private void calculateRightNWrongN() {
int i, j, nData, evaClass;
boolean stop;
Rule rule;
nData = this.train.size();
for (j = 0; j < this.ruleBase.size(); j++) {
rule = this.ruleBase.get(j);
rule.setIni();
}
this.WrongNDefault = 0;
for (i = 0; i < nData; i++) {
for (j = 0; j < this.ruleBase.size(); j++) {
rule = this.ruleBase.get(j);
rule.matching(train.getExample(i));
}
this.sort();
stop = false;
for (j = 0; j < this.ruleBase.size() && !stop; j++) {
rule = this.ruleBase.get(j);
if (train.getOutputAsInteger(i) == rule.getClas()) {
rule.incrRightN();
stop = true;
} else rule.incrWrongN();
}
}
}
/** This function calculates the number of errors */
private void rateError() {
int n_errores, Prediction;
n_errores = 0;
for (int j = 0; j < train.size(); j++) {
Prediction = this.FRM_WR(train.getExample(j));
if (train.getOutputAsInteger(j) != Prediction) n_errores++;
}
this.fitness = n_errores;
}
/** Function to evaluate the whole rule base by using the training dataset */
public void evalua() {
int n_clasificados, Prediction;
n_clasificados = 0;
for (int j = 0; j < train.size(); j++) {
Prediction = this.FRM_WR(train.getExample(j));
if (train.getOutputAsInteger(j) == Prediction) n_clasificados++;
}
this.fitness = n_clasificados;
}
/**
* It generates the output file from a given dataset and stores it in a file
*
* @param dataset myDataset input dataset
* @param filename String the name of the file
* @param lanzar Chc the algorithm class
*/
private void doOutput(myDataset dataset, String filename, Chc lanzar) {
String output = new String("");
output = dataset.copyHeader(); // we insert the header in the outputa file
// We write the output for each example
for (int i = 0; i < dataset.getnData(); i++) {
// for regression:
output +=
dataset.getOutputAsReal(i)
+ " "
+ (double) this.regressionOutput(dataset.getExample(i), lanzar)
+ "\n";
}
Fichero.escribeFichero(filename, output);
}
/**
* It generates the output file from a given dataset and stores it in a file
*
* @param dataset myDataset input dataset
* @param filename String the name of the file
*/
private void doOutput(myDataset dataset, String filename) {
int i;
double fuerza;
String output = new String("");
output = dataset.copyHeader(); // we insert the header in the output file
for (i = 0; i < dataset.getnData(); i++) {
fuerza = Output_fuzzy_system(dataset.getExample(i));
output += (dataset.getOutputAsReal(i) + " " + fuerza + " " + "\n");
}
Files.writeFile(filename, output);
}
/**
* It generates the output file from a given dataset and stores it in a file
*
* @param dataset myDataset input dataset
* @param filename String the name of the file
* @return The classification accuracy
*/
private double doOutput(myDataset dataset, String filename) {
String output = new String("");
int hits = 0;
output = dataset.copyHeader(); // we insert the header in the output file
// We write the output for each example
for (int i = 0; i < dataset.getnData(); i++) {
// for classification:
String classOut = this.classificationOutput(dataset.getExample(i));
output += dataset.getOutputAsString(i) + " " + classOut + "\n";
if (dataset.getOutputAsString(i).equalsIgnoreCase(classOut)) {
hits++;
}
}
Files.writeFile(filename, output);
return (1.0 * hits / dataset.size());
}
/**
* It Evaluates the performance of the fuzzy system. The Mean Square Error (MSE) by training is
* used
*/
public double Evaluate_fuzzy_system() {
int i;
double result, suma, fuerza;
suma = 0.0;
for (i = 0; i < train.getnData(); i++) {
fuerza = Output_fuzzy_system(train.getExample(i));
suma += Math.pow(train.getOutputAsReal(i) - fuerza, 2.0);
}
result = suma / train.getnData();
/* We want to have a maximization problem so, we invert the error */
if (result != 0.0) {
result = 1.0 / result;
}
return (result);
}
/**
* It Evaluates the performance of the best evolved fuzzy system on test data. The Mean Square
* Error (MSE) is used
*
* @return double The MSE error in test data
*/
public double Evaluate_best_fuzzy_system_in_test() {
int i;
double result, suma, fuerza;
SistemaDifuso.clear();
for (i = 0; i < Nr; i++) {
Individual indi = new Individual(BestSistemaDifuso.get(i));
SistemaDifuso.add(indi);
}
suma = 0.0;
for (i = 0; i < test.getnData(); i++) {
fuerza = Output_fuzzy_system(test.getExample(i));
suma += Math.pow(test.getOutputAsReal(i) - fuerza, 2.0);
}
result = suma / test.getnData();
return (result);
}