Import Upstream version 0.7.1

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diff --git a/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/FarthestSumPointsInitialMeansTest.java b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/FarthestSumPointsInitialMeansTest.java
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+++ b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/FarthestSumPointsInitialMeansTest.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2016
+ 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.AbstractKMeans;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.SingleAssignmentKMeans;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.DoubleVector;
+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 single assignment with different k-means initializations.
+ *
+ * @author Erich Schubert
+ * @since 0.4.0
+ */
+public class FarthestSumPointsInitialMeansTest extends AbstractSimpleAlgorithmTest implements JUnit4Test {
+  /**
+   * Run KMeans with fixed parameters and compare the result to a golden
+   * standard.
+   *
+   * @throws ParameterException
+   */
+  @Test
+  public void testFarthestSumPointsInitialMeans() {
+    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(KMeans.K_ID, 5);
+    params.addParameter(KMeans.SEED_ID, 3);
+    params.addParameter(KMeans.INIT_ID, FarthestSumPointsInitialMeans.class);
+    AbstractKMeans<DoubleVector, ?> kmeans = ClassGenericsUtil.parameterizeOrAbort(SingleAssignmentKMeans.class, params);
+    testParameterizationOk(params);
+
+    // run KMeans on database
+    Clustering<?> result = kmeans.run(db);
+    testFMeasure(db, result, 0.80399668933);
+    testClusterSizes(result, new int[] { 32, 169, 199, 201, 399 });
+  }
+}
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