diff options
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/SpatialApproximationMaterializeKNNPreprocessor.java')
-rw-r--r-- | src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/SpatialApproximationMaterializeKNNPreprocessor.java | 47 |
1 files changed, 21 insertions, 26 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 83f8f6d8..cd363091 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) 2013 + Copyright (C) 2014 Ludwig-Maximilians-Universität München Lehr- und Forschungseinheit für Datenbanksysteme ELKI Development Team @@ -23,8 +23,10 @@ package de.lmu.ifi.dbs.elki.index.preprocessed.knn; along with this program. If not, see <http://www.gnu.org/licenses/>. */ +import gnu.trove.impl.Constants; +import gnu.trove.map.hash.TObjectDoubleHashMap; + import java.util.Collection; -import java.util.HashMap; import java.util.List; import de.lmu.ifi.dbs.elki.data.NumberVector; @@ -34,12 +36,11 @@ 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.ids.KNNHeap; +import de.lmu.ifi.dbs.elki.database.ids.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.distancevalue.Distance; import de.lmu.ifi.dbs.elki.index.tree.LeafEntry; import de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialEntry; import de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialIndexTree; @@ -64,13 +65,12 @@ import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException; * * @apiviz.uses SpatialIndexTree * - * @param <D> the type of distance the used distance function will return * @param <N> the type of spatial nodes in the spatial index * @param <E> the type of spatial entries in the spatial index */ @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, KNNList<D>> { +public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector, N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor<O> { /** * Logger to use */ @@ -83,13 +83,13 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect * @param distanceFunction the distance function to use * @param k query k */ - public SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O, D> distanceFunction, int k) { + public SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) { super(relation, distanceFunction, k); } @Override protected void preprocess() { - DistanceQuery<O, D> distanceQuery = relation.getDatabase().getDistanceQuery(relation, distanceFunction); + DistanceQuery<O> distanceQuery = relation.getDatabase().getDistanceQuery(relation, distanceFunction); Collection<SpatialIndexTree<N, E>> indexes = ResultUtil.filterResults(relation, SpatialIndexTree.class); if(indexes.size() != 1) { @@ -118,13 +118,13 @@ 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<>((size * size * 3) >> 3); + TObjectDoubleHashMap<DBIDPair> cache = new TObjectDoubleHashMap<>((size * size * 3) >> 3, Constants.DEFAULT_LOAD_FACTOR, Double.NaN); for(DBIDIter id = ids.iter(); id.valid(); id.advance()) { - KNNHeap<D> kNN = DBIDUtil.newHeap(distanceFunction.getDistanceFactory(), k); + KNNHeap kNN = DBIDUtil.newHeap(k); for(DBIDIter id2 = ids.iter(); id2.valid(); id2.advance()) { DBIDPair key = DBIDUtil.newPair(id, id2); - D d = cache.remove(key); - if(d != null) { + double d = cache.remove(key); + if(d == d) { // Not NaN // consume the previous result. kNN.insert(d, id2); } @@ -145,13 +145,9 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect getLogger().warning("Cache should be empty after each run, but still has " + cache.size() + " elements."); } } - if(progress != null) { - progress.incrementProcessed(getLogger()); - } - } - if(progress != null) { - progress.ensureCompleted(getLogger()); + getLogger().incrementProcessed(progress); } + getLogger().ensureCompleted(progress); if(getLogger().isVerbose()) { getLogger().verbose("Average page size = " + pagesize.getMean() + " +- " + pagesize.getSampleStddev()); getLogger().verbose("On average, " + ksize.getMean() + " +- " + ksize.getSampleStddev() + " neighbors returned."); @@ -187,24 +183,23 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect * @apiviz.uses SpatialApproximationMaterializeKNNPreprocessor oneway - - * «create» * - * @param <D> the type of distance the used distance function will return * @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, KNNList<D>> { + public static class Factory<N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor.Factory<NumberVector> { /** * Constructor. * * @param k k * @param distanceFunction distance function */ - public Factory(int k, DistanceFunction<? super NumberVector<?>, D> distanceFunction) { + public Factory(int k, DistanceFunction<? super NumberVector> distanceFunction) { super(k, distanceFunction); } @Override - public SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>, D, N, E> instantiate(Relation<NumberVector<?>> relation) { - SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>, D, N, E> instance = new SpatialApproximationMaterializeKNNPreprocessor<>(relation, distanceFunction, k); + public SpatialApproximationMaterializeKNNPreprocessor<NumberVector, N, E> instantiate(Relation<NumberVector> relation) { + SpatialApproximationMaterializeKNNPreprocessor<NumberVector, N, E> instance = new SpatialApproximationMaterializeKNNPreprocessor<>(relation, distanceFunction, k); return instance; } @@ -215,9 +210,9 @@ public class SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVect * * @apiviz.exclude */ - public static class Parameterizer<D extends Distance<D>, N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<NumberVector<?>, D> { + public static class Parameterizer<N extends SpatialNode<N, E>, E extends SpatialEntry> extends AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<NumberVector> { @Override - protected Factory<D, N, E> makeInstance() { + protected Factory<N, E> makeInstance() { return new Factory<>(k, distanceFunction); } } |