import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaRDD;import org.apache.spark.api.java.JavaSparkContext;import java.util.LinkedList;import java.util.List;import org.apache.spark.mllib.linalg.Vector;import org.apache.spark.mllib.linalg.Vectors;import org.apache.spark.mllib.regression.LabeledPoint;import org.apache.spark.mllib.classification.NaiveBayes;import org.apache.spark.mllib.classification.NaiveBayesModel;import org.apache.spark.mllib.classification.SVMModel;import org.apache.spark.mllib.classification.SVMWithSGD;import java.util.HashMap;import java.util.Map;import org.apache.spark.mllib.tree.DecisionTree;import org.apache.spark.mllib.tree.model.DecisionTreeModel;import org.apache.spark.mllib.tree.RandomForest;import org.apache.spark.mllib.tree.model.RandomForestModel;public class test {    public static void main(String[] arg){       //生成spark对象        SparkConf conf = new SparkConf();        conf.set("spark.testing.memory","2147480000");  // spark的运行配置，意指占用内存2G        JavaSparkContext sc = new JavaSparkContext("local[*]", "Spark", conf);      //第一个参数为本地模式，[*]尽可能地获取多的cpu；第二个是spark应用程序名，可以任意取;第三个为配置文件                //训练集生成        LabeledPoint pos = new LabeledPoint(1.0, Vectors.dense(2.0, 3.0, 3.0));//规定数据结构为LabeledPoint，1.0为类别标号，Vectors.dense(2.0, 3.0, 3.0)为特征向量        LabeledPoint neg = new LabeledPoint(0.0, Vectors.sparse(3, new int[] {2, 1,1}, new double[] {1.0, 1.0,1.0}));//特征值稀疏时，利用sparse构建       List l = new LinkedList();//利用List存放训练样本        l.add(neg);        l.add(pos);        JavaRDD<LabeledPoint>training = sc.parallelize(l); //ＲＤＤ化，泛化类型为LabeledPoint 而不是List        final NaiveBayesModel nb_model = NaiveBayes.train(training.rdd());                        //测试集生成        double []  d = {1,1,2};        Vector v =  Vectors.dense(d);//测试对象为单个vector，或者是ＲＤＤ化后的vector        //朴素贝叶斯      System.out.println(nb_model.predict(v));// 分类结果      System.out.println(nb_model.predictProbabilities(v)); // 计算概率值            //支持向量机      int numIterations = 100;//迭代次数      final SVMModel svm_model = SVMWithSGD.train(training.rdd(), numIterations);//构建模型      System.out.println(svm_model.predict(v));      //决策树      Integer numClasses = 2;//类别数量      Map<Integer, Integer> categoricalFeaturesInfo = new HashMap();      String impurity = "gini";//对于分类问题，我们可以用熵entropy或Gini来表示信息的无序程度 ,对于回归问题，我们用方差(Variance)来表示无序程度，方差越大，说明数据间差异越大      Integer maxDepth = 5;//最大树深      Integer maxBins = 32;//最大划分数      final DecisionTreeModel tree_model = DecisionTree.trainClassifier(training, numClasses,categoricalFeaturesInfo, impurity, maxDepth, maxBins);//构建模型      System.out.println("决策树分类结果：");         System.out.println(tree_model.predict(v));            //随机森林      Integer numTrees = 3; // Use more in practice.      String featureSubsetStrategy = "auto"; // Let the algorithm choose.      Integer seed = 12345;      // Train a RandomForest model.      final RandomForestModel forest_model = RandomForest.trainRegressor(training,        categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed);//参数与决策数基本一致，除了seed      System.out.println("随机森林结果：");         System.out.println(forest_model.predict(v));    }  }