/***********************************************************************
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. Sánchez (luciano@uniovi.es)
J. Alcalá-Fdez (jalcala@decsai.ugr.es)
S. García (sglopez@ujaen.es)
A. Fernández (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/
**********************************************************************/
//
// SSMA.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 3-10-2005.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Instance_Generation.SSMASFLSDE;
import keel.Algorithms.Preprocess.Basic.*;
import keel.Algorithms.Instance_Generation.Basic.Prototype;
import keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm;
import keel.Algorithms.Instance_Generation.Basic.PrototypeSet;
import keel.Algorithms.Instance_Generation.utilities.*;
import keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator;
import keel.Dataset.Attribute;
import keel.Dataset.Attributes;
import keel.Dataset.InstanceAttributes;
import keel.Dataset.InstanceSet;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class SSMASFLSDE extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private int tamPoblacion;
private double nEval;
private double pCross;
private double pMut;
private int kNeigh;
public String Script; // para releer par�metros..
private PrototypeSet trainingDataSet;
private PrototypeSet testDataSet;
private PrototypeGenerator generador;
//Par�metros DE
private int k;
private int PopulationSize;
private int ParticleSize;
private int MaxIter;
private double ScalingFactor;
private double CrossOverRate;
private int Strategy;
private String CrossoverType; // Binomial, Exponential, Arithmetic
private double tau[] = new double[4];
private double Fl, Fu;
private int iterSFGSS;
private int iterSFHC;
protected int numberOfClass;
protected int numberOfPrototypes; // Particle size is the percentage
protected int numberOfStrategies; // number of strategies in the pool
public SSMASFLSDE (String ficheroScript) {
super (ficheroScript);
}
public SSMASFLSDE(String ficheroScript, InstanceSet train) {
super (ficheroScript, train);
}
public void establishTrain(PrototypeSet trainPG){
trainingDataSet = trainPG.clone();
}
/**
* Reads the prototype set from a data file.
* @param nameOfFile Name of data file to be read.
* @return PrototypeSet built with the data of the file.
*/
public static PrototypeSet readPrototypeSet(String nameOfFile)
{
Attributes.clearAll();//BUGBUGBUG
InstanceSet training = new InstanceSet();
try
{
//System.out.print("PROBANDO:\n"+nameOfFile);
training.readSet(nameOfFile, true);
training.setAttributesAsNonStatic();
InstanceAttributes att = training.getAttributeDefinitions();
Prototype.setAttributesTypes(att);
}
catch(Exception e)
{
System.err.println("readPrototypeSet has failed!");
e.printStackTrace();
}
return new PrototypeSet(training);
}
public static PrototypeSet readPrototypeSet2(InstanceSet training)
{
Attributes.clearAll();//BUGBUGBUG
try
{
//System.out.print("PROBANDO:\n"+nameOfFile);
training.setAttributesAsNonStatic();
InstanceAttributes att = training.getAttributeDefinitions();
Prototype.setAttributesTypes(att);
}
catch(Exception e)
{
System.err.println("readPrototypeSet has failed!");
e.printStackTrace();
}
return new PrototypeSet(training);
}
public void inic_vector_sin(int vector[], int without){
for(int i=0; i<vector.length; i++)
if(i!=without)
vector[i] = i; // Lo inicializo de 1 a n-1
}
public void desordenar_vector_sin(int vector[]){
int tmp, pos;
for(int i=0; i<vector.length-1; i++){
pos = Randomize.Randint(0, vector.length-1);
tmp = vector[i];
vector[i] = vector[pos];
vector[pos] = tmp;
}
}
public PrototypeSet mutant(PrototypeSet population[], int actual, int mejor, double SFi){
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(SFi);
mutant = producto.sumar(r1);
break;
case 2: // Vig = Xbest,G + F(Xr2,G - Xr3,G) De best 1
resta = r2.restar(r3);
producto = resta.mulEscalar(SFi);
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(SFi);
producto2 = resta2.mulEscalar(SFi);
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(SFi);
producto2 = resta2.mulEscalar(SFi);
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(SFi);
producto2 = resta2.mulEscalar(SFi);
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(SFi);
producto2 = resta2.mulEscalar(SFi);
producto3 = resta3.mulEscalar(SFi);
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;
}
/**
* Local Search Fitness Function
* @param Fi
* @param xt
* @param xr
* @param xs
* @param actual
*/
public double lsff(double Fi, double CRi, PrototypeSet population[], int actual, int mejor){
PrototypeSet resta, producto, mutant;
PrototypeSet crossover;
double FitnessFi = 0;
//Mutation:
mutant = new PrototypeSet(population[actual].size());
mutant = mutant(population, actual, mejor, Fi);
//Crossover
crossover =new PrototypeSet(population[actual]);
for(int j=0; j< population[actual].size(); j++){ // For each part of the solution
double randNumber = RandomGenerator.Randdouble(0, 1);
if(randNumber< CRi){
crossover.set(j, mutant.get(j)); // Overwrite.
