Import Debian changes 0.6.0~beta2-1

elki (0.6.0~beta2-1) unstable; urgency=low

  * New upstream beta release.
  * 3DPC extension is not yet included.

8 files changed, 388 insertions, 204 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/AbstractDistanceKNNQuery.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/AbstractDistanceKNNQuery.java
index 0ac2e8ee..f9f7d018 100644
--- a/src/de/lmu/ifi/dbs/elki/database/query/knn/AbstractDistanceKNNQuery.java
+++ b/src/de/lmu/ifi/dbs/elki/database/query/knn/AbstractDistanceKNNQuery.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.database.query.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
@@ -23,10 +23,15 @@ package de.lmu.ifi.dbs.elki.database.query.knn;
  along with this program.  If not, see <http://www.gnu.org/licenses/>.
  */
 
+import java.util.ArrayList;
+import java.util.List;
+
+import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
+import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList;
 import de.lmu.ifi.dbs.elki.database.query.AbstractDataBasedQuery;
 import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNResult;
 import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
 
 /**
@@ -51,8 +56,22 @@ public abstract class AbstractDistanceKNNQuery<O, D extends Distance<D>> extends
   }
 
   @Override
-  abstract public KNNResult<D> getKNNForDBID(DBIDRef id, int k);
+  public List<? extends KNNList<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
+    // throw new
+    // UnsupportedOperationException(ExceptionMessages.UNSUPPORTED_NOT_YET);
+    // TODO: optimize
+    List<KNNList<D>> ret = new ArrayList<>(ids.size());
+    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+      ret.add(getKNNForDBID(iter, k));
+    }
+    return ret;
+  }
+
+  @Override
+  public KNNList<D> getKNNForDBID(DBIDRef id, int k) {
+    return getKNNForObject(relation.get(id), k);
+  }
 
