/*
* 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.ArrayList;
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.DirichletClusteringPolicy;
import org.apache.mahout.clustering.Model;
import org.apache.mahout.clustering.ModelDistribution;
import org.apache.mahout.clustering.dirichlet.DirichletClusterer;
import org.apache.mahout.clustering.dirichlet.models.GaussianClusterDistribution;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.VectorWritable;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class DisplayDirichlet extends DisplayClustering {
private static final Logger log = LoggerFactory.getLogger(DisplayDirichlet.class);
public DisplayDirichlet() {
initialize();
this.setTitle("Dirichlet Process Clusters - Normal Distribution (>"
+ (int) (significance * 100) + "% of population)");
}
// Override the paint() method
@Override
public void paint(Graphics g) {
plotSampleData((Graphics2D) g);
plotClusters((Graphics2D) g);
}
protected static void printModels(Iterable<Cluster[]> result, int significant) {
int row = 0;
StringBuilder models = new StringBuilder(100);
for (Cluster[] r : result) {
models.append("sample[").append(row++).append("]= ");
for (int k = 0; k < r.length; k++) {
Cluster model = r[k];
if (model.count() > significant) {
models.append('m').append(k).append(model.asFormatString(null)).append(", ");
}
}
models.append('\n');
}
models.append('\n');
log.info(models.toString());
}
protected static void generateResults(ModelDistribution<VectorWritable> modelDist,
int numClusters,
int numIterations,
double alpha0,
int thin,
int burnin) throws IOException {
boolean runClusterer = false;
if (runClusterer) {
runSequentialDirichletClusterer(modelDist, numClusters, numIterations, alpha0, thin, burnin);
} else {
runSequentialDirichletClassifier(modelDist, numClusters, numIterations);
}
}
private static void runSequentialDirichletClassifier(ModelDistribution<VectorWritable> modelDist,
int numClusters,
int numIterations) throws IOException {
List<Cluster> models = new ArrayList<Cluster>();
for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(numClusters)) {
models.add((Cluster) cluster);
}
ClusterClassifier prior = new ClusterClassifier(models);
Path samples = new Path("samples");
Path output = new Path("output");
Path priorClassifier = new Path(output, "clusters-0");
Configuration conf = new Configuration();
writeClassifier(prior, conf, priorClassifier);
ClusteringPolicy policy = new DirichletClusteringPolicy(numClusters, numIterations);
new ClusterIterator(policy).iterate(samples, priorClassifier, output, numIterations);
for (int i = 1; i <= numIterations; i++) {
ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
List<Cluster> clusters = new ArrayList<Cluster>();
for (Cluster cluster : posterior.getModels()) {
if (isSignificant(cluster)) {
clusters.add(cluster);
}
}
CLUSTERS.add(clusters);
}
}
private static void runSequentialDirichletClusterer(ModelDistribution<VectorWritable> modelDist,
int numClusters,
int numIterations,
double alpha0,
int thin,
int burnin) {
DirichletClusterer dc = new DirichletClusterer(SAMPLE_DATA, modelDist, alpha0, numClusters, thin, burnin);
List<Cluster[]> result = dc.cluster(numIterations);
printModels(result, burnin);
for (Cluster[] models : result) {
List<Cluster> clusters = new ArrayList<Cluster>();
for (Cluster cluster : models) {
if (isSignificant(cluster)) {
clusters.add(cluster);
}
}
CLUSTERS.add(clusters);
}
}
public static void main(String[] args) throws Exception {
VectorWritable modelPrototype = new VectorWritable(new DenseVector(2));
ModelDistribution<VectorWritable> modelDist = new GaussianClusterDistribution(modelPrototype);
RandomUtils.useTestSeed();
generateSamples();
int numIterations = 20;
int numClusters = 10;
int alpha0 = 1;
int thin = 3;
int burnin = 5;
generateResults(modelDist, numClusters, numIterations, alpha0, thin, burnin);
new DisplayDirichlet();
}
}