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weka.classifiers.bayes.net.estimate.BayesNetEstimator Maven / Gradle / Ivy

/*
 *   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 .
 */

/*
 * BayesNetEstimator.java
 * Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 * 
 */

package weka.classifiers.bayes.net.estimate;

import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.bayes.BayesNet;
import weka.core.Instance;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 *  BayesNetEstimator is the base class for estimating
 * the conditional probability tables of a Bayes network once the structure has
 * been learned.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -A <alpha>
 *  Initial count (alpha)
 * 
 * 
 * 
 * 
 * @author Remco Bouckaert ([email protected])
 * @version $Revision: 10153 $
 */
public class BayesNetEstimator implements OptionHandler, Serializable,
  RevisionHandler {

  /** for serialization */
  static final long serialVersionUID = 2184330197666253884L;

  /**
   * Holds prior on count
   */
  protected double m_fAlpha = 0.5;

  /**
   * estimateCPTs estimates the conditional probability tables for the Bayes Net
   * using the network structure.
   * 
   * @param bayesNet the bayes net to use
   * @throws Exception always throws an exception, since subclass needs to be
   *           used
   */
  public void estimateCPTs(BayesNet bayesNet) throws Exception {
    throw new Exception("Incorrect BayesNetEstimator: use subclass instead.");
  }

  /**
   * Updates the classifier with the given instance.
   * 
   * @param bayesNet the bayes net to use
   * @param instance the new training instance to include in the model
   * @throws Exception always throws an exception, since subclass needs to be
   *           used
   */
  public void updateClassifier(BayesNet bayesNet, Instance instance)
    throws Exception {
    throw new Exception("Incorrect BayesNetEstimator: use subclass instead.");
  }

  /**
   * Calculates the class membership probabilities for the given test instance.
   * 
   * @param bayesNet the bayes net to use
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @throws Exception always throws an exception, since subclass needs to be
   *           used
   */
  public double[] distributionForInstance(BayesNet bayesNet, Instance instance)
    throws Exception {
    throw new Exception("Incorrect BayesNetEstimator: use subclass instead.");
  }

  /**
   * initCPTs reserves space for CPTs and set all counts to zero
   * 
   * @param bayesNet the bayes net to use
   * @throws Exception always throws an exception, since subclass needs to be
   *           used
   */
  public void initCPTs(BayesNet bayesNet) throws Exception {
    throw new Exception("Incorrect BayesNetEstimator: use subclass instead.");
  } // initCPTs

  /**
   * Returns an enumeration describing the available options
   * 
   * @return an enumeration of all the available options
   */
  @Override
  public Enumeration listOptions() {
    Vector newVector = new Vector(1);

    newVector.addElement(new Option("\tInitial count (alpha)\n", "A", 1,
      "-A "));

    return newVector.elements();
  } // listOptions

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -A <alpha>
   *  Initial count (alpha)
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {
    String sAlpha = Utils.getOption('A', options);

    if (sAlpha.length() != 0) {
      m_fAlpha = (new Float(sAlpha)).floatValue();
    } else {
      m_fAlpha = 0.5f;
    }

    Utils.checkForRemainingOptions(options);
  } // setOptions

  /**
   * Gets the current settings of the classifier.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String[] getOptions() {
    String[] options = new String[2];
    int current = 0;

    options[current++] = "-A";
    options[current++] = "" + m_fAlpha;

    return options;
  } // getOptions

  /**
   * Set prior used in probability table estimation
   * 
   * @param fAlpha representing prior
   */
  public void setAlpha(double fAlpha) {
    m_fAlpha = fAlpha;
  }

  /**
   * Get prior used in probability table estimation
   * 
   * @return prior
   */
  public double getAlpha() {
    return m_fAlpha;
  }

  /**
   * @return a string to describe the Alpha option.
   */
  public String alphaTipText() {
    return "Alpha is used for estimating the probability tables and can be interpreted"
      + " as the initial count on each value.";
  }

  /**
   * This will return a string describing the class.
   * 
   * @return The string.
   */
  public String globalInfo() {
    return "BayesNetEstimator is the base class for estimating the "
      + "conditional probability tables of a Bayes network once the "
      + "structure has been learned.";
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10153 $");
  }

} // BayesNetEstimator