/*
* This file is part of the LIRE project: http://lire-project.net
* LIRE 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.
*
* LIRE 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 LIRE; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* We kindly ask you to refer the any or one of the following publications in
* any publication mentioning or employing Lire:
*
* Lux Mathias, Savvas A. Chatzichristofis. Lire: Lucene Image Retrieval –
* An Extensible Java CBIR Library. In proceedings of the 16th ACM International
* Conference on Multimedia, pp. 1085-1088, Vancouver, Canada, 2008
* URL: http://doi.acm.org/10.1145/1459359.1459577
*
* Lux Mathias. Content Based Image Retrieval with LIRE. In proceedings of the
* 19th ACM International Conference on Multimedia, pp. 735-738, Scottsdale,
* Arizona, USA, 2011
* URL: http://dl.acm.org/citation.cfm?id=2072432
*
* Mathias Lux, Oge Marques. Visual Information Retrieval using Java and LIRE
* Morgan & Claypool, 2013
* URL: http://www.morganclaypool.com/doi/abs/10.2200/S00468ED1V01Y201301ICR025
*
* Copyright statement:
* --------------------
* (c) 2002-2013 by Mathias Lux (mathias@juggle.at)
* http://www.semanticmetadata.net/lire, http://www.lire-project.net
*/
/*
* This file is part of the LIRE project: http://lire-project.net.
*
* Lire 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.
*
* Lire 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 Lire; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* (c) 2008-2010 by Mathias Lux, mathias@juggle.at
*/
package net.semanticmetadata.lire.classifiers;
import net.semanticmetadata.lire.imageanalysis.features.FeatureVector;
import net.semanticmetadata.lire.utils.MetricsUtils;
import net.semanticmetadata.lire.utils.SerializationUtils;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Arrays;
import java.util.HashSet;
import java.util.concurrent.atomic.AtomicInteger;
/**
* Provides a simple implementation for a cluster used with the visual bag of words approach.
* Date: 26.03.2010
* Time: 12:10:19
*
* @author Mathias Lux, mathias@juggle.at
* @author Nektarios Anagnostopoulos, nek.anag@gmail.com
* @author Lazaros Tsochatzidis, ltsochat@ee.duth.gr
*/
public class Cluster implements Comparable<Object> {
double[] mean;
AtomicInteger size;
AtomicDouble[] newmean;
private double stress = 0.0;
public Cluster() {
this.mean = new double[4 * 4 * 8];
Arrays.fill(mean, 0d);
size=new AtomicInteger(0);
newmean=new AtomicDouble[mean.length];
for (int i=0;i<mean.length;i++) newmean[i]=new AtomicDouble(0);
}
public Cluster(double[] mean) {
this.mean = mean;
size=new AtomicInteger(0);
newmean=new AtomicDouble[mean.length];
for (int i=0;i<mean.length;i++) newmean[i]=new AtomicDouble(0);
}
public String toString() {
StringBuilder sb = new StringBuilder(512);
for (double next : mean) {
sb.append(next);
sb.append(';');
}
return sb.toString();
}
public int compareTo(Object o) {
return ((Cluster) o).getSize() - getSize();
}
public double getDistance(FeatureVector f) {
return getDistance(f.getFeatureVector());
}
public double getDistance(double[] f) {
// L1
// return MetricsUtils.distL1(mean, f);
// L2
return MetricsUtils.distL2(mean, f);
}
/**
* Creates a byte array representation from the clusters mean.
*
* @return the clusters mean as byte array.
*/
public byte[] getByteRepresentation() {
return SerializationUtils.toByteArray(mean);
}
public void setByteRepresentation(byte[] data) {
mean = SerializationUtils.toDoubleArray(data);
}
public double getStress() {
return stress;
}
public void setStress(double stress) {
this.stress = stress;
}
public int getSize() {
return size.get();
}
public void reset() {
size.set(0);
for (AtomicDouble ad : newmean) ad.set(0);
}
public void assignMember(double[] feat) {
size.addAndGet(1);
int i=0;
for (AtomicDouble ad : newmean) ad.addAndGet(feat[i++]);
}
public void move() {
double lsize=size.get();
stress=0d;
double diff;
for (int i=0;i<mean.length;i++){
diff=mean[i]-newmean[i].divideAndGet(lsize);
stress+=lsize*diff*diff;
mean[i]=newmean[i].get();
}
}
/**
* Returns the cluster mean
*
* @return the cluster mean vector
*/
public double[] getMean() {
return mean;
}
public static void writeClusters(Cluster[] clusters, String path) throws IOException {
File file = new File(path);
if(file.exists()) {
System.out.println("File " + path + " already exists and will be overwritten!!");
}
FileOutputStream fout = new FileOutputStream(file);
fout.write(SerializationUtils.toBytes(clusters.length));
fout.write(SerializationUtils.toBytes((clusters[0].getMean()).length));
for (Cluster cluster : clusters) {
fout.write(cluster.getByteRepresentation());
}
fout.close();
}
// TODO: re-visit here to make the length variable (depending on the actual feature size).
public static Cluster[] readClusters(String file) throws IOException {
FileInputStream fin = new FileInputStream(file);
byte[] tmp = new byte[4];
fin.read(tmp, 0, 4);
Cluster[] result = new Cluster[SerializationUtils.toInt(tmp)];
fin.read(tmp, 0, 4);
int size = SerializationUtils.toInt(tmp);
tmp = new byte[size * 8];
int bytesRead;
for (int i = 0; i < result.length; i++) {
bytesRead = fin.read(tmp, 0, size * 8);
if (bytesRead != size * 8) System.err.println("Didn't read enough bytes ...");
result[i] = new Cluster();
result[i].setByteRepresentation(tmp);
}
fin.close();
return result;
}
}