bayesianinducer.java

bayes network classifier toolbox 贝叶斯网络分类工具箱

/**
 *  JBNC - Bayesian Network Classifiers Toolbox <p>
 *
 *  Latest release available at http://sourceforge.net/projects/jbnc/ <p>
 *
 *  Copyright (C) 1999-2003 Jarek Sacha <p>
 *
 *  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. <p>
 *
 *  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. <p>
 *
 *  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., 59 Temple
 *  Place - Suite 330, Boston, MA 02111-1307, USA. <br>
 *  http://www.fsf.org/licenses/gpl.txt
 */
package jbnc.inducers;

import BayesianNetworks.BayesNet;
import jbnc.dataset.AttributeType;
import jbnc.dataset.DatasetInt;
import jbnc.measures.QualityMeasure;

/**
 *  Abstract class for Bayesian network inducers.
 *
 * @author     Jarek Sacha
 * @since      June 1, 1999
 */
public abstract class BayesianInducer {

  protected static final double DEFAULT_APLHA_IJK = 1.0;

  /**  Bayesian network representing the classifier. */
  protected BayesNet net = null;

  protected NetLayout layout = new NetLayout();

  protected boolean debugMode = false;

  /**
   *  Some Bayesian network inducers may use a quality measure to select the
   *  most optimal networ architecture for a given training data set.
   */
  protected QualityMeasure qualityMeasure = null;


  /**
   *  Sets the debug mode flag.
   *
   * @param  mode  The new DebugMode value
   */
  public void setDebugMode(boolean mode) {
    debugMode = mode;
  }


  /**
   *  Sets new quality measure.
   *
   * @param  qualityMeasure  The new QualityMeasure value
   */
  public void setQualityMeasure(QualityMeasure qualityMeasure) {
    this.qualityMeasure = qualityMeasure;
  }


  /**
   *  Gets Bayesian network representing the classifier.
   *
   * @return                The Network value
   * @exception  Exception  Description of Exception
   */
  public BayesNet getNetwork() throws Exception {
    return net;
  }


  /**
   *  Gets the debug mode flag.
   *
   * @return    The DebugMode value
   */
  public boolean getDebugMode() {
    return debugMode;
  }


  /**
   *  Gets the current quality measure.
   *
   * @return    The QualityMeasure value
   */
  public QualityMeasure getQualityMeasure() {
    return this.qualityMeasure;
  }


  /**  Reset the inducer. Get ready for learning of a new network. */
  public void reset() {
    clean();
  }


  /**
   *  Construct bayesian network from the training data
   *
   * @param  fc             Description of Parameter
   * @exception  Exception  Description of Exception
   */
//  public void train(DatasetInt trainDataset) throws Exception
  public void train(jbnc.util.FrequencyCalc fc) throws Exception {
//    train(trainDataset, false, 0);
    train(fc, false, 0);
  }


  /**
   *  Construct bayesian network from the training data
   *
   * @param  fc             Description of Parameter
   * @param  usePriors      Description of Parameter
   * @param  alpha_ijk      Description of Parameter
   * @exception  Exception  Description of Exception
   */
//  abstract public void train(DatasetInt trainDataset,
  public abstract void train(jbnc.util.FrequencyCalc fc,
                             boolean usePriors,
                             double alpha_ijk) throws Exception;


  /**  Clean member variables. */
  protected void clean() {
    net = null;
  }


  /**
   *  Check validity of the dataset.
   *
   * @param  dataset        Description of Parameter
   * @exception  Exception  Description of Exception
   */
  protected void verifyDataset(DatasetInt dataset) throws Exception {
    if (dataset == null || dataset.names.length < 1) {
      throw new Exception("Training data set has to have at least the class variable.");
    }

    // Verify that all attributes are discrete
    for (int n = 0; n < dataset.names.length; ++n) {
      if (dataset.names[n].getType() != AttributeType.DISCRETE) {
        throw new Exception("All attributes in the data set have to be discrete.");
      }
    }
  }


  /**
   *  Network layout.
   *
   * @author     Jarek Sacha
   * @since      June 1, 1999
   */
  protected class NetLayout {
    int xAttrib = 70;
    int xClass = 240;
    int yOffset = 40;
    int yStep = 50;
  }

}