Import Debian changes 0.5.0-1

elki (0.5.0-1) unstable; urgency=low

  * New upstream release: 0.5.0 final.

1 files changed, 13 insertions, 9 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/RandomlyChosenInitialMeans.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/RandomlyChosenInitialMeans.java
index 30e59453..5b9da923 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/RandomlyChosenInitialMeans.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/RandomlyChosenInitialMeans.java
@@ -25,13 +25,12 @@ package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
 import java.util.ArrayList;
 import java.util.List;
 
-import de.lmu.ifi.dbs.elki.data.NumberVector;
-import de.lmu.ifi.dbs.elki.database.ids.DBID;
+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.linearalgebra.Vector;
 
 /**
  * Initialize K-means by randomly choosing k exsiting elements as cluster
@@ -41,7 +40,7 @@ import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
  * 
  * @param <V> Vector type
  */
-public class RandomlyChosenInitialMeans<V extends NumberVector<V, ?>> extends AbstractKMeansInitialization<V> {
+public class RandomlyChosenInitialMeans<V> extends AbstractKMeansInitialization<V> implements KMedoidsInitialization<V> {
   /**
    * Constructor.
    * 
@@ -52,15 +51,20 @@ public class RandomlyChosenInitialMeans<V extends NumberVector<V, ?>> extends Ab
   }
 
   @Override
-  public List<Vector> chooseInitialMeans(Relation<V> relation, int k, PrimitiveDistanceFunction<? super V, ?> distanceFunction) {
+  public List<V> chooseInitialMeans(Relation<V> relation, int k, PrimitiveDistanceFunction<? super V, ?> distanceFunction) {
     DBIDs ids = DBIDUtil.randomSample(relation.getDBIDs(), k, seed);
-    List<Vector> means = new ArrayList<Vector>(k);
-    for(DBID id : ids) {
-      means.add(relation.get(id).getColumnVector());
+    List<V> means = new ArrayList<V>(k);
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+      means.add(relation.get(iter));
     }
     return means;
   }
 
+  @Override
+  public DBIDs chooseInitialMedoids(int k, DistanceQuery<? super V, ?> distanceFunction) {
+    return DBIDUtil.randomSample(distanceFunction.getRelation().getDBIDs(), k, seed);
+  }
+
   /**
    * Parameterization class.
    * 
@@ -68,7 +72,7 @@ public class RandomlyChosenInitialMeans<V extends NumberVector<V, ?>> extends Ab
    * 
    * @apiviz.exclude
    */
-  public static class Parameterizer<V extends NumberVector<V, ?>> extends AbstractKMeansInitialization.Parameterizer<V> {
+  public static class Parameterizer<V> extends AbstractKMeansInitialization.Parameterizer<V> {
 
     @Override
     protected RandomlyChosenInitialMeans<V> makeInstance() {