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.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.index.preprocessed.snn.SharedNearestNeighborPreprocessor;
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 SNNClustering run, and compares the result with a clustering
* derived from the data set labels. This test ensures that SNNClustering'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 TestSNNClusteringResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run SNNClustering with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testSNNClusteringResults() {
Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d.ascii", 1200);
// Setup algorithm
ListParameterization params = new ListParameterization();
params.addParameter(SNNClustering.EPSILON_ID, 77);
params.addParameter(SNNClustering.MINPTS_ID, 28);
params.addParameter(SharedNearestNeighborPreprocessor.Factory.NUMBER_OF_NEIGHBORS_ID, 100);
SNNClustering<DoubleVector> snn = ClassGenericsUtil.parameterizeOrAbort(SNNClustering.class, params);
testParameterizationOk(params);
// run SNN on database
Clustering<Model> result = snn.run(db);
testFMeasure(db, result, 0.835000);
testClusterSizes(result, new int[] { 76, 213, 219, 225, 231, 236 });
}
}