121 lines
4.4 KiB
C++
121 lines
4.4 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 DiscreteBayesNet_FG.cpp
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* @brief Discrete Bayes Net example using Factor Graphs
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* @author Abhijit
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* @date Jun 4, 2012
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*
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* We use the famous Rain/Cloudy/Sprinkler Example of [Russell & Norvig, 2009,
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* p529] You may be familiar with other graphical model packages like BNT
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* (available at http://bnt.googlecode.com/svn/trunk/docs/usage.html) where this
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* is used as an example. The following demo is same as that in the above link,
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* except that everything is using GTSAM.
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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 <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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// Define keys and a print function
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Key C(1), S(2), R(3), W(4);
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auto print = [=](DiscreteFactor::sharedValues values) {
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cout << boolalpha << "Cloudy = " << static_cast<bool>((*values)[C])
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<< " Sprinkler = " << static_cast<bool>((*values)[S])
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<< " Rain = " << boolalpha << static_cast<bool>((*values)[R])
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<< " WetGrass = " << static_cast<bool>((*values)[W]) << endl;
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};
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// We assume binary state variables
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// we have 0 == "False" and 1 == "True"
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const size_t nrStates = 2;
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// define variables
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DiscreteKey Cloudy(C, nrStates), Sprinkler(S, nrStates), Rain(R, nrStates),
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WetGrass(W, nrStates);
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// create Factor Graph of the bayes net
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DiscreteFactorGraph graph;
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// add factors
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graph.add(Cloudy, "0.5 0.5"); // P(Cloudy)
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graph.add(Cloudy & Sprinkler, "0.5 0.5 0.9 0.1"); // P(Sprinkler | Cloudy)
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graph.add(Cloudy & Rain, "0.8 0.2 0.2 0.8"); // P(Rain | Cloudy)
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graph.add(Sprinkler & Rain & WetGrass,
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"1 0 0.1 0.9 0.1 0.9 0.001 0.99"); // P(WetGrass | Sprinkler, Rain)
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// Alternatively we can also create a DiscreteBayesNet, add
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// DiscreteConditional factors and create a FactorGraph from it. (See
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// testDiscreteBayesNet.cpp)
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// Since this is a relatively small distribution, we can as well print
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// the whole distribution..
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cout << "Distribution of Example: " << endl;
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cout << setw(11) << "Cloudy(C)" << setw(14) << "Sprinkler(S)" << setw(10)
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<< "Rain(R)" << setw(14) << "WetGrass(W)" << setw(15) << "P(C,S,R,W)"
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<< endl;
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for (size_t a = 0; a < nrStates; a++)
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for (size_t m = 0; m < nrStates; m++)
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for (size_t h = 0; h < nrStates; h++)
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for (size_t c = 0; c < nrStates; c++) {
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DiscreteFactor::Values values;
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values[C] = c;
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values[S] = h;
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values[R] = m;
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values[W] = a;
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double prodPot = graph(values);
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cout << setw(8) << static_cast<bool>(c) << setw(14)
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<< static_cast<bool>(h) << setw(12) << static_cast<bool>(m)
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<< setw(13) << static_cast<bool>(a) << setw(16) << prodPot
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<< endl;
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}
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// "Most Probable Explanation", i.e., configuration with largest value
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DiscreteFactor::sharedValues mpe = graph.eliminateSequential()->optimize();
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cout << "\nMost Probable Explanation (MPE):" << endl;
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print(mpe);
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// "Inference" We show an inference query like: probability that the Sprinkler
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// was on; given that the grass is wet i.e. P( S | C=0) = ?
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// add evidence that it is not Cloudy
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graph.add(Cloudy, "1 0");
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// solve again, now with evidence
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DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
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DiscreteFactor::sharedValues mpe_with_evidence = chordal->optimize();
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cout << "\nMPE given C=0:" << endl;
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print(mpe_with_evidence);
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// we can also calculate arbitrary marginals:
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DiscreteMarginals marginals(graph);
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cout << "\nP(S=1|C=0):" << marginals.marginalProbabilities(Sprinkler)[1]
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<< endl;
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cout << "\nP(R=0|C=0):" << marginals.marginalProbabilities(Rain)[0] << endl;
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cout << "\nP(W=1|C=0):" << marginals.marginalProbabilities(WetGrass)[1]
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<< endl;
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// We can also sample from it
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cout << "\n10 samples:" << endl;
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for (size_t i = 0; i < 10; i++) {
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DiscreteFactor::sharedValues sample = chordal->sample();
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print(sample);
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}
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return 0;
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}
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