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) 2012
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 .
*/
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.query.LinearScanQuery;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceQuery;
import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNHeap;
import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNResult;
import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNUtil;
import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
/**
* Instance of this query for a particular database.
*
* @author Erich Schubert
*
* @apiviz.landmark
* @apiviz.has DistanceQuery
*/
public class LinearScanKNNQuery> extends AbstractDistanceKNNQuery implements LinearScanQuery {
/**
* Constructor.
*
* @param distanceQuery Distance function to use
*/
public LinearScanKNNQuery(DistanceQuery distanceQuery) {
super(distanceQuery);
}
/**
* Linear batch knn for arbitrary distance functions.
*
* @param ids DBIDs to process
* @param heaps Heaps to store the results in
*/
private void linearScanBatchKNN(ArrayDBIDs ids, List> heaps) {
// The distance is computed on database IDs
for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
int index = 0;
for(DBIDIter iter2 = ids.iter(); iter2.valid(); iter2.advance()) {
KNNHeap heap = heaps.get(index);
heap.add(distanceQuery.distance(iter2, iter), iter);
index++;
}
}
}
@Override
public KNNResult getKNNForDBID(DBIDRef id, int k) {
KNNHeap heap = KNNUtil.newHeap(distanceQuery.getDistanceFactory(), k);
D max = distanceQuery.getDistanceFactory().infiniteDistance();
if(PrimitiveDistanceQuery.class.isInstance(distanceQuery)) {
O obj = relation.get(id);
for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
final D dist = distanceQuery.distance(obj, relation.get(iter));
if(max.compareTo(dist) > 0) {
heap.add(dist, iter);
if(heap.size() >= k) {
max = heap.getKNNDistance();
}
}
}
}
else {
for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
final D dist = distanceQuery.distance(id, iter);
if(max.compareTo(dist) > 0) {
heap.add(dist, iter);
if(heap.size() >= k) {
max = heap.getKNNDistance();
}
}
}
}
return heap.toKNNList();
}
@Override
public List> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
final int size = ids.size();
final List> heaps = new ArrayList>(size);
for(int i = 0; i < size; i++) {
heaps.add(KNNUtil.newHeap(distanceQuery.getDistanceFactory(), k));
}
linearScanBatchKNN(ids, heaps);
// Serialize heaps
List> result = new ArrayList>(size);
for(KNNHeap heap : heaps) {
result.add(heap.toKNNList());
}
return result;
}
@Override
public KNNResult getKNNForObject(O obj, int k) {
KNNHeap heap = KNNUtil.newHeap(distanceQuery.getDistanceFactory(), k);
for(DBIDIter iter = relation.getDBIDs().iter(); iter.valid(); iter.advance()) {
heap.add(distanceQuery.distance(obj, iter), iter);
}
return heap.toKNNList();
}
}