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Diffstat (limited to 'test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestOPTICSResults.java')
-rw-r--r-- | test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestOPTICSResults.java | 71 |
1 files changed, 0 insertions, 71 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestOPTICSResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestOPTICSResults.java deleted file mode 100644 index dde81a0f..00000000 --- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestOPTICSResults.java +++ /dev/null @@ -1,71 +0,0 @@ -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) 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.data.Clustering; -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 OPTICS run, and compares the result with a clustering derived - * from the data set labels. This test ensures that OPTICS'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 TestOPTICSResults extends AbstractSimpleAlgorithmTest implements JUnit4Test { - /** - * Run OPTICS with fixed parameters and compare the result to a golden - * standard. - * - * @throws ParameterException - */ - @Test - public void testOPTICSResults() { - Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710); - - // Setup algorithm - ListParameterization params = new ListParameterization(); - params.addParameter(OPTICS.MINPTS_ID, 18); - params.addParameter(OPTICSXi.XI_ID, 0.038); - params.addParameter(OPTICSXi.XIALG_ID, OPTICS.class); - OPTICSXi<DoubleDistance> opticsxi = ClassGenericsUtil.parameterizeOrAbort(OPTICSXi.class, params); - testParameterizationOk(params); - - // run OPTICS on database - Clustering<?> clustering = opticsxi.run(db); - - testFMeasure(db, clustering, 0.874062); - testClusterSizes(clustering, new int[] { 109, 121, 210, 270 }); - } -}
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