Package de.lmu.ifi.dbs.elki.algorithm.outlier

Source Code of de.lmu.ifi.dbs.elki.algorithm.outlier.TestLOCI

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) 2011
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 LOCI algorithm.
*
* @author Lucia Cichella
*/
public class TestLOCI extends AbstractSimpleAlgorithmTest implements JUnit4Test {
  @Test
  public void testLOCI() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(LOCI.RMAX_ID, 0.5);

    // setup Algorithm
    LOCI<DoubleVector, DoubleDistance> loci = ClassGenericsUtil.parameterizeOrAbort(LOCI.class, params);
    testParameterizationOk(params);

    // run LOCI on database
    OutlierResult result = loci.run(db);

    testAUC(db, "Noise", result, 0.954444);
    testSingleScore(result, 146, 4.14314916);
  }
}
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