/**
* 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.
*/
package org.apache.mahout.clustering.display;
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.fuzzykmeans.FuzzyKMeansDriver;
import org.apache.mahout.clustering.fuzzykmeans.SoftCluster;
import org.apache.mahout.clustering.iterator.ClusterIterator;
import org.apache.mahout.clustering.iterator.FuzzyKMeansClusteringPolicy;
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 DisplayFuzzyKMeans extends DisplayClustering {
DisplayFuzzyKMeans() {
initialize();
this.setTitle("Fuzzy k-Means Clusters (>" + (int) (significance * 100) + "% of population)");
}
// Override the paint() method
@Override
public void paint(Graphics g) {
plotSampleData((Graphics2D) g);
plotClusters((Graphics2D) g);
}
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, output);
HadoopUtil.delete(conf, samples);
RandomUtils.useTestSeed();
DisplayClustering.generateSamples();
writeSampleData(samples);
boolean runClusterer = true;
int maxIterations = 10;
float threshold = 0.001F;
float m = 1.1F;
if (runClusterer) {
runSequentialFuzzyKClusterer(conf, samples, output, measure, maxIterations, m, threshold);
} else {
int numClusters = 3;
runSequentialFuzzyKClassifier(conf, samples, output, measure, numClusters, maxIterations, m, threshold);
}
new DisplayFuzzyKMeans();
}
private static void runSequentialFuzzyKClassifier(Configuration conf, Path samples, Path output,
DistanceMeasure measure, int numClusters, int maxIterations, float m, double threshold) throws IOException {
Collection<Vector> points = Lists.newArrayList();
for (int i = 0; i < numClusters; i++) {
points.add(SAMPLE_DATA.get(i).get());
}
List<Cluster> initialClusters = Lists.newArrayList();
int id = 0;
for (Vector point : points) {
initialClusters.add(new SoftCluster(point, id++, measure));
}
ClusterClassifier prior = new ClusterClassifier(initialClusters, new FuzzyKMeansClusteringPolicy(m, threshold));
Path priorPath = new Path(output, "classifier-0");
prior.writeToSeqFiles(priorPath);
ClusterIterator.iterateSeq(conf, samples, priorPath, output, maxIterations);
loadClustersWritable(output);
}
private static void runSequentialFuzzyKClusterer(Configuration conf, Path samples, Path output,
DistanceMeasure measure, int maxIterations, float m, double threshold) throws IOException,
ClassNotFoundException, InterruptedException {
Path clustersIn = new Path(output, "random-seeds");
RandomSeedGenerator.buildRandom(conf, samples, clustersIn, 3, measure);
FuzzyKMeansDriver.run(samples, clustersIn, output, measure, threshold, maxIterations, m, true, true, threshold,
true);
loadClustersWritable(output);
}
}