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.
   */
  
  /*
   * BayesNet.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.Instances;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  import weka.estimators.Estimator;
  
  import java.util.Enumeration;
  
  /** 
   <!-- globalinfo-start -->
   * SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned. Estimates probabilities directly from data.
   * <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: 1.6 $
   */
  public class SimpleEstimator 
      extends BayesNetEstimator {
  
      /** for serialization */
      static final long serialVersionUID = 5874941612331806172L;
      
      /**
       * Returns a string describing this object
       * @return a description of the classifier suitable for
       * displaying in the explorer/experimenter gui
       */
      public String globalInfo() {
        return 
            "SimpleEstimator is used for estimating the conditional probability "
        	+ "tables of a Bayes network once the structure has been learned. "
        	+ "Estimates probabilities directly from data.";
      }
    
      /**
       * estimateCPTs estimates the conditional probability tables for the Bayes
       * Net using the network structure.
       * 
       * @param bayesNet the bayes net to use
       * @throws Exception if something goes wrong
       */
      public void estimateCPTs(BayesNet bayesNet) throws Exception {
              initCPTs(bayesNet);
  
              // Compute counts
              Enumeration enumInsts = bayesNet.m_Instances.enumerateInstances();
              while (enumInsts.hasMoreElements()) {
                  Instance instance = (Instance) enumInsts.nextElement();
  
                  updateClassifier(bayesNet, instance);
              }
      } // estimateCPTs
  
      /**
       * 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 if the instance could not be incorporated in
       * the model.
       */
      public void updateClassifier(BayesNet bayesNet, Instance instance) throws Exception {
          for (int iAttribute = 0; iAttribute < bayesNet.m_Instances.numAttributes(); iAttribute++) {
              double iCPT = 0;
  
              for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getNrOfParents(); iParent++) {
                  int nParent = bayesNet.getParentSet(iAttribute).getParent(iParent);
  
                  iCPT = iCPT * bayesNet.m_Instances.attribute(nParent).numValues() + instance.value(nParent);
              }
  
              bayesNet.m_Distributions[iAttribute][(int) iCPT].addValue(instance.value(iAttribute), instance.weight());
          }
      } // updateClassifier
  
  
      /** 
       * initCPTs reserves space for CPTs and set all counts to zero
       * 
       * @param bayesNet the bayes net to use
       * @throws Exception if something goes wrong
       */
      public void initCPTs(BayesNet bayesNet) throws Exception {
          Instances instances = bayesNet.m_Instances;
          
          // Reserve space for CPTs
          int nMaxParentCardinality = 1;
          for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
              if (bayesNet.getParentSet(iAttribute).getCardinalityOfParents() > nMaxParentCardinality) {
                  nMaxParentCardinality = bayesNet.getParentSet(iAttribute).getCardinalityOfParents();
              }
          }
  	
          // Reserve plenty of memory
          bayesNet.m_Distributions = new Estimator[instances.numAttributes()][nMaxParentCardinality];
  	
          // estimate CPTs
          for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
              for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getCardinalityOfParents(); iParent++) {
                  bayesNet.m_Distributions[iAttribute][iParent] =
                      new DiscreteEstimatorBayes(instances.attribute(iAttribute).numValues(), m_fAlpha);
              }
          }
      } // initCPTs
  
      /**
       * 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 if there is a problem generating the prediction
       */
      public double[] distributionForInstance(BayesNet bayesNet, Instance instance) throws Exception {
          Instances instances = bayesNet.m_Instances;
          int nNumClasses = instances.numClasses();
          double[] fProbs = new double[nNumClasses];
  
          for (int iClass = 0; iClass < nNumClasses; iClass++) {
              fProbs[iClass] = 1.0;
          }
  
          for (int iClass = 0; iClass < nNumClasses; iClass++) {
              double logfP = 0;
  
              for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
                  double iCPT = 0;
  
                  for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getNrOfParents(); iParent++) {
                      int nParent = bayesNet.getParentSet(iAttribute).getParent(iParent);
  
                      if (nParent == instances.classIndex()) {
                          iCPT = iCPT * nNumClasses + iClass;
                      } else {
                          iCPT = iCPT * instances.attribute(nParent).numValues() + instance.value(nParent);
                      }
                  }
  
                  if (iAttribute == instances.classIndex()) {
                      //	  fP *= 
                      //	    m_Distributions[iAttribute][(int) iCPT].getProbability(iClass);
                      logfP += Math.log(bayesNet.m_Distributions[iAttribute][(int) iCPT].getProbability(iClass));
                  } else {
                      //	  fP *= 
                      //	    m_Distributions[iAttribute][(int) iCPT]
                      //	      .getProbability(instance.value(iAttribute));
                      logfP
                          += Math.log(bayesNet.m_Distributions[iAttribute][(int) iCPT].getProbability(instance.value(iAttribute)));
                  }
              }
  
              //      fProbs[iClass] *= fP;
              fProbs[iClass] += logfP;
          }
  
          // Find maximum
          double fMax = fProbs[0];
          for (int iClass = 0; iClass < nNumClasses; iClass++) {
              if (fProbs[iClass] > fMax) {
                  fMax = fProbs[iClass];
              }
          }
          // transform from log-space to normal-space
          for (int iClass = 0; iClass < nNumClasses; iClass++) {
              fProbs[iClass] = Math.exp(fProbs[iClass] - fMax);
          }
  
          // Display probabilities
          Utils.normalize(fProbs);
  
          return fProbs;
      } // distributionForInstance
      
      /**
       * Returns the revision string.
       * 
       * @return		the revision
       */
      public String getRevision() {
        return RevisionUtils.extract("$Revision: 1.6 $");
      }
  
  } // SimpleEstimator
  