/**
* It returns the algorithm classification output given an input example
*
* @param example double[] The input example
* @return String the output generated by the algorithm
*/
private String classificationOutput(double[] example) {
String output = new String("?");
/**
* Here we should include the algorithm directives to generate the classification output from
* the input example
*/
int clas = ruleBase.FRM(example);
if (clas >= 0) {
output = train.getOutputValue(clas);
}
return output;
}
@SuppressWarnings("rawtypes")
boolean evalRules(Object asset) {
StatelessSession session = ruleBase.newStatelessSession();
StatelessSessionResult result = session.executeWithResults(asset);
java.util.Iterator objects = result.iterateObjects();
while (objects.hasNext()) {
if (objects.next() instanceof Allow) {
return true;
}
}
return false;
}
/** 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");
}
}
/** This method is for debug */
public void debug() {
R.debug();
}
/**
* This method defuzzified the output and return a value
*
* @param x The output
* @return
*/
public double output(double[] x) {
return R.defuzzify(R.output(x), defuzType);
}
/**
* This method assign the values of the fuzzy rule to another one
*
* @param i The position of the rule
* @param b A fuzzy rule
*/
void setComponent(int i, FuzzyRule b) {
R.setComponent(i, b.clone());
}
/**
* This method obtain the consequent and the weight of the rule
*
* @param n The position of the rule
* @return
*/
FuzzyRule getComponent(int n) {
return R.getComponent(n);
}
/**
* This method return the number of consequents of the fuzzy model
*
* @return The number of consequents
*/
int numConsequents() {
return R.numConsequents();
}
/**
* This methos return the size of the fuzzy model
*
* @return The size of the fuzzy model
*/
int size() {
return R.size();
}
