115 lines
3.6 KiB
C++
115 lines
3.6 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 small.cpp
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* @brief UGM (undirected graphical model) examples: chain
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* @author Frank Dellaert
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* @author Abhijit Kundu
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*
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* See http://www.di.ens.fr/~mschmidt/Software/UGM/chain.html
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* for more explanation. This code demos the same example using GTSAM.
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*/
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#include <gtsam/discrete/DiscreteFactorGraph.h>
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#include <gtsam/discrete/DiscreteSequentialSolver.h>
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#include <gtsam/discrete/DiscreteMarginals.h>
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#include <gtsam/base/timing.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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// Set Number of Nodes in the Graph
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const int nrNodes = 60;
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// Each node takes 1 of 7 possible states denoted by 0-6 in following order:
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// ["VideoGames" "Industry" "GradSchool" "VideoGames(with PhD)"
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// "Industry(with PhD)" "Academia" "Deceased"]
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const size_t nrStates = 7;
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// define variables
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vector<DiscreteKey> nodes;
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for (int i = 0; i < nrNodes; i++){
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DiscreteKey dk(i, nrStates);
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nodes.push_back(dk);
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}
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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(nodes[0], ".3 .6 .1 0 0 0 0");
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for (int i = 1; i < nrNodes; i++)
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graph.add(nodes[i], "1 1 1 1 1 1 1");
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const std::string edgePotential = ".08 .9 .01 0 0 0 .01 "
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".03 .95 .01 0 0 0 .01 "
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".06 .06 .75 .05 .05 .02 .01 "
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"0 0 0 .3 .6 .09 .01 "
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"0 0 0 .02 .95 .02 .01 "
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"0 0 0 .01 .01 .97 .01 "
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"0 0 0 0 0 0 1";
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// add edge potentials
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for (int i = 0; i < nrNodes - 1; i++)
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graph.add(nodes[i] & nodes[i + 1], edgePotential);
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cout << "Created Factor Graph with " << nrNodes << " variable nodes and "
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<< graph.size() << " factors (Unary+Edge).";
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// "Decoding", i.e., configuration with largest value
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// We use sequential variable elimination
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DiscreteSequentialSolver solver(graph);
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DiscreteFactor::sharedValues optimalDecoding = solver.optimize();
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optimalDecoding->print("\nMost Probable Explanation (optimalDecoding)\n");
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// "Inference" Computing marginals for each node
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cout << "\nComputing Node Marginals ..(Sequential Elimination)" << endl;
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gttic_(Sequential);
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for (vector<DiscreteKey>::iterator itr = nodes.begin(); itr != nodes.end();
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++itr) {
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//Compute the marginal
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Vector margProbs = solver.marginalProbabilities(*itr);
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//Print the marginals
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cout << "Node#" << setw(4) << itr->first << " : ";
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print(margProbs);
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}
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gttoc_(Sequential);
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// Here we'll make use of DiscreteMarginals class, which makes use of
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// bayes-tree based shortcut evaluation of marginals
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DiscreteMarginals marginals(graph);
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cout << "\nComputing Node Marginals ..(BayesTree based)" << endl;
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gttic_(Multifrontal);
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for (vector<DiscreteKey>::iterator itr = nodes.begin(); itr != nodes.end();
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++itr) {
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//Compute the marginal
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Vector margProbs = marginals.marginalProbabilities(*itr);
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//Print the marginals
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cout << "Node#" << setw(4) << itr->first << " : ";
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print(margProbs);
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}
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gttoc_(Multifrontal);
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tictoc_print_();
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return 0;
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}
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