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package de.lmu.ifi.dbs.elki.algorithm.outlier;
/*
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.DoubleVector;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
* Tests the KNNWeightOutlier algorithm.
*
* @author Lucia Cichella
*/
public class TestKNNWeightOutlier extends AbstractSimpleAlgorithmTest implements JUnit4Test {
@Test
public void testKNNWeightOutlier() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
// Parameterization
ListParameterization params = new ListParameterization();
params.addParameter(KNNWeightOutlier.K_ID, 5);
// setup Algorithm
KNNWeightOutlier<DoubleVector, DoubleDistance> knnWeightOutlier = ClassGenericsUtil.parameterizeOrAbort(KNNWeightOutlier.class, params);
testParameterizationOk(params);
// run KNNWeightOutlier on database
OutlierResult result = knnWeightOutlier.run(db);
testSingleScore(result, 945, 2.384117261027324);
testAUC(db, "Noise", result, 0.9912777777777778);
}
}
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