/***********************************************************************
This file is part of KEEL-software, the Data Mining tool for regression,
classification, clustering, pattern mining and so on.
Copyright (C) 2004-2010
F. Herrera (herrera@decsai.ugr.es)
L. S�nchez (luciano@uniovi.es)
J. Alcal�-Fdez (jalcala@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
A. Fern�ndez (alberto.fernandez@ujaen.es)
J. Luengo (julianlm@decsai.ugr.es)
This program 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 3 of the License, or
(at your option) any later version.
This program 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 this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
/*
OBDE.java
Isaac Triguero Velazquez.
Created by Isaac Triguero Velazquez 23-7-2009
Copyright (c) 2008 __MyCompanyName__. All rights reserved.
*/
package keel.Algorithms.Instance_Generation.OBDE;
import keel.Algorithms.Instance_Generation.Basic.PrototypeSet;
import keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator;
import keel.Algorithms.Instance_Generation.Basic.Prototype;
import keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm;
import keel.Algorithms.Instance_Generation.Chen.ChenGenerator;
import keel.Algorithms.Instance_Generation.HYB.HYBGenerator;
import keel.Algorithms.Instance_Generation.*;
import java.util.*;
import keel.Algorithms.Instance_Generation.utilities.*;
import keel.Algorithms.Instance_Generation.utilities.KNN.*;
import org.core.*;
import org.core.*;
import java.util.StringTokenizer;
/**
* @param k Number of neighbors
* @param Population Size.
* @param ParticleSize.
* @param Scaling Factor.
* @param Crossover rate.
* @param Strategy (1-5).
* @param MaxIter
* @author Isaac Triguero
* @version 1.0
*/
public class OBDEGenerator extends PrototypeGenerator {
/*Own parameters of the algorithm*/
// We need the variable K to use with k-NN rule
private int k;
private int PopulationSize;
private int ParticleSize;
private int MaxIter;
private double ScalingFactor;
private double CrossOverRate;
private double JumpingRate;
private int Strategy;
private String CrossoverType; // Binomial, Exponential, Arithmetic
protected int numberOfPrototypes; // Particle size is the percentage
/** Parameters of the initial reduction process. */
private String[] paramsOfInitialReducction = null;
/**
* Build a new OBDEGenerator Algorithm
*/
public OBDEGenerator(PrototypeSet _trainingDataSet, int neigbors,int poblacion, int perc, int iteraciones, double F, double CR, double JR, int strg)
{
super(_trainingDataSet);
algorithmName="OBDE";
this.k = neigbors;
this.PopulationSize = poblacion;
this.ParticleSize = perc;
this.MaxIter = iteraciones;
this.numberOfPrototypes = getSetSizeFromPercentage(perc);
this.ScalingFactor = F;
this.CrossOverRate = CR;
this.JumpingRate = JR;
this.Strategy = strg;
}
/**
* Build a new OBDEGenerator Algorithm
* @param t Original prototype set to be reduced.
* @param parameters Parameters of the algorithm (only % of reduced set).
