1 files changed, 84 insertions, 0 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/RandomlyChosenInitialMeans.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/RandomlyChosenInitialMeans.java
new file mode 100644
index 00000000..fe6b7281
--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/RandomlyChosenInitialMeans.java
@@ -0,0 +1,84 @@
+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) 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.data.NumberVector;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
+import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
+import de.lmu.ifi.dbs.elki.math.random.RandomFactory;
+
+/**
+ * Initialize K-means by randomly choosing k exsiting elements as cluster
+ * centers.
+ * 
+ * @author Erich Schubert
+ * 
+ * @param <O> Vector type
+ */
+public class RandomlyChosenInitialMeans<O> extends AbstractKMeansInitialization<NumberVector> implements KMedoidsInitialization<O> {
+  /**
+   * Constructor.
+   * 
+   * @param rnd Random generator.
+   */
+  public RandomlyChosenInitialMeans(RandomFactory rnd) {
+    super(rnd);
+  }
+
+  @Override
+  public <T extends NumberVector, V extends NumberVector> List<V> chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory) {
+    DBIDs ids = DBIDUtil.randomSample(relation.getDBIDs(), k, rnd);
+    List<V> means = new ArrayList<>(k);
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+      means.add(factory.newNumberVector(relation.get(iter)));
+    }
+    return means;
+  }
+  
+  @Override
+  public DBIDs chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction) {
+    return DBIDUtil.randomSample(ids, k, rnd);
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer<V> extends AbstractKMeansInitialization.Parameterizer {
+    @Override
+    protected RandomlyChosenInitialMeans<V> makeInstance() {
+      return new RandomlyChosenInitialMeans<>(rnd);
+    }
+  }
+}
+\ No newline at end of file