}
}
// Compute fitness
PrototypeSet nominalPopulation = new PrototypeSet();
nominalPopulation.formatear(crossover);
FitnessFi = classficationAccuracy1NN(nominalPopulation,trainingDataSet);
return FitnessFi;
}
/**
* SFGSS local Search.
* @param population
* @return
*/
public PrototypeSet SFGSS(PrototypeSet population[], int actual, int mejor, double CRi){
double a=0.1, b=1;
double fi1=0, fi2=0, fitnessFi1=0, fitnessFi2=0;
double phi = (1+ Math.sqrt(5))/5;
double scaling;
PrototypeSet crossover, resta, producto, mutant;
for (int i=0; i<this.iterSFGSS; i++){ // Computation budjet
fi1 = b - (b-a)/phi;
fi2 = a + (b-a)/phi;
fitnessFi1 = lsff(fi1, CRi, population,actual,mejor);
fitnessFi2 = lsff(fi2, CRi,population,actual,mejor);
if(fitnessFi1> fitnessFi2){
b = fi2;
}else{
a = fi1;
}
} // End While
if(fitnessFi1> fitnessFi2){
scaling = fi1;
}else{
scaling = fi2;
}
//Mutation:
mutant = new PrototypeSet(population[actual].size());
mutant = mutant(population, actual, mejor, scaling);
//Crossover
crossover =new PrototypeSet(population[actual]);
for(int j=0; j< population[actual].size(); j++){ // For each part of the solution
double randNumber = RandomGenerator.Randdouble(0, 1);
if(randNumber< CRi){
crossover.set(j, mutant.get(j)); // Overwrite.
}
}
return crossover;
}
/**
* SFHC local search
* @param xt
* @param xr
* @param xs
* @param actual
* @param SFi
* @return
*/
public PrototypeSet SFHC(PrototypeSet population[], int actual, int mejor, double SFi, double CRi){
double fitnessFi1, fitnessFi2, fitnessFi3, bestFi;
PrototypeSet crossover, resta, producto, mutant;
double h= 0.5;
for (int i=0; i<this.iterSFHC; i++){ // Computation budjet
fitnessFi1 = lsff(SFi-h, CRi, population,actual,mejor);
fitnessFi2 = lsff(SFi, CRi, population,actual,mejor);
fitnessFi3 = lsff(SFi+h, CRi, population,actual,mejor);
if(fitnessFi1 >= fitnessFi2 && fitnessFi1 >= fitnessFi3){
bestFi = SFi-h;
}else if(fitnessFi2 >= fitnessFi1 && fitnessFi2 >= fitnessFi3){
bestFi = SFi;
h = h/2; // H is halved.
}else{
bestFi = SFi;
}
SFi = bestFi;
}
//Mutation:
mutant = new PrototypeSet(population[actual].size());
mutant = mutant(population, actual, mejor, SFi);
//Crossover
crossover = new PrototypeSet(population[actual]);
for(int j=0; j< population[actual].size(); j++){ // For each part of the solution
double randNumber = RandomGenerator.Randdouble(0, 1);
if(randNumber< CRi){
crossover.set(j, mutant.get(j)); // Overwrite.
