Import Upstream version 0.6.0

1 files changed, 6 insertions, 2 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 cc7aaa9e..0a97c4d3 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
@@ -31,6 +31,9 @@ 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;
@@ -88,15 +91,16 @@ public class KMediansLloyd<V extends NumberVector<?>, D extends Distance<D>> ext
     // Setup cluster assignment store
     List<ModifiableDBIDs> clusters = new ArrayList<>();
     for (int i = 0; i < k; i++) {
-      clusters.add(DBIDUtil.newHashSet(relation.size() / k));
+      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);
+      boolean changed = assignToNearestCluster(relation, medians, clusters, assignment);
       // Stop if no cluster assignment changed.
       if (!changed) {
         break;