/***********************************************************************
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 HIBRIDO LVq3
//
// 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.SSMALVQ3;
import keel.Algorithms.Preprocess.Basic.*;
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.Basic.PrototypeSet;
import keel.Algorithms.Instance_Generation.LVQ.LVQ3;
import keel.Algorithms.Instance_Generation.utilities.*;
//import keel.Algorithms.Instance_Generation.utilities.KNN.KNN;
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 SSMALVQ3 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 PrototypeGenerator generador;
//Par�metros LVQ3: Solo me hacen falta 4;
private int Maxiter;
private double alpha0;
private double windowW;
private double epsilon;
protected int numberOfClass;
protected int numberOfPrototypes; // Particle size is the percentage
protected int numberOfStrategies; // number of strategies in the pool
public SSMALVQ3 (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);
}
/**
* 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());
}
/* 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);
PrototypeGenerationAlgorithm.readParametersFile(this.Script);
PrototypeGenerationAlgorithm.printParameters();
PrototypeSet training = readPrototypeSet(ficheroSalida[0]);
//training.print(); // Conjunto devuelto POR SSMA
trainingDataSet = readPrototypeSet(this.ficheroTraining); // Conjunto inicial
generador = new PrototypeGenerator(trainingDataSet);
// trainingDataSet.print();
double initialAcc = classficationAccuracy1NN(training,trainingDataSet);
System.out.println("Initial Acc = "+ initialAcc);
PrototypeSet LVQ3 = makeLVQ3Reduction(training, trainingDataSet); // LLAMO al LVQ3
PrototypeSet nominalPopulation = new PrototypeSet();
nominalPopulation.formatear(LVQ3);
initialAcc = classficationAccuracy1NN(nominalPopulation,trainingDataSet);
System.out.println("Final Acc = "+ initialAcc);
LVQ3.print();
LVQ3.save(ficheroSalida[0]); // Lo guardo
// 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],LVQ3.prototypeSetTodouble(), nClases, LVQ3.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],LVQ3.prototypeSetTodouble(), nClases,LVQ3.getClases(), 1);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}
/**
* Performs a LVQ3-reduction of the set.
* @param w Window width.
* @param e Epsilon.
* @param iter Number of iterations.
* @param Np Number of prototypes to be generated.
*/
private PrototypeSet makeLVQ3Reduction(PrototypeSet InitialSet, PrototypeSet training)
{
int size = InitialSet.size();
LVQ3 lvq3 = new LVQ3(InitialSet,training, this.Maxiter, size, this.alpha0, this.windowW, this.epsilon);
PrototypeSet reducedByLVQ3 = lvq3.reduceSet();
return reducedByLVQ3;
}
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 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.Maxiter = Integer.parseInt(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.alpha0= Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.windowW = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
this.epsilon = Double.parseDouble(tokens.nextToken().substring(1));
System.out.print("\nIsaac dice: alpha0= "+this.alpha0+ " Maxiter= "+ this.Maxiter+" epsilon= "+this.epsilon+ "\n");
}
}