1 files changed, 0 insertions, 109 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/TestPreDeConResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/TestPreDeConResults.java
deleted file mode 100644
index d00a703f..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/TestPreDeConResults.java
+++ /dev/null
@@ -1,109 +0,0 @@
-package de.lmu.ifi.dbs.elki.algorithm.clustering.subspace;
-
-/*
- 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.datasource.filter.ClassLabelFilter;
-import de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj.PreDeConSubspaceIndex.Factory;
-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 PreDeCon run, and compare the result with a clustering derived
- * from the data set labels. This test ensures that PreDeCon 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 TestPreDeConResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
-  /**
-   * Run PreDeCon with fixed parameters and compare the result to a golden
-   * standard.
-   * 
-   * @throws ParameterException
-   */
-  @Test
-  public void testPreDeConResults() {
-    // Additional input parameters
-    ListParameterization inp = new ListParameterization();
-    inp.addParameter(ClassLabelFilter.Parameterizer.CLASS_LABEL_INDEX_ID, 1);
-    Class<?>[] filters = new Class<?>[] { ClassLabelFilter.class };
-    Database db = makeSimpleDatabase(UNITTEST + "axis-parallel-subspace-clusters-6d.csv.gz", 2500, inp, filters);
-
-    ListParameterization params = new ListParameterization();
-    // PreDeCon
-    // FIXME: These parameters do NOT work...
-    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 50);
-    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 50);
-    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 2);
-
-    // setup algorithm
-    PreDeCon<DoubleVector> predecon = ClassGenericsUtil.parameterizeOrAbort(PreDeCon.class, params);
-    testParameterizationOk(params);
-
-    // run PredeCon on database
-    Clustering<Model> result = predecon.run(db);
-
-    // FIXME: find working parameters...
-    testFMeasure(db, result, 0.40153);
-    testClusterSizes(result, new int[] { 2500 });
-  }
-
-  /**
-   * Run PreDeCon with fixed parameters and compare the result to a golden
-   * standard.
-   * 
-   * @throws ParameterException
-   */
-  @Test
-  public void testPreDeConSubspaceOverlapping() {
-    Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    // PreDeCon
-    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 2.0);
-    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 7);
-    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 4);
-    params.addParameter(Factory.DELTA_ID, 0.04);
-    PreDeCon<DoubleVector> predecon = ClassGenericsUtil.parameterizeOrAbort(PreDeCon.class, params);
-    testParameterizationOk(params);
-
-    // run PredeCon on database
-    Clustering<Model> result = predecon.run(db);
-    testFMeasure(db, result, 0.6470817);
-    testClusterSizes(result, new int[] { 7, 10, 10, 13, 15, 16, 16, 18, 28, 131, 586 });
-  }
-}