}
}
return crossover;
}
/**
* Implements the 1NN algorithm
* @param current Prototype which the algorithm will find its nearest-neighbor.
* @param dataSet Prototype set in which the algorithm will search.
* @return Nearest prototype to current in the prototype set dataset.
*/
public static Prototype _1nn(Prototype current, PrototypeSet dataSet)
{
Prototype nearestNeighbor = dataSet.get(0);
int indexNN = 0;
//double minDist = Distance.dSquared(current, nearestNeighbor);
//double minDist = Distance.euclideanDistance(current, nearestNeighbor);
double minDist =Double.POSITIVE_INFINITY;
double currDist;
int _size = dataSet.size();
// System.out.println("****************");
// current.print();
for (int i=0; i<_size; i++)
{
Prototype pi = dataSet.get(i);
//if(!current.equals(pi))
//{
// double currDist = Distance.dSquared(current, pi);
currDist = Distance.euclideanDistance(pi,current);
// System.out.println(currDist);
if(currDist >0){
if (currDist < minDist)
{
minDist = currDist;
// nearestNeighbor = pi;
indexNN =i;
}
}
//}
}
// System.out.println("Min dist =" + minDist + " Vecino Cercano = "+ indexNN);
return dataSet.get(indexNN);
}
public double classficationAccuracy1NN(PrototypeSet training, PrototypeSet test)
{
int wellClassificated = 0;
for(Prototype p : test)
{
Prototype nearestNeighbor = _1nn(p, training);
if(p.getOutput(0) == nearestNeighbor.getOutput(0))
++wellClassificated;
}
return 100.0* (wellClassificated / (double)test.size());
}
/**
* Generate a reduced prototype set by the SADEGenerator method.
* @return Reduced set by SADEGenerator's method.
*/
public PrototypeSet reduceSet(PrototypeSet initial)
{
System.out.print("\nThe algorithm SSMA-SFLSDE is starting...\n Computing...\n");
//Algorithm
// First, we create the population, with PopulationSize.
// like a prototypeSet's vector.
PrototypeSet population [] = new PrototypeSet [PopulationSize];
PrototypeSet mutation[] = new PrototypeSet[PopulationSize];
PrototypeSet crossover[] = new PrototypeSet[PopulationSize];
double ScalingFactor[] = new double[this.PopulationSize];
double CrossOverRate[] = new double[this.PopulationSize]; // Inside of the Optimization process.
double fitness[] = new double[PopulationSize];
double fitness_bestPopulation[] = new double[PopulationSize];
PrototypeSet bestParticle = new PrototypeSet();
//Each particle must have Particle Size %
// First Stage, Initialization.
PrototypeSet nominalPopulation;
population[0]= new PrototypeSet(initial.clone()) ;
generador = new PrototypeGenerator(trainingDataSet);
// Por si SSMA falla:
// population[0].print();
if(population[0].size() <2){
this.numberOfPrototypes = (int)Math.round(trainingDataSet.size()*0.02);
population[0]=generador.selecRandomSet(numberOfPrototypes,true).clone() ;
// red .95
// Aseguro que al menos hay un representante de cada clase.
PrototypeSet clases[] = new PrototypeSet [this.numberOfClass];
for(int i=0; i< this.numberOfClass; i++){
clases[i] = new PrototypeSet(trainingDataSet.getFromClass(i));
// System.out.println("Clase "+i+", size= "+ clases[i].size());
}
for(int i=0; i< population[0].size(); i++){
for(int j=0; j< this.numberOfClass; j++){
if(population[0].getFromClass(j).size() ==0 && clases[j].size()!=0){
population[0].add(clases[j].getRandom());
}
}
}
}
nominalPopulation = new PrototypeSet();
nominalPopulation.formatear(population[0]);
fitness[0] = classficationAccuracy1NN(nominalPopulation,trainingDataSet);
System.out.println("Best initial fitness = "+ fitness[0]);
this.numberOfClass = trainingDataSet.getPosibleValuesOfOutput().size();
for(int i=1; i< PopulationSize; i++){
population[i] = new PrototypeSet();
for(int j=0; j< population[0].size(); j++){
Prototype aux = new Prototype(trainingDataSet.getFromClass(population[0].get(j).getOutput(0)).getRandom());
population[i].add(aux);
}
nominalPopulation = new PrototypeSet();
nominalPopulation.formatear(population[i]);
fitness[i] = classficationAccuracy1NN(population[i],trainingDataSet); // PSOfitness
fitness_bestPopulation[i] = fitness[i]; // Initially the same fitness.
