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package de.lmu.ifi.dbs.elki.index.preprocessed.knn;
import de.lmu.ifi.dbs.elki.algorithm.KNNJoin;
import de.lmu.ifi.dbs.elki.data.NumberVector;
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.index.tree.spatial.SpatialEntry;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeNode;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNList;
/*
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 <http://www.gnu.org/licenses/>.
*/
/**
* Class to materialize the kNN using a spatial join on an R-tree.
*
* @author Erich Schubert
*
* @param <V> vector type
* @param <D> distance type
*/
public class KNNJoinMaterializeKNNPreprocessor<V extends NumberVector<V, ?>, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor<V, D, KNNList<D>> {
/**
* Logging class.
*/
private static final Logging logger = Logging.getLogger(KNNJoinMaterializeKNNPreprocessor.class);
/**
* Constructor.
*
* @param relation Relation to index
* @param distanceFunction Distance function
* @param k k
*/
public KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, DistanceFunction<? super V, D> distanceFunction, int k) {
super(relation, distanceFunction, k);
}
@Override
protected void preprocess() {
// Run KNNJoin
KNNJoin<V, D, ?, ?> knnjoin = new KNNJoin<V, D, RStarTreeNode, SpatialEntry>(distanceFunction, k);
storage = knnjoin.run(relation.getDatabase(), relation);
}
@Override
protected Logging getLogger() {
return logger;
}
@Override
public String getLongName() {
return "knn-join materialized neighbors";
}
@Override
public String getShortName() {
return "knn-join";
}
/**
* The parameterizable factory.
*
* @author Erich Schubert
*
* @apiviz.landmark
* @apiviz.stereotype factory
* @apiviz.uses AbstractMaterializeKNNPreprocessor oneway - - «create»
*
* @param <O> The object type
* @param <D> The distance type
*/
public static class Factory<O extends NumberVector<O, ?>, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor.Factory<O, D, KNNList<D>> {
/**
* Constructor.
*
* @param k K
* @param distanceFunction distance function
*/
public Factory(int k, DistanceFunction<? super O, D> distanceFunction) {
super(k, distanceFunction);
}
@Override
public KNNJoinMaterializeKNNPreprocessor<O, D> instantiate(Relation<O> relation) {
return new KNNJoinMaterializeKNNPreprocessor<O, D>(relation, distanceFunction, k);
}
/**
* Parameterization class
*
* @author Erich Schubert
*
* @apiviz.exclude
*
* @param <O> Object type
* @param <D> Distance type
*/
public static class Parameterizer<O extends NumberVector<O, ?>, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<O, D> {
@Override
protected KNNJoinMaterializeKNNPreprocessor.Factory<O, D> makeInstance() {
return new KNNJoinMaterializeKNNPreprocessor.Factory<O, D>(k, distanceFunction);
}
}
}
}
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