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-rw-r--r--src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/PartitionApproximationMaterializeKNNPreprocessor.java49
1 files changed, 21 insertions, 28 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/PartitionApproximationMaterializeKNNPreprocessor.java b/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/PartitionApproximationMaterializeKNNPreprocessor.java
index b6cc5103..ef276e78 100644
--- a/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/PartitionApproximationMaterializeKNNPreprocessor.java
+++ b/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/PartitionApproximationMaterializeKNNPreprocessor.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
@@ -23,24 +23,23 @@ package de.lmu.ifi.dbs.elki.index.preprocessed.knn;
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
-import java.util.HashMap;
-
+import gnu.trove.impl.Constants;
+import gnu.trove.map.hash.TObjectDoubleHashMap;
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.ids.ArrayDBIDs;
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.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
-import de.lmu.ifi.dbs.elki.utilities.RandomFactory;
+import de.lmu.ifi.dbs.elki.math.random.RandomFactory;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
@@ -58,11 +57,10 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.RandomParameter;
* @author Erich Schubert
*
* @param <O> the type of database objects the preprocessor can be applied to
- * @param <D> the type of distance the used distance function will return
*/
@Title("Partitioning Approximate kNN Preprocessor")
@Description("Caterializes the (approximate) k nearest neighbors of objects of a database by partitioning and only computing kNN within each partition.")
-public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor<O, D, KNNList<D>> {
+public class PartitionApproximationMaterializeKNNPreprocessor<O> extends AbstractMaterializeKNNPreprocessor<O> {
/**
* Logger to use
*/
@@ -87,7 +85,7 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
* @param partitions Number of partitions
* @param rnd Random number generator
*/
- public PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O, D> distanceFunction, int k, int partitions, RandomFactory rnd) {
+ public PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, int partitions, RandomFactory rnd) {
super(relation, distanceFunction, k);
this.partitions = partitions;
this.rnd = rnd;
@@ -95,7 +93,7 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
@Override
protected void preprocess() {
- DistanceQuery<O, D> distanceQuery = relation.getDatabase().getDistanceQuery(relation, distanceFunction);
+ DistanceQuery<O> distanceQuery = relation.getDatabase().getDistanceQuery(relation, distanceFunction);
storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, KNNList.class);
MeanVariance ksize = new MeanVariance();
if(LOG.isVerbose()) {
@@ -109,13 +107,13 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
for(int part = 0; part < partitions; part++) {
final ArrayDBIDs ids = parts[part];
final int size = ids.size();
- 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 iter = ids.iter(); iter.valid(); iter.advance()) {
- KNNHeap<D> kNN = DBIDUtil.newHeap(distanceFunction.getDistanceFactory(), k);
+ KNNHeap kNN = DBIDUtil.newHeap(k);
for(DBIDIter iter2 = ids.iter(); iter2.valid(); iter2.advance()) {
DBIDPair key = DBIDUtil.newPair(iter, iter2);
- 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, iter2);
}
@@ -136,13 +134,9 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
LOG.warning("Cache should be empty after each run, but still has " + cache.size() + " elements.");
}
}
- if(progress != null) {
- progress.incrementProcessed(LOG);
- }
- }
- if(progress != null) {
- progress.ensureCompleted(LOG);
+ LOG.incrementProcessed(progress);
}
+ LOG.ensureCompleted(progress);
if(LOG.isVerbose()) {
LOG.verbose("On average, " + ksize.getMean() + " +- " + ksize.getSampleStddev() + " neighbors returned.");
}
@@ -178,9 +172,8 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
* «create»
*
* @param <O> The object type
- * @param <D> The distance type
*/
- public static class Factory<O, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor.Factory<O, D, KNNList<D>> {
+ public static class Factory<O> extends AbstractMaterializeKNNPreprocessor.Factory<O> {
/**
* The number of partitions to use
*/
@@ -199,15 +192,15 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
* @param partitions number of partitions
* @param rnd
*/
- public Factory(int k, DistanceFunction<? super O, D> distanceFunction, int partitions, RandomFactory rnd) {
+ public Factory(int k, DistanceFunction<? super O> distanceFunction, int partitions, RandomFactory rnd) {
super(k, distanceFunction);
this.partitions = partitions;
this.rnd = rnd;
}
@Override
- public PartitionApproximationMaterializeKNNPreprocessor<O, D> instantiate(Relation<O> relation) {
- PartitionApproximationMaterializeKNNPreprocessor<O, D> instance = new PartitionApproximationMaterializeKNNPreprocessor<>(relation, distanceFunction, k, partitions, rnd);
+ public PartitionApproximationMaterializeKNNPreprocessor<O> instantiate(Relation<O> relation) {
+ PartitionApproximationMaterializeKNNPreprocessor<O> instance = new PartitionApproximationMaterializeKNNPreprocessor<>(relation, distanceFunction, k, partitions, rnd);
return instance;
}
@@ -218,7 +211,7 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
*
* @apiviz.exclude
*/
- public static class Parameterizer<O, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<O, D> {
+ public static class Parameterizer<O> extends AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<O> {
/**
* Parameter to specify the number of partitions to use for materializing
* the kNN. Must be an integer greater than 1.
@@ -261,7 +254,7 @@ public class PartitionApproximationMaterializeKNNPreprocessor<O, D extends Dista
}
@Override
- protected Factory<O, D> makeInstance() {
+ protected Factory<O> makeInstance() {
return new Factory<>(k, distanceFunction, partitions, rnd);
}
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-01 15:41:36 +0000