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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.commons.math3.ml.clustering;
import org.hamcrest.CoreMatchers;
import org.junit.Assert;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.ml.distance.CanberraDistance;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.junit.Test;
/**
* Test cases for FuzzyKMeansClusterer.
*
* @since 3.3
*/
public class FuzzyKMeansClustererTest {
@Test
public void testCluster() {
final List<DoublePoint> points = new ArrayList<DoublePoint>();
// create 10 data points: [1], ... [10]
for (int i = 1; i <= 10; i++) {
final DoublePoint p = new DoublePoint(new double[] { i } );
points.add(p);
}
final FuzzyKMeansClusterer<DoublePoint> transformer =
new FuzzyKMeansClusterer<DoublePoint>(3, 2.0);
final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);
// we expect 3 clusters:
// [1], [2], [3]
// [4], [5], [6], [7]
// [8], [9], [10]
final List<DoublePoint> clusterOne = Arrays.asList(points.get(0), points.get(1), points.get(2));
final List<DoublePoint> clusterTwo = Arrays.asList(points.get(3), points.get(4), points.get(5), points.get(6));
final List<DoublePoint> clusterThree = Arrays.asList(points.get(7), points.get(8), points.get(9));
boolean cluster1Found = false;
boolean cluster2Found = false;
boolean cluster3Found = false;
Assert.assertEquals(3, clusters.size());
for (final Cluster<DoublePoint> cluster : clusters) {
if (cluster.getPoints().containsAll(clusterOne)) {
cluster1Found = true;
}
if (cluster.getPoints().containsAll(clusterTwo)) {
cluster2Found = true;
}
if (cluster.getPoints().containsAll(clusterThree)) {
cluster3Found = true;
}
}
Assert.assertTrue(cluster1Found);
Assert.assertTrue(cluster2Found);
Assert.assertTrue(cluster3Found);
}
@Test(expected = MathIllegalArgumentException.class)
public void testTooSmallFuzzynessFactor() {
new FuzzyKMeansClusterer<DoublePoint>(3, 1.0);
}
@Test(expected = NullArgumentException.class)
public void testNullDataset() {
final FuzzyKMeansClusterer<DoublePoint> clusterer = new FuzzyKMeansClusterer<DoublePoint>(3, 2.0);
clusterer.cluster(null);
}
@Test
public void testGetters() {
final DistanceMeasure measure = new CanberraDistance();
final RandomGenerator random = new JDKRandomGenerator();
final FuzzyKMeansClusterer<DoublePoint> clusterer =
new FuzzyKMeansClusterer<DoublePoint>(3, 2.0, 100, measure, 1e-6, random);
Assert.assertEquals(3, clusterer.getK());
Assert.assertEquals(2.0, clusterer.getFuzziness(), 1e-6);
Assert.assertEquals(100, clusterer.getMaxIterations());
Assert.assertEquals(1e-6, clusterer.getEpsilon(), 1e-12);
Assert.assertThat(clusterer.getDistanceMeasure(), CoreMatchers.is(measure));
Assert.assertThat(clusterer.getRandomGenerator(), CoreMatchers.is(random));
}
}