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Diffstat (limited to 'test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestORCLUSResults.java')
-rw-r--r-- | test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestORCLUSResults.java | 98 |
1 files changed, 0 insertions, 98 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestORCLUSResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestORCLUSResults.java deleted file mode 100644 index 5cdb09c9..00000000 --- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestORCLUSResults.java +++ /dev/null @@ -1,98 +0,0 @@ -package de.lmu.ifi.dbs.elki.algorithm.clustering.correlation; - -/* - 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.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 ORCLUS run, and compares the result with a clustering derived - * from the data set labels. This test ensures that ORCLUS performance doesn't - * unexpectedly drop on this data set (and also ensures that the algorithms - * work, as a side effect). - * - * @author Elke Achtert - * @author Katharina Rausch - */ -public class TestORCLUSResults extends AbstractSimpleAlgorithmTest implements JUnit4Test { - /** - * Run ORCLUS with fixed parameters and compare the result to a golden - * standard. - * - * @throws ParameterException on errors. - */ - @Test - public void testORCLUSResults() { - Database db = makeSimpleDatabase(UNITTEST + "correlation-hierarchy.csv", 450); - - ListParameterization params = new ListParameterization(); - params.addParameter(ORCLUS.Parameterizer.K_ID, 3); - params.addParameter(ORCLUS.Parameterizer.L_ID, 1); - params.addParameter(ORCLUS.Parameterizer.SEED_ID, 2); - - // setup algorithm - ORCLUS<DoubleVector> orclus = ClassGenericsUtil.parameterizeOrAbort(ORCLUS.class, params); - testParameterizationOk(params); - - // run ORCLUS on database - Clustering<Model> result = orclus.run(db); - - testFMeasure(db, result, 0.6361108); // Hierarchical pairs scored: 0.789113 - testClusterSizes(result, new int[] { 19, 33, 398 }); - } - - /** - * Run ORCLUS with fixed parameters and compare the result to a golden - * standard. - * - * @throws ParameterException on errors. - */ - @Test - public void testORCLUSSkewedDisjoint() { - Database db = makeSimpleDatabase(UNITTEST + "correlation-skewed-disjoint-3-5d.ascii", 601); - - // Setup algorithm - ListParameterization params = new ListParameterization(); - params.addParameter(ORCLUS.Parameterizer.K_ID, 3); - params.addParameter(ORCLUS.Parameterizer.L_ID, 4); - params.addParameter(ORCLUS.Parameterizer.SEED_ID, 9); - - ORCLUS<DoubleVector> orclus = ClassGenericsUtil.parameterizeOrAbort(ORCLUS.class, params); - testParameterizationOk(params); - - // run ORCLUS on database - Clustering<Model> result = orclus.run(db); - testFMeasure(db, result, 0.8687866); - testClusterSizes(result, new int[] { 170, 200, 231 }); - } -}
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