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;
  
  /**
   * Performs updating operations on a BayesIm by summing over cells in the joint
   * probability table for the BayesIm. Quite flexible and fast if almost all of
   * the variables in the Bayes net are in evidence. Can be excruciatingly slow if
   * numVars - numVarsInEvidence is more than 15.
   *
   * @author Joseph Ramsey
   */
  public final class RowSummingExactUpdater implements ManipulatingBayesUpdater {
      static final long serialVersionUID = 23L;
  
      /**
       * The BayesIm which this updater modifies.
       *
       * @serial Cannot be null.
       */
      private BayesIm bayesIm;
  
      /**
       * Stores evidence for all variables.
       *
       * @serial Cannot be null.
       */
      private Evidence evidence;
  
      /**
       * The last manipulated BayesIm.
       *
       * @serial Can be null.
       */
      private BayesIm manipulatedBayesIm;
  
      /**
       * The BayesIm after update, if this was calculated.
       *
       * @serial Can be null.
       */
      private BayesIm updatedBayesIm;
  
      /**
       * Calculates probabilities from the manipulated Bayes IM.
       *
       * @serial Can be null.
       */
      private BayesImProbs bayesImProbs;
  
      //==============================CONSTRUCTORS===========================//
  
      /**
       * Constructs a new updater for the given Bayes net.
       */
      public RowSummingExactUpdater(BayesIm bayesIm) {
          if (bayesIm == null) {
              throw new NullPointerException();
          }
  
          this.bayesIm = bayesIm;
          setEvidence(Evidence.tautology(bayesIm));
      }
  
      /**
       * Constructs a new updater for the given Bayes net.
       */
      public RowSummingExactUpdater(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 RowSummingExactUpdater serializableInstance() {
          return new RowSummingExactUpdater(MlBayesIm.serializableInstance());
      }
  
      //============================PUBLIC METHODS==========================//
  
      /**
       * The BayesIm that this updater bases its update on. This BayesIm is not
       * modified; rather, a new BayesIm is created and updated.
       */
      public BayesIm getBayesIm() {
          return bayesIm;
      }
  
      /**
       * @return the updated BayesIm.
       */
      public BayesIm getManipulatedBayesIm() {
          return this.manipulatedBayesIm;
      }
  
      public Graph getManipulatedGraph() {
          return getManipulatedBayesIm().getDag();
      }
  
      /**
       * The updated BayesIm. This is a different object from the source BayesIm.
       *
       * @see #getBayesIm
       */
      public BayesIm getUpdatedBayesIm() {
          if (updatedBayesIm == null) {
              updateAll();
          }
  
          return updatedBayesIm;
      }
  
      /**
       * @return a defensive copy of the evidence.
       */
      public Evidence getEvidence() {
          return new Evidence(this.evidence);
      }
  
      public final void setEvidence(Evidence evidence) {
          if (evidence == null) {
              throw new NullPointerException();
          }
  
          if (evidence.isIncompatibleWith(bayesIm)) {
              throw new IllegalArgumentException("The variable list for the " +
                      "given bayesIm must be compatible with the variable list " +
                      "for this evidence.");
          }
  
          this.evidence = evidence;
  
          Graph graph = bayesIm.getBayesPm().getDag();
          Dag manipulatedGraph = createManipulatedGraph(graph);
          BayesPm manipulatedPm = createUpdatedBayesPm(manipulatedGraph);
  
          this.manipulatedBayesIm = createdUpdatedBayesIm(manipulatedPm);
  
          for (int i = 0; i < evidence.getNumNodes(); i++) {
              if (evidence.isManipulated(i)) {
                  for (int j = 0; j < evidence.getNumCategories(i); j++) {
                      if (evidence.getProposition().isAllowed(i, j)) {
                          manipulatedBayesIm.setProbability(i, 0, j, 1.0);
                      } else {
                          manipulatedBayesIm.setProbability(i, 0, j, 0.0);
                      }
                  }
              }
          }
  
  
          this.bayesImProbs = new BayesImProbs(manipulatedBayesIm);
          this.updatedBayesIm = null;
      }
  
      public boolean isJointMarginalSupported() {
          return true;
      }
  
      public double getJointMarginal(int[] variables, int[] values) {
          if (variables.length != values.length) {
              throw new IllegalArgumentException("Values must match variables.");
          }
  
