DisplayKMeans.java Source code

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Here is the source code for DisplayKMeans.java

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/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

import java.awt.Graphics;
import java.awt.Graphics2D;
import java.io.IOException;
import java.util.Collection;
import java.util.List;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.classify.ClusterClassifier;
import org.apache.mahout.clustering.iterator.ClusterIterator;
import org.apache.mahout.clustering.iterator.KMeansClusteringPolicy;
import org.apache.mahout.clustering.kmeans.KMeansDriver;
import org.apache.mahout.clustering.kmeans.RandomSeedGenerator;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.common.distance.ManhattanDistanceMeasure;
import org.apache.mahout.math.Vector;

import com.google.common.collect.Lists;

public class DisplayKMeans extends DisplayClustering {

    DisplayKMeans() {
        initialize();
        this.setTitle("k-Means Clusters (>" + (int) (significance * 100) + "% of population)");
    }

    public static void main(String[] args) throws Exception {
        DistanceMeasure measure = new ManhattanDistanceMeasure();
        Path samples = new Path("samples");
        Path output = new Path("output");
        Configuration conf = new Configuration();
        HadoopUtil.delete(conf, samples);
        HadoopUtil.delete(conf, output);

        RandomUtils.useTestSeed();
        generateSamples();
        writeSampleData(samples);
        boolean runClusterer = true;
        double convergenceDelta = 0.001;
        int numClusters = 2;
        int maxIterations = 10;
        if (runClusterer) {
            runSequentialKMeansClusterer(conf, samples, output, measure, numClusters, maxIterations,
                    convergenceDelta);
        } else {
            runSequentialKMeansClassifier(conf, samples, output, measure, numClusters, maxIterations,
                    convergenceDelta);
        }
        new DisplayKMeans();
    }

    private static void runSequentialKMeansClassifier(Configuration conf, Path samples, Path output,
            DistanceMeasure measure, int numClusters, int maxIterations, double convergenceDelta)
            throws IOException {
        Collection<Vector> points = Lists.newArrayList();
        for (int i = 0; i < numClusters; i++) {
            points.add(SAMPLE_DATA.get(i).get());
            //      System.out.println(SAMPLE_DATA.get(i).toString());
        }
        List<Cluster> initialClusters = Lists.newArrayList();
        int id = 0;
        for (Vector point : points) {
            initialClusters.add(new org.apache.mahout.clustering.kmeans.Kluster(point, id++, measure));
        }
        ClusterClassifier prior = new ClusterClassifier(initialClusters,
                new KMeansClusteringPolicy(convergenceDelta));
        Path priorPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
        prior.writeToSeqFiles(priorPath);

        ClusterIterator.iterateSeq(conf, samples, priorPath, output, maxIterations);
        loadClustersWritable(output);
    }

    private static void runSequentialKMeansClusterer(Configuration conf, Path samples, Path output,
            DistanceMeasure measure, int numClusters, int maxIterations, double convergenceDelta)
            throws IOException, InterruptedException, ClassNotFoundException {
        Path clustersIn = new Path(output, "random-seeds");
        RandomSeedGenerator.buildRandom(conf, samples, clustersIn, numClusters, measure);
        KMeansDriver.run(samples, clustersIn, output, convergenceDelta, maxIterations, true, 0.0, true);
        loadClustersWritable(output);
    }

    // Override the paint() method
    @Override
    public void paint(Graphics g) {
        plotSampleData((Graphics2D) g);
        plotClusters((Graphics2D) g);
    }
}