1 files changed, 0 insertions, 98 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestFourCResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestFourCResults.java
deleted file mode 100644
index 8604a5f0..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestFourCResults.java
+++ /dev/null
@@ -1,98 +0,0 @@
-package de.lmu.ifi.dbs.elki.algorithm.clustering.correlation;
-
-/*
- 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.AbstractProjectedDBSCAN;
-import de.lmu.ifi.dbs.elki.data.Clustering;
-import de.lmu.ifi.dbs.elki.data.DoubleVector;
-import de.lmu.ifi.dbs.elki.data.model.Model;
-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;
-
-/**
- * Perform a full 4C run, and compare the result with a clustering derived from
- * the data set labels. This test ensures that 4C performance doesn't
- * unexpectedly drop on this data set (and also ensures that the algorithms
- * work, as a side effect).
- * 
- * @author Erich Schubert
- * @author Katharina Rausch
- */
-public class TestFourCResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
-  /**
-   * Run 4F with fixed parameters and compare the result to a golden standard.
-   * 
-   * @throws ParameterException on errors.
-   */
-  @Test
-  public void testFourCResults() {
-    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-3d2d1d.csv", 600);
-
-    // Setup 4C
-    ListParameterization params = new ListParameterization();
-    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 0.30);
-    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 20);
-    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 5);
-
-    FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
-    testParameterizationOk(params);
-
-    // run 4C on database
-    Clustering<Model> result = fourc.run(db);
-
-    testFMeasure(db, result, 0.498048); // Hierarchical pairs scored: 0.79467
-    testClusterSizes(result, new int[] { 5, 595 });
-  }
-
-  /**
-   * Run ERiC with fixed parameters and compare the result to a golden standard.
-   * 
-   * @throws ParameterException on errors.
-   */
-  @Test
-  public void testFourCOverlap() {
-    Database db = makeSimpleDatabase(UNITTEST + "correlation-overlap-3-5d.ascii", 650);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    // 4C
-    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 1.2);
-    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 5);
-    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 3);
-
-    FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
-    testParameterizationOk(params);
-
-    // run 4C on database
-    Clustering<Model> result = fourc.run(db);
-    testFMeasure(db, result, 0.48305405);
-    testClusterSizes(result, new int[] { 65, 70, 515 });
-  }
-}
-\ No newline at end of file