*/
public OBDEGenerator(PrototypeSet t, Parameters parameters)
{
super(t, parameters);
algorithmName="OBDE";
this.k = parameters.getNextAsInt();
this.PopulationSize = parameters.getNextAsInt();
this.ParticleSize = parameters.getNextAsInt();
this.MaxIter = parameters.getNextAsInt();
this.ScalingFactor = parameters.getNextAsDouble();
this.CrossOverRate = parameters.getNextAsDouble();
this.JumpingRate = parameters.getNextAsDouble();
this.Strategy = parameters.getNextAsInt();
this.CrossoverType = parameters.getNextAsString();
this.numberOfPrototypes = getSetSizeFromPercentage(ParticleSize);
System.out.print("\nIsaac dice: JR= " + this.JumpingRate + " Swar= "+PopulationSize+ " Particle= "+ ParticleSize + " Maxiter= "+ MaxIter+" CR= "+this.CrossOverRate+ "\n");
//numberOfPrototypes = getSetSizeFromPercentage(parameters.getNextAsDouble());
}
public PrototypeSet mutant(PrototypeSet population[], int actual, int mejor){
PrototypeSet mutant = new PrototypeSet(population.length);
PrototypeSet r1,r2,r3,r4,r5, resta, producto, resta2, producto2, result, producto3, resta3;
//We need three differents solutions of actual
int lista[] = new int[population.length];
inic_vector_sin(lista,actual);
desordenar_vector_sin(lista);
// System.out.println("Lista = "+lista[0]+","+ lista[1]+","+lista[2]);
r1 = population[lista[0]];
r2 = population[lista[1]];
r3 = population[lista[2]];
r4 = population[lista[3]];
r5 = population[lista[4]];
switch(this.Strategy){
case 1: // ViG = Xr1,G + F(Xr2,G - Xr3,G) De rand 1
resta = r2.restar(r3);
producto = resta.mulEscalar(this.ScalingFactor);
mutant = producto.sumar(resta);
break;
case 2: // Vig = Xbest,G + F(Xr2,G - Xr3,G) De best 1
resta = r2.restar(r3);
producto = resta.mulEscalar(this.ScalingFactor);
mutant = population[mejor].sumar(producto);
break;
case 3: // Vig = ... De rand to best 1
resta = r1.restar(r2);
resta2 = population[mejor].restar(population[actual]);
producto = resta.mulEscalar(this.ScalingFactor);
producto2 = resta2.mulEscalar(this.ScalingFactor);
result = population[actual].sumar(producto);
mutant = result.sumar(producto2);
break;
case 4: // DE best 2
resta = r1.restar(r2);
resta2 = r3.restar(r4);
producto = resta.mulEscalar(this.ScalingFactor);
producto2 = resta2.mulEscalar(this.ScalingFactor);
result = population[mejor].sumar(producto);
mutant = result.sumar(producto2);
break;
case 5: //DE rand 2
resta = r2.restar(r3);
resta2 = r4.restar(r5);
producto = resta.mulEscalar(this.ScalingFactor);
producto2 = resta2.mulEscalar(this.ScalingFactor);
result = r1.sumar(producto);
mutant = result.sumar(producto2);
break;
case 6: //DE rand to best 2
resta = r1.restar(r2);
resta2 = r3.restar(r4);
resta3 = population[mejor].restar(population[actual]);
producto = resta.mulEscalar(this.ScalingFactor);
producto2 = resta2.mulEscalar(this.ScalingFactor);
producto3 = resta3.mulEscalar(this.ScalingFactor);
result = population[actual].sumar(producto);
result = result.sumar(producto2);
mutant = result.sumar(producto3);
break;
/*// Para hacer esta estrat�gia, lo que hay que elegir es CrossoverType = Arithmetic
* case 7: //DE current to rand 1
resta = r1.restar(population[actual]);
resta2 = r2.restar(r3);
producto = resta.mulEscalar(RandomGenerator.Randdouble(0, 1));
producto2 = resta2.mulEscalar(this.ScalingFactor);
result = population[actual].sumar(producto);
mutant = result.sumar(producto2);
break;
*/
}
// System.out.println("********Mutante**********");
// mutant.print();
mutant.applyThresholds();
return mutant;
}
public int[] mejoresParticulas(PrototypeSet population[]){
int number = this.PopulationSize;
int index[] = new int[number];
int ind= 0;
double mejor = Double.MIN_VALUE;
double acc;
for(int i=0; i< population.length; i++){
acc =accuracy(population[i],trainingDataSet);
if(acc > mejor )
{
ind = i;
mejor = acc;
}
}
index[0] = ind;
for (int j=1; j<number; j++){
mejor = Double.MIN_VALUE;
for(int i=0; i< population.length; i++){
acc =accuracy(population[i],trainingDataSet);
if(acc > mejor && acc < accuracy(population[index[j-1]],trainingDataSet))
{
ind = i;
mejor = acc;
}
}
index[j] = ind;
}
return index;
}
/**
* Generate a reduced prototype set by the DEGenerator method.
* @return Reduced set by DEGenerator's method.
