/**
* 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 Algorithm(parseParameters parameters) {
train = new myDataset();
val = new myDataset();
test = new myDataset();
try {
System.out.println("\nReading the training set: " + parameters.getTrainingInputFile());
train.readRegressionSet(parameters.getTrainingInputFile(), true);
System.out.println("\nReading the validation set: " + parameters.getValidationInputFile());
val.readRegressionSet(parameters.getValidationInputFile(), false);
System.out.println("\nReading the test set: " + parameters.getTestInputFile());
test.readRegressionSet(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();
outputBC = parameters.getOutputFile(0);
// Now we parse the parameters, for example:
semilla = Long.parseLong(parameters.getParameter(0));
// ...
tamPoblacion = Integer.parseInt(parameters.getParameter(1));
numGeneraciones = Integer.parseInt(parameters.getParameter(2));
numGenMigration = Integer.parseInt(parameters.getParameter(3));
Nr = Integer.parseInt(parameters.getParameter(4));
Nf = Integer.parseInt(parameters.getParameter(5));
K = Integer.parseInt(parameters.getParameter(6));
probMut = Double.parseDouble(parameters.getParameter(7));
entradas = train.getnInputs();
Poblacion = new ArrayList<Individual>(tamPoblacion);
for (int i = 0; i < tamPoblacion; i++) {
Individual indi = new Individual(entradas);
Poblacion.add(indi);
}
Poblacion2 = new ArrayList<Individual>(tamPoblacion);
Hijos = new ArrayList<Individual>(tamPoblacion / 2);
SistemaDifuso = new ArrayList<Individual>(Nr);
BestSistemaDifuso = new ArrayList<Individual>(Nr);
vectorNr = new int[Nr];
}
/**
* 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));
// ...
}