/**
* Obtains the parameters used in the execution of the algorithm and stores them in the private
* variables of the class
*
* @param ficheroScript Name of the configuration script that indicates the parameters that are
* going to be used during the execution of the algorithm
*/
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 names of the 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++) ;
ficheroTest = new String(line, i, j - i);
/*Getting the path and base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the names 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));
}
/**
* It obtains all the necesary information from the configuration file.<br>
* First of all it reads the name of the input data-sets, training, validation and test.<br>
* Then it reads the name of the output files, where the training (validation) and test outputs
* will be stored<br>
* Finally it read the parameters of the algorithm, such as the random seed.<br>
*
* @param fileName Name of the configuration file
*/
public void parseConfigurationFile(String fileName) {
StringTokenizer line;
String file = Fichero.leeFichero(fileName); // file is an string containing the whole file
line = new StringTokenizer(file, "\n\r");
readName(line); // We read the algorithm name
readInputFiles(line); // We read all the input files
readOutputFiles(line); // We read all the output files
readAllParameters(line); // We read all the possible parameters
};
public static void main(String[] args) {
String cadena = "";
StringTokenizer lineas, tokens, tokensT;
String linea, dato1, dato2, token, timesplit[];
boolean algorithms = true;
Vector<String> algoritmos;
Vector<String> datasets;
Vector<Integer> saltos;
int i, j, k, l, m, n;
double accuracyAv[][];
double accuracySD[][];
double reductionAv[][];
double reductionSD[][];
double kappaAv[][];
double kappaSD[][];
double accredAv[][];
double kapredAv[][];
double time[][];
int runs[][];
String alAct, alAnterior;
int configAct;
String datAct;
int salAct;
double acc, red, kappa;
int aciertos, total;
double accV[], redV[], kappaV[];
Vector<String> valoresClase;
int confusionMatrix[][];
int sumKappa, sumi, sumj;
double accSD, redSD, kapSD;
String tiempos;
int postime;
if (args.length != 1) {
System.err.println("Error. Hace falta un parámetro: Fichero de algoritmos y datasets.");
System.exit(1);
}
algoritmos = new Vector<String>();
datasets = new Vector<String>();
saltos = new Vector<Integer>();
/*Lectura del fichero de configuración*/
cadena = Fichero.leeFichero(args[0]);
lineas = new StringTokenizer(cadena, "\n\r");
while (lineas.hasMoreTokens()) {
linea = lineas.nextToken();
if (linea.equalsIgnoreCase("----")) algorithms = false;
else {
if (algorithms) {
algoritmos.addElement(new String(linea));
} else {
tokens = new StringTokenizer(linea, " ");
token = tokens.nextToken();
datasets.addElement(new String(token));
token = tokens.nextToken();
saltos.addElement(new Integer(token));
}
}
}
accuracyAv = new double[datasets.size()][algoritmos.size()];
accuracySD = new double[datasets.size()][algoritmos.size()];
reductionAv = new double[datasets.size()][algoritmos.size()];
reductionSD = new double[datasets.size()][algoritmos.size()];
kappaAv = new double[datasets.size()][algoritmos.size()];
kappaSD = new double[datasets.size()][algoritmos.size()];
accredAv = new double[datasets.size()][algoritmos.size()];
kapredAv = new double[datasets.size()][algoritmos.size()];
time = new double[datasets.size()][algoritmos.size()];
runs = new int[datasets.size()][algoritmos.size()];
accV = new double[10];
redV = new double[10];
kappaV = new double[10];
// tiempos = Fichero.leeFichero("tiempos.txt");
tiempos = Fichero.leeFichero("tiempos.txt");
tokensT = new StringTokenizer(tiempos, "=\n\r");
System.out.println("Numbero de algoritmos " + algoritmos.size());
System.out.println("Dataset Size = " + datasets.size());
/*Cálculo del accuracy, kappa y reducción en KNN (TRAIN)*/
configAct = 0;
for (j = 0; j < datasets.size(); j++) {
System.out.println("Processing dataset: " + datasets.elementAt(j));
for (i = 0; i < algoritmos.size(); i++) {
alAct = (String) algoritmos.elementAt(i);
configAct = i;
datAct = (String) datasets.elementAt(j);
salAct = ((Integer) saltos.elementAt(j)).intValue();
acc = red = kappa = 0.0;
for (k = 0; k < 10; k++) {
/*Accuracy Computation*/
int result =
(j + salAct) * algoritmos.size() * 10
+ i * 10
+ k; // Calculo el result correspondiente.