*/
public PrototypeSet reduceSet()
{
System.out.print("\nThe algorithm DE is starting...\n Computing...\n");
System.out.println("Number of prototypes, result set = "+numberOfPrototypes+ "\n");
if(numberOfPrototypes < 6){
System.out.println("Number of prototypes less than 6, we increse to 6");
numberOfPrototypes = 6;
}
System.out.println("Reduction %, result set = "+((trainingDataSet.size()-numberOfPrototypes)*100)/trainingDataSet.size()+ "\n");
//Algorithm
// First, we create the population, with PopulationSize.
// like a prototypeSet's vector.
PrototypeSet population [] = new PrototypeSet [PopulationSize];
PrototypeSet populationAux [] = new PrototypeSet [PopulationSize];
PrototypeSet mutation[] = new PrototypeSet[PopulationSize];
PrototypeSet crossover[] = new PrototypeSet[PopulationSize];
double fitness[] = new double[PopulationSize];
double fitness_bestPopulation[] = new double[PopulationSize];
PrototypeSet bestParticle = new PrototypeSet();
//Each particle must have Particle Size %
// First Stage, Initialization.
populationAux[0]=selecRandomSet(numberOfPrototypes,true).clone() ;
fitness[0] = accuracy(populationAux[0],trainingDataSet);
for(int i=1; i< PopulationSize; i++){
populationAux[i] = new PrototypeSet();
for(int j=0; j< populationAux[0].size(); j++){
populationAux[i].add(trainingDataSet.getFromClass(populationAux[0].get(j).getOutput(0)).getRandom());
}
fitness[i] = accuracy(populationAux[i],trainingDataSet); // SADE fitness
}
/*
for(int i=0; i< PopulationSize; i++){
populationAux[i]=selecRandomSet(numberOfPrototypes,true).clone() ;
fitness[i] = accuracy(populationAux[i],trainingDataSet); // DE fitness
}
*/
// Then I generate the Opposite Population.
PrototypeSet opposite [] = new PrototypeSet [PopulationSize];
double valor;
for(int i=0; i<populationAux.length; i++){
opposite[i] = populationAux[i].clone();
for(int j=0; j<opposite[i].size(); j++){
for(int k=0; k<opposite[i].get(j).numberOfInputs(); k++){
valor = 1-opposite[i].get(j).getInput(k);
opposite[i].get(j).setInput(k, valor);
}
opposite[i].applyThresholds();
Prototype Nearest = KNN._1nn(opposite[i].get(j),trainingDataSet);
opposite[i].get(j).setClass(Nearest.getOutput(0));
}
}
// population[0].print();
//opposite[0].print();
PrototypeSet PopulationOpposite [] = new PrototypeSet [PopulationSize*2];
for(int i=0; i< population.length; i++){
PopulationOpposite[i] = populationAux[i].clone();
PopulationOpposite[i+populationAux.length] = opposite[i].clone();
}
int indices[]= mejoresParticulas(PopulationOpposite);
for(int i=0; i<indices.length; i++){
// System.out.println(indices[i]);
population[i] = PopulationOpposite[indices[i]].clone();
/* if(indices[i]>PopulationSize){
System.out.println("Cogido del Opuesto");
}*/
}
//We select the best initial particle
double bestFitness=fitness[0];
int bestFitnessIndex=0;
for(int i=1; i< PopulationSize;i++){
if(fitness[i]>bestFitness){
bestFitness = fitness[i];
bestFitnessIndex=i;
}
}
for(int j=0;j<PopulationSize;j++){
//Now, I establish the index of each prototype.
for(int i=0; i<population[j].size(); ++i)
population[j].get(i).setIndex(i);
}
boolean cruceExp [] = new boolean[PopulationSize];
for(int iter=0; iter< MaxIter; iter++){ // Main loop
// If we are going to use exponential, I calculate the index of possible selecting Mutation.
if(this.CrossoverType.equals("Exponential")){
for(int i=0; i<PopulationSize; i++){
cruceExp[i] = false;
}
}
for(int i=0; i<PopulationSize; i++){
//Second: Mutation Operation.
