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Centroid.java

gehe zur Dokumentation dieser Datei

/*
 * $Source: /shared/cvsroot/diplom/app/src/java/de/picana/classifier/Centroid.java,v $
 * $Author: mstolpe $
 * $Date: 2003/04/22 09:51:27 $
 * $Revision: 1.7 $
 * $Release$ 
 *
 * Created on 06. November 2002, 13:05
 *
 * Copyright 2002 by Marco Stolpe
 */

package de.picana.classifier;

import de.picana.control.*;
import de.picana.clusterer.*;
import de.picana.math.*;

import weka.core.*;
import java.io.*;
import java.util.*;


public class Centroid extends Classifier {

    private List centroids; 
    
    
    public Centroid() {
    }

    protected void loadModel(String filename) throws TaskException {
        
        try {
            centroids = new ArrayList();
            
            File model = new File(filename);
            FileReader frd = new FileReader(model);
            BufferedReader in = new BufferedReader(frd);
            String line = in.readLine();
            StringTokenizer tk = new StringTokenizer(line,",");
            int dimension = tk.countTokens();
                            
            logger.debug(LOGSRC, "dim = " + dimension);
            
            int i=0;
            while((line = in.readLine()) != null) {
                StringTokenizer tok = new StringTokenizer(line, ",");
                MLVector vec = new MLVector(dimension);
                int j=0;
                while (tok.hasMoreTokens()) {
                    double d = 0.0;
                    String token = tok.nextToken();
                    try {
                        d = Double.parseDouble(token);
                    } catch (NumberFormatException nfe) {}
                    vec.value[j] = d;
                    j++;
                }
                logger.debug(LOGSRC, "centroid[" + i + "] = " + vec.toString()); 
                centroids.add(vec);
                i++;
            }
            
            in.close();
        
            num_clusters = centroids.size();
            logger.debug(LOGSRC, "num_clusters = " + num_clusters);
            
        } catch (Exception e) {
            throw new TaskException(e.toString());    
        }
    }
    
    protected int classify(Instance i) {
        
        double min_dist = Double.MAX_VALUE;
        double act_dist = 0.0;
        MLVector vec_a = new MLVector(i.numValues());
        for (int j=0; j < i.numValues(); j++) {
            vec_a.value[j] = i.value(j);
        }
        int cl = 0;
        for (int j=0; j < centroids.size(); j++) {
            MLVector vec_b = (MLVector)centroids.get(j);
            act_dist = Distance.euklidian(vec_a.value, vec_b.value);
            if (act_dist < min_dist) {
                cl = j;
                min_dist = act_dist;
            }
        }
        return cl;
    }
}

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