// System.out.println("results//Clas-KNN//"+alAct+"//result"+Integer.toString((j+salAct)*datasets.size()+i*10+k)+".tra");
cadena =
Fichero.leeFichero(
"results//Clas-KNN//" + alAct + "//result" + Integer.toString(result) + ".tra");
lineas = new StringTokenizer(cadena, "\n\r");
aciertos = total = 0;
while (lineas.hasMoreTokens()) {
linea = lineas.nextToken();
if (!(linea.startsWith("@"))) {
tokens = new StringTokenizer(linea, " ");
dato1 = tokens.nextToken();
dato2 = tokens.nextToken();
if (dato1.equalsIgnoreCase(dato2)) {
aciertos++;
total++;
} else {
total++;
}
}
}
accV[k] = (double) aciertos / (double) total;
// System.out.println(accV[k]);
/*Reduction Computation*/
cadena =
Fichero.leeFichero(
"results//Clas-KNN//" + alAct + "//result" + Integer.toString(result) + "e0.txt");
tokens = new StringTokenizer(cadena, "=\n\r");
tokens.nextToken();
dato1 = tokens.nextToken();
redV[k] = Double.parseDouble(dato1.substring(1)) / 100.0;
/*Kappa Computation*/
cadena =
Fichero.leeFichero(
"results//Clas-KNN//" + alAct + "//result" + Integer.toString(result) + ".tra");
System.out.println("Result" + result);
;
lineas = new StringTokenizer(cadena, "\n\r");
aciertos = total = 0;
valoresClase = new Vector<String>();
while (lineas.hasMoreTokens()) {
linea = lineas.nextToken();
if (!(linea.startsWith("@"))) {
tokens = new StringTokenizer(linea, " ");
dato1 = tokens.nextToken();
if (!valoresClase.contains(dato1)) {
valoresClase.addElement(dato1);
}
}
}
confusionMatrix = new int[valoresClase.size()][valoresClase.size()];
lineas = new StringTokenizer(cadena, "\n\r");
total = 0;
while (lineas.hasMoreTokens()) {
linea = lineas.nextToken();
if (!(linea.startsWith("@"))) {
tokens = new StringTokenizer(linea, " ");
dato1 = tokens.nextToken();
dato2 = tokens.nextToken();
try {
confusionMatrix[valoresClase.indexOf(dato1)][valoresClase.indexOf(dato2)]++;
} catch (ArrayIndexOutOfBoundsException e) {
confusionMatrix[valoresClase.indexOf(dato1)][0]++;
}
total++;
}
}
sumKappa = 0;
for (l = 0; l < valoresClase.size(); l++) {
sumKappa += confusionMatrix[l][l];
}
kappa = total * sumKappa;
sumKappa = 0;
for (l = 0; l < valoresClase.size(); l++) {
sumi = 0;
for (m = 0; m < valoresClase.size(); m++) {
sumi += confusionMatrix[l][m];
}
sumj = 0;
for (m = 0; m < valoresClase.size(); m++) {
sumj += confusionMatrix[m][l];
}
sumKappa += sumi * sumj;
}
kappaV[k] = (double) (kappa - sumKappa) / (double) (total * total - sumKappa);
// System.out.println(accV[k]);
}
acc = 0;
for (l = 0; l < accV.length; l++) {
acc += accV[l];
}
acc /= (double) accV.length;
red = 0;
for (l = 0; l < redV.length; l++) {
red += redV[l];
}
red /= (double) redV.length;
kappa = 0;
for (l = 0; l < kappaV.length; l++) {
kappa += kappaV[l];
}
kappa /= (double) kappaV.length;
accSD = redSD = kapSD = 0;
for (l = 0; l < accV.length; l++) {
accSD += (acc - accV[l]) * (acc - accV[l]);
}
accSD = Math.sqrt(accSD / (double) accV.length);
for (l = 0; l < redV.length; l++) {
redSD += (red - redV[l]) * (red - redV[l]);
}
redSD = Math.sqrt(redSD / (double) redV.length);
for (l = 0; l < kappaV.length; l++) {
kapSD += (kappa - kappaV[l]) * (kappa - kappaV[l]);
}
kapSD = Math.sqrt(kapSD / (double) kappaV.length);
/*Store the values in the main matrixes*/
accuracyAv[j][i] = acc;
accuracySD[j][i] = accSD;
reductionAv[j][i] = red;
reductionSD[j][i] = redSD;
kappaAv[j][i] = kappa;
kappaSD[j][i] = kapSD;
accredAv[j][i] = acc * red;
kapredAv[j][i] = kappa * red;
// System.out.println("Acc =" +acc);
/*Time processing*/
/*
for( k=0; k< 10 ;k++){
dato1 = tokensT.nextToken();
time[j][i] += Double.parseDouble(dato1) ;
runs[j][i]++;
//System.out.println(dato1);
}*/
}
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaAccuracyTRA.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena +=
String.format("%6.4f", accuracyAv[i][j])
