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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.
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/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms/*
 *    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.
 */

/*
 *    NBTreeClassifierTree.java
 *    Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.j48;

import weka.core.Capabilities;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Capabilities.Capability;

/**
 * Class for handling a naive bayes tree structure used for
 * classification.
 *
 * @author Mark Hall ([email protected])
 * @version $Revision: 5535 $
 */
public class NBTreeClassifierTree
  extends ClassifierTree {

  /** for serialization */
  private static final long serialVersionUID = -4472639447877404786L;

  public NBTreeClassifierTree(ModelSelection toSelectLocModel) {
    super(toSelectLocModel);
  }

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

    // 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.NOMINAL_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

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

  /**
   * Method for building a naive bayes classifier tree
   *
   * @exception Exception if something goes wrong
   */
  public void buildClassifier(Instances data) throws Exception {
   super.buildClassifier(data);
   cleanup(new Instances(data, 0));
   assignIDs(-1);
  }

  /**
   * Assigns a uniqe id to every node in the tree.
   *
  public int assignIDs(int lastID) {

    int currLastID = lastID + 1;

    m_id = currLastID;
    if (m_sons != null) {
      for (int i = 0; i < m_sons.length; i++) {
	currLastID = m_sons[i].assignIDs(currLastID);
      }
    }
    return currLastID;
    } */

  /**
   * Returns a newly created tree.
   *
   * @param data the training data
   * @exception Exception if something goes wrong
   */
  protected ClassifierTree getNewTree(Instances data) throws Exception {
	 
    ClassifierTree newTree = new NBTreeClassifierTree(m_toSelectModel);
    newTree.buildTree(data, false);
    
    return newTree;
  }

  /**
   * Returns a newly created tree.
   *
   * @param train the training data
   * @param test the pruning data.
   * @exception Exception if something goes wrong
   */
  protected ClassifierTree getNewTree(Instances train, Instances test) 
       throws Exception {
	 
    ClassifierTree newTree = new NBTreeClassifierTree(m_toSelectModel);
    newTree.buildTree(train, test, false);
    
    return newTree;
  }

  /**
   * Print the models at the leaves
   *
   * @return textual description of the leaf models
   */
  public String printLeafModels() {
    StringBuffer text = new StringBuffer();

    if (m_isLeaf) {
      text.append("\nLeaf number: " + m_id+" ");
      text.append(m_localModel.toString());
      text.append("\n");
    } else {
       for (int i=0;i 0) {
	text.append("data =\n" + m_train + "\n");
	text.append(",\n");

      }
      text.append("]\n");
    }else {
      text.append("N" + m_id 
		  + " [label=\"" + 
		  m_localModel.leftSide(m_train) + "\" ");
      if (m_train != null && m_train.numInstances() > 0) {
	text.append("data =\n" + m_train + "\n");
	text.append(",\n");
     }
      text.append("]\n");
      graphTree(text);
    }
    
    return text.toString() +"}\n";
  }

  /**
   * Help method for printing tree structure as a graph.
   *
   * @exception Exception if something goes wrong
   */
  private void graphTree(StringBuffer text) throws Exception {
    
    for (int i = 0; i < m_sons.length; i++) {
      text.append("N" + m_id  
		  + "->" + 
		  "N" + m_sons[i].m_id +
		  " [label=\"" + m_localModel.rightSide(i,m_train).trim() + 
		  "\"]\n");
      if (m_sons[i].m_isLeaf) {
	text.append("N" + m_sons[i].m_id +
		    " [label=\""+"NB Model"+"\" "+ 
		    "shape=box style=filled ");
	if (m_train != null && m_train.numInstances() > 0) {
	  text.append("data =\n" + m_sons[i].m_train + "\n");
	  text.append(",\n");
	}
	text.append("]\n");
      } else {
	text.append("N" + m_sons[i].m_id +
		    " [label=\""+m_sons[i].m_localModel.leftSide(m_train) + 
		    "\" ");
	if (m_train != null && m_train.numInstances() > 0) {
	  text.append("data =\n" + m_sons[i].m_train + "\n");
	  text.append(",\n");
	}
	text.append("]\n");
	((NBTreeClassifierTree)m_sons[i]).graphTree(text);
      }
    }
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 5535 $");
  }
}