waode.java

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/*
 * 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 2 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, write to the Free Software
 * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    WAODE.java
 *    Copyright 2006 Liangxiao Jiang
 */

package weka.classifiers.bayes;

import weka.classifiers.Classifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;

import java.util.Enumeration;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * WAODE contructs the model called Weightily Averaged One-Dependence Estimators.<br/>
 * <br/>
 * For more information, see<br/>
 * <br/>
 * L. Jiang, H. Zhang: Weightily Averaged One-Dependence Estimators. In: Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, 970-974, 2006.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;inproceedings{Jiang2006,
 *    author = {L. Jiang and H. Zhang},
 *    booktitle = {Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006},
 *    pages = {970-974},
 *    series = {LNAI},
 *    title = {Weightily Averaged One-Dependence Estimators},
 *    volume = {4099},
 *    year = {2006}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 * <pre> -I
 *  Whether to print some more internals.
 *  (default: no)</pre>
 * 
 <!-- options-end -->
 *
 * @author  Liangxiao Jiang ([e-mail])
 * @author  H. Zhang ([e-mail])
 * @version $Revision: 1.2 $
 */
public class WAODE 
  extends Classifier
  implements TechnicalInformationHandler {
  
  /** for serialization */
  private static final long serialVersionUID = 2170978824284697882L;

  /** The number of each class value occurs in the dataset */
  private double[] m_ClassCounts;
  
  /** The number of each attribute value occurs in the dataset */
  private double[] m_AttCounts;
  
  /** The number of two attributes values occurs in the dataset */
  private double[][] m_AttAttCounts;
  
  /** The number of class and two attributes values occurs in the dataset */
  private double[][][] m_ClassAttAttCounts;
  
  /** The number of values for each attribute in the dataset */
  private int[] m_NumAttValues;
  
  /** The number of values for all attributes in the dataset */
  private int m_TotalAttValues;
  
  /** The number of classes in the dataset */
  private int m_NumClasses;
  
  /** The number of attributes including class in the dataset */
  private int m_NumAttributes;
  
  /** The number of instances in the dataset */
  private int m_NumInstances;
  
  /** The index of the class attribute in the dataset */
  private int m_ClassIndex;
  
  /** The starting index of each attribute in the dataset */
  private int[] m_StartAttIndex;
  
  /** The array of mutual information between each attribute and class */
  private double[] m_mutualInformation;
  
  /** the header information of the training data */
  private Instances m_Header = null;
  
  /** whether to print more internals in the toString method
   * @see #toString() */
  private boolean m_Internals = false;

  /** a ZeroR model in case no model can be built from the data */
  private Classifier m_ZeroR;
  
  /**
   * Returns a string describing this classifier
   * 
   * @return 		a description of the classifier suitable for
   * 			displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "WAODE contructs the model called Weightily Averaged One-Dependence "
      + "Estimators.\n\n"
      + "For more information, see\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * Gets an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Vector result = new Vector();
    Enumeration enm = super.listOptions();
    while (enm.hasMoreElements())
      result.add(enm.nextElement());
      
    result.addElement(new Option(
	"\tWhether to print some more internals.\n"
	+ "\t(default: no)",
	"I", 0, "-I"));

    return result.elements();
  }


  /**
   * Parses a given list of options. <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   * <pre> -I
   *  Whether to print some more internals.
   *  (default: no)</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {
    super.setOptions(options);

    setInternals(Utils.getFlag('I', options));
  }

  /**
   * Gets the current settings of the filter.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {
    Vector        result;
    String[]      options;
    int           i;

    result = new Vector();

    options = super.getOptions();
    for (i = 0; i < options.length; i++)
      result.add(options[i]);

    if (getInternals())
      result.add("-I");

    return (String[]) result.toArray(new String[result.size()]);
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String internalsTipText() {
    return "Prints more internals of the classifier.";
  }

  /** 
   * Sets whether internals about classifier are printed via toString().
   *
   * @param value if internals should be printed
   * @see #toString()
   */
  public void setInternals(boolean value) {
    m_Internals = value;
  }

  /**
   * Gets whether more internals of the classifier are printed.
   *
   * @return true if more internals are printed
   */
  public boolean getInternals() {
    return m_Internals;
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation 	result;
    
    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR, "L. Jiang and H. Zhang");
    result.setValue(Field.TITLE, "Weightily Averaged One-Dependence Estimators");
    result.setValue(Field.BOOKTITLE, "Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006");
    result.setValue(Field.YEAR, "2006");
    result.setValue(Field.PAGES, "970-974");
    result.setValue(Field.SERIES, "LNAI");
    result.setValue(Field.VOLUME, "4099");

    return result;
  }