}
//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);
}
// Initially the Scaling Factor and crossover for each Individual are randomly generated between 0 and 1.
for(int i=0; i< this.PopulationSize; i++){
ScalingFactor[i] = RandomGenerator.Randdouble(0, 1);
CrossOverRate[i] = RandomGenerator.Randdouble(0, 1);
}
double randj[] = new double[5];
for(int iter=0; iter< MaxIter; iter++){ // Main loop
for(int i=0; i<PopulationSize; i++){
// Generate randj for j=1 to 5.
for(int j=0; j<5; j++){
randj[j] = RandomGenerator.Randdouble(0, 1);
}
if(i==bestFitnessIndex && randj[4] < tau[2]){
// System.out.println("SFGSS applied");
//SFGSS
crossover[i] = SFGSS(population, i, bestFitnessIndex, CrossOverRate[i]);
}else if(i==bestFitnessIndex && tau[2] <= randj[4] && randj[4] < tau[3]){
//SFHC
//System.out.println("SFHC applied");
crossover[i] = SFHC(population, i, bestFitnessIndex, ScalingFactor[i], CrossOverRate[i]);
}else {
// Fi update
if(randj[1] < tau[0]){
ScalingFactor[i] = this.Fl + this.Fu*randj[0];
}
// CRi update
if(randj[3] < tau[1]){
CrossOverRate[i] = randj[2];
}
// Mutation Operation.
mutation[i] = new PrototypeSet(population[i].size());
//Mutation:
mutation[i] = mutant(population, i, bestFitnessIndex, ScalingFactor[i]);
// Crossver Operation.
crossover[i] = new PrototypeSet(population[i]);
for(int j=0; j< population[i].size(); j++){ // For each part of the solution
double randNumber = RandomGenerator.Randdouble(0, 1);
if(randNumber<CrossOverRate[i]){
crossover[i].set(j, mutation[i].get(j)); // Overwrite.
}
}
}
// Fourth: Selection Operation.
nominalPopulation = new PrototypeSet();
nominalPopulation.formatear(population[i]);
fitness[i] = classficationAccuracy1NN(nominalPopulation,trainingDataSet);
nominalPopulation = new PrototypeSet();
nominalPopulation.formatear(crossover[i]);
double trialVector = classficationAccuracy1NN(nominalPopulation,trainingDataSet);
if(trialVector > fitness[i]){
population[i] = new PrototypeSet(crossover[i]);
fitness[i] = trialVector;
}
if(fitness[i]>bestFitness){
bestFitness = fitness[i];
bestFitnessIndex=i;
}
}
}
nominalPopulation = new PrototypeSet();
nominalPopulation.formatear(population[bestFitnessIndex]);
System.err.println("\n% de acierto en training Nominal " + classficationAccuracy1NN(nominalPopulation,trainingDataSet) );
// nominalPopulation.print();
return nominalPopulation;
}
/* MEzcla de algoritmos */
public void ejecutar () {
int i, j, l;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Cromosoma poblacion[];
double ev = 0;
double dMatrix[][];
int sel1, sel2, comp1, comp2;
Cromosoma hijos[];
double umbralOpt;
boolean veryLarge;
double GAeffort=0, LSeffort=0, temporal;
double fAcierto=0, fReduccion=0;
int contAcierto=0, contReduccion=0;
int nClases;
long tiempo = System.currentTimeMillis();
/*Getting the number of different classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
if (datosTrain.length > 9000) {
veryLarge = true;
} else {
veryLarge = false;
}
if (veryLarge == false) {
/*Construct a distance matrix of the instances*/
dMatrix = new double[datosTrain.length][datosTrain.length];
for (i = 0; i < dMatrix.length; i++) {
for (j = i + 1; j < dMatrix[i].length; j++) {
dMatrix[i][j] = KNN.distancia(datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], distanceEu);
