org.montp2.m1decol.ter.clustering.NearestNeighbor.java Source code

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Here is the source code for org.montp2.m1decol.ter.clustering.NearestNeighbor.java

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/* ==========================================
 * CategorizeUserForum : a free Java graph-theory library
 * ==========================================
 * 
 * salmuz : Carranza Alarcon Yonatan Carlos
 * 
 * (C) Copyright 2014, by salmuz and Contributors.
 * 
 * Project Info:  https://github.com/salmuz/Graphes_Multi_Plateformes
 * Project Creator:  salmuz (https://www.assembla.com/spaces/salmuz-java) 
 *
 * This library is free software; you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as published by
 * the Free Software Foundation; either version 2.1 of the License, or
 * (at your option) any later version.
 *
 * This library 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 Lesser General Public
 * License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public License
 * along with this library; if not, write to the Free Software Foundation,
 * Inc.,
 * 
 * ------------------
 * Point.java
 * ------------------
 * (C) Copyright 2014, by salmuz and Contributors
 *
 * Original Author: Carranza Alarcon Yonatan Carlos
 * Contributor(s):  
 *
 * Changes
 * -------
 *
 */

package org.montp2.m1decol.ter.clustering;

import org.montp2.m1decol.ter.utils.WekaUtils;
import weka.clusterers.SimpleKMeans;
import weka.core.EuclideanDistance;
import weka.core.Instances;

import java.util.*;

public class NearestNeighbor {

    public NearestNeighbor() {
    }

    public Map<Integer, List<DistanceUser>> computeNearestNeighbor(String arffData, String inModel,
            Map<Integer, Integer> arffToIdUser) throws Exception {

        SimpleKMeans kmeans = WekaUtils.loadModel(inModel);

        EuclideanDistance eclidean = (EuclideanDistance) kmeans.getDistanceFunction();

        Instances data = new Instances(WekaUtils.loadARFF(arffData));

        int[] clusters = kmeans.getAssignments();
        Instances clusterCentroid = kmeans.getClusterCentroids();

        Map<Integer, List<DistanceUser>> nearUser = new HashMap<Integer, List<DistanceUser>>();

        for (int i = 0; i < clusterCentroid.numInstances(); i++) {
            nearUser.put(i, new ArrayList<DistanceUser>());
        }

        for (int i = 0; i < data.numInstances(); i++) {
            int ind = clusters[i];
            double dist = eclidean.distance(clusterCentroid.instance(ind), data.instance(i));
            List<DistanceUser> nears = nearUser.get(ind);
            if (nears.size() < 10) {
                nears.add(new DistanceUser(i, dist));
            } else {
                DistanceUser max = Collections.max(nears);
                if (max.getDistance() > dist) {
                    int maxIndex = nears.indexOf(max);
                    nears.set(maxIndex, new DistanceUser(i, dist));
                }
            }
        }

        for (Map.Entry<Integer, List<DistanceUser>> item : nearUser.entrySet()) {
            for (DistanceUser user : item.getValue()) {
                user.setIdentifier(arffToIdUser.get(user.getIdentifier()));
            }
        }

        return nearUser;
    }

}