/***********************************************************************
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/
**********************************************************************/
package keel.Algorithms.RE_SL_Methods.mogulIRL;
/**
* <p>Title: </p>
*
* <p>Description: </p>
*
* <p>Copyright: Copyright (c) 2007</p>
*
* <p>Company: </p>
*
* @author not attributable
* @version 1.0
*/
import java.io.*;
import java.util.*;
import java.lang.Math;
import org.core.*;
class Tun_des {
public double semilla;
public long cont_soluciones;
public long Gen, n_genes, n_reglas, n_generaciones;
public int n_soluciones;
public String fich_datos_chequeo, fich_datos_tst;
public String fichero_conf, ruta_salida;
public String fichero_br, fichero_reglas, fich_tra_obli, fich_tst_obli;
public String informe = "";
public String datos_inter = "";
public String cadenaReglas = "";
public MiDataset tabla, tabla_tst;
public BaseR_Tun_des base_reglas;
public Adap_Tun_des fun_adap;
public AG_Tun_des alg_gen;
public Tun_des (String f_e, MiDataset train, MiDataset test) {
fichero_conf = f_e;
this.tabla = train;
this.tabla_tst = test;
}
private String Quita_blancos(String cadena) {
StringTokenizer sT = new StringTokenizer(cadena, "\t ", false);
return (sT.nextToken());
}
/** Reads the data of the configuration file */
public void leer_conf (){
int i, j;
String cadenaEntrada, valor;
double cruce, mutacion, a, b;
int long_poblacion;
// we read the file in a String
cadenaEntrada = Fichero.leeFichero(fichero_conf);
StringTokenizer sT = new StringTokenizer(cadenaEntrada, "\n\r=", false);
// we read the algorithm's name
sT.nextToken();
sT.nextToken();
// we read the name of the training and test files
sT.nextToken();
valor = sT.nextToken();
StringTokenizer ficheros = new StringTokenizer(valor, "\t ", false);
ficheros.nextToken();
fich_datos_chequeo = ((ficheros.nextToken()).replace('\"',' ')).trim();
fich_datos_tst = ((ficheros.nextToken()).replace('\"',' ')).trim();
// fichero_br = ((ficheros.nextToken()).replace('\"',' ')).trim();
// we read the name of the output files
sT.nextToken();
valor = sT.nextToken();
ficheros = new StringTokenizer(valor, "\t ", false);
fich_tra_obli = ((ficheros.nextToken()).replace('\"',' ')).trim();
fich_tst_obli = ((ficheros.nextToken()).replace('\"',' ')).trim();
// fichero_reglas = ((ficheros.nextToken()).replace('\"',' ')).trim();
String aux = ((ficheros.nextToken()).replace('\"',' ')).trim(); //Br inicial
aux = ((ficheros.nextToken()).replace('\"',' ')).trim(); //BD
fichero_br = ((ficheros.nextToken()).replace('\"',' ')).trim(); //BR salida de Select
fichero_reglas = ((ficheros.nextToken()).replace('\"',' ')).trim(); //BR salida de Tuning
ruta_salida = fich_tst_obli.substring(0, fich_tst_obli.lastIndexOf('/') + 1);
// we read the seed of the random generator
sT.nextToken();
valor = sT.nextToken();
semilla = Double.parseDouble(valor.trim());
Randomize.setSeed((long) semilla);;
for (i = 0; i < 14; i++){
sT.nextToken(); //variable
sT.nextToken(); //valor
}
// we read the Number of Iterations
sT.nextToken();
valor = sT.nextToken();
n_generaciones = Long.parseLong(valor.trim());
// we read the Population Size
sT.nextToken();
valor = sT.nextToken();
long_poblacion = Integer.parseInt(valor.trim());
// we read the Parameter a
sT.nextToken();
valor = sT.nextToken();
a = Double.parseDouble(valor.trim());
// we read the Parameter b
sT.nextToken();
valor = sT.nextToken();
b = Double.parseDouble(valor.trim());
// we read the Cross Probability
sT.nextToken();
valor = sT.nextToken();
cruce = Double.parseDouble(valor.trim());
// we read the Mutation Probability
sT.nextToken();
valor = sT.nextToken();
mutacion = Double.parseDouble(valor.trim());
// we create all the objects
// tabla = new MiDataset(fich_datos_chequeo, true);
// if (tabla.salir==false) {
tabla.nuevaTabla();
tabla_tst.nuevaTabla();
base_reglas = new BaseR_Tun_des(fichero_br, tabla);
fun_adap = new Adap_Tun_des(tabla, base_reglas, 0.0, 1);
alg_gen = new AG_Tun_des(long_poblacion, base_reglas.n_etiq_distintas, cruce, mutacion, a, b, fun_adap);
// }
}
public void run () {
int i, j;
double ec, el, ec_tst, el_tst;
/* We read the configutate file and we initialize the structures and variables */
leer_conf();
if (tabla.salir==false) {
Gen = 0;
/* Generation of the initial population */
alg_gen.Initialize(base_reglas, tabla.n_variables);
/* Evaluation of the initial population */
alg_gen.Evaluate ();
Gen++;
/* Main of the Genetic Algorithm */
do {
/* Interchange of the new and old population */
alg_gen.Intercambio();
/* Selection by means of Baker */
alg_gen.Select ();
/* Crossover */
alg_gen.Max_Min_Crossover();
/* Mutation */
alg_gen.Mutacion_No_Uniforme (Gen, n_generaciones);
/* Elitist Selection */
alg_gen.Elitist ();
/* Evaluation of the current population */
alg_gen.Evaluate ();
/* we increment the counter */
Gen++;
fun_adap.Decodifica (alg_gen.solucion());
fun_adap.Error_tra ();
ec = fun_adap.EC;
System.out.println(" Iteration=" + (Gen - 1) + " MSE=" +
ec + " " + " #R=" +
base_reglas.n_reglas);
} while (Gen <= n_generaciones);
/* we calcule the MSEs */
fun_adap.Decodifica (alg_gen.solucion());
fun_adap.Error_tra ();
ec = fun_adap.EC;
el = fun_adap.EL;
tabla_tst = new MiDataset(fich_datos_tst, false);
fun_adap.Error_tst (tabla_tst);
ec_tst = fun_adap.EC;
el_tst = fun_adap.EL;
fun_adap.Cubrimientos_Base ();
/* we write the RB */
cadenaReglas = base_reglas.BRtoString();
cadenaReglas += "\nMSEtra: " + ec + " MSEtst: " + ec_tst + "\nAverage covering degree: " + fun_adap.medcb + " Minimum covering degree: " + fun_adap.mincb;
Fichero.escribeFichero(fichero_reglas, cadenaReglas);
/* we write the obligatory output files*/
String salida_tra = tabla.getCabecera();
salida_tra += fun_adap.getSalidaObli(tabla);
Fichero.escribeFichero(fich_tra_obli, salida_tra);
String salida_tst = tabla_tst.getCabecera();
salida_tst += fun_adap.getSalidaObli(tabla_tst);
Fichero.escribeFichero(fich_tst_obli, salida_tst);
/* we write the MSEs in specific files */
Fichero.AnadirtoFichero(ruta_salida + "tundescomunR.txt", "" + base_reglas.n_reglas + "\n");
Fichero.AnadirtoFichero(ruta_salida + "tundescomunTRA.txt", "" + ec + "\n");
Fichero.AnadirtoFichero(ruta_salida + "tundescomunTST.txt", "" + ec_tst + "\n");
}
}
}