   @Override
-  abstract public KNNResult<D> getKNNForObject(O obj, int k);
-}
\ No newline at end of file
+  abstract public KNNList<D> getKNNForObject(O obj, int k);
+}
diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/DoubleOptimizedKNNQuery.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/DoubleOptimizedKNNQuery.java
new file mode 100644
index 00000000..b18db8f0
--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/database/query/knn/DoubleOptimizedKNNQuery.java
@@ -0,0 +1,309 @@
+package de.lmu.ifi.dbs.elki.database.query.knn;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2013
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+import java.util.ArrayList;
+
+import de.lmu.ifi.dbs.elki.database.ids.DBIDFactory;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.distance.DistanceDBIDPair;
+import de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceDBIDPair;
+import de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceKNNHeap;
+import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList;
+import de.lmu.ifi.dbs.elki.database.ids.generic.DistanceDBIDPairKNNList;
+import de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNHeap;
+import de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNList;
+import de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNList;
+import de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNListHeap;
+import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceQuery;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDoubleDistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distanceresultlist.DistanceDBIDResultUtil;
+import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.Heap;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.TiedTopBoundedHeap;
+
+/**
+ * Optimized linear scan query for {@link PrimitiveDoubleDistanceFunction}s.
+ * 
+ * @author Erich Schubert
+ * 
+ * @apiviz.uses PrimitiveDoubleDistanceFunction
+ * 
+ * @param <O> Object type
+ */
+public class DoubleOptimizedKNNQuery<O> extends LinearScanKNNQuery<O, DoubleDistance> {
+  /**
+   * Raw distance function.
+   */
+  PrimitiveDoubleDistanceFunction<O> rawdist;
+
+  /**
+   * Constructor.
+   * 
+   * @param distanceQuery Distance function to use
+   */
+  @SuppressWarnings("unchecked")
+  public DoubleOptimizedKNNQuery(PrimitiveDistanceQuery<O, DoubleDistance> distanceQuery) {
+    super(distanceQuery);
+    if(!(distanceQuery.getDistanceFunction() instanceof PrimitiveDoubleDistanceFunction)) {
+      throw new UnsupportedOperationException("DoubleOptimizedKNNQuery instantiated for non-PrimitiveDoubleDistanceFunction!");
+    }
+    rawdist = (PrimitiveDoubleDistanceFunction<O>) distanceQuery.getDistanceFunction();
+  }
+
+  @Override
+  public KNNList<DoubleDistance> getKNNForDBID(DBIDRef id, int k) {
+    return getKNNForObject(relation.get(id), k);
+  }
+
+  @Override
+  public KNNList<DoubleDistance> getKNNForObject(O obj, int k) {
+    return getKNNForObjectBenchmarked(obj, k);
+  }
+
+  /**
+   * This is the straightforward implementation using the optimized heap.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectKNNHeap(O obj, int k) {
+    // Optimization for double distances.
+    final DoubleDistanceKNNHeap heap = (DoubleDistanceKNNHeap) DBIDUtil.newHeap(DoubleDistance.FACTORY, k);
+    for(DBIDIter iter = relation.iterDBIDs(); iter.valid(); iter.advance()) {
+      heap.add(rawdist.doubleDistance(obj, relation.get(iter)), iter);
+    }
+    return heap.toKNNList();
+  }
+
+  /**
+   * This is the cleaner, supposedly faster implementation.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectClean(O obj, int k) {
+    // Optimization for double distances.
+    final TiedTopBoundedHeap<DoubleDistanceDBIDPair> heap = new TiedTopBoundedHeap<>(k, DoubleDistanceDBIDPairKNNHeap.COMPARATOR);
+    final DBIDIter iter = relation.iterDBIDs();
+
+    // First k elements don't need checking.
+    double max = 0.;
+    for(int i = 0; i < k && iter.valid(); i++, iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      heap.add(DBIDFactory.FACTORY.newDistancePair(doubleDistance, iter));
+      max = Math.max(max, doubleDistance);
+    }
+    // Remaining elements
+    for(; iter.valid(); iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      if(doubleDistance <= max) {
+        heap.add(DBIDFactory.FACTORY.newDistancePair(doubleDistance, iter));
+      }
+      if(doubleDistance < max) {
+        max = heap.peek().doubleDistance();
+      }
+    }
+    return new DoubleDistanceDBIDPairKNNList(heap, k);
+  }
+
+  /**
+   * It does not make sense, but this version is faster in our larger
+   * benchmarks. Apparently, some JIT optimization kicks in better.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectBenchmarked(O obj, int k) {
+    // THIS SHOULD BE SLOWER THAN THE VERSION ABOVE, BUT ISN'T!
+    final TiedTopBoundedHeap<DistanceDBIDPair<DoubleDistance>> heap = new TiedTopBoundedHeap<>(k, DistanceDBIDResultUtil.BY_REVERSE_DISTANCE);
+    final DBIDIter iter = relation.iterDBIDs();
+    // First k elements don't need checking.
+    double max = 0.;
+    for(int i = 0; i < k && iter.valid(); i++, iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      heap.add(DBIDFactory.FACTORY.newDistancePair(new DoubleDistance(doubleDistance), iter));
+      max = Math.max(max, doubleDistance);
+    }
+    // Remaining elements
