Better comments
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@ -30,18 +30,22 @@
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namespace gtsam {
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/**
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* In gtsam a junction tree is an intermediate data structure in multifrontal
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* variable elimination. Each node is a cluster of factors, along with a
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* clique of variables that are eliminated all at once. The tree structure and
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* elimination method are exactly analagous to the EliminationTree, except that
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* in the JunctionTree, at each node multiple variables are eliminated at the same
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* time.
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* A ClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with
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* the additional property that it represents the clique tree associated with a Bayes net.
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*
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* In GTSAM a junction tree is an intermediate data structure in multifrontal
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* variable elimination. Each node is a cluster of factors, along with a
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* clique of variables that are eliminated all at once. In detail, every node k represents
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* a clique (maximal fully connected subset) of an associated chordal graph, such as a
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* chordal Bayes net resulting from elimination.
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*
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* The difference with the BayesTree is that a JunctionTree stores factors, whereas a
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* BayesTree stores conditionals, that are the product of eliminating the factors in the
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* corresponding JunctionTree cliques.
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*
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* The tree structure and elimination method are exactly analagous to the EliminationTree,
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* except that in the JunctionTree, at each node multiple variables are eliminated at a time.
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*
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* A junction tree (or clique-tree) is a cluster-tree where each node k represents a
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* clique (maximal fully connected subset) of an associated chordal graph, such as a
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* chordal Bayes net resulting from elimination. In GTSAM the BayesTree is used to
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* represent the clique tree associated with a Bayes net, and the JunctionTree is
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* used to collect the factors associated with each clique during the elimination process.
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*
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* \ingroup Multifrontal
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*/
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@ -27,9 +27,13 @@
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namespace gtsam {
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/* ************************************************************************* */
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/**
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* GaussianJunctionTree that does the optimization
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* A JunctionTree where all the factors are of type GaussianFactor.
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*
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* In GTSAM, typically, a GaussianJunctionTree is created directly from a GaussianFactorGraph,
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* after which you call optimize() to solve for the mean, or JunctionTree::eliminate() to
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* create a BayesTree<GaussianConditional>. In both cases, you need to provide a basic
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* GaussianFactorGraph::Eliminate function that will be used to
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*
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* \ingroup Multifrontal
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*/
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@ -60,7 +64,9 @@ namespace gtsam {
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GaussianJunctionTree(const GaussianFactorGraph& fg, const VariableIndex& variableIndex)
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: Base(fg, variableIndex) {}
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// optimize the linear graph
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/**
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* optimize the linear graph
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*/
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VectorValues optimize(Eliminate function) const;
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// convenient function to return dimensions of all variables in the BayesTree<GaussianConditional>
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