OpenDataset.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.net;

import java.util.Vector;

import keel.Dataset.Attributes;
import keel.Dataset.InstanceSet;

/**
 * <p>
 * Dataset interface simplification
 * </p>
 * @author Written by Nicolas Garcia Pedrajas (University of Cordoba) 27/02/2007
 * @version 0.1
 * @since JDK1.5
 */
public class OpenDataset {

    /** Number of examples*/
    private int ndatos;

    /** Number of variables */
    private int nvariables;

    /** Number of inputs */
    private int nentradas;

    /**  Number of outputs */
    private int nsalidas;

    /** Number of classes */
    private int nclases;

    /** Set of data instances */
    private InstanceSet IS;

    /**
     * <p>
     * Gets the number of examples
     * </p>
     * @return number of examples
     */
    public int getndatos() {
        return ndatos;
    }

    /**
     * <p>
     * Gets the number of variables
     * </p>
     * @return number of variables
     */
    public int getnvariables() {
        return nvariables;
    }

    /**
     * <p>
     * Gets the number of inputs
     * </p>
     * @return number of inputs
     */
    public int getnentradas() {
        return nentradas;
    }

    /**
     * <p>
     * Gets the number of outputs
     * </p>
     * @return number of outputs
     */
    public int getnsalidas() {
        return nsalidas;
    }

    /**
     * <p>
     * Return type (0 nominal, 1 integer, 2 float,..) of attribute at index.
     * </p>
     * @param index
     * @return type of attribute.
     */
    public int getTiposAt(int index) {
        return Attributes.getAttribute(index).getType();
    }

    /**
     * <p>
     * It returns the nominal value list of an attribute
     * </p>
     * @param index Index of the attribute
     * @return Nominal value list
     */
    public Vector getRangosVar(int index) {
        return Attributes.getAttribute(index).getNominalValuesList();
    }

    /**
     * <p>
     * It returns the list of values for the output class
     * </p>
     * @return Vector list of values for the output class
     */
    public Vector getRangeOutput(){
        return Attributes.getOutputAttribute(0).getNominalValuesList();
    }

    /**
     * <p>
     * Return example data at index in a string separated by comma without spaces
     * </p>
     * @param index Index of the instance
     * @return String representation of the instance
     */
    public String getDatosAt(int index) {
        return IS.getInstance(index).toString();
    }

    /**
     * <p>
     * Empty Constructor
     * </p>
     */
    public OpenDataset() {
        // Init a new set of instances
        IS = new InstanceSet();
    }

    /**
     * <p>
     * Load a file and parse it
     * </p>
     * @param nfejemplos Name of the file
     * @param b Is a training file?
     */
    public void processClassifierDataset(String nfejemplos, boolean b) {

        try {
            // Load in memory a dataset that contains a classification problem
            IS.readSet(nfejemplos, b);
            ndatos = IS.getNumInstances();
            nentradas = Attributes.getInputNumAttributes();
            nvariables = nentradas + Attributes.getOutputNumAttributes();
            nsalidas = Attributes.getOutputNumAttributes();

            // Check that there is only one output variable and
            // it is nominal

            if (Attributes.getOutputNumAttributes() > 1) {
                System.out.println(
                        "This algorithm can not process MIMO datasets");
                System.out.println(
                        "All outputs but the first one will be removed");
            }

            boolean noOutputs = false;
            if (Attributes.getOutputNumAttributes() < 1) {
                System.out
                        .println(
                        "This algorithm can not process datasets without outputs");
                System.out.println("Zero-valued output generated");
                noOutputs = true;
            }

        } catch (Exception e) {
            System.out.println("DBG: Exception in readSet");
            e.printStackTrace();
            System.exit( -1);
        }

    }
    
    /**
     * Returns the InstanceSet associated
     * @return the InstanceSet used by this object
     */
    public InstanceSet getInstaceSet(){
    	return IS;
    }
}