1 files changed, 148 insertions, 147 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMediansLloyd.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMediansLloyd.java
index 0a97c4d3..49b67599 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMediansLloyd.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMediansLloyd.java
@@ -1,147 +1,148 @@
-package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
-
-/*
- This file is part of ELKI:
- Environment for Developing KDD-Applications Supported by Index-Structures
-
- Copyright (C) 2013
- 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 java.util.ArrayList;
-import java.util.List;
-
-import de.lmu.ifi.dbs.elki.data.Cluster;
-import de.lmu.ifi.dbs.elki.data.Clustering;
-import de.lmu.ifi.dbs.elki.data.NumberVector;
-import de.lmu.ifi.dbs.elki.data.model.MeanModel;
-import de.lmu.ifi.dbs.elki.database.Database;
-import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
-import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
-import de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
-import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
-import de.lmu.ifi.dbs.elki.database.relation.Relation;
-import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
-import de.lmu.ifi.dbs.elki.logging.Logging;
-import de.lmu.ifi.dbs.elki.logging.progress.IndefiniteProgress;
-import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
-import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
-
-/**
- * Provides the k-medians clustering algorithm, using Lloyd-style bulk
- * iterations.
- * 
- * Reference:
- * <p>
- * Clustering via Concave Minimization<br />
- * P. S. Bradley, O. L. Mangasarian, W. N. Street<br />
- * in: Advances in neural information processing systems
- * </p>
- * 
- * @author Erich Schubert
- * 
- * @param <V> vector datatype
- * @param <D> distance value type
- */
-@Title("K-Medians")
-@Reference(title = "Clustering via Concave Minimization", authors = "P. S. Bradley, O. L. Mangasarian, W. N. Street", booktitle = "Advances in neural information processing systems", url = "http://nips.djvuzone.org/djvu/nips09/0368.djvu")
-public class KMediansLloyd<V extends NumberVector<?>, D extends Distance<D>> extends AbstractKMeans<V, D, MeanModel<V>> {
-  /**
-   * The logger for this class.
-   */
-  private static final Logging LOG = Logging.getLogger(KMediansLloyd.class);
-
-  /**
-   * Constructor.
-   * 
-   * @param distanceFunction distance function
-   * @param k k parameter
-   * @param maxiter Maxiter parameter
-   * @param initializer Initialization method
-   */
-  public KMediansLloyd(PrimitiveDistanceFunction<NumberVector<?>, D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) {
-    super(distanceFunction, k, maxiter, initializer);
-  }
-
-  @Override
-  public Clustering<MeanModel<V>> run(Database database, Relation<V> relation) {
-    if (relation.size() <= 0) {
-      return new Clustering<>("k-Medians Clustering", "kmedians-clustering");
-    }
-    // Choose initial medians
-    List<? extends NumberVector<?>> medians = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction());
-    // Setup cluster assignment store
-    List<ModifiableDBIDs> clusters = new ArrayList<>();
-    for (int i = 0; i < k; i++) {
-      clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
-    }
-    WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
-
-    IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Medians iteration", LOG) : null;
-    for (int iteration = 0; maxiter <= 0 || iteration < maxiter; iteration++) {
-      if (prog != null) {
-        prog.incrementProcessed(LOG);
-      }
-      boolean changed = assignToNearestCluster(relation, medians, clusters, assignment);
-      // Stop if no cluster assignment changed.
-      if (!changed) {
-        break;
-      }
-      // Recompute medians.
-      medians = medians(clusters, medians, relation);
-    }
-    if (prog != null) {
-      prog.setCompleted(LOG);
-    }
-    // Wrap result
-    final NumberVector.Factory<V, ?> factory = RelationUtil.getNumberVectorFactory(relation);
-    Clustering<MeanModel<V>> result = new Clustering<>("k-Medians Clustering", "kmedians-clustering");
-    for (int i = 0; i < clusters.size(); i++) {
-      MeanModel<V> model = new MeanModel<>(factory.newNumberVector(medians.get(i).getColumnVector().getArrayRef()));
-      result.addToplevelCluster(new Cluster<>(clusters.get(i), model));
-    }
-    return result;
-  }
-
-  @Override
-  protected Logging getLogger() {
-    return LOG;
-  }
-
-  /**
-   * Parameterization class.
