等集成学习方法的基分类器，这里不给出adaboost代码，因为好久以前写的，当时有问题也懒得改了。。。。。。
scala实现
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDDimport scala.util.Randomobject Stump {def buildTree(data:RDD[Array[Double]]) = {val rows = data.count().toIntval features = 2val poi = data.filter(s => s(3) == 1).count().toDoubleval neg = data.filter(s => s(3) == 0).count().toDoubleval gain_array1 = new collection.mutable.ArrayBuffer[(Double,Double)]()val gain_array2 = new collection.mutable.ArrayBuffer[(Double,Double)]()val ent = -((poi/rows.toDouble)*(Math.log(poi/rows)/Math.log(2.0))+(neg/rows.toDouble)*(Math.log(neg/rows)/Math.log(2.0)))println("ent is " + ent)val best_candidate = new collection.mutable.ArrayBuffer[(Double,Double)]()var best_split = 0.0var curr_feature = 0for (i <- 0 to features-1){val candidate_attr = new collection.mutable.ArrayBuffer[Double]()val gain_array = new collection.mutable.ArrayBuffer[(Double,Double)]()val max = new collection.mutable.ArrayBuffer[Double]()//[index,feature1,feature2,label]//[feature_x,label]val attr = data.map(s => (s(i+1),s(3))).sortBy(s => s._1)val attr_value = attr.collect()//[(feature_x,label)]for(j <- 0 to attr_value.length-2) {val curr_value = attr_value(j)._1val next_value = attr_value(j + 1)._1val candidate_value = (curr_value + next_value) / 2candidate_attr.append(candidate_value)}for(k <-0 to candidate_attr.length-1){val curr_candidate = candidate_attr(k)
// println("当前候选者为" + curr_candidate)val left_data = attr_value.filter(s => s._1<curr_candidate)val right_data = attr_value.filter(s => s._1>curr_candidate)val l_poi = left_data.filter(s => s._2 == 1.0).length.toDoubleval l_neg = left_data.filter(s => s._2 == 0.0).length.toDoubleval l_count = left_data.length.toDoubleval r_poi = right_data.filter(s => s._2 == 1.0).length.toDoubleval r_neg = right_data.filter(s => s._2 == 0.0).length.toDoubleval r_count = right_data.length.toDoubleval l_ent = -(((l_poi/l_count)*(Math.log(l_poi/l_count)/Math.log(2.0)))+((l_neg/l_count)*(Math.log(l_neg/l_count)/Math.log(2.0))))val r_ent = -(((r_poi/r_count)*(Math.log(r_poi/r_count)/Math.log(2.0)))+((r_neg/r_count)*(Math.log(r_neg/r_count)/Math.log(2.0))))val gain = (curr_candidate,(ent-(l_ent*(l_count/rows.toDouble)) + (r_ent*(r_count/rows.toDouble))))
// if(i == 0){
// gain_array1.append(gain)
// }else{
// gain_array2.append(gain)
// }gain_array.append(gain)max.append(0.0)max.append(0.0)for (m <- 0 to gain_array.length-1){if(gain_array(m)._2>max(1)){max(0) = gain_array(m)._1max(1) = gain_array(m)._2}}}best_candidate.append((max(0),max(1)))}println(best_candidate.length)var max = 0.0for (n <- 0 to best_candidate.length-1){if(best_candidate(n)._2>best_split){max = best_candidate(n)._2best_split = best_candidate(n)._1curr_feature = n}}println("the best split value is" + best_split + " in attr"+curr_feature )// val randomFeatures = Random.nextInt(features)
// val colValue = data.map(_.apply(randomFeatures))
// val randomRows = Random.nextInt(rows)
// val colMax = colValue.max()
// val colMin = colValue.min()
// val splitValue = colMin + (colMax-colMin) * Random.nextDouble()val dataLeft = data.filter(s => s(features)<best_split)val dataRight = data.filter(s => s(features)>best_split)new Stump(best_split,curr_feature)}def main(args: Array[String]): Unit = {val conf = new SparkConf().setAppName(s"${this.getClass.getSimpleName}").setMaster("local[4]")val sc = new SparkContext(conf)sc.setLogLevel("ERROR")val originData = sc.textFile("C:\\Users\\dell\\Desktop\\data\\adaboostingData.txt")
// val data = originData.map(_.split(" ")).map(t => t.slice(1,3)).map(s => s.map(_.toDouble))
// val test_data = originData.map(_.split(" ")).map(s => s.map(_.toDouble))// val stump = buildTree(data)val data = originData.map(_.split(" ")).map(s => s.map(_.toDouble))val stump = buildTree(data)val result = data.map(t => {val prediction = stump.predict(Array(t(1),t(2)))(prediction.toDouble,t(3))})val errRate = result.filter(t => t._1 != t._2).count().toDouble/data.count().toDoubleresult.collect().foreach(println)println("errRate is "+errRate)}
}class Stump(splitValue:Double,features:Int) extends Serializable {def predict(x:Array[Double]):Int = {if(x(features)<splitValue){0}else {1}}
}