code: ///////////////////////////////////////////////////////////////////////////////
  // For information as to what this class does, see the Javadoc, below.       //
  // Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,       //
  // 2007, 2008, 2009, 2010, 2014, 2015 by Peter Spirtes, Richard Scheines, Joseph   //
  // Ramsey, and Clark Glymour.                                                //
  //                                                                           //
  // 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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA //
  ///////////////////////////////////////////////////////////////////////////////
  
  package edu.cmu.tetrad.bayes;
  
  import edu.cmu.tetrad.graph.Dag;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.graph.Node;
  
  import java.io.IOException;
  import java.io.ObjectInputStream;
  import java.util.List;
  
  /**
   * Calculates updated probabilities for variables conditional on their parents
   * as well as single-variable updated marginals for a Bayes IM using an
   * algorithm that restricts expensive updating summations only to conditional
   * probabilities of variables with respect to their parents that change from
   * non-updated to updated values.
   *
   * @author Joseph Ramsey
   */
  public final class CptInvariantUpdater implements ManipulatingBayesUpdater {
      static final long serialVersionUID = 23L;
  
      /**
       * The IM which this updater modifies.
       *
       * @serial Cannot be null.
       */
      private BayesIm bayesIm;
  
      /**
       * The manipulated Bayes IM--that is, bayesIm after the manipulations in
       * evidence have been applied to it.
       *
       * @serial
       */
      private BayesIm manipulatedBayesIm;
  
      /**
       * The IM after update.
       *
       * @serial
       */
      private UpdatedBayesIm updatedBayesIm;
  
      /**
       * Stores evidence for all variables.
       *
       * @serial Cannot be null
       */
      private Evidence evidence;
  
      /**
       * The gadget that calculates marginals.
       *
       * @serial
       */
      private CptInvariantMarginalCalculator cptInvariantMarginalCalculator;
  
      //==============================CONSTRUCTORS===========================//
  
      public CptInvariantUpdater(BayesIm bayesIm) {
          if (bayesIm == null) {
              throw new NullPointerException();
          }
  
          this.bayesIm = bayesIm;
          setEvidence(Evidence.tautology(this.bayesIm));
      }
  
      /**
       * Constructs a new updater for the given Bayes net.
       */
      public CptInvariantUpdater(BayesIm bayesIm, Evidence evidence) {
          if (bayesIm == null) {
              throw new NullPointerException();
          }
  
          this.bayesIm = bayesIm;
          setEvidence(evidence);
      }
  
      /**
       * Generates a simple exemplar of this class to test serialization.
       */
      public static CptInvariantUpdater serializableInstance() {
          return new CptInvariantUpdater(MlBayesIm.serializableInstance());
      }
  
      //============================PUBLIC METHODS==========================//
  
      public BayesIm getBayesIm() {
          return this.bayesIm;
      }
  
      public BayesIm getManipulatedBayesIm() {
          return this.manipulatedBayesIm;
      }
  
      public Graph getManipulatedGraph() {
          return getManipulatedBayesIm().getDag();
      }
  
      public BayesIm getUpdatedBayesIm() {
          return this.updatedBayesIm;
      }
  
      public Evidence getEvidence() {
          return new Evidence(this.evidence);
      }
  
      public void setEvidence(Evidence evidence) {
          if (evidence == null) {
              throw new NullPointerException();
          }
  
          if (evidence.isIncompatibleWith(bayesIm)) {
              throw new IllegalArgumentException("The variable list for this evidence " +
                      "must be compatible with the variable list of the stored IM.");
          }
  
          this.evidence = evidence;
  
          // Create the manipulated Bayes Im.
          Graph graph = bayesIm.getBayesPm().getDag();
          Dag manipulatedGraph = createManipulatedGraph(graph);
          BayesPm manipulatedBayesPm = createManipulatedBayesPm(manipulatedGraph);
          this.manipulatedBayesIm = createdManipulatedBayesIm(manipulatedBayesPm);
  
          // Create the updated BayesIm from the manipulated Bayes Im.
          Evidence evidence2 = new Evidence(evidence, manipulatedBayesIm);
          this.updatedBayesIm = new UpdatedBayesIm(manipulatedBayesIm, evidence2);
  
