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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) 2011
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.List;
import java.util.Map;
import java.util.Map.Entry;
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.query.DistanceResultPair;
import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceQuery;
import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNHeap;
/**
* Instance of this query for a particular database.
*
* This is a subtle optimization: for primitive queries, it is clearly faster to
* retrieve the query object from the relation only once!
*
* @author Erich Schubert
*
* @apiviz.uses PrimitiveDistanceQuery
*/
public class LinearScanPrimitiveDistanceKNNQuery<O, D extends Distance<D>> extends LinearScanKNNQuery<O, D> {
/**
* Constructor.
*
* @param distanceQuery Distance function to use
*/
public LinearScanPrimitiveDistanceKNNQuery(PrimitiveDistanceQuery<O, D> distanceQuery) {
super(distanceQuery);
}
/**
* Perform a linear scan batch kNN for primitive distance functions.
*
* @param objs Objects list
* @param heaps Heaps array
*/
protected void linearScanBatchKNN(List<O> objs, List<KNNHeap<D>> heaps) {
final int size = objs.size();
// Linear scan style KNN.
for(DBID candidateID : relation.iterDBIDs()) {
O candidate = relation.get(candidateID);
for(int index = 0; index < size; index++) {
O object = objs.get(index);
KNNHeap<D> heap = heaps.get(index);
heap.add(distanceQuery.distance(object, candidate), candidateID);
}
}
}
@Override
public List<DistanceResultPair<D>> getKNNForDBID(DBID id, int k) {
return getKNNForObject(relation.get(id), k);
}
@Override
public List<List<DistanceResultPair<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);
for(DBID id : ids) {
heaps.add(new KNNHeap<D>(k));
objs.add(relation.get(id));
}
linearScanBatchKNN(objs, heaps);
List<List<DistanceResultPair<D>>> result = new ArrayList<List<DistanceResultPair<D>>>(heaps.size());
for(KNNHeap<D> heap : heaps) {
result.add(heap.toSortedArrayList());
}
return result;
}
@Override
public void getKNNForBulkHeaps(Map<DBID, KNNHeap<D>> heaps) {
List<O> objs = new ArrayList<O>(heaps.size());
List<KNNHeap<D>> kheaps = new ArrayList<KNNHeap<D>>(heaps.size());
for(Entry<DBID, KNNHeap<D>> ent : heaps.entrySet()) {
objs.add(relation.get(ent.getKey()));
kheaps.add(ent.getValue());
}
linearScanBatchKNN(objs, kheaps);
}
}
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