Import Debian changes 0.6.5~20141030-1

elki (0.6.5~20141030-1) unstable; urgency=medium

  * New upstream beta release
  * Urgency medium: 0.6.0 suffers from a performance issue with duplicates.
  * Repackaged tarball from .jar to .tar.bz2
  * Add dependency on libsvm3-java
  * Enable line numbers for debugging (ant debuglevel)

1 files changed, 0 insertions, 90 deletions

diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestDeLiCluResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestDeLiCluResults.java
deleted file mode 100644
index b70c0f67..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestDeLiCluResults.java
+++ /dev/null
@@ -1,90 +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 static org.junit.Assert.assertEquals;
-
-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.algorithm.clustering.trivial.ByLabelClustering;
-import de.lmu.ifi.dbs.elki.data.Clustering;
-import de.lmu.ifi.dbs.elki.data.model.Model;
-import de.lmu.ifi.dbs.elki.database.Database;
-import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
-import de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable;
-import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu.DeLiCluTreeFactory;
-import de.lmu.ifi.dbs.elki.persistent.AbstractPageFileFactory;
-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 DeLiClu run, and compares the result with a clustering
- * derived from the data set labels. This test ensures that DeLiClu'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 TestDeLiCluResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
-  /**
-   * Run DeLiClu with fixed parameters and compare the result to a golden
-   * standard.
-   * 
-   * @throws ParameterException
-   */
-  @Test
-  public void testDeLiCluResults() {
-    ListParameterization indexparams = new ListParameterization();
-    // We need a special index for this algorithm:
-    indexparams.addParameter(StaticArrayDatabase.Parameterizer.INDEX_ID, DeLiCluTreeFactory.class);
-    indexparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 1000);
-    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710, indexparams, null);
-
-    // Setup actual algorithm
-    ListParameterization params = new ListParameterization();
-    params.addParameter(DeLiClu.MINPTS_ID, 18);
-    params.addParameter(OPTICSXi.XI_ID, 0.038);
-    params.addParameter(OPTICSXi.XIALG_ID, DeLiClu.class);
-    OPTICSXi<DoubleDistance> opticsxi = ClassGenericsUtil.parameterizeOrAbort(OPTICSXi.class, params);
-    testParameterizationOk(params);
-
-    // run DeLiClu on database
-    Clustering<?> clustering = opticsxi.run(db);
-
-    // Test F-Measure    
-    ByLabelClustering bylabel = new ByLabelClustering();
-    Clustering<Model> rbl = bylabel.run(db);
-    ClusterContingencyTable ct = new ClusterContingencyTable(true, false);
-    ct.process(clustering, rbl);
-    double score = ct.getPaircount().f1Measure();
-    // We cannot test exactly - due to Hashing, DeLiClu sequence is not
-    // identical each time, the results will vary slightly.
-    assertEquals(this.getClass().getSimpleName() + ": Score does not match: " + score, score, 0.807415, 1E-5);
-  }
-}
\ No newline at end of file