public static void main(String[] args) {

    SparkConf sparkConf = new SparkConf()

          .setAppName("Regression")

          .setMaster("local[2]");

    JavaSparkContext sc = new JavaSparkContext(sparkConf);

    JavaRDD<String> data = sc.textFile("/home/yurnom/lpsa.txt");

    JavaRDD<LabeledPoint> parsedData = data.map(line -> {

        String[] parts = line.split(",");

        double[] ds = Arrays.stream(parts[1].split(" "))

              .mapToDouble(Double::parseDouble)

              .toArray();

        return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(ds));

    }).cache();

    int numIterations = 100; //迭代次数

    LinearRegressionModel model = LinearRegressionWithSGD.train(parsedData.rdd(), numIterations);

    RidgeRegressionModel model1 = RidgeRegressionWithSGD.train(parsedData.rdd(), numIterations);

    LassoModel model2 = LassoWithSGD.train(parsedData.rdd(), numIterations);

    print(parsedData, model);

    print(parsedData, model1);

    print(parsedData, model2);

    //预测一条新数据方法

    double[] d = new double[]{1.0, 1.0, 2.0, 1.0, 3.0, -1.0, 1.0, -2.0};

    Vector v = Vectors.dense(d);

    System.out.println(model.predict(v));

    System.out.println(model1.predict(v));

    System.out.println(model2.predict(v));

}

public static void print(JavaRDD<LabeledPoint> parsedData, GeneralizedLinearModel model) {

    JavaPairRDD<Double, Double> valuesAndPreds = parsedData.mapToPair(point -> {

        double prediction = model.predict(point.features()); //用模型预测训练数据

        return new Tuple2<>(point.label(), prediction);

    });

    Double MSE = valuesAndPreds.mapToDouble((Tuple2<Double, Double> t) -> Math.pow(t._1() - t._2(), 2)).mean(); //计算预测值与实际值差值的平方值的均值

    System.out.println(model.getClass().getName() + " training Mean Squared Error = " + MSE);

}

运行结果

LinearRegressionModel training Mean Squared Error = 6.206807793307759

RidgeRegressionModel training Mean Squared Error = 6.416002077543526

LassoModel training Mean Squared Error = 6.972349839013683

Prediction of linear: 0.805390219777772

Prediction of ridge: 1.0907608111865237

Prediction of lasso: 0.18652645118913225