1 files changed, 95 insertions, 0 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
+
+/*
+ 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.algorithm.clustering.kmeans.KMeansLloyd;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.DoubleVector;
+import de.lmu.ifi.dbs.elki.data.model.MeanModel;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
+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 KMeans run, and compares the result with a clustering derived
+ * from the data set labels. This test ensures that KMeans'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 TestKMeansResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
+  /**
+   * Run KMeans with fixed parameters and compare the result to a golden
+   * standard.
+   * 
+   * @throws ParameterException
+   */
+  @Test
+  public void testKMeansLloyd() {
+    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(AbstractKMeans.K_ID, 5);
+    params.addParameter(AbstractKMeans.SEED_ID, 3);
+    AbstractKMeans<DoubleVector, DoubleDistance> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansLloyd.class, params);
+    testParameterizationOk(params);
+
+    // run KMeans on database
+    Clustering<MeanModel<DoubleVector>> result = kmeans.run(db);
+    testFMeasure(db, result, 0.998005);
+    testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 });
+  }
+
+  /**
+   * Run KMeans with fixed parameters and compare the result to a golden
+   * standard.
+   * 
+   * @throws ParameterException
+   */
+  @Test
+  public void testKMeansMacQueen() {
+    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(AbstractKMeans.K_ID, 5);
+    params.addParameter(AbstractKMeans.SEED_ID, 3);
+    AbstractKMeans<DoubleVector, DoubleDistance> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansMacQueen.class, params);
+    testParameterizationOk(params);
+
+    // run KMeans on database
+    Clustering<MeanModel<DoubleVector>> result = kmeans.run(db);
+    testFMeasure(db, result, 0.998005);
+    testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 });
+  }
+}
+\ No newline at end of file