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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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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 3 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, see .
 */

/*
 *    M5P.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees;

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

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;

/**
 *  M5Base. Implements base routines for generating M5
 * Model trees and rules

 * The original algorithm M5 was invented by R. Quinlan and Yong Wang made
 * improvements.

 * 

 * For more information see:

 * 

 * Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint
 * Conference on Artificial Intelligence, Singapore, 343-348, 1992.

 * 

 * 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.
 * 
 * 
 * 
 *  BibTeX:
 * 
 * 
 * @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}
 * }
 * 
 * @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}
 * }
 * 
 * 
 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -N
 *  Use unpruned tree/rules
 * 
 * 
 *  * -U
 *  Use unsmoothed predictions
 * 
 * 
 *  * -R
 *  Build regression tree/rule rather than a model tree/rule
 * 
 * 
 *  * -M <minimum number of instances>
 *  Set minimum number of instances per leaf
 *  (default 4)
 * 
 * 
 *  * -L
 *  Save instances at the nodes in
 *  the tree (for visualization purposes)
 * 
 * 
 * 
 * 
 * @author Mark Hall
 * @version $Revision: 10153 $
 */
public class M5P extends M5Base implements Drawable {

  /** for serialization */
  static final long serialVersionUID = -6118439039768244417L;

  /**
   * Creates a new M5P instance.
   */
  public M5P() {
    super();
    setGenerateRules(false);
  }

  /**
   * Returns the type of graph this classifier represents.
   * 
   * @return Drawable.TREE
   */
  @Override
  public int graphType() {
    return Drawable.TREE;
  }

  /**
   * Return a dot style String describing the tree.
   * 
   * @return a String value
   * @throws Exception if an error occurs
   */
  @Override
  public String graph() throws Exception {
    StringBuffer text = new StringBuffer();

    text.append("digraph M5Tree {\n");
    Rule temp = m_ruleSet.get(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 boolean value
   */
  public void setSaveInstances(boolean save) {
    m_saveInstances = save;
  }

  /**
   * Get whether instance data is being save.
   * 
   * @return a boolean value
   */
  public boolean getSaveInstances() {
    return m_saveInstances;
  }

  /**
   * Returns an enumeration describing the available options
   * 
   * @return an enumeration of all the available options
   */
  @Override
  public Enumeration