1 files changed, 8 insertions, 9 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java
index 7f397790..9b4534fe 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java
@@ -3,7 +3,7 @@ package de.lmu.ifi.dbs.elki.algorithm.outlier.spatial;
 This file is part of ELKI:
 Environment for Developing KDD-Applications Supported by Index-Structures
 
-Copyright (C) 2011
+Copyright (C) 2012
 Ludwig-Maximilians-Universität München
 Lehr- und Forschungseinheit für Datenbanksysteme
 ELKI Development Team
@@ -29,7 +29,7 @@ import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
 import de.lmu.ifi.dbs.elki.data.type.VectorFieldTypeInformation;
 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.WritableDataStore;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
 import de.lmu.ifi.dbs.elki.database.ids.DBID;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
 import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
@@ -37,10 +37,10 @@ import de.lmu.ifi.dbs.elki.database.relation.Relation;
 import de.lmu.ifi.dbs.elki.logging.Logging;
 import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
 import de.lmu.ifi.dbs.elki.math.MeanVariance;
-import de.lmu.ifi.dbs.elki.math.statistics.QuickSelect;
 import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
 import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
 import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.QuickSelect;
 import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
 import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
 
@@ -91,7 +91,7 @@ public class CTLuMedianAlgorithm<N> extends AbstractNeighborhoodOutlier<N> {
    */
   public OutlierResult run(Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
     final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
-    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
+    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
 
     MeanVariance mv = new MeanVariance();
     for(DBID id : relation.iterDBIDs()) {
@@ -110,15 +110,14 @@ public class CTLuMedianAlgorithm<N> extends AbstractNeighborhoodOutlier<N> {
         }
 
         if(c > 0) {
-          // Note: only use up to c-1, since we may have used a too big array
-          median = QuickSelect.median(fi, 0, c - 1);
+          median = QuickSelect.median(fi, 0, c);
         }
         else {
           median = relation.get(id).doubleValue(1);
         }
       }
       double h = relation.get(id).doubleValue(1) - median;
-      scores.put(id, h);
+      scores.putDouble(id, h);
       mv.put(h);
     }
 
@@ -127,9 +126,9 @@ public class CTLuMedianAlgorithm<N> extends AbstractNeighborhoodOutlier<N> {
     final double stddev = mv.getNaiveStddev();
     DoubleMinMax minmax = new DoubleMinMax();
     for(DBID id : relation.iterDBIDs()) {
-      double score = Math.abs((scores.get(id) - mean) / stddev);
+      double score = Math.abs((scores.doubleValue(id) - mean) / stddev);
       minmax.put(score);
-      scores.put(id, score);
+      scores.putDouble(id, score);
     }
 
     Relation<Double> scoreResult = new MaterializedRelation<Double>("MO", "Median-outlier", TypeUtil.DOUBLE, scores, relation.getDBIDs());