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.data.DiscreteVariable;
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.util.TetradSerializable;
  
  import java.io.IOException;
  import java.io.ObjectInputStream;
  import java.util.Collections;
  import java.util.LinkedList;
  import java.util.List;
  
  /**
   * Calculates cell probabilities from conditional BayesIm probabilities on the
   * fly without constructing the entire table. (To force the entire table to be
   * constructed, use StoredCellProbs.)
   *
   * @author Joseph Ramsey
   */
  public final class BayesImProbs implements DiscreteProbs, TetradSerializable {
      static final long serialVersionUID = 23L;
  
      /**
       * @serial Cannot be null.
       */
      private BayesIm bayesIm;
  
      /**
       * @serial Cannot be null.
       */
      private List<Node> variables;
  
      //===========================CONSTRUCTORS==========================//
  
      public BayesImProbs(BayesIm bayesIm) {
          if (bayesIm == null) {
              throw new NullPointerException();
          }
  
          this.bayesIm = bayesIm;
          List<Node> variables = new LinkedList<>();
          BayesPm bayesPm = bayesIm.getBayesPm();
  
          for (int i = 0; i < bayesIm.getNumNodes(); i++) {
              Node node = bayesIm.getNode(i);
              String name = node.getName();
  
              int numCategories = bayesPm.getNumCategories(node);
              List<String> categories = new LinkedList<>();
  
              for (int j = 0; j < numCategories; j++) {
                  categories.add(bayesPm.getCategory(node, j));
              }
  
              variables.add(new DiscreteVariable(name, categories));
          }
  
          this.variables = Collections.unmodifiableList(variables);
      }
  
      /**
       * Generates a simple exemplar of this class to test serialization.
       */
      public static BayesImProbs serializableInstance() {
          return new BayesImProbs(MlBayesIm.serializableInstance());
      }
  
      //==========================PUBLIC METHODS==========================//
  
      /**
       * Calculates the probability in the given cell from the conditional
       * probabilities in the BayesIm. It's the product of the probabilities
       * that each variable takes on the value it does given that the other
       * variables take on the values they do in that cell. The returned value
       * will be undefined (Double.NaN) if any of the conditional probabilities
       * being multiplied together is undefined.
       *
       * @return the cell probability, or NaN if this probability is undefined.
       */
      public double getCellProb(int[] variableValues) {
          double p = 1.0;
  
          VALUES:
          for (int node = 0; node < variableValues.length; node++) {
  
              // If a value is missing, count its probability as 1.0.
              if (Double.isNaN(variableValues[node])) {
                  continue;
              }
  
              int[] parents = bayesIm.getParents(node);
              int[] parentValues = new int[parents.length];
              for (int parentIndex = 0;
                   parentIndex < parentValues.length; parentIndex++) {
                  parentValues[parentIndex] =
                          variableValues[parents[parentIndex]];
  
                  if (parentValues[parentIndex] == DiscreteVariable.MISSING_VALUE) {
                      continue VALUES;
                  }
              }
  
              int rowIndex = bayesIm.getRowIndex(node, parentValues);
              int colIndex = variableValues[node];
  
              if (colIndex == DiscreteVariable.MISSING_VALUE) {
                  continue;
              }
  
              p *= bayesIm.getProbability(node, rowIndex, colIndex);
          }
  
          return p;
      }
  
      public double getProb(Proposition assertion) {
  
          // Initialize to 0's.
          int[] variableValues = new int[assertion.getNumVariables()];
  
          for (int i = 0; i < assertion.getNumVariables(); i++) {
              variableValues[i] = nextValue(assertion, i, -1);
          }
  
          variableValues[variableValues.length - 1] = -1;
          double p = 0.0;
  
          loop:
          while (true) {
              for (int i = assertion.getNumVariables() - 1; i >= 0; i--) {
                  if (hasNextValue(assertion, i, variableValues[i])) {
                      variableValues[i] =
                              nextValue(assertion, i, variableValues[i]);
  
                      for (int j = i + 1; j < assertion.getNumVariables(); j++) {
                          if (hasNextValue(assertion, j, -1)) {
                              variableValues[j] = nextValue(assertion, j, -1);
                          } else {
                              break loop;
                          }
                      }
  
                      double cellProb = getCellProb(variableValues);
  
                      if (Double.isNaN(cellProb)) {
                          continue;
                      }
  
                      p += cellProb;
                      continue loop;
                  }
              }
  
              break;
          }
  
          return p;
      }
  
      public double getConditionalProb(Proposition assertion,
                                       Proposition condition) {
          if (assertion.getVariableSource() != condition.getVariableSource()) {
              throw new IllegalArgumentException(
                      "Assertion and condition must be " +
                              "for the same Bayes IM.");
          }
  
          int[] variableValues = new int[condition.getNumVariables()];
  
          for (int i = 0; i < condition.getNumVariables(); i++) {
              variableValues[i] = nextValue(condition, i, -1);
          }
  
          variableValues[variableValues.length - 1] = -1;
          double conditionTrue = 0.0;
          double assertionTrue = 0.0;
  
          loop:
          while (true) {
              for (int i = condition.getNumVariables() - 1; i >= 0; i--) {
                  if (hasNextValue(condition, i, variableValues[i])) {
                      variableValues[i] =
                              nextValue(condition, i, variableValues[i]);
  
                      for (int j = i + 1; j < condition.getNumVariables(); j++) {
                          if (hasNextValue(condition, j, -1)) {
                              variableValues[j] = nextValue(condition, j, -1);
                          } else {
                              break loop;
                          }
                      }
  
                      double cellProb = getCellProb(variableValues);
  
                      if (Double.isNaN(cellProb)) {
                          continue;
                      }
  
                      boolean assertionHolds = true;
  
                      for (int j = 0; j < assertion.getNumVariables(); j++) {
                          if (!assertion.isAllowed(j, variableValues[j])) {
                              assertionHolds = false;
                              break;
                          }
                      }
  
                      if (assertionHolds) {
                          assertionTrue += cellProb;
                      }
  
                      conditionTrue += cellProb;
                      continue loop;
                  }
              }
  
              break;
          }
  
          return assertionTrue / conditionTrue;
      }
  
      public boolean isMissingValueCaseFound() {
          return false;
      }
  
      public List<Node> getVariables() {
          return this.variables;
      }
  
      private static boolean hasNextValue(Proposition proposition, int variable,
                                          int currentIndex) {
          return nextValue(proposition, variable, currentIndex) != -1;
      }
  
      private static int nextValue(Proposition proposition, int variable,
                                   int currentIndex) {
          for (int i = currentIndex + 1;
               i < proposition.getNumCategories(variable); i++) {
              if (proposition.isAllowed(variable, i)) {
                  return i;
              }
          }
  
          return -1;
      }
  
      /**
       * 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 (variables == null) {
              throw new NullPointerException();
          }
      }
  }
  
  
  
  
  
  