+ "\t"
+ String.format("%6.4f", accuracySD[i][j])
+ "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaAccuracyTRA.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaReduction.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena +=
String.format("%6.4f", reductionAv[i][j])
+ "\t"
+ String.format("%6.4f", reductionSD[i][j])
+ "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaReduction.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaTime.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena += String.format("%6.4f", time[i][j] / (double) runs[i][j]) + "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaTime.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaKappaTRA.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena +=
String.format("%6.4f", kappaAv[i][j])
+ "\t"
+ String.format("%6.4f", kappaSD[i][j])
+ "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaKappaTRA.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaAccRedTRA.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena += String.format("%6.4f", accredAv[i][j]) + "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaAccRedTRA.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaKappaRedTRA.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena += String.format("%6.4f", kapredAv[i][j]) + "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaKappaRedTRA.txt", cadena);
}
configAct = 0;
/*Cálculo del accuracy, kappa y reducción en KNN (TST)*/
for (j = 0; j < datasets.size(); j++) {
for (i = 0; i < algoritmos.size(); i++) {
alAct = (String) algoritmos.elementAt(i);
// System.out.println("Processing algorithm: " + alAct);
salAct = ((Integer) saltos.elementAt(j)).intValue();
acc = red = kappa = 0.0;
for (k = 0; k < 10; k++) {
/*Accuracy Computation*/
int result =
(j + salAct) * algoritmos.size() * 10
+ i * 10
+ k; // Calculo el result correspondiente.
cadena =
Fichero.leeFichero(
"results//Clas-KNN//" + alAct + "//result" + Integer.toString(result) + ".tst");
lineas = new StringTokenizer(cadena, "\n\r");
aciertos = total = 0;
while (lineas.hasMoreTokens()) {
linea = lineas.nextToken();
if (!(linea.startsWith("@"))) {
tokens = new StringTokenizer(linea, " ");
dato1 = tokens.nextToken();
dato2 = tokens.nextToken();
if (dato1.equalsIgnoreCase(dato2)) {
aciertos++;
total++;
} else {
total++;
}
}
}
accV[k] = (double) aciertos / (double) total;
/*Reduction Computation*/
cadena =
Fichero.leeFichero(
"results//Clas-KNN//" + alAct + "//result" + Integer.toString(result) + "e0.txt");
tokens = new StringTokenizer(cadena, "=\n\r");
tokens.nextToken();
dato1 = tokens.nextToken();
redV[k] = Double.parseDouble(dato1.substring(1)) / 100.0;
/*Kappa Computation*/
cadena =
Fichero.leeFichero(
"results//Clas-KNN//" + alAct + "//result" + Integer.toString(result) + ".tst");
lineas = new StringTokenizer(cadena, "\n\r");
aciertos = total = 0;
valoresClase = new Vector<String>();
while (lineas.hasMoreTokens()) {
linea = lineas.nextToken();
if (!(linea.startsWith("@"))) {
tokens = new StringTokenizer(linea, " ");
dato1 = tokens.nextToken();
if (!valoresClase.contains(dato1)) {
valoresClase.addElement(dato1);
}
}
}
confusionMatrix = new int[valoresClase.size()][valoresClase.size()];
lineas = new StringTokenizer(cadena, "\n\r");
total = 0;
while (lineas.hasMoreTokens()) {
linea = lineas.nextToken();
if (!(linea.startsWith("@"))) {
tokens = new StringTokenizer(linea, " ");
dato1 = tokens.nextToken();
dato2 = tokens.nextToken();
try {
confusionMatrix[valoresClase.indexOf(dato1)][valoresClase.indexOf(dato2)]++;
} catch (ArrayIndexOutOfBoundsException e) {
confusionMatrix[valoresClase.indexOf(dato1)][0]++;
}
total++;
}
}
sumKappa = 0;
for (l = 0; l < valoresClase.size(); l++) {
sumKappa += confusionMatrix[l][l];
}
kappa = total * sumKappa;
