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 edu.cmu.tetrad.util.RandomUtil;
  
  import java.io.IOException;
  import java.io.ObjectInputStream;
  import java.util.Collection;
  import java.util.List;
  
  /**
   * Calculates updated marginals for a Bayes net by simulating data and
   * calculating likelihood ratios. The method is as follows. For P(A | B), enough
   * sample points are simulated from the underlying BayesIm so that 1000 satisfy
   * the condition B. Then the maximum likelihood estimate of condition A is
   * calculated.
   *
   * @author Joseph Ramsey
   */
  public final class ApproximateUpdater implements ManipulatingBayesUpdater {
      static final long serialVersionUID = 23L;
  
      /**
       * The IM which this updater modifies.
       *
       * @serial Cannot be null.
       */
      private BayesIm bayesIm;
  
      /**
       * Stores evidence for all variables.
       *
       * @serial Cannot be null.
       */
      private Evidence evidence;
  
      /**
       * Counts of data points for each variable value.
       *
       * @serial
       */
      private int[][] counts;
  
      /**
       * This is the source BayesIm after manipulation; all data simulations
       * should be taken from this.
       *
       * @serial
       */
      private BayesIm manipulatedBayesIm;
  
      //==============================CONSTRUCTORS===========================//
  
      public ApproximateUpdater(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 ApproximateUpdater(BayesIm bayesIm, Evidence evidence) {
          if (bayesIm == null) {
              throw new NullPointerException();
          }
  
          this.bayesIm = bayesIm;
          setEvidence(evidence);
      }
  
      /**
       * @return a simple exemplar of this class to test serialization.
       */
      public static ApproximateUpdater serializableInstance() {
          return new ApproximateUpdater(MlBayesIm.serializableInstance());
      }
  
      //============================PUBLIC METHODS==========================//
  
      /**
       * @return the Bayes instantiated model that is being updated.
       */
      public BayesIm getBayesIm() {
          return bayesIm;
      }
  
      /**
       * @return the Bayes instantiated model after manipulations have been
       * applied.
       */
      public BayesIm getManipulatedBayesIm() {
          return this.manipulatedBayesIm;
      }
  
      /**
       * @return the graph for getManipulatedBayesIm().
       */
      public Graph getManipulatedGraph() {
          return this.manipulatedBayesIm.getDag();
      }
  
      /**
       * @return the updated Bayes IM, or null if there is no updated Bayes IM.
       */
      public BayesIm getUpdatedBayesIm() {
          return null;
      }
  
      /**
       * @return a copy of the getModel evidence.
       */
      public Evidence getEvidence() {
          return new Evidence(this.evidence);
      }
  
      /**
       * Sets new evidence for the next update operation.
       */
      public final void setEvidence(Evidence evidence) {
          if (evidence == null) {
              throw new NullPointerException();
          }
  
          if (evidence.isIncompatibleWith(bayesIm)) {
              throw new IllegalArgumentException("The variables for the given " +
                      "evidence must be compatible with the Bayes IM being updated.");
          }
  
          this.evidence = new Evidence(evidence);
  
          Graph graph = bayesIm.getBayesPm().getDag();
          Dag manipulatedGraph = createManipulatedGraph(graph);
          BayesPm manipulatedBayesPm = createUpdatedBayesPm(manipulatedGraph);
          this.manipulatedBayesIm = createdUpdatedBayesIm(manipulatedBayesPm);
  
          this.counts = null;
      }
  
      public double getMarginal(int variable, int value) {
          doUpdate();
          int sum = 0;
  
          for (int i = 0; i < manipulatedBayesIm.getNumColumns(variable); i++) {
              sum += counts[variable][i];
          }
  
          return counts[variable][value] / (double) sum;
      }
  
      public boolean isJointMarginalSupported() {
          return false;
      }
  
      /**
       * @return the joint marginal.
       */
      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 "Approximate updater, evidence = " + evidence;
      }
  
      //==============================PRIVATE METHODS=======================//
  
      private void doUpdate() {
          if (counts != null) {
              return;
          }
  
          this.counts = new int[manipulatedBayesIm.getNumNodes()][];
  
          for (int i = 0; i < manipulatedBayesIm.getNumNodes(); i++) {
              this.counts[i] = new int[manipulatedBayesIm.getNumColumns(i)];
          }
  
          // Get a tier ordering and convert it to an int array.
          Graph graph = getManipulatedGraph();
          List<Node> tierOrdering = graph.getCausalOrdering();
          int[] tiers = new int[tierOrdering.size()];
  
          for (int i = 0; i < tierOrdering.size(); i++) {
              tiers[i] = getManipulatedBayesIm().getNodeIndex(tierOrdering.get(i));
          }
  
          int numCounted = 0;
  
          // Adding an "ur"-counter--if the counting procedure exceeds this many
          // iterations, just give up. Handles the case there there simply aren't
          // enough (say, none) legitimate combinations to count, which was
          // leading to infinite loops. jdramsey 2/7/12
          int numSurveyed = 0;
  
          // Construct the sample.
          while (numCounted < 1000 && ++numSurveyed < 10000) {
              int[] point = getSinglePoint(getBayesIm(), tiers);
  
              if (evidence.getProposition().isPermissibleCombination(point)) {
                  numCounted++;
  
                  for (int j = 0; j < getManipulatedBayesIm().getNumNodes(); j++) {
                      counts[j][point[j]]++;
                  }
              }
          }
      }
  
      private BayesIm createdUpdatedBayesIm(BayesPm updatedBayesPm) {
          return new MlBayesIm(updatedBayesPm, bayesIm, MlBayesIm.RANDOM);
      }
  
      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 = evidence.getNode(i);
                  node = updatedGraph.getNode(node.getName());
                  Collection<Node> parents = updatedGraph.getParents(node);
  
                  for (Node parent1 : parents) {
                      updatedGraph.removeEdge(node, parent1);
                  }
              }
          }
  
          return updatedGraph;
      }
  
      private static int[] getSinglePoint(BayesIm bayesIm, int[] tiers) {
          int[] point = new int[bayesIm.getNumNodes()];
          int[] combination = new int[bayesIm.getNumNodes()];
          RandomUtil randomUtil = RandomUtil.getInstance();
  
          for (int nodeIndex : tiers) {
              double cutoff = randomUtil.nextDouble();
  
              for (int k = 0; k < bayesIm.getNumParents(nodeIndex); k++) {
                  combination[k] = point[bayesIm.getParent(nodeIndex, k)];
              }
  
              int rowIndex = bayesIm.getRowIndex(nodeIndex, combination);
              double sum = 0.0;
  
              for (int k = 0; k < bayesIm.getNumColumns(nodeIndex); k++) {
                  double probability =
                          bayesIm.getProbability(nodeIndex, rowIndex, k);
  
                  if (Double.isNaN(probability)) {
                      throw new IllegalStateException("Some probability " +
                              "values in the BayesIm are not filled in; " +
                              "cannot simulate data to do approximate updating.");
                  }
  
                  sum += probability;
  
                  if (sum >= cutoff) {
                      point[nodeIndex] = k;
                      break;
                  }
              }
          }
  
          return point;
      }
  
      /**
       * 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();
          }
      }
  }
  
  
  
  
  