91 lines
3.0 KiB
C++
91 lines
3.0 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 inference.h
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* @brief Contains *generic* inference algorithms that convert between templated
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* graphical models, i.e., factor graphs, Bayes nets, and Bayes trees
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* @author Frank Dellaert, Richard Roberts
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*/
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#pragma once
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#include <gtsam/base/types.h>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/inference/BayesNet.h>
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#include <gtsam/inference/VariableIndex.h>
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#include <gtsam/inference/Permutation.h>
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#include <vector>
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#include <deque>
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namespace gtsam {
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class Inference {
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private:
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/* Static members only, private constructor */
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Inference() {}
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public:
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/**
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* Compute a permutation (variable ordering) using colamd
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*/
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template<class VARIABLEINDEXTYPE>
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static boost::shared_ptr<Permutation> PermutationCOLAMD(const VARIABLEINDEXTYPE& variableIndex) { return PermutationCOLAMD(variableIndex, std::vector<Index>()); }
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template<class VARIABLEINDEXTYPE, typename CONSTRAINTCONTAINER>
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static boost::shared_ptr<Permutation> PermutationCOLAMD(const VARIABLEINDEXTYPE& variableIndex, const CONSTRAINTCONTAINER& constrainLast);
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};
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// ELIMINATE: FACTOR GRAPH -> BAYES NET
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// /**
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// * Eliminate a single node yielding a Conditional
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// * Eliminates the factors from the factor graph through findAndRemoveFactors
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// * and adds a new factor on the separator to the factor graph
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// */
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// template<class Factor, class Conditional>
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// boost::shared_ptr<Conditional>
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// eliminateOne(FactorGraph<Factor>& factorGraph, Index key);
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//
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// /**
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// * eliminate factor graph using the given (not necessarily complete)
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// * ordering, yielding a chordal Bayes net and (partially eliminated) FG
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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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// FACTOR/MARGINALIZE: BAYES NET -> FACTOR GRAPH
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// /**
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// * Factor P(X) as P(not keys|keys) P(keys)
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// * @return P(not keys|keys) as an incomplete BayesNet, and P(keys) as a factor graph
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// */
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// template<class Factor, class Conditional>
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// std::pair< BayesNet<Conditional>, FactorGraph<Factor> >
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// factor(const BayesNet<Conditional>& bn, const Ordering& keys);
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//
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// /**
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// * integrate out all except ordering, might be inefficient as the ordering
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// * will simply be the current ordering with the keys put in the back
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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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/**
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* Hacked-together function to compute a Gaussian marginal for the given variable.
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* todo: This is inefficient!
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*/
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//std::pair<Vector,Matrix> marginalGaussian(const GaussianFactorGraph& fg, const Symbol& key);
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} /// namespace gtsam
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