Explanation.java

/*
 * Explanation.java
 * @author Fabio G. Cozman 
 * Copyright 1996 - 1999, Fabio G. Cozman,
 *          Carnergie Mellon University, Universidade de Sao Paulo
 * fgcozman@usp.br, http://www.cs.cmu.edu/~fgcozman/home.html
 *
 * The JavaBayes distribution 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), 
 * provided that this notice and the name of the author appear in all 
 * copies. Upon request to the author, some of the packages in the 
 * JavaBayes distribution can be licensed under the GNU Lesser General
 * Public License as published by the Free Software Foundation (either
 * version 2 of the License, or (at your option) any later version).
 * If you're using the software, please notify fgcozman@usp.br so
 * that you can receive updates and patches. JavaBayes is distributed
 * "as is", 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 the JavaBayes distribution. If not, write to the Free
 * Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

package BayesianInferences;

import BayesianNetworks.*;

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

/*
 * There are two cases to be considered:                             
 *     EXPLANATION or FULL_EXPLANATION                               
 * the only difference is which variables are considered             
 * explanation variables.                                            
 *                                                                   
 * In FULL_EXPLANATION all variables are                             
 * explanation variables except variables that are observed.         
 *                                                                   
 * In EXPLANATION only variables that are marked as explanation      
 * variables are used in the maximization; an observed variable is   
 * not used even if it is marked as explanation variable.            
 * Note that in EXPLANATION mode, if there are no variables          
 * marked as explanation variables, the final result is the posterior
 * marginal.                                                         
 */

public class Explanation {
  BayesNet bn;
  BucketTree bucket_tree;
  ProbabilityFunction results[];

  /**
   * Constructor for an Explanation.
   */
  public Explanation(BayesNet b_n) {
    bn = b_n;
  }

  /*
   * Methods for calculation of explanations.
   */

  /**
   * Calculation of an Explanation.
   */
  public void explanation() {
    explanation(Inference.EXPLANATION);
  }

  /** 
   * Calculation of a full Explanation.
   */
  public void full_explanation() {
    explanation(Inference.FULL_EXPLANATION);
  }

  /**
   * Calculation of an Explanation accordingly 
   * to the flag explanation_status.
   */
  public void explanation(int explanation_status) {
    bucket_tree = new BucketTree(new Ordering(bn, (String)null,
                                              explanation_status, Ordering.MINIMUM_WEIGHT));
    do_inference_from_bucket_tree();
  }

  /**
   * Calculation of an Explanation given order.
   */
  public void explanation(String order[]) {
    explanation(order, Inference.EXPLANATION);
  }

  /**
   * Calculation of a full Explanation given order.
   */
  public void full_explanation(String order[]) {
    explanation(order, Inference.FULL_EXPLANATION);
  }

  /**
   * Calculation of an Explanation accordingly 
   * to the flag explanation_status.
   */
  public void explanation(String order[], int explanation_status) {
    bucket_tree =
        new BucketTree(new Ordering(bn, order, explanation_status));
    do_inference_from_bucket_tree();
  }

  /*
   * Do the Explanation.
   */
  void do_inference_from_bucket_tree() {
    results = new ProbabilityFunction[1];    
    bucket_tree.reduce();
    results[0] = bucket_tree.get_normalized_result();
  }

  /*
   * Generic, auxiliary methods.
   */

  /**
   * Print Explanation.
   */
  public void print() {
    print(System.out, (boolean)true);
  }

  /**
   * Print Explanation.
   */
  public void print(PrintStream out) {
    print(out, (boolean)true);
  }

  /**
   * Print Explanation.
   */
  public void print(boolean should_print_bucket_tree) {
    print(System.out, should_print_bucket_tree);
  }

  /**
   * Print Explanation.
   */
  public void print(PrintStream out, boolean should_print_bucket_tree) {
   int i, bp[];
   ProbabilityVariable pv;
 
   // Do explanation if Explanation is null.
    if (results == null)
       explanation();
   
   // Print it all.
   out.println("Explanation:");

   if (should_print_bucket_tree == true)
    bucket_tree.print(out);

   if (bucket_tree.backward_pointers == null) {
       out.println("No explanatory variable; posterior distribution:");
       for (i=0; i<results.length; i++) {
           results[i].print(out);
       }
   }
   else {
       bp = bucket_tree.backward_pointers;
       for (i=0; i<bp.length; i++) {
           if (bp[i] != BayesNet.INVALID_INDEX) {
               pv = bn.get_probability_variable(i);
               out.println("Variable " + pv.get_name() + ": " +
                           pv.get_value( bp[i] ) );
           }
       }
   }
  }

  /* ************************************************************* */
  /* Methods that allow basic manipulation of non-public variables */
  /* ************************************************************* */
  
  /**
   * Get the results in the Explanation.
   */
  public ProbabilityFunction[] get_results() {
    return(results);
  }
}