regressiongenerator.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.
 */

/*
 * RegressionGenerator.java
 * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.datagenerators;

import weka.core.Option;
import weka.core.Utils;
import weka.datagenerators.DataGenerator;

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

/** 
 * Abstract class for data generators for regression classifiers. <p/>
 *
 * Example usage as the main of a datagenerator called RandomGenerator:
 * <pre>
 * public static void main(String[] args) {
 *   try {
 *     DataGenerator.makeData(new RandomGenerator(), args);
 *   } 
 *   catch (Exception e) {
 *     e.printStackTrace();
 *     System.err.println(e.getMessage());
 *   }
 * }
 * </pre>
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 1.2 $
 */
public abstract class RegressionGenerator 
  extends DataGenerator {

  /** Number of instances*/
  protected int m_NumExamples;

  /**
   * initializes the generator with default values
   */
  public RegressionGenerator() {
    super();

    setNumExamples(defaultNumExamples());
  }
  
  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Vector result = enumToVector(super.listOptions());

    result.addElement(new Option(
        "\tThe number of examples to generate (default " 
        + defaultNumExamples() + ")",
        "n", 1, "-n <num>"));
    
    return result.elements();
  }

  /**
   * Sets the options.
   *
   * @param options the options 
   * @throws Exception if invalid option
   */
  public void setOptions(String[] options) throws Exception { 
    String        tmpStr;
    
    super.setOptions(options);

    tmpStr = Utils.getOption('n', options);
    if (tmpStr.length() != 0)
      setNumExamples(Integer.parseInt(tmpStr));
    else
      setNumExamples(defaultNumExamples());
  }
  
  /**
   * Gets the current settings of the classifier.
   *
   * @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]);
    
    result.add("-n");
    result.add("" + getNumExamples());
    
    return (String[]) result.toArray(new String[result.size()]);
  }

  /**
   * returns the default number of examples
   * 
   * @return the default number of examples
   */
  protected int defaultNumExamples() {
    return 100;
  }

  /**
   * Sets the number of examples, given by option.
   * @param numExamples the new number of examples
   */
  public void setNumExamples(int numExamples) { 
    m_NumExamples = numExamples; 
  }

  /**
   * Gets the number of examples, given by option.
   * @return the number of examples, given by option 
   */
  public int getNumExamples() { 
    return m_NumExamples; 
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String numExamplesTipText() {
    return "The number of examples to generate.";
  }
}