+    for(; iter.valid(); iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      if(doubleDistance <= max) {
+        heap.add(DBIDFactory.FACTORY.newDistancePair(new DoubleDistance(doubleDistance), iter));
+      }
+      if(doubleDistance < max) {
+        max = heap.peek().getDistance().doubleValue();
+      }
+    }
+    return new DistanceDBIDPairKNNList<>(heap, k);
+  }
+
+  /**
+   * Another attempt at getting a faster knn heap.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectBenchmarked2(O obj, int k) {
+    final Heap<DoubleDistanceDBIDPair> heap = new Heap<>(k, DistanceDBIDResultUtil.BY_REVERSE_DISTANCE);
+    final ArrayList<DoubleDistanceDBIDPair> ties = new ArrayList<>();
+    final DBIDIter iter = relation.iterDBIDs();
+    // First k elements don't need checking.
+    double max = 0.;
+    for(int i = 0; i < k && iter.valid(); i++, iter.advance()) {
+      final double doubleDistance = rawdist.distance(obj, relation.get(iter)).doubleValue();
+      heap.add(DBIDFactory.FACTORY.newDistancePair(doubleDistance, iter));
+      max = Math.max(max, doubleDistance);
+    }
+    // Remaining elements
+    for(; iter.valid(); iter.advance()) {
+      final double doubleDistance = rawdist.distance(obj, relation.get(iter)).doubleValue();
+      if(doubleDistance <= max) {
+        if(doubleDistance < max) {
+          DoubleDistanceDBIDPair prev = heap.replaceTopElement(DBIDFactory.FACTORY.newDistancePair(doubleDistance, iter));
+          double newkdist = heap.peek().doubleDistance();
+          if(newkdist < max) {
+            max = newkdist;
+            ties.clear();
+          }
+          else {
+            ties.add(prev);
+          }
+        }
+        else {
+          ties.add(DBIDFactory.FACTORY.newDistancePair(doubleDistance, iter));
+        }
+      }
+    }
+
+    DoubleDistanceIntegerDBIDKNNList ret = new DoubleDistanceIntegerDBIDKNNList(k, k + ties.size());
+    for(DoubleDistanceDBIDPair pair : ties) {
+      ret.add(pair);
+    }
+    while(!heap.isEmpty()) {
+      ret.add(heap.poll());
+    }
+    ret.sort(); // Actually, reverse.
+    return ret;
+  }
+
+  /**
+   * Next attempt at exploiting the JIT fully.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectBenchmarked3(O obj, int k) {
+    DoubleDistanceKNNHeap heap = new DoubleDistanceIntegerDBIDKNNListHeap(k);
+    final DBIDIter iter = relation.iterDBIDs();
+    // First k elements don't need checking.
+    double max = 0.;
+    for(int i = 0; i < k && iter.valid(); i++, iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      heap.add(doubleDistance, iter);
+      max = Math.max(max, doubleDistance);
+    }
+    // Remaining elements
+    for(; iter.valid(); iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      if(doubleDistance <= max) {
+        heap.add(doubleDistance, iter);
+      }
+      if(doubleDistance < max) {
+        max = heap.peek().doubleDistance();
+      }
+    }
+    return heap.toKNNList();
+  }
+
+  /**
+   * Next attempt at exploiting the JIT fully.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectBenchmarked4(O obj, int k) {
+    DoubleDistanceKNNHeap heap = new DoubleDistanceIntegerDBIDKNNListHeap(k);
+    final DBIDIter iter = relation.iterDBIDs();
+    // First k elements don't need checking.
+    for(int i = 0; i < k && iter.valid(); i++, iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      heap.add(doubleDistance, iter);
+    }
+    double max = heap.doubleKNNDistance();
+    // Remaining elements
+    for(; iter.valid(); iter.advance()) {
+      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
+      if(doubleDistance <= max) {
+        heap.add(doubleDistance, iter);
+        max = heap.doubleKNNDistance();
+      }
+    }
+    return heap.toKNNList();
+  }
+
+  /**
+   * Next attempt at exploiting the JIT fully.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectBenchmarked5(O obj, int k) {
+    DoubleDistanceKNNHeap heap = new DoubleDistanceIntegerDBIDKNNListHeap(k);
+    double max = Double.POSITIVE_INFINITY;
+    for(DBIDIter iter = relation.iterDBIDs(); iter.valid(); iter.advance()) {
+      final double distance = rawdist.doubleDistance(obj, relation.get(iter));
+      if(distance <= max) {
+        heap.add(distance, iter);
+        max = heap.doubleKNNDistance();
+      }
+    }
+    return heap.toKNNList();
+  }
+
+  /**
+   * Next attempt at exploiting the JIT fully.
+   * 
+   * @param obj Query object
+   * @param k Desired number of neighbors
+   * @return kNN result
+   */
+  KNNList<DoubleDistance> getKNNForObjectBenchmarked6(O obj, int k) {
+    DoubleDistanceKNNHeap heap = new DoubleDistanceIntegerDBIDKNNListHeap(k);
+    for(DBIDIter iter = relation.iterDBIDs(); iter.valid(); iter.advance()) {
+      final double distance = rawdist.doubleDistance(obj, relation.get(iter));
+      heap.add(distance, iter);
+    }
+    return heap.toKNNList();
+  }
+}
diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/KNNQuery.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/KNNQuery.java
index 5b517509..c94f3d42 100644
--- a/src/de/lmu/ifi/dbs/elki/database/query/knn/KNNQuery.java
+++ b/src/de/lmu/ifi/dbs/elki/database/query/knn/KNNQuery.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.database.query.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
@@ -27,8 +27,8 @@ import java.util.List;
 