// I want to generate a PrototypeSet Mutation for each item of the population.
mutation[i] = new PrototypeSet(population[i].size());
// Pasamos la poblaci�n, y la mejor actual, por si se usa /best/
mutation[i] = mutant(population, i,bestFitnessIndex).clone();
// Third: Crossver Operation.
// Now, we decide if the mutation will be the trial vector.
// Three Types of Crossover Operations, BINOMIAL, EXPONENTIAL ARITHMETIC
crossover[i] = new PrototypeSet(population[i]);
for(int j=0; j< population[i].size(); j++){ // For each part of the solution
if(this.CrossoverType.equals("Binomial")){
double randNumber = RandomGenerator.Randdouble(0, 1);
if(randNumber<this.CrossOverRate){
crossover[i].set(j, mutation[i].get(j)); // Overwrite.
}
}else if(this.CrossoverType.equals("Exponential")){
int startingPoint = RandomGenerator.Randint(0, PopulationSize);
int L=0;
do{
L++;
}while(RandomGenerator.Randdouble(0, 1)<this.CrossOverRate && (L< population[i].size()));
for(int m=startingPoint; m< startingPoint+L; m++){
crossover[i].set(m%population[i].size(), mutation[i].get(j)); // Overwrite
}
}else if(this.CrossoverType.equals("Arithmetic")){ // Uig = XiG + K*(Vi - Xi)
PrototypeSet resta = mutation[i].restar(population[i]);
crossover[i] = population[i].sumar(resta.mulEscalar(RandomGenerator.Randdouble(0, 1)));
}else{
System.err.println("ERROR, Crossover Type incorrect.");
}
}
// Fourth: Selection Operation.
// Decide if the trial vector is better than initial population.
//Crossover has the trialVector, we check its fitness.
double trialVector = accuracy(crossover[i],trainingDataSet);
fitness[i] = accuracy(population[i],trainingDataSet);
//System.out.println("Trial Vector fitness = "+ trialVector);
//System.out.println("fitness de la particula = "+ fitness[i]);
if(trialVector > fitness[i]){
// System.out.println("Selecting");
population[i] = crossover[i].clone();
}
fitness[i] = accuracy(population[i],trainingDataSet);
if(fitness[i]>bestFitness){
bestFitness = fitness[i];
bestFitnessIndex=i;
}
}
double randNumber = RandomGenerator.Randdouble(0, 1);
if(randNumber < this.JumpingRate){
populationAux = new PrototypeSet [PopulationSize];
populationAux = population.clone();
//CALCULAR EL M�NIMO Y EL M�XIMO DE ESTE VALOR EN EL RESTO DE LA POBLACI�N.
double min[][] = new double[population[0].size()][opposite[0].get(0).numberOfInputs()];
double max[][] = new double[population[0].size()][opposite[0].get(0).numberOfInputs()];
for(int i=0; i< population[0].size(); i++){
for(int j=0; j<opposite[0].get(0).numberOfInputs(); j++ ){
min[i][j] = 1;
max[i][j] = 0;
}
}
for(int i=0; i< population.length; i++){
for(int j=0; j< population[i].size(); j++){
for(int k=0; k<population[i].get(j).numberOfInputs(); k++){
if(population[i].get(j).getInput(k)<min[j][k])
{
min[j][k] = population[i].get(j).getInput(k);
}
if(population[i].get(j).getInput(k)>max[j][k])
{
max[j][k] = population[i].get(j).getInput(k);
}
}
}
}
// Fin de calcular cada uno de los minimos.
