/**
* 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 double doOutput(myDataset dataset, String filename, boolean test) {
String output = new String("");
confusionMatrix = new int[dataset.getnClasses()][dataset.getnClasses()];
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++) {
String clReal = dataset.getOutputAsString(i);
String clPred = classificationOutput(i, test);
confusionMatrix[dataset.getOutputAsInteger(i)][dataset.numericClass(clPred)]++;
output += clReal + " " + clPred + "\n";
}
double acc = 0;
int nClasses = 0;
for (int i = 0; i < confusionMatrix.length; i++) {
int count = 0;
for (int j = 0; j < confusionMatrix[i].length; j++) {
count += confusionMatrix[i][j];
}
if (count > 0) {
acc += 1.0 * confusionMatrix[i][i] / count;
nClasses++;
}
}
Files.writeFile(filename, output);
return acc / nClasses;
// return 1.0*hits/dataset.size();
}
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 launches the algorithm */
public void execute() {
int i, j, k, l;
int t;
int ele;
double prob[];
double aux;
double NUmax = 1.5; // used for lineal ranking
double NUmin = 0.5; // used for lineal ranking
double pos1, pos2;
int sel1, sel2;
int data[][];
int infoAttr[];
int classData[];
Vector<Rule> contenedor = new Vector<Rule>();
Vector<Rule> conjR = new Vector<Rule>();
Rule tmpRule;
Condition tmpCondition[] = new Condition[1];
RuleSet population[];
RuleSet hijo1, hijo2;
if (somethingWrong) { // We do not execute the program
System.err.println("An error was found, the data-set has numerical values.");
System.err.println("Aborting the program");
// We should not use the statement: System.exit(-1);
} else {
Randomize.setSeed(seed);
nClasses = train.getnClasses();
/*Build the nominal data information*/
infoAttr = new int[train.getnInputs()];
for (i = 0; i < infoAttr.length; i++) {
infoAttr[i] = train.numberValues(i);
}
data = new int[train.getnData()][train.getnInputs()];
for (i = 0; i < data.length; i++) {
for (j = 0; j < data[i].length; j++) {
if (train.isMissing(i, j)) data[i][j] = -1;
else data[i][j] = train.valueExample(i, j);
}
}
classData = new int[train.getnData()];
for (i = 0; i < classData.length; i++) {
classData[i] = train.getOutputAsInteger(i);
}
/*Find first-order rules which result interesting*/
for (i = 0; i < nClasses; i++) {
for (j = 0; j < infoAttr.length; j++) {
for (k = 0; k < infoAttr[j]; k++) {
tmpCondition[0] = new Condition(j, k);
tmpRule = new Rule(tmpCondition);
if (Math.abs(computeAdjustedResidual(data, classData, tmpRule, i)) > 1.96) {
if (!contenedor.contains(tmpRule)) {
contenedor.add(tmpRule);
conjR.add(tmpRule);
}
}
}
}
}
// Construct the Baker selection roulette
prob = new double[popSize];
for (j = 0; j < popSize; j++) {
aux = (double) (NUmax - NUmin) * ((double) j / (popSize - 1));
prob[j] = (double) (1.0 / (popSize)) * (NUmax - aux);
}
for (j = 1; j < popSize; j++) prob[j] = prob[j] + prob[j - 1];
/*Steady-State Genetic Algorithm*/
ele = 2;
population = new RuleSet[popSize];
while (conjR.size() >= 2) {
t = 0;
System.out.println("Producing rules of level " + ele);
for (i = 0; i < population.length; i++) {
population[i] = new RuleSet(conjR);
population[i].computeFitness(data, classData, infoAttr, contenedor, nClasses);
}
Arrays.sort(population);
while (t < numGenerations && !population[0].equals(population[popSize - 1])) {
System.out.println("Generation " + t);
t++;
/*Baker's selection*/
pos1 = Randomize.Rand();
pos2 = Randomize.Rand();
for (l = 0; l < popSize && prob[l] < pos1; l++) ;
sel1 = l;
for (l = 0; l < popSize && prob[l] < pos2; l++) ;
sel2 = l;
hijo1 = new RuleSet(population[sel1]);
hijo2 = new RuleSet(population[sel2]);
if (Randomize.Rand() < pCross) {
RuleSet.crossover1(hijo1, hijo2);
} else {
RuleSet.crossover2(hijo1, hijo2);
}
RuleSet.mutation(hijo1, conjR, pMut, data, classData, infoAttr, contenedor, nClasses);
RuleSet.mutation(hijo2, conjR, pMut, data, classData, infoAttr, contenedor, nClasses);
hijo1.computeFitness(data, classData, infoAttr, contenedor, nClasses);
hijo2.computeFitness(data, classData, infoAttr, contenedor, nClasses);
population[popSize - 2] = new RuleSet(hijo1);
population[popSize - 1] = new RuleSet(hijo2);
Arrays.sort(population);
}
/*Decode function*/
ele++;
conjR.removeAllElements();
System.out.println(
"Fitness of the best chromosome in rule level " + ele + ": " + population[0].fitness);
for (i = 0; i < population[0].getRuleSet().length; i++) {
if (Math.abs(computeAdjustedResidual(data, classData, population[0].getRule(i), i))
> 1.96) {
if (validarRegla(population[0].getRule(i))
&& !contenedor.contains(population[0].getRule(i))) {
contenedor.add(population[0].getRule(i));
conjR.add(population[0].getRule(i));
}
}
}
}
// Finally we should fill the training and test output files
doOutput(this.val, this.outputTr, data, classData, infoAttr, contenedor, nClasses);
doOutput(this.test, this.outputTst, data, classData, infoAttr, contenedor, nClasses);
/*Print the rule obtained*/
for (i = contenedor.size() - 1; i >= 0; i--) {
if (reglaPositiva(
this.train, data, classData, infoAttr, nClasses, contenedor.elementAt(i))) {
Fichero.AnadirtoFichero(outputRule, contenedor.elementAt(i).toString(train));
Fichero.AnadirtoFichero(
outputRule,
" -> "
+ consecuente(
this.train, data, classData, infoAttr, nClasses, contenedor.elementAt(i))
+ "\n");
}
}
System.out.println("Algorithm Finished");
}
}