}
}
for (i = 0; i < dMatrix.length; i++) {
dMatrix[i][i] = Double.POSITIVE_INFINITY;
}
for (i = 0; i < dMatrix.length; i++) {
for (j = i - 1; j >= 0; j--) {
dMatrix[i][j] = dMatrix[j][i];
}
}
} else {
dMatrix = null;
}
/*Random inicialization of the population*/
Randomize.setSeed (semilla);
poblacion = new Cromosoma[tamPoblacion];
for (i=0; i<tamPoblacion; i++)
poblacion[i] = new Cromosoma (kNeigh, datosTrain.length, dMatrix, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
/*Initial evaluation of the population*/
for (i=0; i<tamPoblacion; i++) {
poblacion[i].evaluacionCompleta(nClases, kNeigh, clasesTrain);
}
umbralOpt = 0;
/*Until stop condition*/
while (ev < nEval) {
Arrays.sort(poblacion);
if (fAcierto >= (double)poblacion[0].getFitnessAc()*100.0/(double)datosTrain.length) {
contAcierto++;
} else {
contAcierto=0;
}
fAcierto = (double)poblacion[0].getFitnessAc()*100.0/(double)datosTrain.length;
if (fReduccion >= (1.0-((double)poblacion[0].genesActivos()/(double)datosTrain.length))*100.0) {
contReduccion++;
} else {
contReduccion=0;
}
fReduccion = (1.0-((double)poblacion[0].genesActivos()/(double)datosTrain.length))*100.0;
if (contReduccion >= 10 || contAcierto >= 10){
if (Randomize.Randint(0,1)==0) {
if (contAcierto >= 10) {
contAcierto = 0;
umbralOpt++;
} else {
contReduccion = 0;
umbralOpt--;
}
} else {
if (contReduccion >= 10) {
contReduccion = 0;
umbralOpt--;
} else {
contAcierto = 0;
umbralOpt++;
}
}
}
/*Binary tournament selection*/
comp1 = Randomize.Randint(0,tamPoblacion-1);
do {
comp2 = Randomize.Randint(0,tamPoblacion-1);
} while (comp2 == comp1);
if (poblacion[comp1].getFitness() > poblacion[comp2].getFitness())
sel1 = comp1;
else sel1 = comp2;
comp1 = Randomize.Randint(0,tamPoblacion-1);
do {
comp2 = Randomize.Randint(0,tamPoblacion-1);
} while (comp2 == comp1);
if (poblacion[comp1].getFitness() > poblacion[comp2].getFitness())
sel2 = comp1;
else
sel2 = comp2;
hijos = new Cromosoma[2];
hijos[0] = new Cromosoma (kNeigh, poblacion[sel1], poblacion[sel2], pCross,datosTrain.length);
hijos[1] = new Cromosoma (kNeigh, poblacion[sel2], poblacion[sel1], pCross,datosTrain.length);
hijos[0].mutation (kNeigh, pMut, dMatrix, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
hijos[1].mutation (kNeigh, pMut, dMatrix, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
/*Evaluation of offsprings*/
hijos[0].evaluacionCompleta(nClases, kNeigh, clasesTrain);
hijos[1].evaluacionCompleta(nClases, kNeigh, clasesTrain);
ev+=2;
GAeffort += 2;
temporal = ev;
if (hijos[0].getFitness() > poblacion[tamPoblacion-1].getFitness() || Randomize.Rand() < 0.0625) {
ev += hijos[0].optimizacionLocal(nClases, kNeigh, clasesTrain,dMatrix,umbralOpt, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
}
if (hijos[1].getFitness() > poblacion[tamPoblacion-1].getFitness() || Randomize.Rand() < 0.0625) {
ev += hijos[1].optimizacionLocal(nClases, kNeigh, clasesTrain,dMatrix,umbralOpt, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
}
LSeffort += (ev - temporal);
/*Replace the two worst*/
if (hijos[0].getFitness() > poblacion[tamPoblacion-1].getFitness()) {
poblacion[tamPoblacion-1] = new Cromosoma (kNeigh, datosTrain.length, hijos[0]);
}
