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/***************************************************************************
* Copyright (C) 2017 iObserve Project (https://www.iobserve-devops.net)
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
***************************************************************************/
package kieker.analysis.generic.clustering;
import java.util.Set;
import kieker.analysis.generic.clustering.mtree.IDistanceFunction;
import kieker.analysis.generic.clustering.mtree.TrimmedAlgorithm;
import teetime.stage.basic.AbstractTransformation;
/**
* This stage calculates the medoid of the clusters using the trimmed algorithm.
* A medoid is a representative object of a cluster where the medoid has the least
* difference to all other objects in the cluster.
*
* @param
* data type
*
* @author Lars Jürgensen
* @since 2.0.0
*/
public class MedoidGenerator extends AbstractTransformation, T> {
private final IDistanceFunction distanceFunction;
public MedoidGenerator(final IDistanceFunction distanceFunction) {
this.distanceFunction = distanceFunction;
}
@Override
protected void execute(final Clustering clustering) throws Exception {
for (final Set clusterSet : clustering.getClusters()) {
@SuppressWarnings("unchecked")
final T[] cluster = (T[]) clusterSet.toArray(); // NOPMD
// The trimmed algorithm needs at least one element.
if (cluster.length == 0) {
this.logger.warn("Empty cluster received");
return;
}
final TrimmedAlgorithm trimed = new TrimmedAlgorithm<>(cluster, this.distanceFunction);
this.outputPort.send(trimed.calculate());
}
this.logger.debug("gernerated all mediods of a clustering");
}
}
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