1 files changed, 16 insertions, 12 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/SpatialApproximationMaterializeKNNPreprocessor.java b/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/SpatialApproximationMaterializeKNNPreprocessor.java
index c7963e14..739dcc7c 100644
--- a/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/SpatialApproximationMaterializeKNNPreprocessor.java
+++ b/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/SpatialApproximationMaterializeKNNPreprocessor.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.index.preprocessed.knn;
  This file is part of ELKI:
  Environment for Developing KDD-Applications Supported by Index-Structures
 
- Copyright (C) 2012
+ Copyright (C) 2013
  Ludwig-Maximilians-Universität München
  Lehr- und Forschungseinheit für Datenbanksysteme
  ELKI Development Team
@@ -34,12 +34,11 @@ import de.lmu.ifi.dbs.elki.database.ids.ArrayModifiableDBIDs;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDPair;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.distance.KNNHeap;
+import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList;
 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.DistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNHeap;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNResult;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNUtil;
 import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
 import de.lmu.ifi.dbs.elki.index.tree.LeafEntry;
 import de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialEntry;
@@ -57,7 +56,7 @@ import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
  * A preprocessor for annotation of the k nearest neighbors (and their
  * distances) to each database object.
  * 
- * Used for example by {@link de.lmu.ifi.dbs.elki.algorithm.outlier.LOF}.
+ * Used for example by {@link de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF}.
  * 
  * TODO correct handling of datastore events
  * 
@@ -71,7 +70,7 @@ import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
  */
 @Title("Spatial Approximation Materialize kNN Preprocessor")
 @Description("Caterializes the (approximate) k nearest neighbors of objects of a database using a spatial approximation.")
-public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>, D extends Distance<D>, N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor<O, D, KNNResult<D>> {
+public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>, D extends Distance<D>, N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor<O, D, KNNList<D>> {
   /**
    * Logger to use
    */
@@ -98,7 +97,7 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect
     }
     SpatialIndexTree<N, E> index = indexes.iterator().next();
 
-    storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, KNNResult.class);
+    storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, KNNList.class);
     MeanVariance pagesize = new MeanVariance();
     MeanVariance ksize = new MeanVariance();
     if(getLogger().isVerbose()) {
@@ -119,9 +118,9 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect
       for(int i = 0; i < size; i++) {
         ids.add(((LeafEntry) node.getEntry(i)).getDBID());
       }
-      HashMap<DBIDPair, D> cache = new HashMap<DBIDPair, D>((size * size * 3) >> 3);
+      HashMap<DBIDPair, D> cache = new HashMap<>((size * size * 3) >> 3);
       for(DBIDIter id = ids.iter(); id.valid(); id.advance()) {
-        KNNHeap<D> kNN = KNNUtil.newHeap(distanceFunction, k);
+        KNNHeap<D> kNN = DBIDUtil.newHeap(distanceFunction.getDistanceFactory(), k);
         for(DBIDIter id2 = ids.iter(); id2.valid(); id2.advance()) {
           DBIDPair key = DBIDUtil.newPair(id, id2);
           D d = cache.remove(key);
@@ -174,6 +173,11 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect
     return "spatial-approximate-knn";
   }
 
+  @Override
+  public void logStatistics() {
+    // No statistics to log.
+  }
+
   /**
    * The actual preprocessor instance.
    * 
@@ -187,7 +191,7 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect
    * @param <N> the type of spatial nodes in the spatial index
    * @param <E> the type of spatial entries in the spatial index
    */
-  public static class Factory<D extends Distance<D>, N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor.Factory<NumberVector<?>, D, KNNResult<D>> {
+  public static class Factory<D extends Distance<D>, N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor.Factory<NumberVector<?>, D, KNNList<D>> {
     /**
      * Constructor.
      * 
@@ -200,7 +204,7 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect
 
     @Override
     public SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>, D, N, E> instantiate(Relation<NumberVector<?>> relation) {
-      SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>, D, N, E> instance = new SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>, D, N, E>(relation, distanceFunction, k);
+      SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>, D, N, E> instance = new SpatialApproximationMaterializeKNNPreprocessor<>(relation, distanceFunction, k);
       return instance;
     }
 
@@ -214,7 +218,7 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect
     public static class Parameterizer<D extends Distance<D>, N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<NumberVector<?>, D> {
       @Override
       protected Factory<D, N, E> makeInstance() {
-        return new Factory<D, N, E>(k, distanceFunction);
+        return new Factory<>(k, distanceFunction);
       }
     }
   }