diff options
author | Andrej Shadura <andrewsh@debian.org> | 2019-03-09 22:30:46 +0000 |
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committer | Andrej Shadura <andrewsh@debian.org> | 2019-03-09 22:30:46 +0000 |
commit | 0a055548ae9f9d5c639bb29ca32e0fd88de37c1d (patch) | |
tree | 3bd93fd4bb0ae6025a6fcfadeb7844669fea457c /elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java | |
parent | 38212b3127e90751fb39cda34250bc11be62b76c (diff) |
Import Upstream version 0.7.1
Diffstat (limited to 'elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java')
-rw-r--r-- | elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java | 96 |
1 files changed, 96 insertions, 0 deletions
diff --git a/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java new file mode 100644 index 00000000..7c2b0bce --- /dev/null +++ b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java @@ -0,0 +1,96 @@ +package de.lmu.ifi.dbs.elki.algorithm.clustering; + +/* + 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.Model; +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 DBSCAN run, and compares the result with a clustering derived + * from the data set labels. This test ensures that DBSCAN performance doesn't + * unexpectedly drop on this data set (and also ensures that the algorithms + * work, as a side effect). + * + * @author Elke Achtert + * @author Erich Schubert + * @author Katharina Rausch + * @since 0.3 + */ +public class DBSCANTest extends AbstractSimpleAlgorithmTest implements JUnit4Test { + /** + * Run DBSCAN with fixed parameters and compare the result to a golden + * standard. + * + * @throws ParameterException + */ + @Test + public void testDBSCANResults() { + Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330); + + // setup algorithm + ListParameterization params = new ListParameterization(); + params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 0.04); + params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 20); + DBSCAN<DoubleVector> dbscan = ClassGenericsUtil.parameterizeOrAbort(DBSCAN.class, params); + testParameterizationOk(params); + + // run DBSCAN on database + Clustering<Model> result = dbscan.run(db); + + testFMeasure(db, result, 0.996413); + testClusterSizes(result, new int[] { 29, 50, 101, 150 }); + } + + /** + * Run DBSCAN with fixed parameters and compare the result to a golden + * standard. + * + * @throws ParameterException + */ + @Test + public void testDBSCANOnSingleLinkDataset() { + Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638); + + // Setup algorithm + ListParameterization params = new ListParameterization(); + params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 11.5); + params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 120); + DBSCAN<DoubleVector> dbscan = ClassGenericsUtil.parameterizeOrAbort(DBSCAN.class, params); + testParameterizationOk(params); + + // run DBSCAN on database + Clustering<Model> result = dbscan.run(db); + testFMeasure(db, result, 0.954382); + testClusterSizes(result, new int[] { 11, 200, 203, 224 }); + } +}
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