1 files changed, 0 insertions, 70 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java
deleted file mode 100644
index 065307be..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java
+++ /dev/null
@@ -1,70 +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.algorithm.clustering.kmeans.KMeans;
-import de.lmu.ifi.dbs.elki.data.Clustering;
-import de.lmu.ifi.dbs.elki.data.DoubleVector;
-import de.lmu.ifi.dbs.elki.data.model.EMModel;
-import de.lmu.ifi.dbs.elki.database.Database;
-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 EM run, and compares the result with a clustering derived
- * from the data set labels. This test ensures that EM'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 TestEMResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
-  /**
-   * Run EM with fixed parameters and compare the result to a golden standard.
-   * 
-   * @throws ParameterException
-   */
-  @Test
-  public void testEMResults() {
-    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    params.addParameter(KMeans.SEED_ID, 0);
-    params.addParameter(EM.Parameterizer.K_ID, 6);
-    EM<DoubleVector> em = ClassGenericsUtil.parameterizeOrAbort(EM.class, params);
-    testParameterizationOk(params);
-
-    // run EM on database
-    Clustering<EMModel<DoubleVector>> result = em.run(db);
-    testFMeasure(db, result, 0.7551098);
-    testClusterSizes(result, new int[] { 50, 99, 102, 120, 141, 198 });
-  }
-}
-\ No newline at end of file