1 files changed, 0 insertions, 114 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestCOPACResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestCOPACResults.java
deleted file mode 100644
index 94c59557..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/TestCOPACResults.java
+++ /dev/null
@@ -1,114 +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.DBSCAN;
-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.distance.distancevalue.DoubleDistance;
-import de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeightedCovarianceMatrixBuilder;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions.ErfcWeight;
-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 COPAC run, and compare the result with a clustering derived
- * from the data set labels. This test ensures that COPAC 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 TestCOPACResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
-  /**
-   * Run COPAC with fixed parameters and compare the result to a golden
-   * standard.
-   * 
-   * @throws ParameterException on errors.
-   */
-  @Test
-  public void testCOPACResults() {
-    Database db = makeSimpleDatabase(UNITTEST + "correlation-hierarchy.csv", 450);
-
-    // these parameters are not picked too smartly - room for improvement.
-    ListParameterization params = new ListParameterization();
-    params.addParameter(COPAC.PARTITION_ALGORITHM_ID, DBSCAN.class);
-    params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 0.02);
-    params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 50);
-    params.addParameter(COPAC.PREPROCESSOR_ID, KNNQueryFilteredPCAIndex.Factory.class);
-    params.addParameter(KNNQueryFilteredPCAIndex.Factory.K_ID, 15);
-
-    COPAC<DoubleVector, DoubleDistance> copac = ClassGenericsUtil.parameterizeOrAbort(COPAC.class, params);
-    testParameterizationOk(params);
-
-    // run COPAC on database
-    Clustering<Model> result = copac.run(db);
-
-    testFMeasure(db, result, 0.842521);
-    testClusterSizes(result, new int[] { 6, 16, 32, 196, 200 });
-  }
-
-  /**
-   * Run COPAC with fixed parameters and compare the result to a golden
-   * standard.
-   * 
-   * @throws ParameterException on errors.
-   */
-  @Test
-  public void testCOPACOverlap() {
-    Database db = makeSimpleDatabase(UNITTEST + "correlation-overlap-3-5d.ascii", 650);
-
-    // Setup algorithm
-    ListParameterization params = new ListParameterization();
-    params.addParameter(COPAC.PARTITION_ALGORITHM_ID, DBSCAN.class);
-    params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 0.5);
-    params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 20);
-    params.addParameter(COPAC.PREPROCESSOR_ID, KNNQueryFilteredPCAIndex.Factory.class);
-    params.addParameter(KNNQueryFilteredPCAIndex.Factory.K_ID, 45);
-    // PCA
-    params.addParameter(PCARunner.PCA_COVARIANCE_MATRIX, WeightedCovarianceMatrixBuilder.class);
-    params.addParameter(WeightedCovarianceMatrixBuilder.WEIGHT_ID, ErfcWeight.class);
-    params.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, PercentageEigenPairFilter.class);
-    params.addParameter(PercentageEigenPairFilter.ALPHA_ID, 0.8);
-
-    COPAC<DoubleVector, DoubleDistance> copac = ClassGenericsUtil.parameterizeOrAbort(COPAC.class, params);
-    testParameterizationOk(params);
-
-    Clustering<Model> result = copac.run(db);
-    testFMeasure(db, result, 0.84687864);
-    testClusterSizes(result, new int[] { 1, 22, 22, 29, 34, 158, 182, 202 });
-  }
-}
-\ No newline at end of file