KMeans from apache spark - Java Big Data

Java examples for Big Data:apache spark

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Description

KMeans from apache spark

Demo Code

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 * this work for additional information regarding copyright ownership.
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 * (the "License"); you may not use this file except in compliance with
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package eduonix.spark.mllib;

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.mllib.clustering.KMeans;
import org.apache.spark.mllib.clustering.KMeansModel;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;

import java.net.URL;
import java.util.regex.Pattern;

/**
 * Example using MLlib KMeans from Java.
 */
public final class JavaKMeans {

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

        @Override
        public Vector call(String line) {
            String[] tok = SPACE.split(line);
            double[] point = new double[tok.length];
            for (int i = 0; i < tok.length; ++i) {
                point[i] = Double.parseDouble(tok[i]);
            }
            return Vectors.dense(point);
        }

    }

    static boolean runOnCluster = false;

    public static void main(String[] args) {

        URL resource = null;

        if (!runOnCluster) {
            resource = JavaKMeans.class.getResource("input_k_small.txt");
        } else if (args.length < 3) {
            System.err
                    .println("Usage: JavaKMeans <input_file> <k> <max_iterations> [<runs>]");
            System.exit(1);
        }

        String inputFile = null;
        int k = 0;
        int iterations = 0;
        int runs = 0;

        if (!runOnCluster) {

            inputFile = resource.getFile();
            System.out.println(inputFile);
            k = 10;
            iterations = 10;
            runs = 1;
        } else {

            inputFile = args[0];
            k = Integer.parseInt(args[1]);
            iterations = Integer.parseInt(args[2]);
            runs = 1;
            if (args.length >= 4) {
                runs = Integer.parseInt(args[3]);
            }

        }

        SparkConf sparkConf = new SparkConf().setAppName("JavaKMeans");

        if (!runOnCluster) {

            sparkConf.setMaster("local");
            sparkConf.setSparkHome(resource.getPath().replaceAll(
                    "/input_k_small.txt", ""));
            sparkConf
                    .setJars(new String[] { "build/libs/spark-gradle-0.1.0.jar" });

        }

        JavaSparkContext sc = new JavaSparkContext(sparkConf);
        JavaRDD<String> lines = sc.textFile(inputFile);

        JavaRDD<Vector> points = lines.map(new ParsePoint());

        KMeansModel model = KMeans.train(points.rdd(), k, iterations, runs,
                KMeans.K_MEANS_PARALLEL());

        System.out.println("Cluster centers:");
        for (Vector center : model.clusterCenters()) {
            System.out.println(" " + center);
        }
        double cost = model.computeCost(points.rdd());
        System.out.println("Cost: " + cost);

        sc.stop();
    }
}

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