/*
* 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
*/
package net.semanticmetadata.lire.classifiers;
import net.semanticmetadata.lire.builders.DocumentBuilder;
import java.util.LinkedList;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.LinkedBlockingQueue;
/**
* Created by Mathias on 12/10/11.
*
* @author Mathias Lux, mathias@juggle.at
* @author Nektarios Anagnostopoulos, nek.anag@gmail.com
* @author Lazaros Tsochatzidis, ltsochat@ee.duth.gr
*/
public class ParallelKMeans extends KMeans {
int numThreads = DocumentBuilder.NUM_OF_THREADS;
private LinkedBlockingQueue<Item> queue = new LinkedBlockingQueue<Item>(100);
public ParallelKMeans(int numClusters) {
super(numClusters);
}
/**
* Re-shuffle all features.
*/
protected void reOrganizeFeatures() {
LinkedList<Thread> threads = new LinkedList<Thread>();
Thread thread;
Thread p = new Thread(new ProducerForFeatures());
p.start();
for (int i = 0; i < numThreads; i++) {
thread = new Thread(new FeatureToClass());
thread.start();
threads.add(thread);
}
for (Thread next : threads) {
try {
next.join();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
threads.clear();
}
protected void recomputeMeans() {
LinkedList<Thread> threads = new LinkedList<Thread>();
Thread p = new Thread(new ProducerForClusters());
p.start();
Thread thread;
for (int i = 0; i < numThreads; i++) {
thread = new Thread(new MeanOfCluster());
thread.start();
threads.add(thread);
}
for (Thread next : threads) {
try {
next.join();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
threads.clear();
}
protected double overallStress() {
double v = 0.0;
LinkedList<ComputeStress> tasks = new LinkedList<ComputeStress>();
LinkedList<Thread> threads = new LinkedList<Thread>();
ComputeStress computeStress;
Thread thread;
Thread p = new Thread(new ProducerForClusters());
p.start();
for (int i = 0; i < numThreads; i++) {
computeStress = new ComputeStress();
thread = new Thread(computeStress);
thread.start();
tasks.add(computeStress);
threads.add(thread);
}
for (Thread next : threads) {
try {
next.join();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
for (ComputeStress task : tasks) {
v += task.getResult();
}
tasks.clear();
threads.clear();
// for(Cluster cluster : clusters){
// v += cluster.getStress();
// }
return v;
}
private class ComputeStress implements Runnable {
private boolean locallyEnded;
double result;
private ComputeStress() {
this.result = 0.0;
this.locallyEnded = false;
}
public void run() {
Item tmp;
while (!locallyEnded) {
try {
tmp = queue.take();
if (tmp.getNum() == -1) locallyEnded = true;
if (!locallyEnded) { // && tmp != -1
// for (Integer member : tmp.getCluster().members) {
// for (int j = 0; j < length; j++) {
// result += Math.abs(tmp.getCluster().mean[j] - features.get(member)[j]);
// }
// }
result += tmp.getCluster().getStress();
}
} catch (InterruptedException e) {
e.getMessage();
}
}
}
public double getResult() {
return result;
}
}
private class MeanOfCluster implements Runnable {
private boolean locallyEnded;
private MeanOfCluster() {
this.locallyEnded = false;
}
public void run() {
Cluster cluster;
double[] mean, f;
Item tmp;
double size, stress;
while (!locallyEnded) {
try {
tmp = queue.take();
if (tmp.getNum() == -1) locallyEnded = true;
if (!locallyEnded) { // && tmp != -1
cluster = tmp.getCluster();
if (cluster.getSize() == 1) {
System.err.println("** There is just one member in cluster " + tmp.getNum());
} else if (cluster.getSize() < 1) {
System.err.println("** There is NO member in cluster " + tmp.getNum());
// fill it with a random member?!?
cluster.assignMember(features.get((int) Math.floor(Math.random() * features.size())));
}
cluster.move();
}
} catch (InterruptedException e) {
e.getMessage();
}
}
}
}
private class FeatureToClass implements Runnable {
private boolean locallyEnded;
private FeatureToClass() {
this.locallyEnded = false;
}
public void run() {
double v, minDistance;
double[] f;
Item tmp;
int best;
while (!locallyEnded) {
try {
tmp = queue.take();
if (tmp.getNum() == -1) locallyEnded = true;
if (!locallyEnded) { // && tmp != -1
f = tmp.getArray();
best = 0;
minDistance = clusters[0].getDistance(f);
for (int i = 1; i < clusters.length; i++) {
v = clusters[i].getDistance(f);
if (minDistance > v) {
best = i;
minDistance = v;
}
}
clusters[best].assignMember(f);
}
} catch (InterruptedException e) {
e.getMessage();
}
}
}
}
class ProducerForClusters implements Runnable {
private ProducerForClusters() {
queue.clear();
}
public void run() {
int counter = 0;
for(Cluster cluster : clusters){
try {
queue.put(new Item(counter, cluster));
} catch (InterruptedException e) {
e.printStackTrace();
}
counter++;
}
Cluster cluster = null;
for (int i = 0; i < numThreads * 3; i++) {
try {
queue.put(new Item(-1, cluster));
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
}
class ProducerForFeatures implements Runnable {
private ProducerForFeatures() {
queue.clear();
}
public void run() {
int counter = 0;
for (double[] feature : features) {
try {
queue.put(new Item(counter, feature));
} catch (InterruptedException e) {
e.printStackTrace();
}
counter++;
}
double[] tmp = null;
for (int i = 0; i < numThreads * 3; i++) {
try {
queue.put(new Item(-1, tmp));
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
}
private class Item {
private double[] array;
private Cluster cluster;
private int num;
Item(int num, double[] array) {
this.num = num;
this.array = array;
}
Item(int num, Cluster cluster) {
this.num = num;
this.cluster = cluster;
}
private int getNum() { return num; }
private Cluster getCluster() { return cluster; }
private double[] getArray() { return array; }
}
}