classif.kmeans.KMeansSymbolicSequence.java Source code

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Here is the source code for classif.kmeans.KMeansSymbolicSequence.java

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/*******************************************************************************
 * Copyright (C) 2014 Anonymized
 * Contributors:
 *    Anonymized
 * 
 * This file is part of ICDM2014SUBMISSION. 
 * This is a program related to the paper "Dynamic Time Warping Averaging of 
 * Time Series allows more Accurate and Faster Classification" submitted to the
 * 2014 Int. Conf. on Data Mining.
 * 
 * ICDM2014SUBMISSION 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, version 3 of the License.
 * 
 * ICDM2014SUBMISSION 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 ICDM2014SUBMISSION.  If not, see <http://www.gnu.org/licenses/>.
 ******************************************************************************/
package classif.kmeans;

import items.Sequence;
import items.Sequences;

import java.io.File;
import java.io.FilenameFilter;
import java.io.IOException;
import java.util.ArrayList;

import org.apache.commons.math3.random.RandomDataGenerator;

public class KMeansSymbolicSequence {
    public int nbClusters;
    public ArrayList<Sequence> data;
    public Sequence[] centers;
    public int[] clusterMap;
    public RandomDataGenerator randGen;
    public ArrayList<Sequence>[] affectation;

    public KMeansSymbolicSequence(int nbClusters, ArrayList<Sequence> data) {
        if (data.size() < nbClusters) {
            this.nbClusters = data.size();
        } else {
            this.nbClusters = nbClusters;
        }
        this.data = data;
        this.clusterMap = new int[data.size()];
        this.randGen = new RandomDataGenerator();
    }

    public void cluster() {
        centers = new Sequence[nbClusters];
        affectation = new ArrayList[nbClusters];

        // pickup centers
        int[] selected = randGen.nextPermutation(data.size(), nbClusters);
        for (int i = 0; i < selected.length; i++) {
            centers[i] = data.get(selected[i]);
        }

        // for each iteration i
        for (int i = 0; i < 15; i++) {
            // init
            for (int k = 0; k < affectation.length; k++) {
                affectation[k] = new ArrayList<Sequence>();
            }
            // for each data point j
            for (int j = 0; j < data.size(); j++) {

                double minDist = Double.MAX_VALUE;
                // for each cluster k
                for (int k = 0; k < centers.length; k++) {
                    // distance between cluster k and data point j
                    double currentDist = centers[k].distance(data.get(j));
                    if (currentDist < minDist) {
                        clusterMap[j] = k;
                        minDist = currentDist;
                    }
                }

                // affect data point j to cluster affected to j
                affectation[clusterMap[j]].add(data.get(j));
            }

            // redefine
            for (int j = 0; j < nbClusters; j++) {
                if (affectation[j].size() == 0) {
                    centers[j] = null;
                } else {
                    centers[j] = Sequences.mean(affectation[j].toArray(new Sequence[0]));
                }
            }
        }
    }
}