gtsam/gtsam/inference/BayesTreeCliqueBase.h

186 lines
6.3 KiB
C++

/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file BayesTreeCliqueBase.h
* @brief Base class for cliques of a BayesTree
* @author Richard Roberts and Frank Dellaert
*/
#pragma once
#include <gtsam/inference/Key.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/base/types.h>
#include <gtsam/base/FastVector.h>
#include <boost/optional.hpp>
#include <string>
namespace gtsam {
// Forward declarations
template<class CLIQUE> class BayesTree;
template<class GRAPH> struct EliminationTraits;
/**
* This is the base class for BayesTree cliques. The default and standard derived type is
* BayesTreeClique, but some algorithms, like iSAM2, use a different clique type in order to store
* extra data along with the clique.
*
* This class is templated on the derived class (i.e. the curiously recursive template pattern).
* The advantage of this over using virtual classes is that it avoids the need for casting to get
* the derived type. This is possible because all cliques in a BayesTree are the same type - if
* they were not then we'd need a virtual class.
*
* @tparam DERIVED The derived clique type.
* @tparam CONDITIONAL The conditional type.
* \nosubgrouping */
template<class DERIVED, class FACTORGRAPH>
class BayesTreeCliqueBase
{
private:
typedef BayesTreeCliqueBase<DERIVED, FACTORGRAPH> This;
typedef DERIVED DerivedType;
typedef EliminationTraits<FACTORGRAPH> EliminationTraitsType;
typedef boost::shared_ptr<This> shared_ptr;
typedef boost::weak_ptr<This> weak_ptr;
typedef boost::shared_ptr<DerivedType> derived_ptr;
typedef boost::weak_ptr<DerivedType> derived_weak_ptr;
public:
typedef FACTORGRAPH FactorGraphType;
typedef typename EliminationTraitsType::BayesNetType BayesNetType;
typedef typename BayesNetType::ConditionalType ConditionalType;
typedef boost::shared_ptr<ConditionalType> sharedConditional;
typedef typename FactorGraphType::FactorType FactorType;
typedef typename FactorGraphType::Eliminate Eliminate;
protected:
/// @name Standard Constructors
/// @{
/** Default constructor */
BayesTreeCliqueBase() : problemSize_(1) {}
/** Construct from a conditional, leaving parent and child pointers uninitialized */
BayesTreeCliqueBase(const sharedConditional& conditional) : conditional_(conditional), problemSize_(1) {}
/// @}
/// This stores the Cached separator margnal P(S)
mutable boost::optional<FactorGraphType> cachedSeparatorMarginal_;
public:
sharedConditional conditional_;
derived_weak_ptr parent_;
FastVector<derived_ptr> children;
int problemSize_;
bool is_root = false;
/// Fill the elimination result produced during elimination. Here this just stores the
/// conditional and ignores the remaining factor, but this is overridden in ISAM2Clique
/// to also cache the remaining factor.
void setEliminationResult(const typename FactorGraphType::EliminationResult& eliminationResult);
/// @name Testable
/// @{
/** check equality */
bool equals(const DERIVED& other, double tol = 1e-9) const;
/** print this node */
virtual void print(const std::string& s = "", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
/// @}
/// @name Standard Interface
/// @{
/** Access the conditional */
const sharedConditional& conditional() const { return conditional_; }
/** is this the root of a Bayes tree ? */
inline bool isRoot() const { return parent_.expired(); }
/** The size of subtree rooted at this clique, i.e., nr of Cliques */
size_t treeSize() const;
/** Collect number of cliques with cached separator marginals */
size_t numCachedSeparatorMarginals() const;
/** return a shared_ptr to the parent clique */
derived_ptr parent() const { return parent_.lock(); }
/** Problem size (used for parallel traversal) */
int problemSize() const { return problemSize_; }
/// @}
/// @name Advanced Interface
/// @{
/** return the conditional P(S|Root) on the separator given the root */
BayesNetType shortcut(const derived_ptr& root, Eliminate function = EliminationTraitsType::DefaultEliminate) const;
/** return the marginal P(S) on the separator */
FactorGraphType separatorMarginal(Eliminate function = EliminationTraitsType::DefaultEliminate) const;
/** return the marginal P(C) of the clique, using marginal caching */
FactorGraphType marginal2(Eliminate function = EliminationTraitsType::DefaultEliminate) const;
/**
* This deletes the cached shortcuts of all cliques (subtree) below this clique.
* This is performed when the bayes tree is modified.
*/
void deleteCachedShortcuts();
const boost::optional<FactorGraphType>& cachedSeparatorMarginal() const {
return cachedSeparatorMarginal_; }
friend class BayesTree<DerivedType>;
protected:
/// Calculate set \f$ S \setminus B \f$ for shortcut calculations
KeyVector separator_setminus_B(const derived_ptr& B) const;
/** Determine variable indices to keep in recursive separator shortcut calculation The factor
* graph p_Cp_B has keys from the parent clique Cp and from B. But we only keep the variables
* not in S union B. */
KeyVector shortcut_indices(const derived_ptr& B, const FactorGraphType& p_Cp_B) const;
/** Non-recursive delete cached shortcuts and marginals - internal only. */
void deleteCachedShortcutsNonRecursive() { cachedSeparatorMarginal_ = boost::none; }
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
if(!parent_.lock()) {
is_root = true;
}
ar & BOOST_SERIALIZATION_NVP(is_root);
ar & BOOST_SERIALIZATION_NVP(conditional_);
if (!is_root) { // TODO(fan): Workaround for boost/serialization #119
ar & BOOST_SERIALIZATION_NVP(parent_);
}
ar & BOOST_SERIALIZATION_NVP(children);
}
/// @}
};
}