private void config_read(String fileParam) { parseParameters parameters; parameters = new parseParameters(); parameters.parseConfigurationFile(fileParam); input_train_name = parameters.getTrainingInputFile(); // input_validation_name = parameters.getValidationInputFile(); input_test_name = parameters.getTestInputFile(); output_train_name = parameters.getTrainingOutputFile(); output_test_name = parameters.getTestOutputFile(); seed = Long.parseLong(parameters.getParameter(0)); kernelType = parameters.getParameter(1); C = Double.parseDouble(parameters.getParameter(2)); eps = Double.parseDouble(parameters.getParameter(3)); degree = Integer.parseInt(parameters.getParameter(4)); gamma = Double.parseDouble(parameters.getParameter(5)); coef0 = Double.parseDouble(parameters.getParameter(6)); nu = Double.parseDouble(parameters.getParameter(7)); p = Double.parseDouble(parameters.getParameter(8)); shrinking = Integer.parseInt(parameters.getParameter(9)); }
/** * 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)); // ... }