 import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
+import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList;
 import de.lmu.ifi.dbs.elki.database.query.DatabaseQuery;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNResult;
 import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
 
 /**
@@ -37,7 +37,7 @@ import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
  * @author Erich Schubert
  * 
  * @apiviz.landmark
- * @apiviz.has KNNResult oneway - - «create»
+ * @apiviz.has KNNList oneway - - «create»
  * 
  * @param <O> Object type
  * @param <D> Distance type
@@ -50,7 +50,7 @@ public interface KNNQuery<O, D extends Distance<D>> extends DatabaseQuery {
    * @param k Number of neighbors requested
    * @return neighbors
    */
-  public KNNResult<D> getKNNForDBID(DBIDRef id, int k);
+  public KNNList<D> getKNNForDBID(DBIDRef id, int k);
 
   /**
    * Bulk query method
@@ -59,7 +59,7 @@ public interface KNNQuery<O, D extends Distance<D>> extends DatabaseQuery {
    * @param k Number of neighbors requested
    * @return neighbors
    */
-  public List<? extends KNNResult<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k);
+  public List<? extends KNNList<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k);
 
   /**
    * Get the k nearest neighbors for a particular id.
@@ -68,5 +68,5 @@ public interface KNNQuery<O, D extends Distance<D>> extends DatabaseQuery {
    * @param k Number of neighbors requested
    * @return neighbors
    */
-  public KNNResult<D> getKNNForObject(O obj, int k);
+  public KNNList<D> getKNNForObject(O obj, int k);
 }
\ No newline at end of file
diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanKNNQuery.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanKNNQuery.java
index 29fa9931..485330ea 100644
--- a/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanKNNQuery.java
+++ b/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanKNNQuery.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.database.query.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
@@ -29,12 +29,12 @@ import java.util.List;
 import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
+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.LinearScanQuery;
 import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
 import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceQuery;
-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;
 
 /**
@@ -63,9 +63,9 @@ public class LinearScanKNNQuery<O, D extends Distance<D>> extends AbstractDistan
    */
   private void linearScanBatchKNN(ArrayDBIDs ids, List<KNNHeap<D>> heaps) {
     // The distance is computed on database IDs
-    for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
+    for (DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
       int index = 0;
-      for(DBIDIter iter2 = ids.iter(); iter2.valid(); iter2.advance()) {
+      for (DBIDIter iter2 = ids.iter(); iter2.valid(); iter2.advance()) {
         KNNHeap<D> heap = heaps.get(index);
         heap.add(distanceQuery.distance(iter2, iter), iter);
         index++;
@@ -74,27 +74,26 @@ public class LinearScanKNNQuery<O, D extends Distance<D>> extends AbstractDistan
   }
 
   @Override
-  public KNNResult<D> getKNNForDBID(DBIDRef id, int k) {
-    KNNHeap<D> heap = KNNUtil.newHeap(distanceQuery.getDistanceFactory(), k);
+  public KNNList<D> getKNNForDBID(DBIDRef id, int k) {
+    KNNHeap<D> heap = DBIDUtil.newHeap(distanceQuery.getDistanceFactory(), k);
     D max = distanceQuery.getDistanceFactory().infiniteDistance();
-    if(PrimitiveDistanceQuery.class.isInstance(distanceQuery)) {
+    if (PrimitiveDistanceQuery.class.isInstance(distanceQuery)) {
       O obj = relation.get(id);
-      for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
+      for (DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
         final D dist = distanceQuery.distance(obj, relation.get(iter));
-        if(max.compareTo(dist) > 0) {
+        if (max.compareTo(dist) > 0) {
           heap.add(dist, iter);
-          if(heap.size() >= k) {
+          if (heap.size() >= k) {
             max = heap.getKNNDistance();
           }
         }
       }
-    }
-    else {
-      for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
+    } else {
+      for (DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
         final D dist = distanceQuery.distance(id, iter);
-        if(max.compareTo(dist) > 0) {
+        if (max.compareTo(dist) > 0) {
           heap.add(dist, iter);
-          if(heap.size() >= k) {
+          if (heap.size() >= k) {
             max = heap.getKNNDistance();
           }
         }
@@ -104,27 +103,27 @@ public class LinearScanKNNQuery<O, D extends Distance<D>> extends AbstractDistan
   }
 
