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package de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2014
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 de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
import de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter;
import de.lmu.ifi.dbs.elki.database.ids.KNNList;
import de.lmu.ifi.dbs.elki.parallel.Executor;
import de.lmu.ifi.dbs.elki.parallel.processor.AbstractDoubleProcessor;
import de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor;
import de.lmu.ifi.dbs.elki.parallel.variables.SharedDouble;
import de.lmu.ifi.dbs.elki.parallel.variables.SharedObject;
/**
* Compute the kNN weight score, used by {@link ParallelKNNWeightOutlier}.
*
* Needs the k nearest neighbors as input, for example from {@link KNNProcessor}
*
* @author Erich Schubert
*/
public class KNNWeightProcessor extends AbstractDoubleProcessor {
/**
* K parameter
*/
int k;
/**
* Constructor.
*
* @param k K parameter
*/
public KNNWeightProcessor(int k) {
super();
this.k = k;
}
/**
* KNN query object
*/
SharedObject<? extends KNNList> input;
/**
* Connect the input channel.
*
* @param input Input channel
*/
public void connectKNNInput(SharedObject<? extends KNNList> input) {
this.input = input;
}
@Override
public Instance instantiate(Executor executor) {
return new Instance(k, executor.getInstance(input), executor.getInstance(output));
}
/**
* Instance for precomputing the kNN.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
private static class Instance extends AbstractDoubleProcessor.Instance {
/**
* k Parameter
*/
int k;
/**
* kNN query
*/
SharedObject.Instance<? extends KNNList> input;
/**
* Constructor.
*
* @param k K parameter
* @param input kNN list input
* @param store Datastore to write to
*/
protected Instance(int k, SharedObject.Instance<? extends KNNList> input, SharedDouble.Instance store) {
super(store);
this.k = k;
this.input = input;
}
@Override
public void map(DBIDRef id) {
final KNNList list = input.get();
int i = 0;
double sum = 0;
for(DoubleDBIDListIter iter = list.iter(); iter.valid() && i < k; iter.advance(), ++i) {
sum += iter.doubleValue();
}
output.set(sum);
}
}
}
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