sumKappa = 0;
for (l = 0; l < valoresClase.size(); l++) {
sumi = 0;
for (m = 0; m < valoresClase.size(); m++) {
sumi += confusionMatrix[l][m];
}
sumj = 0;
for (m = 0; m < valoresClase.size(); m++) {
sumj += confusionMatrix[m][l];
}
sumKappa += sumi * sumj;
}
kappaV[k] = (double) (kappa - sumKappa) / (double) (total * total - sumKappa);
}
acc = 0;
for (l = 0; l < accV.length; l++) {
acc += accV[l];
}
acc /= (double) accV.length;
red = 0;
for (l = 0; l < redV.length; l++) {
red += redV[l];
}
red /= (double) redV.length;
kappa = 0;
for (l = 0; l < kappaV.length; l++) {
kappa += kappaV[l];
}
kappa /= (double) kappaV.length;
accSD = redSD = kapSD = 0;
for (l = 0; l < accV.length; l++) {
accSD += (acc - accV[l]) * (acc - accV[l]);
}
accSD = Math.sqrt(accSD / (double) accV.length);
for (l = 0; l < redV.length; l++) {
redSD += (red - redV[l]) * (red - redV[l]);
}
redSD = Math.sqrt(redSD / (double) redV.length);
for (l = 0; l < kappaV.length; l++) {
kapSD += (kappa - kappaV[l]) * (kappa - kappaV[l]);
}
kapSD = Math.sqrt(kapSD / (double) kappaV.length);
/*Store the values in the main matrixes*/
accuracyAv[j][i] = acc;
accuracySD[j][i] = accSD;
reductionAv[j][i] = red;
reductionSD[j][i] = redSD;
kappaAv[j][i] = kappa;
kappaSD[j][i] = kapSD;
accredAv[j][i] = acc * red;
kapredAv[j][i] = kappa * red;
}
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaAccuracyTST.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena +=
String.format("%6.4f", accuracyAv[i][j])
+ "\t"
+ String.format("%6.4f", accuracySD[i][j])
+ "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaAccuracyTST.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaKappaTST.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena +=
String.format("%6.4f", kappaAv[i][j])
+ "\t"
+ String.format("%6.4f", kappaSD[i][j])
+ "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaKappaTST.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaAccRedTST.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena += String.format("%6.4f", accredAv[i][j]) + "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaAccRedTST.txt", cadena);
}
/*Print the results*/
cadena = "Datasets\t\t";
for (i = 0; i < algoritmos.size(); i++) {
cadena += algoritmos.elementAt(i) + "\t\t";
}
cadena += "\n";
Fichero.escribeFichero("tablaKappaRedTST.txt", cadena);
for (i = 0; i < datasets.size(); i++) {
cadena = datasets.elementAt(i) + "\t\t";
for (j = 0; j < algoritmos.size(); j++) {
cadena += String.format("%6.4f", kapredAv[i][j]) + "\t";
}
cadena += "\n";
Fichero.AnadirtoFichero("tablaKappaRedTST.txt", cadena);
}
System.out.println("FIN");
}
/** Reads the data of the configuration file */
public void leer_conf() {
int i, j;
String cadenaEntrada, valor;
double cruce, mutacion, a, b, tau;
int long_poblacion, tipo_fitness;
// 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();
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(Quita_blancos(valor));
Randomize.setSeed((long) semilla);
;
// we read the Number of Iterations
sT.nextToken();
valor = sT.nextToken();
n_generaciones = Long.parseLong(Quita_blancos(valor));
// we read the Population Size
sT.nextToken();
valor = sT.nextToken();
long_poblacion = Integer.parseInt(Quita_blancos(valor));
// we read the Tau parameter for the minimun maching degree required to the KB
sT.nextToken();
valor = sT.nextToken();
tau = Double.parseDouble(Quita_blancos(valor));
// we read the Parameter a
sT.nextToken();
valor = sT.nextToken();
a = Double.parseDouble(Quita_blancos(valor));
// we read the Parameter b
sT.nextToken();
valor = sT.nextToken();
b = Double.parseDouble(Quita_blancos(valor));
// we read the Type of Fitness Function
sT.nextToken();
valor = sT.nextToken();
tipo_fitness = Integer.parseInt(Quita_blancos(valor));
// we read the Cross Probability
