/**
* Parameters Constructor
*
* @param dataBase Set of training data which is necessary to generate a rule
* @param train Training data set with information to construct the rule base (mainly, the
* training examples)
*/
public RuleBase(DataBase dataBase, myDataset train) {
this.ruleBase = new ArrayList<Rule>();
this.dataBase = dataBase;
this.train = train;
this.n_variables = dataBase.numVariables();
this.fitness = 0;
this.totalLabels = new int[this.n_variables];
for (int i = 0; i < this.n_variables; i++) this.totalLabels[i] = dataBase.numLabels(i);
}
/** It launches the algorithm */
public void execute() {
if (somethingWrong) { // We do not execute the program
System.err.println("An error was found, either the data-set has missing values.");
System.err.println(
"Please remove the examples with missing data or apply a MV preprocessing.");
System.err.println("Aborting the program");
// We should not use the statement: System.exit(-1);
} else {
// We do here the algorithm's operations
nClasses = train.getnClasses();
dataBase =
new DataBase(
train.getnInputs(), train.getRanges(), train.varNames(), train.getNominals());
Population pobl =
new Population(
train,
dataBase,
populationSize,
nRules,
crossProb,
ruleWeight,
combinationType,
inferenceType,
p_DC,
michProb);
pobl.Generation(this.nGenerations);
dataBase.writeFile(this.fileDB);
ruleBase = pobl.bestRB();
ruleBase.writeFile(this.fileBR);
// Finally we should fill the training and test output files
double accTra = doOutput(this.val, this.outputTr);
double accTst = doOutput(this.test, this.outputTst);
System.out.println("Accuracy obtained in training: " + accTra);
System.out.println("Accuracy obtained in test: " + accTst);
System.out.println("Algorithm Finished");
}
}
