vote.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

/*
 *    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.
 */

/*
 *    Vote.java
 *    Copyright (C) 2000 Alexander K. Seewald
 *
 */

package weka.classifiers.meta;

import weka.classifiers.MultipleClassifiersCombiner;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.SelectedTag;
import weka.core.Tag;
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 -->
 * Class for combining classifiers. Different combinations of probability estimates for classification are available.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc..<br/>
 * <br/>
 * J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(3):226-239.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -B &lt;classifier specification&gt;
 *  Full class name of classifier to include, followed
 *  by scheme options. May be specified multiple times.
 *  (default: "weka.classifiers.rules.ZeroR")</pre>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 * <pre> -R &lt;AVG|PROD|MAJ|MIN|MAX|MED&gt;
 *  The combination rule to use
 *  (default: AVG)</pre>
 * 
 <!-- options-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;book{Kuncheva2004,
 *    author = {Ludmila I. Kuncheva},
 *    publisher = {John Wiley and Sons, Inc.},
 *    title = {Combining Pattern Classifiers: Methods and Algorithms},
 *    year = {2004}
 * }
 * 
 * &#64;article{Kittler1998,
 *    author = {J. Kittler and M. Hatef and Robert P.W. Duin and J. Matas},
 *    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
 *    number = {3},
 *    pages = {226-239},
 *    title = {On combining classifiers},
 *    volume = {20},
 *    year = {1998}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 * @author Alexander K. Seewald ([e-mail])
 * @author Eibe Frank ([e-mail])
 * @author Roberto Perdisci ([e-mail])
 * @version $Revision: 1.12 $
 */
public class Vote
  extends MultipleClassifiersCombiner
  implements TechnicalInformationHandler {
    
  /** for serialization */
  static final long serialVersionUID = -637891196294399624L;
  
  /** combination rule: Average of Probabilities */
  public static final int AVERAGE_RULE = 1;
  /** combination rule: Product of Probabilities (only nominal classes) */
  public static final int PRODUCT_RULE = 2;
  /** combination rule: Majority Voting (only nominal classes) */
  public static final int MAJORITY_VOTING_RULE = 3;
  /** combination rule: Minimum Probability */
  public static final int MIN_RULE = 4;
  /** combination rule: Maximum Probability */
  public static final int MAX_RULE = 5;
  /** combination rule: Median Probability (only numeric class) */
  public static final int MEDIAN_RULE = 6;
  /** combination rules */
  public static final Tag[] TAGS_RULES = {
    new Tag(AVERAGE_RULE, "AVG", "Average of Probabilities"),
    new Tag(PRODUCT_RULE, "PROD", "Product of Probabilities"),
    new Tag(MAJORITY_VOTING_RULE, "MAJ", "Majority Voting"),
    new Tag(MIN_RULE, "MIN", "Minimum Probability"),
    new Tag(MAX_RULE, "MAX", "Maximum Probability"),
    new Tag(MEDIAN_RULE, "MED", "Median")
  };
  
  /** Combination Rule variable */
  protected int m_CombinationRule = AVERAGE_RULE;
  
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Class for combining classifiers. Different combinations of "
      + "probability estimates for classification are available.\n\n"
      + "For more information see:\n\n"
      + getTechnicalInformation().toString();
  }
  
  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Enumeration 	enm;
    Vector		result;
    
    result = new Vector();
    
    enm = super.listOptions();
    while (enm.hasMoreElements())
      result.addElement(enm.nextElement());

    result.addElement(new Option(
	"\tThe combination rule to use\n"
	+ "\t(default: AVG)",
	"R", 1, "-R " + Tag.toOptionList(TAGS_RULES)));
    
    return result.elements();
  }
  
  /**
   * Gets the current settings of Vote.
   *
   * @return an array of strings suitable for passing to setOptions()
   */
  public String [] getOptions() {
    int       	i;
    Vector    	result;
    String[]  	options;

    result = new Vector();

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

    result.add("-R");
    result.add("" + getCombinationRule());

    return (String[]) result.toArray(new String[result.size()]);
  }
  
  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -B &lt;classifier specification&gt;
   *  Full class name of classifier to include, followed
   *  by scheme options. May be specified multiple times.
   *  (default: "weka.classifiers.rules.ZeroR")</pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   * <pre> -R &lt;AVG|PROD|MAJ|MIN|MAX|MED&gt;
   *  The combination rule to use
   *  (default: AVG)</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 {
    String 	tmpStr;
    
    tmpStr = Utils.getOption('R', options);
    if (tmpStr.length() != 0) 
      setCombinationRule(new SelectedTag(tmpStr, TAGS_RULES));
    else
      setCombinationRule(new SelectedTag(AVERAGE_RULE, TAGS_RULES));

    super.setOptions(options);
  }

  /**
   * 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;
    TechnicalInformation 	additional;
    
    result = new TechnicalInformation(Type.BOOK);
    result.setValue(Field.AUTHOR, "Ludmila I. Kuncheva");
    result.setValue(Field.TITLE, "Combining Pattern Classifiers: Methods and Algorithms");
    result.setValue(Field.YEAR, "2004");
    result.setValue(Field.PUBLISHER, "John Wiley and Sons, Inc.");

    additional = result.add(Type.ARTICLE);
    additional.setValue(Field.AUTHOR, "J. Kittler and M. Hatef and Robert P.W. Duin and J. Matas");
    additional.setValue(Field.YEAR, "1998");
    additional.setValue(Field.TITLE, "On combining classifiers");
    additional.setValue(Field.JOURNAL, "IEEE Transactions on Pattern Analysis and Machine Intelligence");
    additional.setValue(Field.VOLUME, "20");
    additional.setValue(Field.NUMBER, "3");
    additional.setValue(Field.PAGES, "226-239");
    
    return result;
  }
  
  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // class
    if (    (m_CombinationRule == PRODUCT_RULE) 
	 || (m_CombinationRule == MAJORITY_VOTING_RULE) ) {
      result.disableAllClasses();
      result.disableAllClassDependencies();
      result.enable(Capability.NOMINAL_CLASS);
      result.enableDependency(Capability.NOMINAL_CLASS);
    }
    else if (m_CombinationRule == MEDIAN_RULE) {
      result.disableAllClasses();
      result.disableAllClassDependencies();
      result.enable(Capability.NUMERIC_CLASS);
      result.enableDependency(Capability.NUMERIC_CLASS);
    }
    
    return result;
  }

  /**
   * Buildclassifier selects a classifier from the set of classifiers
   * by minimising error on the training data.
   *
   * @param data the training data to be used for generating the
   * boosted classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    Instances newData = new Instances(data);
    newData.deleteWithMissingClass();
    
    for (int i = 0; i < m_Classifiers.length; i++) {
      getClassifier(i).buildClassifier(newData);
    }
  }

  /**
   * Classifies the given test instance.
   *
   * @param instance the instance to be classified
   * @return the predicted most likely class for the instance or 
   * Instance.missingValue() if no prediction is made
   * @throws Exception if an error occurred during the prediction
   */
  public double classifyInstance(Instance instance) throws Exception {
    double result;
    
    switch (m_CombinationRule) {
      case AVERAGE_RULE:
      case PRODUCT_RULE:
      case MAJORITY_VOTING_RULE:
      case MIN_RULE: