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.
   */
  
  /*
   * BayesNetEstimator.java
   * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
   * 
   */
  
  package weka.classifiers.bayes.net.estimate;
  
  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;
  
  import java.io.Serializable;
  import java.util.Enumeration;
  import java.util.Vector;
  
  /** 
   <!-- globalinfo-start -->
   * BayesNetEstimator is the base class for estimating the conditional probability tables of a Bayes network once the structure has been learned.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -A &lt;alpha&gt;
   *  Initial count (alpha)
   * </pre>
   * 
   <!-- options-end -->
   * 
   * @author Remco Bouckaert (rrb@xm.co.nz)
   * @version $Revision: 1.4 $
   */
  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
       */
      public Enumeration listOptions() {
          Vector newVector = new Vector(1);
  
          newVector.addElement(new Option("\tInitial count (alpha)\n", "A", 1, "-A <alpha>"));
  
          return newVector.elements();
      } // listOptions
  
      /**
       * Parses a given list of options. <p/>
       *
       <!-- options-start -->
       * Valid options are: <p/>
       * 
       * <pre> -A &lt;alpha&gt;
       *  Initial count (alpha)
       * </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 {
          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
       */
      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
       */
      public String getRevision() {
        return RevisionUtils.extract("$Revision: 1.4 $");
      }
  
  } // BayesNetEstimator
  