   @Override
-  public List<KNNResult<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
+  public List<KNNList<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
     final int size = ids.size();
-    final List<KNNHeap<D>> heaps = new ArrayList<KNNHeap<D>>(size);
-    for(int i = 0; i < size; i++) {
-      heaps.add(KNNUtil.newHeap(distanceQuery.getDistanceFactory(), k));
+    final List<KNNHeap<D>> heaps = new ArrayList<>(size);
+    for (int i = 0; i < size; i++) {
+      heaps.add(DBIDUtil.newHeap(distanceQuery.getDistanceFactory(), k));
     }
     linearScanBatchKNN(ids, heaps);
     // Serialize heaps
-    List<KNNResult<D>> result = new ArrayList<KNNResult<D>>(size);
-    for(KNNHeap<D> heap : heaps) {
+    List<KNNList<D>> result = new ArrayList<>(size);
+    for (KNNHeap<D> heap : heaps) {
       result.add(heap.toKNNList());
     }
     return result;
   }
 
   @Override
-  public KNNResult<D> getKNNForObject(O obj, int k) {
-    KNNHeap<D> heap = KNNUtil.newHeap(distanceQuery.getDistanceFactory(), k);
-    for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
+  public KNNList<D> getKNNForObject(O obj, int k) {
+    KNNHeap<D> heap = DBIDUtil.newHeap(distanceQuery.getDistanceFactory(), k);
+    for (DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
       heap.add(distanceQuery.distance(obj, iter), iter);
     }
     return heap.toKNNList();
   }
-}
\ No newline at end of file
+}
diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanPrimitiveDistanceKNNQuery.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanPrimitiveDistanceKNNQuery.java
index c4f09744..3f3fbc3f 100644
--- a/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanPrimitiveDistanceKNNQuery.java
+++ b/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanPrimitiveDistanceKNNQuery.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.database.query.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
@@ -29,10 +29,10 @@ import java.util.List;
 import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
+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.PrimitiveDistanceQuery;
-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;
 
 /**
@@ -74,22 +74,22 @@ public class LinearScanPrimitiveDistanceKNNQuery<O, D extends Distance<D>> exten
   }
 
   @Override
-  public KNNResult<D> getKNNForDBID(DBIDRef id, int k) {
+  public KNNList<D> getKNNForDBID(DBIDRef id, int k) {
     return getKNNForObject(relation.get(id), k);
   }
 
   @Override
-  public List<KNNResult<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
+  public List<KNNList<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
     final int size = ids.size();
-    final List<KNNHeap<D>> heaps = new ArrayList<KNNHeap<D>>(size);
-    List<O> objs = new ArrayList<O>(size);
+    final List<KNNHeap<D>> heaps = new ArrayList<>(size);
+    List<O> objs = new ArrayList<>(size);
     for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
-      heaps.add(KNNUtil.newHeap(distanceQuery.getDistanceFactory(), k));
+      heaps.add(DBIDUtil.newHeap(distanceQuery.getDistanceFactory(), k));
       objs.add(relation.get(iter));
     }
     linearScanBatchKNN(objs, heaps);
 