if (hijos[1].getFitness() > poblacion[tamPoblacion-2].getFitness()) {
poblacion[tamPoblacion-2] = new Cromosoma (kNeigh, datosTrain.length, hijos[1]);
}
/* System.out.println(ev + " - (" + umbralOpt + ")" +
(double)poblacion[0].getFitnessAc()*100.0/(double)datosTrain.length + " / " +
(double)poblacion[tamPoblacion-1].getFitnessAc()*100.0/(double)datosTrain.length + " - " +
(1.0-((double)poblacion[0].genesActivos()/(double)datosTrain.length))*100.0 + " / " +
(1.0-((double)poblacion[tamPoblacion-1].genesActivos()/(double)datosTrain.length))*100.0);*/
}
Arrays.sort(poblacion);
nSel = poblacion[0].genesActivos();
/*Construction of S set from the best cromosome*/
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i=0, l=0; i<datosTrain.length; i++) {
if (poblacion[0].getGen(i)) { //the instance must be copied to the solution
for (j=0; j<datosTrain[i].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
System.out.println("SSMA "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
OutputIS.escribeSalida(ficheroSalida[0], conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
/** AHORA A�ADO MI DE!! **/
Parameters.assertBasicArgs(ficheroSalida);
if(!this.Script.equals("NOFILE")){
PrototypeGenerationAlgorithm.readParametersFile(this.Script);
PrototypeGenerationAlgorithm.printParameters();
trainingDataSet = readPrototypeSet(this.ficheroTraining); // Conjunto inicial
testDataSet = readPrototypeSet(this.ficheroTest);
}
PrototypeSet training = readPrototypeSet(ficheroSalida[0]);
// training.print(); // Conjunto devuelto POR SSMA
// trainingDataSet.print();
//this.numberOfPrototypes = (int)Math.floor((trainingDataSet.size())*ParticleSize/100.0);
//System.out.println("**************DENTRO");
// training.print();
// System.out.println("**************FUERA");
PrototypeSet SADE = reduceSet(training); // LLAMO al SADE
SADE.save(ficheroSalida[0]); // Lo guardo
SADE.print();
//Copy the test input file to the output test file
// KeelFile.copy(inputFilesPath.get(TEST), outputFilesPath.get(TEST));
System.out.println("Time elapse:" + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
if(!this.Script.equals("NOFILE")){
/*ADDING KNN FOR TEST FILE */
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
nClases = SADE.getPosibleValuesOfOutput().size();
//Working on training
int cont=0;
for (i=0; i<trainingDataSet.size(); i++) {
trainRealClass[i][0] = (int) trainingDataSet.get(i).getOutput(0);
trainPrediction[i][0] = evaluate(trainingDataSet.get(i).getInputs(),SADE.prototypeSetTodouble(), nClases, SADE.getClases(), 1);
if(trainRealClass[i][0] == trainPrediction[i][0]){ cont++;}
}
System.out.println("Acierto = "+ (cont*1.0)/(trainingDataSet.size()));
Attribute entradas[];
Attribute salida;
entradas = Attributes.getInputAttributes();
salida = Attributes.getOutputAttribute(0);
String relation = Attributes.getRelationName();
writeOutput(this.ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
for (i=0; i<realClass.length; i++) {
realClass[i][0] = (int) testDataSet.get(i).getOutput(0);
prediction[i][0]= evaluate(testDataSet.get(i).getInputs(),SADE.prototypeSetTodouble(), nClases, SADE.getClases(), 1);
}
writeOutput(this.ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}
}
/**
* Prints output files.
