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package de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
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 java.util.HashMap;
import java.util.List;
import java.util.Map;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.database.QueryUtil;
import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
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.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.index.tree.query.DoubleDistanceSearchCandidate;
import de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialDirectoryEntry;
import de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialPointLeafEntry;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.AbstractRStarTree;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.AbstractRStarTreeNode;
import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.ComparableMinHeap;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
/**
* Instance of a KNN query for a particular spatial index.
*
* Reference:
* <p>
* G. R. Hjaltason, H. Samet<br />
* Ranking in spatial databases<br />
* In: 4th Symposium on Advances in Spatial Databases, SSD'95
* </p>
*
* @author Erich Schubert
* @since 0.7.0
*
* @apiviz.uses EuclideanDistanceFunction
* @apiviz.uses SquaredEuclideanDistanceFunction
*/
@Reference(authors = "G. R. Hjaltason, H. Samet", //
title = "Ranking in spatial databases", //
booktitle = "Advances in Spatial Databases - 4th Symposium, SSD'95", //
url = "http://dx.doi.org/10.1007/3-540-60159-7_6")
public class EuclideanRStarTreeKNNQuery<O extends NumberVector> extends RStarTreeKNNQuery<O> {
/**
* Squared euclidean distance function.
*/
private static final SquaredEuclideanDistanceFunction SQUARED = SquaredEuclideanDistanceFunction.STATIC;
/**
* Constructor.
*
* @param tree Index to use
* @param relation Data relation to query
*/
public EuclideanRStarTreeKNNQuery(AbstractRStarTree<?, ?, ?> tree, Relation<? extends O> relation) {
super(tree, relation, EuclideanDistanceFunction.STATIC);
}
@Override
public KNNList getKNNForObject(O obj, int k) {
if(k < 1) {
throw new IllegalArgumentException("At least one neighbor has to be requested!");
}
tree.statistics.countKNNQuery();
final KNNHeap knnList = DBIDUtil.newHeap(k);
final ComparableMinHeap<DoubleDistanceSearchCandidate> pq = new ComparableMinHeap<>(Math.min(knnList.getK() << 1, 21));
// expand root
double maxDist = expandNode(obj, knnList, pq, Double.MAX_VALUE, tree.getRootID());
// search in tree
while(!pq.isEmpty()) {
DoubleDistanceSearchCandidate pqNode = pq.poll();
if(pqNode.mindist > maxDist) {
break;
}
maxDist = expandNode(obj, knnList, pq, maxDist, pqNode.nodeID);
}
return QueryUtil.applySqrt(knnList.toKNNList());
}
private double expandNode(O object, KNNHeap knnList, final ComparableMinHeap<DoubleDistanceSearchCandidate> pq, double maxDist, final int nodeID) {
AbstractRStarTreeNode<?, ?> node = tree.getNode(nodeID);
// data node
if(node.isLeaf()) {
for(int i = 0; i < node.getNumEntries(); i++) {
SpatialPointLeafEntry entry = (SpatialPointLeafEntry) node.getEntry(i);
double distance = SQUARED.minDist(entry, object);
tree.statistics.countDistanceCalculation();
if(distance <= maxDist) {
maxDist = knnList.insert(distance, entry.getDBID());
}
}
}
// directory node
else {
for(int i = 0; i < node.getNumEntries(); i++) {
SpatialDirectoryEntry entry = (SpatialDirectoryEntry) node.getEntry(i);
double distance = SQUARED.minDist(entry, object);
tree.statistics.countDistanceCalculation();
// Greedy expand, bypassing the queue
if(distance <= 0) {
expandNode(object, knnList, pq, maxDist, entry.getPageID());
}
else {
if(distance <= maxDist) {
pq.add(new DoubleDistanceSearchCandidate(distance, entry.getPageID()));
}
}
}
}
return maxDist;
}
@Override
public List<KNNList> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
if(k < 1) {
throw new IllegalArgumentException("At least one enumeration has to be requested!");
}
// While this works, it seems to be slow at least for large sets!
// TODO: use a DataStore instead of a map.
final Map<DBID, KNNHeap> knnLists = new HashMap<>(ids.size());
for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
DBID id = DBIDUtil.deref(iter);
knnLists.put(id, DBIDUtil.newHeap(k));
}
batchNN(tree.getRoot(), knnLists);
List<KNNList> result = new ArrayList<>();
for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
DBID id = DBIDUtil.deref(iter);
tree.statistics.countKNNQuery();
result.add(QueryUtil.applySqrt(knnLists.get(id).toKNNList()));
}
return result;
}
}
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