net.sf.mzmine.modules.peaklistmethods.dataanalysis.clustering.hierarchical.HierarClusterer.java Source code

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Here is the source code for net.sf.mzmine.modules.peaklistmethods.dataanalysis.clustering.hierarchical.HierarClusterer.java

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/*
 * Copyright 2006-2015 The MZmine 2 Development Team
 *
 * This file is part of MZmine 2.
 *
 * MZmine 2 is free software; you can redistribute it and/or modify it under the
 * terms of the GNU General Public License as published by the Free Software
 * Foundation; either version 2 of the License, or (at your option) any later
 * version.
 *
 * MZmine 2 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 General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License along with
 * MZmine 2; if not, write to the Free Software Foundation, Inc., 51 Franklin St,
 * Fifth Floor, Boston, MA 02110-1301 USA
 */

package net.sf.mzmine.modules.peaklistmethods.dataanalysis.clustering.hierarchical;

import java.util.logging.Level;
import java.util.logging.Logger;

import javax.annotation.Nonnull;

import net.sf.mzmine.modules.peaklistmethods.dataanalysis.clustering.ClusteringAlgorithm;
import net.sf.mzmine.modules.peaklistmethods.dataanalysis.clustering.ClusteringResult;
import net.sf.mzmine.parameters.ParameterSet;
import weka.clusterers.HierarchicalClusterer;
import weka.core.Instances;

public class HierarClusterer implements ClusteringAlgorithm {

    private Logger logger = Logger.getLogger(this.getClass().getName());

    private static final String MODULE_NAME = "Hierarchical clusterer";

    @Override
    public @Nonnull String getName() {
        return MODULE_NAME;
    }

    @Override
    public ClusteringResult performClustering(Instances dataset, ParameterSet parameters) {
        HierarchicalClusterer clusterer = new HierarchicalClusterer();
        String[] options = new String[5];
        LinkType link = parameters.getParameter(HierarClustererParameters.linkType).getValue();
        DistanceType distanceType = parameters.getParameter(HierarClustererParameters.distanceType).getValue();
        options[0] = "-L";
        options[1] = link.name();
        options[2] = "-A";
        switch (distanceType) {
        case EUCLIDIAN:
            options[3] = "weka.core.EuclideanDistance";
            break;
        case CHEBYSHEV:
            options[3] = "weka.core.ChebyshevDistance";
            break;
        case MANHATTAN:
            options[3] = "weka.core.ManhattanDistance";
            break;
        case MINKOWSKI:
            options[3] = "weka.core.MinkowskiDistance";
            break;
        }

        options[4] = "-P";
        try {
            clusterer.setOptions(options);
            clusterer.setPrintNewick(true);
            clusterer.buildClusterer(dataset);
            // clusterer.graph() gives only the first cluster and in the case
            // there
            // are more than one cluster the variables in the second cluster are
            // missing.
            // I'm using clusterer.toString() which contains all the clusters in
            // Newick format.
            ClusteringResult result = new ClusteringResult(null, clusterer.toString(), clusterer.getNumClusters(),
                    null);
            return result;
        } catch (Exception ex) {
            logger.log(Level.SEVERE, null, ex);
            return null;
        }
    }

    @Override
    public @Nonnull Class<? extends ParameterSet> getParameterSetClass() {
        return HierarClustererParameters.class;
    }

}