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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.
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package org.apache.mahout.clustering.display;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.io.IOException;
import java.util.ArrayList;
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.ClusterClassifier;
import org.apache.mahout.clustering.ClusterIterator;
import org.apache.mahout.clustering.ClusteringPolicy;
import org.apache.mahout.clustering.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;
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();
DisplayClustering.generateSamples();
writeSampleData(samples);
boolean runClusterer = false;
if (runClusterer) {
int numClusters = 3;
runSequentialKMeansClusterer(conf, samples, output, measure, numClusters);
} else {
int maxIterations = 10;
runSequentialKMeansClassifier(conf, samples, output, measure, maxIterations);
}
new DisplayKMeans();
}
private static void runSequentialKMeansClassifier(Configuration conf,
Path samples,
Path output,
DistanceMeasure measure,
int numClusters) throws IOException {
Collection<Vector> points = new ArrayList<Vector>();
for (int i = 0; i < numClusters; i++) {
points.add(SAMPLE_DATA.get(i).get());
}
List<Cluster> initialClusters = new ArrayList<Cluster>();
int id = 0;
for (Vector point : points) {
initialClusters.add(new org.apache.mahout.clustering.kmeans.Cluster(
point, id++, measure));
}
ClusterClassifier prior = new ClusterClassifier(initialClusters);
Path priorClassifier = new Path(output, "clusters-0");
writeClassifier(prior, conf, priorClassifier);
int maxIter = 10;
ClusteringPolicy policy = new KMeansClusteringPolicy();
new ClusterIterator(policy).iterate(samples, priorClassifier, output, maxIter);
for (int i = 1; i <= maxIter; i++) {
ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
CLUSTERS.add(posterior.getModels());
}
}
private static void runSequentialKMeansClusterer(Configuration conf,
Path samples,
Path output,
DistanceMeasure measure,
int maxIterations)
throws IOException, InterruptedException, ClassNotFoundException {
Path clusters = RandomSeedGenerator.buildRandom(conf, samples, new Path(
output, "clusters-0"), 3, measure);
double distanceThreshold = 0.001;
KMeansDriver.run(samples, clusters, output, measure, distanceThreshold,
maxIterations, true, true);
loadClusters(output);
}
// Override the paint() method
@Override
public void paint(Graphics g) {
plotSampleData((Graphics2D) g);
plotClusters((Graphics2D) g);
}
}