*
* @param filename Name of output file
* @param realClass Real output of instances
* @param prediction Predicted output for instances
*/
public static void writeOutput(String filename, int [][] realClass, int [][] prediction, Attribute inputs[], Attribute output, String relation) {
String text = "";
/*Printing input attributes*/
text += "@relation "+ relation +"\n";
for (int i=0; i<inputs.length; i++) {
text += "@attribute "+ inputs[i].getName()+" ";
if (inputs[i].getType() == Attribute.NOMINAL) {
text += "{";
for (int j=0; j<inputs[i].getNominalValuesList().size(); j++) {
text += (String)inputs[i].getNominalValuesList().elementAt(j);
if (j < inputs[i].getNominalValuesList().size() -1) {
text += ", ";
}
}
text += "}\n";
} else {
if (inputs[i].getType() == Attribute.INTEGER) {
text += "integer";
} else {
text += "real";
}
text += " ["+String.valueOf(inputs[i].getMinAttribute()) + ", " + String.valueOf(inputs[i].getMaxAttribute())+"]\n";
}
}
/*Printing output attribute*/
text += "@attribute "+ output.getName()+" ";
if (output.getType() == Attribute.NOMINAL) {
text += "{";
for (int j=0; j<output.getNominalValuesList().size(); j++) {
text += (String)output.getNominalValuesList().elementAt(j);
if (j < output.getNominalValuesList().size() -1) {
text += ", ";
}
}
text += "}\n";
} else {
text += "integer ["+String.valueOf(output.getMinAttribute()) + ", " + String.valueOf(output.getMaxAttribute())+"]\n";
}
/*Printing data*/
text += "@data\n";
Files.writeFile(filename, text);
if (output.getType() == Attribute.INTEGER) {
text = "";
for (int i=0; i<realClass.length; i++) {
for (int j=0; j<realClass[0].length; j++){
text += "" + realClass[i][j] + " ";
}
for (int j=0; j<realClass[0].length; j++){
text += "" + prediction[i][j] + " ";
}
text += "\n";
if((i%10)==9){
Files.addToFile(filename, text);
text = "";
}
}
if((realClass.length%10)!=0){
Files.addToFile(filename, text);
}
}
else{
text = "";
for (int i=0; i<realClass.length; i++) {
for (int j=0; j<realClass[0].length; j++){
text += "" + (String)output.getNominalValuesList().elementAt(realClass[i][j]) + " ";
}
for (int j=0; j<realClass[0].length; j++){
if(prediction[i][j]>-1){
text += "" + (String)output.getNominalValuesList().elementAt(prediction[i][j]) + " ";
}
else{
text += "" + "Unclassified" + " ";
}
}
text += "\n";
if((i%10)==9){
Files.addToFile(filename, text);
text = "";
}
}
if((realClass.length%10)!=0){
Files.addToFile(filename, text);
}
}
}//end-method
/**
* Calculates the Euclidean distance between two instances
*
* @param instance1 First instance
* @param instance2 Second instance
* @return The Euclidean distance
*
*/
protected static double distance(double instance1[],double instance2[]){
double length=0.0;
for (int i=0; i<instance1.length; i++) {
length += (instance1[i]-instance2[i])*(instance1[i]-instance2[i]);
}
length = Math.sqrt(length);
return length;
} //end-method
/**
* Calculates the Euclidean distance between two instances
*
* @param instance1 First instance
* @param instance2 Second instance
* @return The Euclidean distance
*
*/
protected static double distanceWeighting(double instance1[],double instance2[], double Weights[]){
double length=0.0;
for (int i=0; i<instance1.length; i++) {
length += ((instance1[i]-instance2[i])*(instance1[i]-instance2[i]))*Weights[i];
}
length = Math.sqrt(length);
return length;
} //end-method
/**
* Evaluates a instance to predict its class.
