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package de.lmu.ifi.dbs.elki.algorithm.clustering.subspace;
/*
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.database.Database;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
* Test DOC on a simple test data set.
*
* On the first set, its an all-or-nothing depending on the parameters.
*
* @author Erich Schubert
* @since 0.3
*/
public class DOCTest extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run DOC with fixed parameters and compare the result to a golden standard.
*/
@Test
public void testDOCSimple() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-simple.csv", 600);
ListParameterization params = new ListParameterization();
params.addParameter(DOC.Parameterizer.RANDOM_ID, 0);
params.addParameter(DOC.Parameterizer.ALPHA_ID, 0.4);
params.addParameter(DOC.Parameterizer.BETA_ID, 0.85);
// setup algorithm
DOC<DoubleVector> doc = ClassGenericsUtil.parameterizeOrAbort(DOC.class, params);
testParameterizationOk(params);
// run DOC on database
Clustering<?> result = doc.run(db);
testClusterSizes(result, new int[] { 200, 400 });
testFMeasure(db, result, 1.0);
}
/**
* Run DOC with fixed parameters and compare the result to a golden standard.
*/
@Test
public void testDOCOverlapping() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);
// Setup algorithm
ListParameterization params = new ListParameterization();
params.addParameter(DOC.Parameterizer.RANDOM_ID, 0);
params.addParameter(DOC.Parameterizer.ALPHA_ID, 0.4);
params.addParameter(DOC.Parameterizer.BETA_ID, 0.95);
params.addFlag(DOC.Parameterizer.HEURISTICS_ID);
params.addParameter(DOC.Parameterizer.D_ZERO_ID, 1);
DOC<DoubleVector> doc = ClassGenericsUtil.parameterizeOrAbort(DOC.class, params);
testParameterizationOk(params);
// run DOC on database
Clustering<?> result = doc.run(db);
testFMeasure(db, result, .54271816);
testClusterSizes(result, new int[] { 1, 20, 33, 40, 56, 104, 274, 322 });
}
}
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