82 lines
2.4 KiB
C++
82 lines
2.4 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file DiscreteBayesNetExample.cpp
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* @brief Discrete Bayes Net example with famous Asia Bayes Network
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* @author Frank Dellaert
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* @date JULY 10, 2020
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*/
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#include <gtsam/discrete/DiscreteFactorGraph.h>
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#include <gtsam/discrete/DiscreteMarginals.h>
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#include <gtsam/inference/BayesNet.h>
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#include <iomanip>
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using namespace std;
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using namespace gtsam;
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int main(int argc, char **argv) {
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DiscreteBayesNet asia;
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DiscreteKey Asia(0, 2), Smoking(4, 2), Tuberculosis(3, 2), LungCancer(6, 2),
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Bronchitis(7, 2), Either(5, 2), XRay(2, 2), Dyspnea(1, 2);
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asia.add(Asia % "99/1");
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asia.add(Smoking % "50/50");
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asia.add(Tuberculosis | Asia = "99/1 95/5");
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asia.add(LungCancer | Smoking = "99/1 90/10");
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asia.add(Bronchitis | Smoking = "70/30 40/60");
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asia.add((Either | Tuberculosis, LungCancer) = "F T T T");
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asia.add(XRay | Either = "95/5 2/98");
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asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
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// print
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vector<string> pretty = {"Asia", "Dyspnea", "XRay", "Tuberculosis",
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"Smoking", "Either", "LungCancer", "Bronchitis"};
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auto formatter = [pretty](Key key) { return pretty[key]; };
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asia.print("Asia", formatter);
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// Convert to factor graph
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DiscreteFactorGraph fg(asia);
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// Create solver and eliminate
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const Ordering ordering{0, 1, 2, 3, 4, 5, 6, 7};
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// solve
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auto mpe = fg.optimize();
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GTSAM_PRINT(mpe);
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// We can also build a Bayes tree (directed junction tree).
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// The elimination order above will do fine:
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auto bayesTree = fg.eliminateMultifrontal(ordering);
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bayesTree->print("bayesTree", formatter);
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// add evidence, we were in Asia and we have dyspnea
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fg.add(Asia, "0 1");
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fg.add(Dyspnea, "0 1");
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// solve again, now with evidence
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auto mpe2 = fg.optimize();
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GTSAM_PRINT(mpe2);
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// We can also sample from it
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DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
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cout << "\n10 samples:" << endl;
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for (size_t i = 0; i < 10; i++) {
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auto sample = chordal->sample();
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GTSAM_PRINT(sample);
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}
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return 0;
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}
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