-   * 
-   * @author Erich Schubert
-   * 
-   * @apiviz.exclude
-   */
-  public static class Parameterizer<V extends NumberVector<?>, D extends Distance<D>> extends AbstractKMeans.Parameterizer<V, D> {
-    @Override
-    protected Logging getLogger() {
-      return LOG;
-    }
-
-    @Override
-    protected KMediansLloyd<V, D> makeInstance() {
-      return new KMediansLloyd<>(distanceFunction, k, maxiter, initializer);
-    }
-  }
-}
+package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2014
+ 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 java.util.ArrayList;
+import java.util.List;
+
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansInitialization;
+import de.lmu.ifi.dbs.elki.data.Cluster;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.model.MeanModel;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
+import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.logging.progress.IndefiniteProgress;
+import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
+
+/**
+ * k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
+ * of the more complicated approach suggested by Kaufman and Rousseeuw (see
+ * {@link KMedoidsPAM} instead).
+ * 
+ * Reference:
+ * <p>
+ * Clustering via Concave Minimization<br />
+ * P. S. Bradley, O. L. Mangasarian, W. N. Street<br />
+ * in: Advances in Neural Information Processing Systems (NIPS)
+ * </p>
+ * 
+ * @author Erich Schubert
+ * 
+ * @apiviz.has MeanModel
+ * 
+ * @param <V> vector datatype
+ */
+@Title("K-Medians")
+@Reference(title = "Clustering via Concave Minimization", //
+authors = "P. S. Bradley, O. L. Mangasarian, W. N. Street", //
+booktitle = "Advances in Neural Information Processing Systems", //
+url = "https://papers.nips.cc/paper/1260-clustering-via-concave-minimization.pdf")
+public class KMediansLloyd<V extends NumberVector> extends AbstractKMeans<V, MeanModel> {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging LOG = Logging.getLogger(KMediansLloyd.class);
+
+  /**
+   * Constructor.
+   * 
+   * @param distanceFunction distance function
+   * @param k k parameter
+   * @param maxiter Maxiter parameter
+   * @param initializer Initialization method
+   */
+  public KMediansLloyd(PrimitiveDistanceFunction<? super NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) {
+    super(distanceFunction, k, maxiter, initializer);
+  }
+
+  @Override
+  public Clustering<MeanModel> run(Database database, Relation<V> relation) {
+    if(relation.size() <= 0) {
+      return new Clustering<>("k-Medians Clustering", "kmedians-clustering");
+    }
+    // Choose initial medians
+    List<Vector> medians = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction(), Vector.FACTORY);
+    // Setup cluster assignment store
+    List<ModifiableDBIDs> clusters = new ArrayList<>();
+    for(int i = 0; i < k; i++) {
+      clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
+    }
+    WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
+    double[] distsum = new double[k];
+
+    IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Medians iteration", LOG) : null;
+    for(int iteration = 0; maxiter <= 0 || iteration < maxiter; iteration++) {
+      LOG.incrementProcessed(prog);
+      boolean changed = assignToNearestCluster(relation, medians, clusters, assignment, distsum);
+      // Stop if no cluster assignment changed.
+      if(!changed) {
+        break;
+      }
+      // Recompute medians.
+      medians = medians(clusters, medians, relation);
+    }
+    LOG.setCompleted(prog);
+    // Wrap result
+    Clustering<MeanModel> result = new Clustering<>("k-Medians Clustering", "kmedians-clustering");
+    for(int i = 0; i < clusters.size(); i++) {
+      MeanModel model = new MeanModel(medians.get(i));
+      result.addToplevelCluster(new Cluster<>(clusters.get(i), model));
+    }
+    return result;
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer<V extends NumberVector> extends AbstractKMeans.Parameterizer<V> {
+    @Override
+    protected Logging getLogger() {
+      return LOG;
+    }
+
+    @Override
+    protected KMediansLloyd<V> makeInstance() {
+      return new KMediansLloyd<>(distanceFunction, k, maxiter, initializer);
+    }
+  }
+}