1 files changed, 0 insertions, 140 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/TestNaiveAgglomerativeHierarchicalClustering.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/TestNaiveAgglomerativeHierarchicalClustering.java
deleted file mode 100644
index 8ed18823..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/TestNaiveAgglomerativeHierarchicalClustering.java
+++ /dev/null
@@ -1,140 +0,0 @@
-package de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical;
-
-/*
- 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.database.Database;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
-import de.lmu.ifi.dbs.elki.result.Result;
-import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
-import de.lmu.ifi.dbs.elki.workflow.AlgorithmStep;
-
-/**
- * Perform agglomerative hierarchical clustering, using the naive algorithm.
- * 
- * @author Erich Schubert
- */
-public class TestNaiveAgglomerativeHierarchicalClustering extends AbstractSimpleAlgorithmTest implements JUnit4Test {
-  // TODO: add more data sets.
-
-  /**
-   * Run agglomerative hierarchical clustering with fixed parameters and compare
-   * the result to a golden standard.
-   */
-  @Test
-  public void testSingleLink() {
-    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.OUTPUTMODE_ID, ExtractFlatClusteringFromHierarchy.OutputMode.STRICT_PARTITIONS);
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
-    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, NaiveAgglomerativeHierarchicalClustering.class);
-    params.addParameter(NaiveAgglomerativeHierarchicalClustering.Parameterizer.LINKAGE_ID, SingleLinkageMethod.class);
-    ExtractFlatClusteringFromHierarchy<DoubleDistance> c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
-    testParameterizationOk(params);
-
-    // run clustering algorithm on database
-    Result result = c.run(db);
-    Clustering<?> clustering = findSingleClustering(result);
-    testFMeasure(db, clustering, 0.6829722);
-    testClusterSizes(clustering, new int[] { 9, 200, 429 });
-  }
-
-  /**
-   * Run agglomerative hierarchical clustering with fixed parameters and compare
-   * the result to a golden standard.
-   */
-  @Test
-  public void testWard() {
-    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.OUTPUTMODE_ID, ExtractFlatClusteringFromHierarchy.OutputMode.STRICT_PARTITIONS);
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
-    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, NaiveAgglomerativeHierarchicalClustering.class);
-    ExtractFlatClusteringFromHierarchy<DoubleDistance> c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
-    testParameterizationOk(params);
-
-    // run clustering algorithm on database
-    Result result = c.run(db);
-    Clustering<?> clustering = findSingleClustering(result);
-    testFMeasure(db, clustering, 0.93866265);
-    testClusterSizes(clustering, new int[] { 200, 211, 227 });
-  }
-
-  /**
-   * Run agglomerative hierarchical clustering with fixed parameters and compare
-   * the result to a golden standard.
-   */
-  @Test
-  public void testGroupAverage() {
-    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.OUTPUTMODE_ID, ExtractFlatClusteringFromHierarchy.OutputMode.STRICT_PARTITIONS);
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
-    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, NaiveAgglomerativeHierarchicalClustering.class);
-    params.addParameter(NaiveAgglomerativeHierarchicalClustering.Parameterizer.LINKAGE_ID, GroupAverageLinkageMethod.class);
-    ExtractFlatClusteringFromHierarchy<DoubleDistance> c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
-    testParameterizationOk(params);
-
-    // run clustering algorithm on database
-    Result result = c.run(db);
-    Clustering<?> clustering = findSingleClustering(result);
-    testFMeasure(db, clustering, 0.93866265);
-    testClusterSizes(clustering, new int[] { 200, 211, 227 });
-  }
-
-  /**
-   * Run agglomerative hierarchical clustering with fixed parameters and compare
-   * the result to a golden standard.
-   */
-  @Test
-  public void testCompleteLink() {
-    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.OUTPUTMODE_ID, ExtractFlatClusteringFromHierarchy.OutputMode.STRICT_PARTITIONS);
-    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
-    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, NaiveAgglomerativeHierarchicalClustering.class);
-    params.addParameter(NaiveAgglomerativeHierarchicalClustering.Parameterizer.LINKAGE_ID, CompleteLinkageMethod.class);
-    ExtractFlatClusteringFromHierarchy<DoubleDistance> c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
-    testParameterizationOk(params);
-
-    // run clustering algorithm on database
-    Result result = c.run(db);
-    Clustering<?> clustering = findSingleClustering(result);
-    testFMeasure(db, clustering, 0.938167802);
-    testClusterSizes(clustering, new int[] { 200, 217, 221 });
-  }
-}