sT.nextToken();
valor = sT.nextToken();
cruce = Double.parseDouble(Quita_blancos(valor));
// we read the Mutation Probability
sT.nextToken();
valor = sT.nextToken();
mutacion = Double.parseDouble(Quita_blancos(valor));
// we create all the objects
tipoc = 1;
tabla = new MiDataset(fich_datos_chequeo, true);
if (tabla.salir == false) {
base_reglas = new BaseR(fichero_br, tabla);
fun_adap = new Adap(tabla, base_reglas, tau, tipo_fitness);
alg_gen = new AG(long_poblacion, base_reglas.n_reglas, cruce, mutacion, a, b, fun_adap);
}
}
/**
* Reads configuration script, and extracts its contents.
*
* @param ficheroScript Name of the configuration script
*/
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 names 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++) ;
ficheroTest = new String(line, i, j - i);
/*Getting the path and base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the names 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 mutation and cross probability*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
pMutacion1to0 = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
pMutacion0to1 = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
pCruce = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the size of the population and 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 = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the alfa equilibrate factor*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
alfa = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the type of selection*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
token = tokens.nextToken();
token = token.substring(1);
if (token.equalsIgnoreCase("binary_tournament")) torneo = true;
else torneo = false;
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;
} // end-method
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 names of the 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++) ;
ficheroTest = new String(line, i, j - i);
/*Getting the path and base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the names 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 number of neighbours*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
k = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the k' */
linea = lineasFichero.nextToken();
tokens = new StringTokenizer(linea, "=");
tokens.nextToken();
k2 = 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;
}
/** Reads the data of the configuration file */
public void leer_conf() {
int i, n_etiquetas;
String cadenaEntrada, valor;
// 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();
// 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();
fichero_inf = ((ficheros.nextToken()).replace('\"', ' ')).trim();
ruta_salida = fichero_reglas.substring(0, fichero_reglas.lastIndexOf('/') + 1);
// we read the Number of labels
sT.nextToken();
valor = sT.nextToken();
n_etiquetas = Integer.parseInt(valor.trim());
// we read the KB Output File Format with Weight values to 1 (0/1)
peso = 0;
// we create all the objects
tabla = new MiDataset(fich_datos_chequeo, true);
tabla_tst = new MiDataset(fich_datos_tst, false);
base_datos = new BaseD(n_etiquetas, tabla.n_variables);
for (i = 0; i < tabla.n_variables; i++) {
base_datos.n_etiquetas[i] = n_etiquetas;
base_datos.extremos[i].min = tabla.extremos[i].min;
base_datos.extremos[i].max = tabla.extremos[i].max;
}
MaxReglas = tabla.long_tabla; // MaxReglas == #Ejemplos
base_reglas = new BaseR(MaxReglas, base_datos, tabla);
fun_adap = new Adap(tabla, tabla_tst, base_reglas);
Regla_act = new int[tabla.n_variables];
grado_pertenencia = new double[tabla.n_variables];
Conjunto_Reglas = new TipoRegla[tabla.long_tabla];
for (i = 0; i < tabla.long_tabla; i++) {
Conjunto_Reglas[i] = new TipoRegla(tabla.n_variables);
}
}
