88 lines
2.7 KiB
C++
88 lines
2.7 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 UGM_small.cpp
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* @brief UGM (undirected graphical model) examples: small
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* @author Frank Dellaert
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*
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* See http://www.di.ens.fr/~mschmidt/Software/UGM/small.html
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*/
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#include <gtsam/base/Vector.h>
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#include <gtsam/discrete/DiscreteFactorGraph.h>
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#include <gtsam/discrete/DiscreteMarginals.h>
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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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// We will assume 2-state variables, where, to conform to the "small" example
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// we have 0 == "right answer" and 1 == "wrong answer"
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size_t nrStates = 2;
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// define variables
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DiscreteKey Cathy(1, nrStates), Heather(2, nrStates), Mark(3, nrStates),
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Allison(4, nrStates);
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// create graph
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DiscreteFactorGraph graph;
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// add node potentials
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graph.add(Cathy, "1 3");
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graph.add(Heather, "9 1");
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graph.add(Mark, "1 3");
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graph.add(Allison, "9 1");
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// add edge potentials
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graph.add(Cathy & Heather, "2 1 1 2");
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graph.add(Heather & Mark, "2 1 1 2");
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graph.add(Mark & Allison, "2 1 1 2");
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// Print the UGM distribution
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cout << "\nUGM distribution:" << endl;
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vector<DiscreteFactor::Values> allPosbValues = cartesianProduct(
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Cathy & Heather & Mark & Allison);
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for (size_t i = 0; i < allPosbValues.size(); ++i) {
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DiscreteFactor::Values values = allPosbValues[i];
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double prodPot = graph(values);
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cout << values[Cathy.first] << " " << values[Heather.first] << " "
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<< values[Mark.first] << " " << values[Allison.first] << " :\t"
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<< prodPot << "\t" << prodPot / 3790 << endl;
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}
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// "Decoding", i.e., configuration with largest value (MPE)
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// We use sequential variable elimination
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DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
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DiscreteFactor::sharedValues optimalDecoding = chordal->optimize();
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optimalDecoding->print("\noptimalDecoding");
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// "Inference" Computing marginals
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cout << "\nComputing Node Marginals .." << endl;
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DiscreteMarginals marginals(graph);
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Vector margProbs = marginals.marginalProbabilities(Cathy);
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print(margProbs, "Cathy's Node Marginal:");
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margProbs = marginals.marginalProbabilities(Heather);
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print(margProbs, "Heather's Node Marginal");
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margProbs = marginals.marginalProbabilities(Mark);
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print(margProbs, "Mark's Node Marginal");
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margProbs = marginals.marginalProbabilities(Allison);
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print(margProbs, "Allison's Node Marginal");
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return 0;
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}
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