wie die lineare regression der MLlib von apache spark?

package org.apache.spark.examples;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;

import java.io.Serializable;
import java.util.Arrays;
import java.util.Random;
import java.util.regex.Pattern;

/**
 * Logistic regression based classification.
 *
 * This is an example implementation for learning how to use Spark. For more conventional use,
 * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
 * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
 */
public final class JavaHdfsLR {

  private static final int D = 10;   //Number of dimensions
  private static final Random rand = new Random(42);

  static void showWarning() {
    String warning = "WARN: This is a naive implementation of Logistic Regression " +
            "and is given as an example!\n" +
            "Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD " +
            "or org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS " +
            "for more conventional use.";
    System.err.println(warning);
  }

  static class DataPoint implements Serializable {
    DataPoint(double[] x, double y) {
      this.x = x;
      this.y = y;
    }

    double[] x;
    double y;
  }

  static class ParsePoint implements Function<String, DataPoint> {
    private static final Pattern SPACE = Pattern.compile(" ");

    @Override
    public DataPoint call(String line) {
      String[] tok = SPACE.split(line);
      double y = Double.parseDouble(tok[0]);
      double[] x = new double[D];
      for (int i = 0; i < D; i++) {
        x[i] = Double.parseDouble(tok[i + 1]);
      }
      return new DataPoint(x, y);
    }
  }

  static class VectorSum implements Function2<double[], double[], double[]> {
    @Override
    public double[] call(double[] a, double[] b) {
      double[] result = new double[D];
      for (int j = 0; j < D; j++) {
        result[j] = a[j] + b[j];
      }
      return result;
    }
  }

  static class ComputeGradient implements Function<DataPoint, double[]> {
    private final double[] weights;

    ComputeGradient(double[] weights) {
      this.weights = weights;
    }

    @Override
    public double[] call(DataPoint p) {
      double[] gradient = new double[D];
      for (int i = 0; i < D; i++) {
        double dot = dot(weights, p.x);
        gradient[i] = (1 / (1 + Math.exp(-p.y * dot)) - 1) * p.y * p.x[i];
      }
      return gradient;
    }
  }

  public static double dot(double[] a, double[] b) {
    double x = 0;
    for (int i = 0; i < D; i++) {
      x += a[i] * b[i];
    }
    return x;
  }

  public static void printWeights(double[] a) {
    System.out.println(Arrays.toString(a));
  }

  public static void main(String[] args) {

    if (args.length < 2) {
      System.err.println("Usage: JavaHdfsLR <file> <iters>");
      System.exit(1);
    }

    showWarning();

    SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
    JavaSparkContext sc = new JavaSparkContext(sparkConf);
    JavaRDD<String> lines = sc.textFile(args[0]);
    JavaRDD<DataPoint> points = lines.map(new ParsePoint()).cache();
    int ITERATIONS = Integer.parseInt(args[1]);

    //Initialize w to a random value
    double[] w = new double[D];
    for (int i = 0; i < D; i++) {
      w[i] = 2 * rand.nextDouble() - 1;
    }

    System.out.print("Initial w: ");
    printWeights(w);

    for (int i = 1; i <= ITERATIONS; i++) {
      System.out.println("On iteration " + i);

      double[] gradient = points.map(
        new ComputeGradient(w)
      ).reduce(new VectorSum());

      for (int j = 0; j < D; j++) {
        w[j] -= gradient[j];
      }

    }

    System.out.print("Final w: ");
    printWeights(w);
    sc.stop();
  }
}