DiscreteBayesNetExample.cpp

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/* ----------------------------------------------------------------------------
 
 * GTSAM Copyright 2010, Georgia Tech Research Corporation,
 * Atlanta, Georgia 30332-0415
 * All Rights Reserved
 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
 
 * See LICENSE for the license information
 
 * -------------------------------------------------------------------------- */
 
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteMarginals.h>
#include <gtsam/inference/BayesNet.h>
 
#include <iomanip>
 
using namespace std;
using namespace gtsam;
 
int main(int argc, char **argv) {
  DiscreteBayesNet asia;
  DiscreteKey Asia(0, 2), Smoking(4, 2), Tuberculosis(3, 2), LungCancer(6, 2),
      Bronchitis(7, 2), Either(5, 2), XRay(2, 2), Dyspnea(1, 2);
  asia.add(Asia % "99/1");
  asia.add(Smoking % "50/50");
 
  asia.add(Tuberculosis | Asia = "99/1 95/5");
  asia.add(LungCancer | Smoking = "99/1 90/10");
  asia.add(Bronchitis | Smoking = "70/30 40/60");
 
  asia.add((Either | Tuberculosis, LungCancer) = "F T T T");
 
  asia.add(XRay | Either = "95/5 2/98");
  asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
 
  // print
  vector<string> pretty = {"Asia",    "Dyspnea", "XRay",       "Tuberculosis",
                           "Smoking", "Either",  "LungCancer", "Bronchitis"};
  auto formatter = [pretty](Key key) { return pretty[key]; };
  asia.print("Asia", formatter);
 
  // Convert to factor graph
  DiscreteFactorGraph fg(asia);
 
  // Create solver and eliminate
  const Ordering ordering{0, 1, 2, 3, 4, 5, 6, 7};
 
  // solve
  auto mpe = fg.optimize();
  GTSAM_PRINT(mpe);
 
  // We can also build a Bayes tree (directed junction tree).
  // The elimination order above will do fine:
  auto bayesTree = fg.eliminateMultifrontal(ordering);
  bayesTree->print("bayesTree", formatter);
 
  // add evidence, we were in Asia and we have dyspnea
  fg.add(Asia, "0 1");
  fg.add(Dyspnea, "0 1");
 
  // solve again, now with evidence
  auto mpe2 = fg.optimize();
  GTSAM_PRINT(mpe2);
 
  // We can also sample from it
  DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
  cout << "\n10 samples:" << endl;
  for (size_t i = 0; i < 10; i++) {
    auto sample = chordal->sample();
    GTSAM_PRINT(sample);
  }
  return 0;
}