/**
* 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.dirichlet;
import java.util.ArrayList;
import java.util.List;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.dirichlet.models.DistanceMeasureClusterDistribution;
import org.apache.mahout.clustering.dirichlet.models.GaussianClusterDistribution;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.VectorWritable;
import org.junit.Before;
import org.junit.Test;
public final class TestDirichletClustering extends MahoutTestCase {
private List<VectorWritable> sampleData;
@Override
@Before
public void setUp() throws Exception {
super.setUp();
sampleData = new ArrayList<VectorWritable>();
}
/**
* Generate random samples and add them to the sampleData
*
* @param num int number of samples to generate
* @param mx double x-value of the sample mean
* @param my double y-value of the sample mean
* @param sd double standard deviation of the samples
* @param card int cardinality of the generated sample vectors
*/
private void generateSamples(int num, double mx, double my, double sd, int card) {
System.out.println("Generating " + num + " samples m=[" + mx + ", " + my + "] sd=" + sd);
for (int i = 0; i < num; i++) {
DenseVector v = new DenseVector(card);
for (int j = 0; j < card; j++) {
v.set(j, UncommonDistributions.rNorm(mx, sd));
}
sampleData.add(new VectorWritable(v));
}
}
/**
* Generate 2-d samples for backwards compatibility with existing tests
* @param num int number of samples to generate
* @param mx double x-value of the sample mean
* @param my double y-value of the sample mean
* @param sd double standard deviation of the samples
*/
private void generateSamples(int num, double mx, double my, double sd) {
generateSamples(num, mx, my, sd, 2);
}
private static void printResults(Iterable<Cluster[]> result, int significant) {
int row = 0;
for (Cluster[] r : result) {
System.out.print("sample[" + row++ + "]= ");
for (Cluster model : r) {
if (model.count() > significant) {
System.out.print(model.asFormatString(null) + ", ");
}
}
System.out.println();
}
System.out.println();
}
@Test
public void testDirichletCluster100() {
System.out.println("testDirichletCluster100");
generateSamples(40, 1, 1, 3);
generateSamples(30, 1, 0, 0.1);
generateSamples(30, 0, 1, 0.1);
DirichletClusterer dc = new DirichletClusterer(sampleData,
new GaussianClusterDistribution(new VectorWritable(new DenseVector(2))),
1.0,
10,
1,
0);
List<Cluster[]> result = dc.cluster(30);
printResults(result, 2);
assertNotNull(result);
}
@Test
public void testDirichletGaussianCluster100() {
System.out.println("testDirichletGaussianCluster100");
generateSamples(40, 1, 1, 3);
generateSamples(30, 1, 0, 0.1);
generateSamples(30, 0, 1, 0.1);
DirichletClusterer dc = new DirichletClusterer(sampleData,
new GaussianClusterDistribution(new VectorWritable(new DenseVector(2))),
1.0,
10,
1,
0);
List<Cluster[]> result = dc.cluster(30);
printResults(result, 2);
assertNotNull(result);
}
@Test
public void testDirichletDMCluster100() {
System.out.println("testDirichletDMCluster100");
generateSamples(40, 1, 1, 3);
generateSamples(30, 1, 0, 0.1);
generateSamples(30, 0, 1, 0.1);
DirichletClusterer dc = new DirichletClusterer(sampleData,
new DistanceMeasureClusterDistribution(new VectorWritable(new DenseVector(2))),
1.0,
10,
1,
0);
List<Cluster[]> result = dc.cluster(30);
printResults(result, 2);
assertNotNull(result);
}
}