88 lines
3.1 KiB
C++
88 lines
3.1 KiB
C++
/**
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* @file inference-inl.h
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* @brief inference template definitions
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* @author Frank Dellaert
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*/
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//#include "inference.h"
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#include "FactorGraph-inl.h"
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#include "BayesNet-inl.h"
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using namespace std;
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namespace gtsam {
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/* ************************************************************************* */
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/* eliminate one node from the factor graph */
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/* ************************************************************************* */
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template<class Factor,class Conditional>
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boost::shared_ptr<Conditional> eliminateOne(FactorGraph<Factor>& graph, const string& key) {
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// combine the factors of all nodes connected to the variable to be eliminated
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// if no factors are connected to key, returns an empty factor
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boost::shared_ptr<Factor> joint_factor = removeAndCombineFactors(graph,key);
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// eliminate that joint factor
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boost::shared_ptr<Factor> factor;
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boost::shared_ptr<Conditional> conditional;
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boost::tie(conditional, factor) = joint_factor->eliminate(key);
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// add new factor on separator back into the graph
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if (!factor->empty()) graph.push_back(factor);
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// return the conditional Gaussian
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return conditional;
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}
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/* ************************************************************************* */
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// This doubly templated function is generic. There is a GaussianFactorGraph
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// version that returns a more specific GaussianBayesNet.
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// Note, you will need to include this file to instantiate the function.
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/* ************************************************************************* */
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template<class Factor,class Conditional>
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BayesNet<Conditional> eliminate(FactorGraph<Factor>& factorGraph, const Ordering& ordering)
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{
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BayesNet<Conditional> bayesNet; // empty
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BOOST_FOREACH(string key, ordering) {
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boost::shared_ptr<Conditional> cg = eliminateOne<Factor,Conditional>(factorGraph,key);
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bayesNet.push_back(cg);
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}
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return bayesNet;
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}
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/* ************************************************************************* */
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template<class Factor, class Conditional>
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pair< BayesNet<Conditional>, FactorGraph<Factor> >
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factor(const BayesNet<Conditional>& bn, const Ordering& keys) {
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// Convert to factor graph
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FactorGraph<Factor> factorGraph(bn);
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// Get the keys of all variables and remove all keys we want the marginal for
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Ordering ord = bn.ordering();
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BOOST_FOREACH(string key, keys) ord.remove(key); // TODO: O(n*k), faster possible?
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// eliminate partially,
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BayesNet<Conditional> conditional = eliminate<Factor,Conditional>(factorGraph,ord);
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// at this moment, the factor graph only encodes P(keys)
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return make_pair(conditional,factorGraph);
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}
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/* ************************************************************************* */
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template<class Factor, class Conditional>
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FactorGraph<Factor> marginalize(const BayesNet<Conditional>& bn, const Ordering& keys) {
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// factor P(X,Y) as P(X|Y)P(Y), where Y corresponds to keys
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pair< BayesNet<Conditional>, FactorGraph<Factor> > factors =
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gtsam::factor<Factor,Conditional>(bn,keys);
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// throw away conditional, return marginal P(Y)
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return factors.second;
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}
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/* ************************************************************************* */
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} // namespace gtsam
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