          Proposition assertion = Proposition.tautology(manipulatedBayesIm);
          Proposition condition =
                  new Proposition(manipulatedBayesIm, evidence.getProposition());
  
          for (int i = 0; i < variables.length; i++) {
              assertion.setCategory(variables[i], values[i]);
          }
  
          if (condition.existsCombination()) {
              return bayesImProbs.getConditionalProb(assertion, condition);
          } else {
              return Double.NaN;
          }
      }
  
      /**
       * @return P&lpar;variable&equals;value &vbar; evidence&rpar; where evidence is getEvidence().
       */
      public double getMarginal(int variable, int value) {
          Proposition assertion = Proposition.tautology(manipulatedBayesIm);
          Proposition condition =
                  new Proposition(manipulatedBayesIm, evidence.getProposition());
          assertion.setCategory(variable, value);
  
          if (condition.existsCombination()) {
              return bayesImProbs.getConditionalProb(assertion, condition);
          } else {
              return Double.NaN;
          }
      }
  
      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 "Row summing exact updater, evidence = " + evidence;
      }
  
      //==============================PRIVATE METHODS=======================//
  
      private void updateAll() {
          BayesIm updatedBayesIm = new MlBayesIm(manipulatedBayesIm);
          int numNodes = manipulatedBayesIm.getNumNodes();
  
          Proposition assertion = Proposition.tautology(manipulatedBayesIm);
          Proposition condition = Proposition.tautology(manipulatedBayesIm);
          Evidence evidence2 = new Evidence(evidence, manipulatedBayesIm);
  
          for (int node = 0; node < numNodes; node++) {
              int numRows = manipulatedBayesIm.getNumRows(node);
              int numCols = manipulatedBayesIm.getNumColumns(node);
              int[] parents = manipulatedBayesIm.getParents(node);
  
              for (int row = 0; row < numRows; row++) {
                  int[] parentValues =
                          manipulatedBayesIm.getParentValues(node, row);
  
                  for (int col = 0; col < numCols; col++) {
                      assertion.setToTautology();
                      condition.setToTautology();
  
                      for (int i = 0; i < numNodes; i++) {
                          for (int j = 0; j < evidence2.getNumCategories(i); j++) {
                              if (!evidence2.getProposition().isAllowed(i, j)) {
                                  condition.removeCategory(i, j);
                              }
                          }
                      }
  
                      assertion.disallowComplement(node, col);
  
                      for (int k = 0; k < parents.length; k++) {
                          condition.disallowComplement(parents[k],
                                  parentValues[k]);
                      }
  
                      if (condition.existsCombination()) {
                          double p = bayesImProbs.getConditionalProb(assertion,
                                  condition);
                          updatedBayesIm.setProbability(node, row, col, p);
                      } else {
                          updatedBayesIm.setProbability(node, row, col,
                                  Double.NaN);
                      }
                  }
              }
          }
  
          this.updatedBayesIm = updatedBayesIm;
      }
  
      private BayesIm createdUpdatedBayesIm(BayesPm updatedBayesPm) {
  
          // Switching this to MANUAL since the initial values don't matter.
          return new MlBayesIm(updatedBayesPm, bayesIm, MlBayesIm.MANUAL);
      }
  
      private BayesPm createUpdatedBayesPm(Dag updatedGraph) {
          return new BayesPm(updatedGraph, bayesIm.getBayesPm());
      }
  
      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();
          }
      }
  }
  
  
  
  
  