*
* @param example Instance evaluated
* @return Class predicted
*
*/
public static int evaluate (double example[], double trainData[][],int nClasses,int trainOutput[],int k) {
double minDist[];
int nearestN[];
int selectedClasses[];
double dist;
int prediction;
int predictionValue;
boolean stop;
nearestN = new int[k];
minDist = new double[k];
for (int i=0; i<k; i++) {
nearestN[i] = 0;
minDist[i] = Double.MAX_VALUE;
}
//KNN Method starts here
for (int i=0; i<trainData.length; i++) {
dist = distance(trainData[i],example);
if (dist > 0.0){ //leave-one-out
//see if it's nearer than our previous selected neighbors
stop=false;
for(int j=0;j<k && !stop;j++){
if (dist < minDist[j]) {
for (int l = k - 1; l >= j+1; l--) {
minDist[l] = minDist[l - 1];
nearestN[l] = nearestN[l - 1];
}
minDist[j] = dist;
nearestN[j] = i;
stop=true;
}
}
}
}
//we have check all the instances... see what is the most present class
selectedClasses= new int[nClasses];
for (int i=0; i<nClasses; i++) {
selectedClasses[i] = 0;
}
for (int i=0; i<k; i++) {
// System.out.println("nearestN i ="+i + " =>"+nearestN[i]);
// System.out.println("trainOutput ="+trainOutput[nearestN[i]]);
selectedClasses[trainOutput[nearestN[i]]]+=1;
}
prediction=0;
predictionValue=selectedClasses[0];
for (int i=1; i<nClasses; i++) {
if (predictionValue < selectedClasses[i]) {
predictionValue = selectedClasses[i];
prediction = i;
}
}
return prediction;
} //end-method
public void leerConfiguracion (String ficheroScript) {
String fichero, linea, token;
StringTokenizer lineasFichero, tokens;
byte line[];
int i, j;
ficheroSalida = new String[2];
if(ficheroScript.equals("NOFILE")){
System.out.println("There is no configuration file: Applying Auto-parameters");
ficheroSalida[0] = "salida.dat";
ficheroSalida[1] = "otro.dat";
ficheroTraining = "intermediate.dat";
tamPoblacion = 30;
nEval = 10000;
pCross = 0.5;
pMut = 0.001;
kNeigh = 1;
distanceEu = true;
PopulationSize = 50;
this.MaxIter = 500;
this.iterSFGSS = 8;
this.iterSFHC = 20;
this.Fl = 0.1;
this.Fu = 0.9;
tau = new double[4];
this.tau[0] = 0.1;
this.tau[1] = 0.1;
this.tau[2] = 0.03;
this.tau[3] = 0.07;;
this.Strategy = 3;
}else{
fichero = Fichero.leeFichero (ficheroScript);
lineasFichero = new StringTokenizer (fichero,"\n\r");
lineasFichero.nextToken();
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the name of training and test files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTraining = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroValidation = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTest = new String (line,i,j-i);
//Parameters.assertBasicArgs(ficheroSalida);
/*Obtainin the path and the base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the name of output files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[0] = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[1] = new String (line,i,j-i);
/*Getting the seed*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
semilla = Long.parseLong(tokens.nextToken().substring(1));
/*Getting the size of the poblation and the number of evaluations*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
tamPoblacion = Integer.parseInt(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
nEval = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the probabilities of evolutionary operators*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pCross = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pMut = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
kNeigh = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the type of distance function*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
distanceEu = tokens.nextToken().substring(1).equalsIgnoreCase("Euclidean")?true:false;
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.PopulationSize = Integer.parseInt(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.MaxIter = Integer.parseInt(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.iterSFGSS = Integer.parseInt(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.iterSFHC = Integer.parseInt(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.Fl = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.Fu = Double.parseDouble(tokens.nextToken().substring(1));
tau = new double[4];
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.tau[0] = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.tau[1] = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.tau[2] = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.tau[3] = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.Strategy = Integer.parseInt(tokens.nextToken().substring(1));
System.out.print("\nIsaac dice: tau3"+this.tau[3] +"\n");
}
}
}