votedperceptron.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

/*
 *    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.
 */

/*
 *    VotedPerceptron.java
 *    Copyright (C) 1999 Eibe Frank
 *
 */


package weka.classifiers.functions;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.NominalToBinary;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

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

/**
 <!-- globalinfo-start -->
 * Implementation of the voted perceptron algorithm by Freund and Schapire. Globally replaces all missing values, and transforms nominal attributes into binary ones.<br/>
 * <br/>
 * For more information, see:<br/>
 * <br/>
 * Y. Freund, R. E. Schapire: Large margin classification using the perceptron algorithm. In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;inproceedings{Freund1998,
 *    address = {New York, NY},
 *    author = {Y. Freund and R. E. Schapire},
 *    booktitle = {11th Annual Conference on Computational Learning Theory},
 *    pages = {209-217},
 *    publisher = {ACM Press},
 *    title = {Large margin classification using the perceptron algorithm},
 *    year = {1998}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -I &lt;int&gt;
 *  The number of iterations to be performed.
 *  (default 1)</pre>
 * 
 * <pre> -E &lt;double&gt;
 *  The exponent for the polynomial kernel.
 *  (default 1)</pre>
 * 
 * <pre> -S &lt;int&gt;
 *  The seed for the random number generation.
 *  (default 1)</pre>
 * 
 * <pre> -M &lt;int&gt;
 *  The maximum number of alterations allowed.
 *  (default 10000)</pre>
 * 
 <!-- options-end -->
 *
 * @author Eibe Frank ([e-mail])
 * @version $Revision: 1.20 $ 
 */
public class VotedPerceptron 
  extends Classifier 
  implements OptionHandler, TechnicalInformationHandler {
  
  /** for serialization */
  static final long serialVersionUID = -1072429260104568698L;
  
  /** The maximum number of alterations to the perceptron */
  private int m_MaxK = 10000;

  /** The number of iterations */
  private int m_NumIterations = 1;

  /** The exponent */
  private double m_Exponent = 1.0;

  /** The actual number of alterations */
  private int m_K = 0;

  /** The training instances added to the perceptron */
  private int[] m_Additions = null;

  /** Addition or subtraction? */
  private boolean[] m_IsAddition = null;

  /** The weights for each perceptron */
  private int[] m_Weights = null;
  
  /** The training instances */
  private Instances m_Train = null;

  /** Seed used for shuffling the dataset */
  private int m_Seed = 1;

  /** The filter used to make attributes numeric. */
  private NominalToBinary m_NominalToBinary;

  /** The filter used to get rid of missing values. */
  private ReplaceMissingValues m_ReplaceMissingValues;

  /**
   * Returns a string describing this classifier
   * @return a description of the classifier suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Implementation of the voted perceptron algorithm by Freund and "
      + "Schapire. Globally replaces all missing values, and transforms "
      + "nominal attributes into binary ones.\n\n"
      + "For more information, see:\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * 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, "Y. Freund and R. E. Schapire");
    result.setValue(Field.TITLE, "Large margin classification using the perceptron algorithm");
    result.setValue(Field.BOOKTITLE, "11th Annual Conference on Computational Learning Theory");
    result.setValue(Field.YEAR, "1998");
    result.setValue(Field.PAGES, "209-217");
    result.setValue(Field.PUBLISHER, "ACM Press");
    result.setValue(Field.ADDRESS, "New York, NY");
    
    return result;
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(4);

    newVector.addElement(new Option("\tThe number of iterations to be performed.\n"
				    + "\t(default 1)",
				    "I", 1, "-I <int>"));
    newVector.addElement(new Option("\tThe exponent for the polynomial kernel.\n"
				    + "\t(default 1)",
				    "E", 1, "-E <double>"));
    newVector.addElement(new Option("\tThe seed for the random number generation.\n"
				    + "\t(default 1)",
				    "S", 1, "-S <int>"));
    newVector.addElement(new Option("\tThe maximum number of alterations allowed.\n"
				    + "\t(default 10000)",
				    "M", 1, "-M <int>"));

    return newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -I &lt;int&gt;
   *  The number of iterations to be performed.
   *  (default 1)</pre>
   * 
   * <pre> -E &lt;double&gt;
   *  The exponent for the polynomial kernel.
   *  (default 1)</pre>
   * 
   * <pre> -S &lt;int&gt;
   *  The seed for the random number generation.
   *  (default 1)</pre>
   * 
   * <pre> -M &lt;int&gt;
   *  The maximum number of alterations allowed.
   *  (default 10000)</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 iterationsString = Utils.getOption('I', options);
    if (iterationsString.length() != 0) {
      m_NumIterations = Integer.parseInt(iterationsString);
    } else {
      m_NumIterations = 1;
    }
    String exponentsString = Utils.getOption('E', options);
    if (exponentsString.length() != 0) {
      m_Exponent = (new Double(exponentsString)).doubleValue();
    } else {
      m_Exponent = 1.0;
    }
    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) {
      m_Seed = Integer.parseInt(seedString);
    } else {
      m_Seed = 1;
    }
    String alterationsString = Utils.getOption('M', options);
    if (alterationsString.length() != 0) {
      m_MaxK = Integer.parseInt(alterationsString);
    } else {
      m_MaxK = 10000;
    }
  }

  /**
   * Gets the current settings of the classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {

    String[] options = new String [8];
    int current = 0;

    options[current++] = "-I"; options[current++] = "" + m_NumIterations;
    options[current++] = "-E"; options[current++] = "" + m_Exponent;
    options[current++] = "-S"; options[current++] = "" + m_Seed;
    options[current++] = "-M"; options[current++] = "" + m_MaxK;
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.enable(Capability.BINARY_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

    // instances
    result.setMinimumNumberInstances(0);
    
    return result;
  }

  /**
   * Builds the ensemble of perceptrons.
   *
   * @param insts the data to train the classifier with
   * @throws Exception if something goes wrong during building