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) 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.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
 * @since 0.4.0
 */
public class SNNClusteringTest 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.Parameterizer.EPSILON_ID, 77);
    params.addParameter(SNNClustering.Parameterizer.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, 0.832371422);
    testClusterSizes(result, new int[] { 73, 228, 213, 219, 231, 236 });
  }
}