content: /*
   *    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.
   */
  
  /*
   *    M5P.java
   *    Copyright (C) 2001 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.trees;
  
  import weka.classifiers.trees.m5.M5Base;
  import weka.classifiers.trees.m5.Rule;
  import weka.core.Drawable;
  import weka.core.Option;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * M5Base. Implements base routines for generating M5 Model trees and rules<br/>
   * The original algorithm M5 was invented by R. Quinlan and Yong Wang made improvements.<br/>
   * <br/>
   * For more information see:<br/>
   * <br/>
   * Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.<br/>
   * <br/>
   * Y. Wang, I. H. Witten: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning, 1997.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;inproceedings{Quinlan1992,
   *    address = {Singapore},
   *    author = {Ross J. Quinlan},
   *    booktitle = {5th Australian Joint Conference on Artificial Intelligence},
   *    pages = {343-348},
   *    publisher = {World Scientific},
   *    title = {Learning with Continuous Classes},
   *    year = {1992}
   * }
   * 
   * &#64;inproceedings{Wang1997,
   *    author = {Y. Wang and I. H. Witten},
   *    booktitle = {Poster papers of the 9th European Conference on Machine Learning},
   *    publisher = {Springer},
   *    title = {Induction of model trees for predicting continuous classes},
   *    year = {1997}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -N
   *  Use unpruned tree/rules</pre>
   * 
   * <pre> -U
   *  Use unsmoothed predictions</pre>
   * 
   * <pre> -R
   *  Build regression tree/rule rather than a model tree/rule</pre>
   * 
   * <pre> -M &lt;minimum number of instances&gt;
   *  Set minimum number of instances per leaf
   *  (default 4)</pre>
   * 
   * <pre> -L
   *  Save instances at the nodes in
   *  the tree (for visualization purposes)</pre>
   * 
   <!-- options-end -->
   *
   * @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
   * @version $Revision: 1.10 $
   */
  public class M5P 
    extends M5Base 
    implements Drawable {
  
    /** for serialization */
    static final long serialVersionUID = -6118439039768244417L;
    
    /**
     * Creates a new <code>M5P</code> instance.
     */
    public M5P() {
      super();
      setGenerateRules(false);
    }
  
    /**
     *  Returns the type of graph this classifier
     *  represents.
     *  @return Drawable.TREE
     */   
    public int graphType() {
        return Drawable.TREE;
    }
  
    /**
     * Return a dot style String describing the tree.
     *
     * @return a <code>String</code> value
     * @throws Exception if an error occurs
     */
    public String graph() throws Exception {
      StringBuffer text = new StringBuffer();
      
      text.append("digraph M5Tree {\n");
      Rule temp = (Rule)m_ruleSet.elementAt(0);
      temp.topOfTree().graph(text);
      text.append("}\n");
      return text.toString();
    }
  
    /**
     * Returns the tip text for this property
     * 
     * @return 		tip text for this property suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String saveInstancesTipText() {
      return 
          "Whether to save instance data at each node in the tree for "
        + "visualization purposes.";
    }
  
    /**
     * Set whether to save instance data at each node in the
     * tree for visualization purposes
     *
     * @param save a <code>boolean</code> value
     */
    public void setSaveInstances(boolean save) {
      m_saveInstances = save;
    }
  
    /**
     * Get whether instance data is being save.
     *
     * @return a <code>boolean</code> value
     */
    public boolean getSaveInstances() {
      return m_saveInstances;
    }
  
    /**
     * Returns an enumeration describing the available options
     * 
     * @return an enumeration of all the available options
     */
    public Enumeration listOptions() {
      Enumeration superOpts = super.listOptions();
      
      Vector newVector = new Vector();
      while (superOpts.hasMoreElements()) {
        newVector.addElement((Option)superOpts.nextElement());
      }
  
      newVector.addElement(new Option("\tSave instances at the nodes in\n"
  				    +"\tthe tree (for visualization purposes)",
  				    "L", 0, "-L"));
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -N
     *  Use unpruned tree/rules</pre>
     * 
     * <pre> -U
     *  Use unsmoothed predictions</pre>
     * 
     * <pre> -R
     *  Build regression tree/rule rather than a model tree/rule</pre>
     * 
     * <pre> -M &lt;minimum number of instances&gt;
     *  Set minimum number of instances per leaf
     *  (default 4)</pre>
     * 
     * <pre> -L
     *  Save instances at the nodes in
     *  the tree (for visualization purposes)</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 {
      setSaveInstances(Utils.getFlag('L', options));
      super.setOptions(options);
    }
  
    /**
     * Gets the current settings of the classifier.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    public String [] getOptions() {
      String[] superOpts = super.getOptions();
      String [] options = new String [superOpts.length+1];
      int current = superOpts.length;
      for (int i = 0; i < current; i++) {
        options[i] = superOpts[i];
      }
      
      if (getSaveInstances()) {
        options[current++] = "-L";
      }
  
      while (current < options.length) {
        options[current++] = "";
      }
  
      return options;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.10 $");
    }
  
    /**
     * Main method by which this class can be tested
     * 
     * @param args an array of options
     */
    public static void main(String[] args) {
      runClassifier(new M5P(), args);
    } 
  }
  