/**
* It reads the data from the input files (training, validation and test) and parse all the
* parameters from the parameters array.
*
* @param parameters parseParameters It contains the input files, output files and parameters
*/
public DMEL(parseParameters parameters) {
train = new myDataset();
val = new myDataset();
test = new myDataset();
try {
System.out.println("\nReading the training set: " + parameters.getTrainingInputFile());
train.readClassificationSet(parameters.getTrainingInputFile(), true);
System.out.println("\nReading the validation set: " + parameters.getValidationInputFile());
val.readClassificationSet(parameters.getValidationInputFile(), false);
System.out.println("\nReading the test set: " + parameters.getTestInputFile());
test.readClassificationSet(parameters.getTestInputFile(), false);
} catch (IOException e) {
System.err.println("There was a problem while reading the input data-sets: " + e);
somethingWrong = true;
}
// We may check if there are some numerical attributes, because our algorithm may not handle
// them:
somethingWrong = somethingWrong || train.hasRealAttributes();
outputTr = parameters.getTrainingOutputFile();
outputTst = parameters.getTestOutputFile();
outputRule = parameters.getOutputFile(0);
// Now we parse the parameters, for example:
seed = Long.parseLong(parameters.getParameter(0));
popSize = Integer.parseInt(parameters.getParameter(1));
pCross = Double.parseDouble(parameters.getParameter(2));
pMut = Double.parseDouble(parameters.getParameter(3));
numGenerations = Integer.parseInt(parameters.getParameter(4));
// ...
}
/**
* It reads the data from the input files (training, validation and test) and parse all the
* parameters from the parameters array.
*
* @param parameters parseParameters It contains the input files, output files and parameters
*/
public Fuzzy_Ish(parseParameters parameters) {
train = new myDataset();
val = new myDataset();
test = new myDataset();
try {
System.out.println("\nReading the training set: " + parameters.getTrainingInputFile());
train.readClassificationSet(parameters.getTrainingInputFile(), true);
System.out.println("\nReading the validation set: " + parameters.getValidationInputFile());
val.readClassificationSet(parameters.getValidationInputFile(), false);
System.out.println("\nReading the test set: " + parameters.getTestInputFile());
test.readClassificationSet(parameters.getTestInputFile(), false);
} catch (IOException e) {
System.err.println("There was a problem while reading the input data-sets: " + e);
somethingWrong = true;
}
// We may check if there are some numerical attributes, because our algorithm may not handle
// them:
// somethingWrong = somethingWrong || train.hasNumericalAttributes();
// somethingWrong = somethingWrong || train.hasMissingAttributes();
outputTr = parameters.getTrainingOutputFile();
outputTst = parameters.getTestOutputFile();
fileDB = parameters.getOutputFile(0);
fileBR = parameters.getOutputFile(1);
// Now we parse the parameters
long seed = Long.parseLong(parameters.getParameter(0));
// String aux = parameters.getParameter(1); //Computation of the compatibility degree
combinationType = PRODUCT;
/*if (aux.compareToIgnoreCase("minimum") == 0) {
combinationType = MINIMUM;
}
aux = parameters.getParameter(2);*/
ruleWeight = PCF_IV;
/*if (aux.compareToIgnoreCase("Certainty_Factor") == 0) {
ruleWeight = CF;
}
else if (aux.compareToIgnoreCase("Mansoory_Rule_Weight_System") == 0) {
ruleWeight = MCF;
}
else if (aux.compareToIgnoreCase("Average_Penalized_Certainty_Factor") == 0) {
ruleWeight = PCF_II;
}
aux = parameters.getParameter(3);*/
inferenceType = WINNING_RULE;
/*if (aux.compareToIgnoreCase("Additive_Combination") == 0) {
inferenceType = ADDITIVE_COMBINATION;
}*/
nRules = Integer.parseInt(parameters.getParameter(1));
if (nRules == 0) {
if (train.getnInputs() < 10) {
nRules = 5 * train.getnInputs(); // heuristic
} else {
nRules = 50;
}
}
while (nRules % 10 != 0) {
nRules++;
} // In order to have no problems with "n_replace"
if (nRules > train.getnData()) {
nRules = train.getnInputs() / 10;
nRules *= 10;
}
populationSize = Integer.parseInt(parameters.getParameter(2));
while (populationSize % 10 != 0) {
populationSize++;
}
crossProb = Double.parseDouble(parameters.getParameter(3));
this.nGenerations = Integer.parseInt(parameters.getParameter(4));
p_DC = Double.parseDouble(parameters.getParameter(5));
michProb = Double.parseDouble(parameters.getParameter(6));
Randomize.setSeed(seed);
}