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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) 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 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.PrimitiveDistanceQuery;
import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
/**
* 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 AbstractDistanceKNNQuery<O, D> implements LinearScanQuery {
/**
* 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(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
O candidate = relation.get(iter);
for(int index = 0; index < size; index++) {
O object = objs.get(index);
heaps.get(index).insert(distanceQuery.distance(object, candidate), iter);
}
}
}
@Override
public KNNList<D> getKNNForDBID(DBIDRef id, int k) {
final O obj = relation.get(id);
KNNHeap<D> heap = DBIDUtil.newHeap(distanceQuery.getDistanceFactory(), k);
for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
heap.insert(distanceQuery.distance(obj, iter), iter);
}
return heap.toKNNList();
}
@Override
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.insert(distanceQuery.distance(obj, iter), iter);
}
return heap.toKNNList();
}
@Override
public List<KNNList<D>> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
final int size = ids.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(DBIDUtil.newHeap(distanceQuery.getDistanceFactory(), k));
objs.add(relation.get(iter));
}
linearScanBatchKNN(objs, heaps);
List<KNNList<D>> result = new ArrayList<>(heaps.size());
for(KNNHeap<D> heap : heaps) {
result.add(heap.toKNNList());
}
return result;
}
}
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