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package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import org.junit.Test;
import de.lmu.ifi.dbs.elki.JUnit4Test;
import de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.model.MedoidModel;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
* Performs a full KMeans run, and compares the result with a clustering derived
* from the data set labels. This test ensures that KMeans's performance doesn't
* unexpectedly drop on this data set (and also ensures that the algorithms
* work, as a side effect).
*
* @author Katharina Rausch
* @author Erich Schubert
* @since 0.4.0
*/
public class KMedoidsPAMTest extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run KMedians PAM with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testKMedoidsPAM() {
Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
// Setup algorithm
ListParameterization params = new ListParameterization();
params.addParameter(KMeans.K_ID, 5);
KMedoidsPAM<DoubleVector> kmedians = ClassGenericsUtil.parameterizeOrAbort(KMedoidsPAM.class, params);
testParameterizationOk(params);
// run KMedians on database
Clustering<MedoidModel> result = kmedians.run(db);
testFMeasure(db, result, 0.998005);
testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 });
}
}
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