Import Debian changes 0.5.0~beta2-1

elki (0.5.0~beta2-1) unstable; urgency=low

  * New upstream beta release.
  * Needs GNU Trove 3, in NEW.
  * Build with OpenJDK7, as OpenJDK6 complains.

elki (0.5.0~beta1-1) unstable; urgency=low

  * New upstream beta release.
  * Needs GNU Trove 3, not yet in Debian (private package)
  * Build with OpenJDK7, as OpenJDK6 complains.

1 files changed, 107 insertions, 0 deletions

diff --git a/test/de/lmu/ifi/dbs/elki/evaluation/paircounting/TestClusterContingencyTable.java b/test/de/lmu/ifi/dbs/elki/evaluation/paircounting/TestClusterContingencyTable.java
new file mode 100644
index 00000000..42188f56
--- /dev/null
+++ b/test/de/lmu/ifi/dbs/elki/evaluation/paircounting/TestClusterContingencyTable.java
@@ -0,0 +1,107 @@
+package de.lmu.ifi.dbs.elki.evaluation.paircounting;
+
+/*
+ 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 static org.junit.Assert.assertTrue;
+
+import org.junit.Test;
+
+import de.lmu.ifi.dbs.elki.JUnit4Test;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.TrivialAllInOne;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.TrivialAllNoise;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.model.Model;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection;
+import de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable;
+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;
+
+/**
+ * Validate {@link ClusterContingencyTable} with respect to its ability to compare
+ * data clusterings.
+ * 
+ * @author Erich Schubert
+ */
+public class TestClusterContingencyTable implements JUnit4Test {
+  // the following values depend on the data set used!
+  String dataset = "data/testdata/unittests/hierarchical-3d2d1d.csv";
+
+  // size of the data set
+  int shoulds = 600;
+
+  /**
+   * Validate {@link ClusterContingencyTable} with respect to its ability to
+   * compare data clusterings.
+   * 
+   * @throws ParameterException on errors.
+   */
+  @Test
+  public void testCompareDatabases() {
+    ListParameterization params = new ListParameterization();
+    // Input
+    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
+
+    // get database
+    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, params);
+    db.initialize();
+
+    // verify data set size.
+    Relation<?> rel = db.getRelation(TypeUtil.ANY);
+    assertTrue(rel.size() == shoulds);
+
+    // run all-in-one
+    TrivialAllInOne allinone = new TrivialAllInOne();
+    Clustering<Model> rai = allinone.run(db);
+
+    // run all-in-noise
+    TrivialAllNoise allinnoise = new TrivialAllNoise();
+    Clustering<Model> ran = allinnoise.run(db);
+
+    // run by-label
+    ByLabelClustering bylabel = new ByLabelClustering();
+    Clustering<?> rbl = bylabel.run(db);
+
+    assertEquals(1.0, computeFMeasure(rai, rai, false), Double.MIN_VALUE);
+    assertEquals(1.0, computeFMeasure(ran, ran, false), Double.MIN_VALUE);
+    assertEquals(1.0, computeFMeasure(rbl, rbl, false), Double.MIN_VALUE);
+
+    assertEquals(0.009950248756218905, computeFMeasure(ran, rbl, true), Double.MIN_VALUE);
+    assertEquals(0.0033277870216306157, computeFMeasure(rai, ran, true), Double.MIN_VALUE);
+
+    assertEquals(0.5 /* 0.3834296724470135 */, computeFMeasure(rai, rbl, false), Double.MIN_VALUE);
+  }
+
+  private double computeFMeasure(Clustering<?> c1, Clustering<?> c2, boolean noise) {
+    ClusterContingencyTable ct = new ClusterContingencyTable(true, noise);
+    ct.process(c1, c2);
+    return ct.getPaircount().f1Measure();
+  }
+}
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