/*
* Source code for Listing 9.8
*
*/
package mia.clustering.ch09;
import java.util.ArrayList;
import java.util.List;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.dirichlet.DirichletClusterer;
import org.apache.mahout.clustering.dirichlet.models.GaussianClusterDistribution;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
public class DirichletExample {
public static void main(String[] args) {
List<Vector> sampleData = new ArrayList<Vector>();
RandomPointsUtil.generateSamples(sampleData, 400, 1, 1, 3);
RandomPointsUtil.generateSamples(sampleData, 300, 1, 0, 0.5);
RandomPointsUtil.generateSamples(sampleData, 300, 0, 2, 0.1);
List<VectorWritable> points = new ArrayList<VectorWritable>();
for (Vector sd : sampleData) {
points.add(new VectorWritable(sd));
}
DirichletClusterer dc = new DirichletClusterer(points,
new GaussianClusterDistribution(new VectorWritable(
new DenseVector(2))), 1.0, 10, 2, 2);
List<Cluster[]> result = dc.cluster(20);
for (Cluster cluster : result.get(result.size() - 1)) {
System.out.println("Cluster id: " + cluster.getId() + " center: "
+ cluster.getCenter().asFormatString());
}
}
}