-    List<KNNResult<D>> result = new ArrayList<KNNResult<D>>(heaps.size());
+    List<KNNList<D>> result = new ArrayList<>(heaps.size());
     for(KNNHeap<D> heap : heaps) {
       result.add(heap.toKNNList());
     }
diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanRawDoubleDistanceKNNQuery.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanRawDoubleDistanceKNNQuery.java
deleted file mode 100644
index 5e5eae2f..00000000
--- a/src/de/lmu/ifi/dbs/elki/database/query/knn/LinearScanRawDoubleDistanceKNNQuery.java
+++ /dev/null
@@ -1,143 +0,0 @@
-package de.lmu.ifi.dbs.elki.database.query.knn;
-
-/*
- This file is part of ELKI:
- Environment for Developing KDD-Applications Supported by Index-Structures
-
- Copyright (C) 2012
- Ludwig-Maximilians-Universität München
- Lehr- und Forschungseinheit für Datenbanksysteme
- ELKI Development Team
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU Affero General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU Affero General Public License for more details.
-
- You should have received a copy of the GNU Affero General Public License
- along with this program.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-import de.lmu.ifi.dbs.elki.database.ids.DBIDFactory;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
-import de.lmu.ifi.dbs.elki.database.ids.DistanceDBIDPair;
-import de.lmu.ifi.dbs.elki.database.ids.DoubleDistanceDBIDPair;
-import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceQuery;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDoubleDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.AbstractKNNHeap;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.DoubleDistanceKNNHeap;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.DoubleDistanceKNNList;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.GenericKNNList;
-import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNResult;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
-import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.TiedTopBoundedHeap;
-
-/**
- * Optimized linear scan query for {@link PrimitiveDoubleDistanceFunction}s.
- * 
- * @author Erich Schubert
- * 
- * @apiviz.uses PrimitiveDoubleDistanceFunction
- * 
- * @param <O> Object type
- */
-public class LinearScanRawDoubleDistanceKNNQuery<O> extends LinearScanPrimitiveDistanceKNNQuery<O, DoubleDistance> {
-  /**
-   * Raw distance function.
-   */
-  PrimitiveDoubleDistanceFunction<O> rawdist;
-
-  /**
-   * Constructor.
-   * 
-   * @param distanceQuery Distance function to use
-   */
-  @SuppressWarnings("unchecked")
-  public LinearScanRawDoubleDistanceKNNQuery(PrimitiveDistanceQuery<O, DoubleDistance> distanceQuery) {
-    super(distanceQuery);
-    if (!(distanceQuery.getDistanceFunction() instanceof PrimitiveDoubleDistanceFunction)) {
-      throw new UnsupportedOperationException("LinearScanRawDoubleDistance instantiated for non-RawDoubleDistance!");
-    }
-    rawdist = (PrimitiveDoubleDistanceFunction<O>) distanceQuery.getDistanceFunction();
-  }
-
-  @Override
-  public KNNResult<DoubleDistance> getKNNForDBID(DBIDRef id, int k) {
-    return getKNNForObject(relation.get(id), k);
-  }
-
-  @Override
-  public KNNResult<DoubleDistance> getKNNForObject(O obj, int k) {
-    return getKNNForObjectBenchmarked(obj, k);
-  }
-
-  /**
-   * This is the cleaner, supposedly faster implementation.
-   * 
-   * @param obj Query object
-   * @param k Desired number of neighbors
-   * @return kNN result
-   */
-  KNNResult<DoubleDistance> getKNNForObjectClean(O obj, int k) {
-    // Optimization for double distances.
-    final TiedTopBoundedHeap<DoubleDistanceDBIDPair> heap = new TiedTopBoundedHeap<DoubleDistanceDBIDPair>(k, DoubleDistanceKNNHeap.COMPARATOR);
-    final DBIDIter iter = relation.iterDBIDs();
-
-    // First k elements don't need checking.
-    double max = 0.;
-    for (int i = 0; i < k && iter.valid(); i++, iter.advance()) {
-      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
-      heap.add(DBIDFactory.FACTORY.newDistancePair(doubleDistance, iter));
-      max = Math.max(max, doubleDistance);
-    }
-    // Remaining elements
-    for (; iter.valid(); iter.advance()) {
-      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
-      if (doubleDistance <= max) {
-        heap.add(DBIDFactory.FACTORY.newDistancePair(doubleDistance, iter));
-      }
-      if (doubleDistance < max) {
-        max = heap.peek().doubleDistance();
-      }
-    }
-    return new DoubleDistanceKNNList(heap, k);
-  }
-
-  /**
-   * It does not make sense, but this version is faster in our larger
-   * benchmarks. Apparently, some JIT optimization kicks in better.
-   * 
-   * @param obj Query object
-   * @param k Desired number of neighbors
-   * @return kNN result
-   */
-  KNNResult<DoubleDistance> getKNNForObjectBenchmarked(O obj, int k) {
-    // THIS SHOULD BE SLOWER THAN THE VERSION ABOVE, BUT ISN'T!
-    final TiedTopBoundedHeap<DistanceDBIDPair<DoubleDistance>> heap = new TiedTopBoundedHeap<DistanceDBIDPair<DoubleDistance>>(k, AbstractKNNHeap.COMPARATOR);
-    final DBIDIter iter = relation.iterDBIDs();
-    // First k elements don't need checking.
-    double max = 0.;
-    for (int i = 0; i < k && iter.valid(); i++, iter.advance()) {
-      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
-      heap.add(DBIDFactory.FACTORY.newDistancePair(new DoubleDistance(doubleDistance), iter));
-      max = Math.max(max, doubleDistance);
-    }
-    // Remaining elements
-    for (; iter.valid(); iter.advance()) {
-      final double doubleDistance = rawdist.doubleDistance(obj, relation.get(iter));
-      if (doubleDistance <= max) {
-        heap.add(DBIDFactory.FACTORY.newDistancePair(new DoubleDistance(doubleDistance), iter));
-      }
-      if (doubleDistance < max) {
-        max = heap.peek().getDistance().doubleValue();
-      }
-    }
-    return new GenericKNNList<DoubleDistance>(heap, k);
-  }
-}
diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/PreprocessorKNNQuery.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/PreprocessorKNNQuery.java
index a1e12fc3..0f555c79 100644
--- a/src/de/lmu/ifi/dbs/elki/database/query/knn/PreprocessorKNNQuery.java
+++ b/src/de/lmu/ifi/dbs/elki/database/query/knn/PreprocessorKNNQuery.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.database.query.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
@@ -29,10 +29,10 @@ import java.util.List;
 import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList;
 import de.lmu.ifi.dbs.elki.database.query.AbstractDataBasedQuery;
 import de.lmu.ifi.dbs.elki.database.relation.Relation;
-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.preprocessed.knn.AbstractMaterializeKNNPreprocessor;
 import de.lmu.ifi.dbs.elki.logging.LoggingUtil;
@@ -43,7 +43,7 @@ import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
  * 
  * @author Erich Schubert
  */
-public class PreprocessorKNNQuery<O, D extends Distance<D>, T extends KNNResult<D>> extends AbstractDataBasedQuery<O> implements KNNQuery<O, D> {
+public class PreprocessorKNNQuery<O, D extends Distance<D>, T extends KNNList<D>> extends AbstractDataBasedQuery<O> implements KNNQuery<O, D> {
   /**
    * The last preprocessor result
    */
@@ -76,12 +76,12 @@ public class PreprocessorKNNQuery<O, D extends Distance<D>, T extends KNNResult<
   }
 
