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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.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.Model;
import org.apache.mahout.clustering.ModelDistribution;
import org.apache.mahout.clustering.classify.ClusterClassifier;
import org.apache.mahout.clustering.dirichlet.DirichletDriver;
import org.apache.mahout.clustering.dirichlet.models.DistributionDescription;
import org.apache.mahout.clustering.dirichlet.models.GaussianClusterDistribution;
import org.apache.mahout.clustering.iterator.ClusterIterator;
import org.apache.mahout.clustering.iterator.DirichletClusteringPolicy;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.common.distance.ManhattanDistanceMeasure;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.VectorWritable;
import com.google.common.collect.Lists;
public class DisplayDirichlet extends DisplayClustering {
public DisplayDirichlet() {
initialize();
setTitle("Dirichlet Process Clusters - Normal Distribution (>" + (int) (significance * 100) + "% of population)");
}
@Override
public void paint(Graphics g) {
plotSampleData((Graphics2D) g);
plotClusters((Graphics2D) g);
}
protected static void generateResults(Path input, Path output, ModelDistribution<VectorWritable> modelDist,
int numClusters, int numIterations, double alpha0, int thin, int burnin) throws IOException,
ClassNotFoundException, InterruptedException {
boolean runClusterer = true;
if (runClusterer) {
runSequentialDirichletClusterer(input, output, modelDist, numClusters, numIterations, alpha0);
} else {
runSequentialDirichletClassifier(input, output, modelDist, numClusters, numIterations, alpha0);
}
for (int i = 1; i <= numIterations; i++) {
ClusterClassifier posterior = new ClusterClassifier();
String name = i == numIterations ? "clusters-" + i + "-final" : "clusters-" + i;
posterior.readFromSeqFiles(new Configuration(), new Path(output, name));
List<Cluster> clusters = Lists.newArrayList();
for (Cluster cluster : posterior.getModels()) {
if (isSignificant(cluster)) {
clusters.add(cluster);
}
}
CLUSTERS.add(clusters);
}
}
private static void runSequentialDirichletClassifier(Path input, Path output,
ModelDistribution<VectorWritable> modelDist, int numClusters, int numIterations, double alpha0)
throws IOException {
List<Cluster> models = Lists.newArrayList();
for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(numClusters)) {
models.add((Cluster) cluster);
}
ClusterClassifier prior = new ClusterClassifier(models, new DirichletClusteringPolicy(numClusters, alpha0));
Path priorPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
prior.writeToSeqFiles(priorPath);
Configuration conf = new Configuration();
ClusterIterator.iterateSeq(conf, input, priorPath, output, numIterations);
}
private static void runSequentialDirichletClusterer(Path input, Path output,
ModelDistribution<VectorWritable> modelDist, int numClusters, int numIterations, double alpha0)
throws IOException, ClassNotFoundException, InterruptedException {
DistributionDescription description = new DistributionDescription(modelDist.getClass().getName(),
RandomAccessSparseVector.class.getName(), ManhattanDistanceMeasure.class.getName(), 2);
DirichletDriver.run(new Configuration(), input, output, description, numClusters, numIterations, alpha0, true,
true, 0, true);
}
public static void main(String[] args) throws Exception {
VectorWritable modelPrototype = new VectorWritable(new DenseVector(2));
ModelDistribution<VectorWritable> modelDist = new GaussianClusterDistribution(modelPrototype);
Configuration conf = new Configuration();
Path output = new Path("output");
HadoopUtil.delete(conf, output);
Path samples = new Path("samples");
HadoopUtil.delete(conf, samples);
RandomUtils.useTestSeed();
generateSamples();
writeSampleData(samples);
int numIterations = 20;
int numClusters = 10;
int alpha0 = 1;
int thin = 3;
int burnin = 5;
generateResults(samples, output, modelDist, numClusters, numIterations, alpha0, thin, burnin);
new DisplayDirichlet();
}
}