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Improving documentation

release/4.3a0
Richard Roberts 2011-09-05 18:37:43 +00:00
parent 6512c6b822
commit e56b6ff392
4 changed files with 44 additions and 15 deletions
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4
gtsam/inference/EliminationTree.h
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@ -110,8 +110,8 @@ public:
bool equals(const EliminationTree& other, double tol = 1e-9) const;
/** Eliminate the factors to a Bayes Net
* @param function The function to use to eliminate, see the static member
* functions of GaussianFactorGraph
* @param function The function to use to eliminate, see the namespace functions
* in GaussianFactorGraph.h
* @return The BayesNet resulting from elimination
*/
typename BayesNet::shared_ptr eliminate(Eliminate function) const;

39
gtsam/inference/JunctionTree.h
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@ -30,6 +30,13 @@
namespace gtsam {
/**
* In gtsam a junction tree is an intermediate data structure in multifrontal
* variable elimination. Each node is a cluster of factors, along with a
* clique of variables that are eliminated all at once. The tree structure and
* elimination method are exactly analagous to the EliminationTree, except that
* in the JunctionTree, at each node multiple variables are eliminated at the same
* time.
*
* A junction tree (or clique-tree) is a cluster-tree where each node k represents a
* clique (maximal fully connected subset) of an associated chordal graph, such as a
* chordal Bayes net resulting from elimination. In GTSAM the BayesTree is used to
@ -41,15 +48,17 @@ namespace gtsam {
public:
// In a junction tree each cluster is associated with a clique
/// In a junction tree each cluster is associated with a clique
typedef typename ClusterTree<FG>::Cluster Clique;
typedef typename Clique::shared_ptr sharedClique;
typedef typename Clique::shared_ptr sharedClique; ///< Shared pointer to a clique
/// The BayesTree type produced by elimination
typedef class BayesTree<typename FG::FactorType::ConditionalType> BayesTree;
/// Shared pointer to this class
typedef boost::shared_ptr<JunctionTree<FG> > shared_ptr;
// And we will frequently refer to a symbolic Bayes tree
/// We will frequently refer to a symbolic Bayes tree, used to find the clique structure
typedef gtsam::BayesTree<IndexConditional> SymbolicBayesTree;
private:
@ -73,13 +82,29 @@ namespace gtsam {
/** Default constructor */
JunctionTree() {}
/** Construct from a factor graph. Computes a variable index. */
JunctionTree(const FG& fg);
/** Named constructor to build the junction tree of a factor graph. Note
* that this has to compute the column structure as a VariableIndex, so if you
* already have this precomputed, use the JunctionTree(const FG&, const VariableIndex&)
* constructor instead.
* @param factorGraph The factor graph for which to build the elimination tree
*/
JunctionTree(const FG& factorGraph);
/** Construct from a factor graph and a pre-computed variable index. */
/** Construct from a factor graph and pre-computed variable index.
* @param factorGraph The factor graph for which to build the junction tree
* @param structure The set of factors involving each variable. If this is not
* precomputed, you can call the JunctionTree(const FG&)
* constructor instead.
*/
JunctionTree(const FG& fg, const VariableIndex& variableIndex);
// eliminate the factors in the subgraphs
/** Eliminate the factors in the subgraphs to produce a BayesTree.
* @param function The function used to eliminate, see the namespace functions
* in GaussianFactorGraph.h
* @param cache Whether to cache the intermediate elimination factors for use in ISAM2 - this
* should always be false when called outside of ISAM2 (this will be fixed in the future).
* @return The BayesTree resulting from elimination
*/
typename BayesTree::sharedClique eliminate(typename FG::Eliminate function,
bool cache = false) const;

7
gtsam/linear/GaussianFactor.h
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@ -11,8 +11,8 @@
/**
* @file GaussianFactor.h
* @brief Linear Factor....A Gaussian
* @brief linearFactor
* @brief A factor with a quadratic error function - a Gaussian
* @brief GaussianFactor
* @author Richard Roberts, Christian Potthast
*/
@ -34,7 +34,8 @@ namespace gtsam {
template<class C> class BayesNet;
/**
* Base Class for a linear factor.
* An abstract virtual base class for JacobianFactor and HessianFactor.
* A GaussianFactor has a quadratic error function.
* GaussianFactor is non-mutable (all methods const!).
* The factor value is exp(-0.5*||Ax-b||^2)
*/

9
gtsam/linear/GaussianFactorGraph.h
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@ -32,7 +32,8 @@ namespace gtsam {
struct SharedDiagonal;
/** unnormalized error */
/** unnormalized error
* \todo Make this a member function - affects SubgraphPreconditioner */
template<class FACTOR>
double gaussianError(const FactorGraph<FACTOR>& fg, const VectorValues& x) {
double total_error = 0.;
@ -42,13 +43,15 @@ namespace gtsam {
return total_error;
}
/** return A*x-b */
/** return A*x-b
* \todo Make this a member function - affects SubgraphPreconditioner */
template<class FACTOR>
Errors gaussianErrors(const FactorGraph<FACTOR>& fg, const VectorValues& x) {
return *gaussianErrors_(fg, x);
}
/** shared pointer version */
/** shared pointer version
* \todo Make this a member function - affects SubgraphPreconditioner */
template<class FACTOR>
boost::shared_ptr<Errors> gaussianErrors_(const FactorGraph<FACTOR>& fg, const VectorValues& x) {
boost::shared_ptr<Errors> e(new Errors);