for(int i=0; i<populationAux.length; i++){
opposite[i] = populationAux[i].clone();
for(int j=0; j<opposite[i].size(); j++){
for(int k=0; k<opposite[i].get(j).numberOfInputs(); k++){
//System.out.println(max[j][k]);
valor = min[j][k]+max[j][k]-opposite[i].get(j).getInput(k);
opposite[i].get(j).setInput(k, valor);
}
opposite[i].applyThresholds();
Prototype Nearest = KNN._1nn(opposite[i].get(j),trainingDataSet);
opposite[i].get(j).setClass(Nearest.getOutput(0));
}
}
PopulationOpposite = new PrototypeSet [PopulationSize*2];
for(int i=0; i< population.length; i++){
PopulationOpposite[i] = populationAux[i].clone();
PopulationOpposite[i+populationAux.length] = opposite[i].clone();
}
indices= mejoresParticulas(PopulationOpposite);
for(int i=0; i<indices.length; i++){
// System.out.println(indices[i]);
population[i] = PopulationOpposite[indices[i]].clone();
/* if(indices[i]>PopulationSize){
System.out.println("Cogido del Opuesto");
}*/
}
}
}// End Main LOOP
bestFitness = 0;
bestFitnessIndex = 0;
for(int i=0; i< PopulationSize;i++){
// Where is the best?
if(fitness[i]>bestFitness){
bestFitness = fitness[i];
bestFitnessIndex=i;
}
}
//System.err.println("\n% de acierto en training " + DEGenerator.accuracy(population[bestFitnessIndex], trainingDataSet) );
//population[bestFitnessIndex].print();
System.err.println("\n% de acierto en training " + KNN.classficationAccuracy(population[bestFitnessIndex],trainingDataSet,1)*100./trainingDataSet.size());
return population[bestFitnessIndex];
}
/**
* General main for all the prototoype generators
* Arguments:
* 0: Filename with the training data set to be condensed.
* 1: Filename which contains the test data set.
* 3: Seed of the random number generator. Always.
* **************************
* 4: .Number of neighbors
* 5: Swarm Size
* 6: Particle Size
* 7: Max Iter
* 8: C1
* 9: c2
* 10: vmax
* 11: wstart
* 12: wend
* @param args Arguments of the main function.
*/
public static void main(String[] args)
{
Parameters.setUse("OBDE", "<seed> <Number of neighbors>\n<Swarm size>\n<Particle Size>\n<MaxIter>\n<DistanceFunction>");
Parameters.assertBasicArgs(args);
PrototypeSet training = PrototypeGenerationAlgorithm.readPrototypeSet(args[0]);
PrototypeSet test = PrototypeGenerationAlgorithm.readPrototypeSet(args[1]);
long seed = Parameters.assertExtendedArgAsInt(args,2,"seed",0,Long.MAX_VALUE);
OBDEGenerator.setSeed(seed);
int k = Parameters.assertExtendedArgAsInt(args,3,"number of neighbors", 1, Integer.MAX_VALUE);
int swarm = Parameters.assertExtendedArgAsInt(args,4,"swarm size", 1, Integer.MAX_VALUE);
int particle = Parameters.assertExtendedArgAsInt(args,5,"particle size", 1, Integer.MAX_VALUE);
int iter = Parameters.assertExtendedArgAsInt(args,6,"max iter", 1, Integer.MAX_VALUE);
double c1 = Parameters.assertExtendedArgAsInt(args,7,"c1", 1, Double.MAX_VALUE);
double c2 =Parameters.assertExtendedArgAsInt(args,8,"c2", 1, Double.MAX_VALUE);
double vmax =Parameters.assertExtendedArgAsInt(args,9,"vmax", 1, Double.MAX_VALUE);
double wstart = Parameters.assertExtendedArgAsInt(args,10,"wstart", 1, Double.MAX_VALUE);
double wend =Parameters.assertExtendedArgAsInt(args,11,"wend", 1, Double.MAX_VALUE);
//String[] parametersOfInitialReduction = Arrays.copyOfRange(args, 4, args.length);
//System.out.print(" swarm ="+swarm+"\n");
OBDEGenerator generator = new OBDEGenerator(training, k,swarm,particle,iter, 0.5,0.5,0.3,1);
PrototypeSet resultingSet = generator.execute();
//resultingSet.save(args[1]);
//int accuracyKNN = KNN.classficationAccuracy(resultingSet, test, k);
int accuracy1NN = KNN.classficationAccuracy(resultingSet, test);
generator.showResultsOfAccuracy(Parameters.getFileName(), accuracy1NN, test);
}
}