EnsembleParameters.java

/***********************************************************************

	This file is part of KEEL-software, the Data Mining tool for regression, 
	classification, clustering, pattern mining and so on.

	Copyright (C) 2004-2010
	
	F. Herrera (herrera@decsai.ugr.es)
    L. S�nchez (luciano@uniovi.es)
    J. Alcal�-Fdez (jalcala@decsai.ugr.es)
    S. Garc�a (sglopez@ujaen.es)
    A. Fern�ndez (alberto.fernandez@ujaen.es)
    J. Luengo (julianlm@decsai.ugr.es)

	This program is free software: you can redistribute it and/or modify
	it under the terms of the GNU General Public License as published by
	the Free Software Foundation, either version 3 of the License, or
	(at your option) any later version.

	This program is distributed in the hope that it will be useful,
	but WITHOUT ANY WARRANTY; without even the implied warranty of
	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
	GNU General Public License for more details.

	You should have received a copy of the GNU General Public License
	along with this program.  If not, see http://www.gnu.org/licenses/
  
**********************************************************************/

package keel.Algorithms.Neural_Networks.ensemble;

import keel.Algorithms.Neural_Networks.net.Parameters;

import java.io.InputStream;
import java.util.Properties;
import java.io.FileInputStream;
import java.io.IOException;

import org.core.Randomize;

/**
 * <p>
 * Class representing the parameters of an ensemble
 * </p>
 * @author Written by Nicolas Garcia Pedrajas (University of Cordoba) 27/02/2007
 * @version 0.1
 * @since JDK1.5
 */
public class EnsembleParameters extends Parameters {

    /** Type of sample (NONE | ADA | ARCING | BAGGING) */
    String sampling;

    /** Ensemble method (GEM | BEM) */
    String ensemble_method;

    /** Ensemble combination (WEIGHTED | SUM | MAJORITY | VOTING) */
    String combination;

    /** No of networks */
    int n_networks;

    /**
     * <p>
     * Empty constructor
     * </p>
     */
    public EnsembleParameters() {
    }

    /**
     * {@inheritDoc}
     */
    public void LoadParameters(String file_name) {
        InputStream paramsFile;
        String line;
        int pos2;

        Properties props = new Properties();

        try {
            paramsFile = new FileInputStream(file_name);
            props.load(paramsFile);
            paramsFile.close();
        } catch (IOException ioe) {
            System.out.println("I/O Exception.");
            System.exit(0);
        }

        // Load global parameters
        Nhidden_layers = Integer.parseInt(props.getProperty("Hidden_layers"));
        Nhidden = new int[Nhidden_layers + 1];
        line = props.getProperty("Hidden_nodes");

        System.out.println(line);

        // Number of nodes per layer
        int j = 0;
        int pos1 = 0;
        do {
            pos2 = line.indexOf(" ", pos1);
            if (pos2 != -1) {
                Nhidden[j] = Integer.parseInt(line.substring(pos1, pos2));
                pos1 = pos2 + 1;
                j++;
            } else {
                Nhidden[j] = Integer.parseInt(line.substring(pos1));
                j++;
            }
        } while (pos2 != -1 && j < Nhidden_layers);

        for (int i = j; i < Nhidden_layers - 1; i++) {
            Nhidden[i] = Nhidden[j - 1];
        }
        Nhidden[Nhidden_layers - 1] = Integer.parseInt(line.substring(pos1));

        // Learning coefficients
        eta = Double.parseDouble(props.getProperty("Eta"));
        alpha = Double.parseDouble(props.getProperty("Alpha"));
        lambda = Double.parseDouble(props.getProperty("Lambda"));

        // Transfer functions per layer
        transfer = new String[Nhidden_layers + 1];
        line = props.getProperty("Transfer");
        j = pos1 = 0;
        do {
            pos2 = line.indexOf(" ", pos1);
            if (pos2 != -1) {
                transfer[j] = line.substring(pos1, pos2);
                pos1 = pos2 + 1;
                j++;
            } else {
                transfer[j] = line.substring(pos1);
                j++;
            }

        } while (pos2 != -1 && j < Nhidden_layers);

        for (int i = j; i < Nhidden_layers; i++) {
            transfer[i] = transfer[j - 1];
        }
        transfer[Nhidden_layers] = line.substring(pos1);

        // threshold = Double.parseDouble(props.getProperty("Threshold"));
        test_data = Boolean.valueOf(props.getProperty("Test_data")).
                    booleanValue();
        val_data = Boolean.valueOf(props.getProperty("Validation_data")).
                   booleanValue();
        line = props.getProperty("inputData");
        pos1 = line.indexOf("\"", 0);
        pos2 = line.indexOf("\"", pos1 + 1);
        train_file = line.substring(pos1 + 1, pos2);
        pos1 = line.indexOf("\"", pos2 + 1);
        pos2 = line.indexOf("\"", pos1 + 1);
        val_file = line.substring(pos1 + 1, pos2);
        pos1 = line.indexOf("\"", pos2 + 1);
        pos2 = line.indexOf("\"", pos1 + 1);
        test_file = line.substring(pos1 + 1, pos2);

        line = props.getProperty("outputData");
        pos1 = line.indexOf("\"", 0);
        pos2 = line.indexOf("\"", pos1 + 1);
        train_output = line.substring(pos1 + 1, pos2);

        pos1 = line.indexOf("\"", pos2 + 1);
        pos2 = line.indexOf("\"", pos1 + 1);
        test_output = line.substring(pos1 + 1, pos2);
        
        pos1 = line.indexOf("\"", pos2 + 1);
        pos2 = line.indexOf("\"", pos1 + 1);
        model_output = line.substring(pos1 + 1, pos2);

        cross_validation = Boolean.valueOf(props.getProperty("Crossvalidation")).
                           booleanValue();
        cycles = Integer.parseInt(props.getProperty("Cycles"));
        improve = Double.parseDouble(props.getProperty("Improve"));
        seed = Long.parseLong(props.getProperty("seed"));
        if (seed != -1) {
            Randomize.setSeed(seed);
        }
        problem = props.getProperty("Problem");
        // bp_type = props.getProperty("BPtype");
        bp_type = "BPstd";
        tipify_inputs = Boolean.valueOf(props.getProperty("Tipify_inputs")).
                        booleanValue();
        verbose = Boolean.valueOf(props.getProperty("Verbose")).booleanValue();
        save = Boolean.valueOf(props.getProperty("SaveAll")).booleanValue();
        n_networks = Integer.parseInt(props.getProperty("Networks"));
        sampling = props.getProperty("Sampling");
        ensemble_method = props.getProperty("Ensemble_method");
        combination = props.getProperty("Combination");
    }

}