1 files changed, 0 insertions, 59 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/outlier/TestGaussianModel.java b/test/de/lmu/ifi/dbs/elki/algorithm/outlier/TestGaussianModel.java
deleted file mode 100644
index e18c2f46..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/outlier/TestGaussianModel.java
+++ /dev/null
@@ -1,59 +0,0 @@
-package de.lmu.ifi.dbs.elki.algorithm.outlier;
-
-/*
- 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.data.DoubleVector;
-import de.lmu.ifi.dbs.elki.database.Database;
-import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
-import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
-
-/**
- * Tests the GaussianModel algorithm.
- * 
- * @author Lucia Cichella
- */
-public class TestGaussianModel extends AbstractSimpleAlgorithmTest implements JUnit4Test {
-  @Test
-  public void testGaussianModel() {
-    Database db = makeSimpleDatabase(UNITTEST + "outlier-fire.ascii", 1025);
-
-    // Parameterization
-    ListParameterization params = new ListParameterization();
-
-    // setup Algorithm
-    GaussianModel<DoubleVector> gaussianModel = ClassGenericsUtil.parameterizeOrAbort(GaussianModel.class, params);
-    testParameterizationOk(params);
-
-    // run GaussianModel on database
-    OutlierResult result = gaussianModel.run(db);
-
-    testSingleScore(result, 1025, 2.8312466458765426);
-    testAUC(db, "Noise", result, 0.9937641025641025);
-  }
-}
-\ No newline at end of file