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Diffstat (limited to 'test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java')
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diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java deleted file mode 100644 index 3601b977..00000000 --- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java +++ /dev/null @@ -1,163 +0,0 @@ -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) 2012 - 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.algorithm.clustering.kmeans.KMeansLloyd; -import de.lmu.ifi.dbs.elki.data.Clustering; -import de.lmu.ifi.dbs.elki.data.DoubleVector; -import de.lmu.ifi.dbs.elki.data.model.MeanModel; -import de.lmu.ifi.dbs.elki.data.model.MedoidModel; -import de.lmu.ifi.dbs.elki.database.Database; -import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance; -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 - */ -public class TestKMeansResults extends AbstractSimpleAlgorithmTest implements JUnit4Test { - /** - * Run KMeans with fixed parameters and compare the result to a golden - * standard. - * - * @throws ParameterException - */ - @Test - public void testKMeansLloyd() { - Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000); - - // Setup algorithm - ListParameterization params = new ListParameterization(); - params.addParameter(KMeans.K_ID, 5); - params.addParameter(KMeans.SEED_ID, 2); - AbstractKMeans<DoubleVector, DoubleDistance, ?> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansLloyd.class, params); - testParameterizationOk(params); - - // run KMeans on database - Clustering<? extends MeanModel<DoubleVector>> result = kmeans.run(db); - testFMeasure(db, result, 0.998005); - testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 }); - } - - /** - * Run KMeans with fixed parameters and compare the result to a golden - * standard. - * - * @throws ParameterException - */ - @Test - public void testKMeansMacQueen() { - Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000); - - // Setup algorithm - ListParameterization params = new ListParameterization(); - params.addParameter(KMeans.K_ID, 5); - params.addParameter(KMeans.SEED_ID, 2); - AbstractKMeans<DoubleVector, DoubleDistance, ?> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansMacQueen.class, params); - testParameterizationOk(params); - - // run KMeans on database - Clustering<? extends MeanModel<DoubleVector>> result = kmeans.run(db); - testFMeasure(db, result, 0.998005); - testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 }); - } - - /** - * Run KMedians with fixed parameters and compare the result to a golden - * standard. - * - * @throws ParameterException - */ - @Test - public void testKMediansLloyd() { - Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000); - - // Setup algorithm - ListParameterization params = new ListParameterization(); - params.addParameter(KMeans.K_ID, 5); - params.addParameter(KMeans.SEED_ID, 2); - AbstractKMeans<DoubleVector, DoubleDistance, ?> kmedians = ClassGenericsUtil.parameterizeOrAbort(KMediansLloyd.class, params); - testParameterizationOk(params); - - // run KMedians on database - Clustering<? extends MeanModel<DoubleVector>> result = kmedians.run(db); - testFMeasure(db, result, 0.998005); - testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 }); - } - - /** - * 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, DoubleDistance> 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 }); - } - - /** - * Run KMedoidsEM with fixed parameters and compare the result to a golden - * standard. - * - * @throws ParameterException - */ - @Test - public void testKMedoidsEM() { - Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000); - - // Setup algorithm - ListParameterization params = new ListParameterization(); - params.addParameter(KMeans.K_ID, 5); - KMedoidsEM<DoubleVector, DoubleDistance> kmedians = ClassGenericsUtil.parameterizeOrAbort(KMedoidsEM.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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