/***********************************************************************
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.DROP3SFLSDE;
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.Attributes;
import keel.Dataset.InstanceAttributes;
import keel.Dataset.InstanceSet;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
import java.util.Vector;
public class DROP3SFLSDE extends Metodo {
/*Own parameters of the algorithm*/
private int k;
private double semilla;
public String Script; // para releer par�metros..
private PrototypeSet trainingDataSet;
private PrototypeGenerator generador;
//Par�metros DE
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 DROP3SFLSDE (String ficheroScript) {
super (ficheroScript);
}
/**
* 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 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
*/
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
*/
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
*/
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 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);
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, m, n, o;
int nClases;
int claseObt;
boolean marcas[];
int nSel;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int vecinos[][];
Vector asociados[];
int aciertosSin;
int vecinosTemp[];
double distTemp[];
double dist, bestD;
boolean parar;
Referencia orden[];
int mayoria;
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++;
/*Inicialization of the instance flagged vector of the S set*/
marcas = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++) {
marcas[i] = true;
}
nSel = datosTrain.length;
/*Do ENN before sorting*/
for (i=0; i<datosTrain.length; i++) {
claseObt = KNN.evaluacionKNN2 (k, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu);
if (claseObt != clasesTrain[i]) { //is included in the solution set if it is agree with your mayority
marcas[i] = false;
nSel--;
}
}
/*Construction of an instance vector with distances to the nearest enemy*/
orden = new Referencia[datosTrain.length];
for (i=0; i<datosTrain.length; i++) {
bestD = Double.POSITIVE_INFINITY;
for (j=0; j<datosTrain.length; j++) {
if (clasesTrain[i] != clasesTrain[j]) {
dist = KNN.distancia (datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], distanceEu);
if (dist < bestD)
bestD = dist;
}
}
orden[i] = new Referencia (i, bestD);
}
/*Sorting the previous vector*/
Arrays.sort(orden);
/*Inicialization of the data structures of neighbors and associates*/
distTemp = new double[k+1];
vecinosTemp = new int[k+1];
vecinos = new int[datosTrain.length][k+1];
asociados = new Vector[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
asociados[i] = new Vector ();
/*Body of the algorithm DROP3 (same as DROP2).*/
for (i=0; i<datosTrain.length; i++) {
/*Get the k+1 nearest neighbors of each instance*/
if (marcas[i]) {
KNN.evaluacionKNN2 (k+1, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu, vecinos[i]);
for (j=0; j<vecinos[i].length; j++) {
if (vecinos[i][j] >= 0)
asociados[vecinos[i][j]].addElement (new Referencia (i,0));
}
}
}
/*Check if delete or not the instances considering the WITH and WITHOUT sets*/
for (o=0; o<datosTrain.length; o++){
i = orden[o].entero;
if (marcas[i]) { //only for instances haven�t noise filtered
aciertosSin = 0;
marcas[i] = false;
nSel--;
/*Construction of S set from the temporaly flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance will evaluate
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
marcas[i] = true;
nSel++;
/*Evaluation of associates without the instance in T*/
for (j=0; j<k+1; j++) {
if (vecinos[i][j] >= 0) {
claseObt = KNN.evaluacionKNN2 (k, conjS, conjR, conjN, conjM, clasesS, datosTrain[vecinos[i][j]], realTrain[vecinos[i][j]], nominalTrain[vecinos[i][j]], nulosTrain[vecinos[i][j]], nClases, distanceEu);
if (claseObt == clasesTrain[vecinos[i][j]]) //classify it correctly
aciertosSin++;
}
}
mayoria = (k+1) / 2;
if (aciertosSin > mayoria) {
/*Delete P from S*/
marcas[i] = false;
nSel--;
/*For each associate of P, search a new nearest neighbor*/
for (j=0; j<asociados[i].size(); j++) {
for (l=0; l<k+1; l++) {
vecinosTemp[l] = vecinos[((Referencia)(asociados[i].elementAt(j))).entero][l];
vecinos[((Referencia)(asociados[i].elementAt(j))).entero][l] = -1;
distTemp[l] = Double.POSITIVE_INFINITY;
}
for (l=0; l<datosTrain.length; l++) {
if (marcas[l]) { //it is in S
dist = KNN.distancia (datosTrain[((Referencia)(asociados[i].elementAt(j))).entero], realTrain[((Referencia)(asociados[i].elementAt(j))).entero], nominalTrain[((Referencia)(asociados[i].elementAt(j))).entero], nulosTrain[((Referencia)(asociados[i].elementAt(j))).entero], datosTrain[l], realTrain[l], nominalTrain[l], nulosTrain[l], distanceEu);
parar = false;
/*Calculate the nearest neighbors in this situation again*/
for (m=0; m<(k+1) && !parar; m++) {
if (dist < distTemp[m]) {
parar = true;
for (n=m+1; n<k+1; n++) {
distTemp[n] = distTemp[n-1];
vecinos[((Referencia)(asociados[i].elementAt(j))).entero][n] = vecinos[((Referencia)(asociados[i].elementAt(j))).entero][n-1];
}
distTemp[m] = dist;
vecinos[((Referencia)(asociados[i].elementAt(j))).entero][m] = l;
}
}
}
}
/*Add to the list of associates of the new neighbor this instance*/
for (l=0; l<k+1; l++) {
parar = false;
for (m=0; m<asociados[vecinosTemp[l]].size() && !parar; m++) {
if (((Referencia)(asociados[vecinosTemp[l]].elementAt(m))).entero == ((Referencia)(asociados[i].elementAt(j))).entero
&& vecinosTemp[l] != i) {
asociados[vecinosTemp[l]].removeElementAt(m);
parar = true;
}
}
}
for (l=0; l<k+1; l++) {
asociados[vecinos[((Referencia)(asociados[i].elementAt(j))).entero][l]].addElement(new Referencia (((Referencia)(asociados[i].elementAt(j))).entero,0));
}
}
}
}
}
/*Construction of the S set from the flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance will evaluate
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
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);
PrototypeGenerationAlgorithm.readParametersFile(this.Script);
PrototypeGenerationAlgorithm.printParameters();
PrototypeSet training = readPrototypeSet(ficheroSalida[0]);
// training.print(); // Conjunto devuelto POR SSMA
trainingDataSet = readPrototypeSet(this.ficheroTraining); // Conjunto inicial
// trainingDataSet.print();
//this.numberOfPrototypes = (int)Math.floor((trainingDataSet.size())*ParticleSize/100.0);
PrototypeSet SADE = reduceSet(training); // LLAMO al SADE
SADE.save(ficheroSalida[0]); // Lo guardo
//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");
// COn conjS me vale.
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
//Working on training
for ( i=0; i<datosTrain.length; i++) {
trainRealClass[i][0] = clasesTrain[i];
trainPrediction[i][0] = KNN.evaluate(datosTrain[i],SADE.prototypeSetTodouble(), nClases, SADE.getClases(), 1);
}
KNN.writeOutput(ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
//Working on test
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
//Check time
for (i=0; i<realClass.length; i++) {
realClass[i][0] = clasesTest[i];
prediction[i][0]= KNN.evaluate(datosTest[i],SADE.prototypeSetTodouble(), nClases, SADE.getClases(), 1);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}
public void leerConfiguracion (String ficheroScript) {
String fichero, linea, token;
StringTokenizer lineasFichero, tokens;
byte line[];
int i, j;
ficheroSalida = new String[2];
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 number of neighbors*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
k = 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));
}
}