UGM_small.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/base/Vector.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteMarginals.h>
 
using namespace std;
using namespace gtsam;
 
int main(int argc, char** argv) {
 
  // We will assume 2-state variables, where, to conform to the "small" example
  // we have 0 == "right answer" and 1 == "wrong answer"
  size_t nrStates = 2;
 
  // define variables
  DiscreteKey Cathy(1, nrStates), Heather(2, nrStates), Mark(3, nrStates),
      Allison(4, nrStates);
 
  // create graph
  DiscreteFactorGraph graph;
 
  // add node potentials
  graph.add(Cathy,   "1 3");
  graph.add(Heather, "9 1");
  graph.add(Mark,    "1 3");
  graph.add(Allison, "9 1");
 
  // add edge potentials
  graph.add(Cathy & Heather, "2 1 1 2");
  graph.add(Heather & Mark,  "2 1 1 2");
  graph.add(Mark & Allison,  "2 1 1 2");
 
  // Print the UGM distribution
  cout << "\nUGM distribution:" << endl;
  auto allPosbValues =
      DiscreteValues::CartesianProduct(Cathy & Heather & Mark & Allison);
  for (size_t i = 0; i < allPosbValues.size(); ++i) {
    DiscreteFactor::Values values = allPosbValues[i];
    double prodPot = graph(values);
    cout << values[Cathy.first] << " " << values[Heather.first] << " "
        << values[Mark.first] << " " << values[Allison.first] << " :\t"
        << prodPot << "\t" << prodPot / 3790 << endl;
  }
 
  // "Decoding", i.e., configuration with largest value (MPE)
  // Uses max-product
  auto optimalDecoding = graph.optimize();
  GTSAM_PRINT(optimalDecoding);
 
  // "Inference" Computing marginals
  cout << "\nComputing Node Marginals .." << endl;
  DiscreteMarginals marginals(graph);
 
  Vector margProbs = marginals.marginalProbabilities(Cathy);
  print(margProbs, "Cathy's Node Marginal:");
 
  margProbs = marginals.marginalProbabilities(Heather);
  print(margProbs, "Heather's Node Marginal");
 
  margProbs = marginals.marginalProbabilities(Mark);
  print(margProbs, "Mark's Node Marginal");
 
  margProbs = marginals.marginalProbabilities(Allison);
  print(margProbs, "Allison's Node Marginal");
 
  return 0;
}