1 files changed, 6 insertions, 7 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java b/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java
index c1eb118d..353c1b02 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java
@@ -29,7 +29,7 @@ import java.util.Collections;
 import java.util.HashMap;
 
 import de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
-import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelOrAllInOneClustering;
 import de.lmu.ifi.dbs.elki.data.Cluster;
 import de.lmu.ifi.dbs.elki.data.DoubleVector;
 import de.lmu.ifi.dbs.elki.data.NumberVector;
@@ -39,6 +39,7 @@ import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
 import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
 import de.lmu.ifi.dbs.elki.database.Database;
 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.query.distance.DistanceQuery;
 import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
 import de.lmu.ifi.dbs.elki.database.query.knn.KNNResult;
@@ -114,11 +115,8 @@ public class EvaluateRankingQuality<V extends NumberVector<V, ?>, D extends Numb
    */
   int numbins = 20;
 
-  /**
-   * Run the algorithm.
-   */
   @Override
-  public HistogramResult<DoubleVector> run(Database database) throws IllegalStateException {
+  public HistogramResult<DoubleVector> run(Database database) {
     final Relation<V> relation = database.getRelation(getInputTypeRestriction()[0]);
     final DistanceQuery<V, D> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
     final KNNQuery<V, D> knnQuery = database.getKNNQuery(distQuery, relation.size());
@@ -127,7 +125,7 @@ public class EvaluateRankingQuality<V extends NumberVector<V, ?>, D extends Numb
       logger.verbose("Preprocessing clusters...");
     }
     // Cluster by labels
-    Collection<Cluster<Model>> split = (new ByLabelClustering()).run(database).getAllClusters();
+    Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters();
 
     // Compute cluster averages and covariance matrix
     HashMap<Cluster<?>, V> averages = new HashMap<Cluster<?>, V>(split.size());
@@ -150,7 +148,8 @@ public class EvaluateRankingQuality<V extends NumberVector<V, ?>, D extends Numb
       Vector av = averages.get(clus).getColumnVector();
       Matrix covm = covmats.get(clus);
 
-      for(DBID i1 : clus.getIDs()) {
+      for(DBIDIter iter = clus.getIDs().iter(); iter.valid(); iter.advance()) {
+        DBID i1 = iter.getDBID();
         Double d = MathUtil.mahalanobisDistance(covm, av.minus(relation.get(i1).getColumnVector()));
         cmem.add(new FCPair<Double, DBID>(d, i1));
       }