   @Override
-  public KNNResult<D> getKNNForDBID(DBIDRef id, int k) {
+  public KNNList<D> getKNNForDBID(DBIDRef id, int k) {
     if(!warned && k > preprocessor.getK()) {
       LoggingUtil.warning("Requested more neighbors than preprocessed!");
     }
     if(!warned && k < preprocessor.getK()) {
-      KNNResult<D> dr = preprocessor.get(id);
+      KNNList<D> dr = preprocessor.get(id);
       int subk = k;
       D kdist = dr.get(subk - 1).getDistance();
       while(subk < dr.size()) {
@@ -95,7 +95,7 @@ public class PreprocessorKNNQuery<O, D extends Distance<D>, T extends KNNResult<
         }
       }
       if(subk < dr.size()) {
-        return KNNUtil.subList(dr, subk);
+        return DBIDUtil.subList(dr, subk);
       }
       else {
         return dr;
@@ -105,14 +105,14 @@ public class PreprocessorKNNQuery<O, D extends Distance<D>, T extends KNNResult<
   }
 
   @Override
-  public List<KNNResult<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
+  public List<KNNList<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
     if(!warned && k > preprocessor.getK()) {
       LoggingUtil.warning("Requested more neighbors than preprocessed!");
     }
-    List<KNNResult<D>> result = new ArrayList<KNNResult<D>>(ids.size());
+    List<KNNList<D>> result = new ArrayList<>(ids.size());
     if(k < preprocessor.getK()) {
       for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {      
-        KNNResult<D> dr = preprocessor.get(iter);
+        KNNList<D> dr = preprocessor.get(iter);
         int subk = k;
         D kdist = dr.get(subk - 1).getDistance();
         while(subk < dr.size()) {
@@ -126,7 +126,7 @@ public class PreprocessorKNNQuery<O, D extends Distance<D>, T extends KNNResult<
           }
         }
         if(subk < dr.size()) {
-          result.add(KNNUtil.subList(dr, subk));
+          result.add(DBIDUtil.subList(dr, subk));
         }
         else {
           result.add(dr);
@@ -142,7 +142,7 @@ public class PreprocessorKNNQuery<O, D extends Distance<D>, T extends KNNResult<
   }
 
   @Override
-  public KNNResult<D> getKNNForObject(O obj, int k) {
+  public KNNList<D> getKNNForObject(O obj, int k) {
     throw new AbortException("Preprocessor KNN query only supports ID queries.");
   }
 
diff --git a/src/de/lmu/ifi/dbs/elki/database/query/knn/package-info.java b/src/de/lmu/ifi/dbs/elki/database/query/knn/package-info.java
index 63c3aa16..17d8e4b0 100644
--- a/src/de/lmu/ifi/dbs/elki/database/query/knn/package-info.java
+++ b/src/de/lmu/ifi/dbs/elki/database/query/knn/package-info.java
@@ -8,7 +8,7 @@
 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