          // Create the marginal calculator from the updated Bayes Im.
          this.cptInvariantMarginalCalculator =
                  new CptInvariantMarginalCalculator(bayesIm,
                          evidence2);
      }
  
      public double getMarginal(int variable, int value) {
          return cptInvariantMarginalCalculator.getMarginal(variable, value);
      }
  
      public boolean isJointMarginalSupported() {
          return false;
      }
  
      public double getJointMarginal(int[] variables, int[] values) {
          throw new UnsupportedOperationException();
      }
  
      public double[] calculatePriorMarginals(int nodeIndex) {
          Evidence evidence = getEvidence();
          setEvidence(Evidence.tautology(evidence.getVariableSource()));
  
          double[] marginals = new double[evidence.getNumCategories(nodeIndex)];
  
          for (int i = 0;
               i < getBayesIm().getNumColumns(nodeIndex); i++) {
              marginals[i] = getMarginal(nodeIndex, i);
          }
  
          setEvidence(evidence);
          return marginals;
      }
  
      public double[] calculateUpdatedMarginals(int nodeIndex) {
          double[] marginals = new double[evidence.getNumCategories(nodeIndex)];
  
          for (int i = 0;
               i < getBayesIm().getNumColumns(nodeIndex); i++) {
              marginals[i] = getMarginal(nodeIndex, i);
          }
  
          return marginals;
      }
  
      /**
       * Prints out the most recent marginal.
       */
      public String toString() {
          return "CPT Invariant Updater, evidence = " + evidence;
      }
  
      //==============================PRIVATE METHODS=======================//
  
      private BayesIm createdManipulatedBayesIm(BayesPm updatedBayesPm) {
          return new MlBayesIm(updatedBayesPm, bayesIm, MlBayesIm.MANUAL);
      }
  
      private BayesPm createManipulatedBayesPm(Dag updatedGraph) {
          return new BayesPm(updatedGraph, bayesIm.getBayesPm());
      }
  
  //    private Dag createManipulatedGraph(Graph graph) {
  //        Dag updatedGraph = new Dag(graph);
  //
  //        for (int i = 0; i < bayesIm.getNumNodes(); ++i) {
  //            if (evidence.isManipulated(i)) {
  //                Node node = evidence.getNode(i);
  //                List<Node> parents = updatedGraph.getParents(node);
  //
  //                for (Node parent1 : parents) {
  //                    updatedGraph.removeEdge(node, parent1);
  //                }
  //            }
  //        }
  //
  //        return updatedGraph;
  //    }
  
      private Dag createManipulatedGraph(Graph graph) {
          Dag updatedGraph = new Dag(graph);
  
          // alters graph for manipulated evidenceItems
          for (int i = 0; i < evidence.getNumNodes(); ++i) {
              if (evidence.isManipulated(i)) {
                  Node node = updatedGraph.getNode(evidence.getNode(i).getName());
                  List<Node> parents = updatedGraph.getParents(node);
  
                  for (Object parent1 : parents) {
                      Node parent = (Node) parent1;
                      updatedGraph.removeEdge(node, parent);
                  }
              }
          }
  
          return updatedGraph;
      }
  
      /**
       * Adds semantic checks to the default deserialization method. This method
       * must have the standard signature for a readObject method, and the body of
       * the method must begin with "s.defaultReadObject();". Other than that, any
       * semantic checks can be specified and do not need to stay the same from
       * version to version. A readObject method of this form may be added to any
       * class, even if Tetrad sessions were previously saved out using a version
       * of the class that didn't include it. (That's what the
       * "s.defaultReadObject();" is for. See J. Bloch, Effective Java, for help.
       *
       * @throws java.io.IOException
       * @throws ClassNotFoundException
       */
      private void readObject(ObjectInputStream s)
              throws IOException, ClassNotFoundException {
          s.defaultReadObject();
  
          if (bayesIm == null) {
              throw new NullPointerException();
          }
  
          if (evidence == null) {
              throw new NullPointerException();
          }
      }
  }
  
  
  
  
  