1 files changed, 0 insertions, 72 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestSNNClusteringResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestSNNClusteringResults.java
deleted file mode 100644
index 302d5c0b..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestSNNClusteringResults.java
+++ /dev/null
@@ -1,72 +0,0 @@
-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, 0.832371422);
-    testClusterSizes(result, new int[] { 73, 228, 213, 219, 231, 236 });
-  }
-}
-\ No newline at end of file