gtsam/inference/JunctionTree-inl.h

210 lines
8.4 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
* -------------------------------------------------------------------------- */
/*
* JunctionTree-inl.h
* Created on: Feb 4, 2010
* @Author: Kai Ni
* @Author: Frank Dellaert
* @brief: The junction tree, template bodies
*/
#pragma once
#include <gtsam/inference/SymbolicFactorGraph.h>
#include <gtsam/inference/BayesTree-inl.h>
#include <gtsam/inference/JunctionTree.h>
#include <gtsam/inference/inference-inl.h>
#include <gtsam/inference/VariableSlots-inl.h>
#include <gtsam/inference/SymbolicSequentialSolver.h>
#include <boost/foreach.hpp>
#include <boost/pool/pool_alloc.hpp>
#include <boost/lambda/bind.hpp>
#include <boost/lambda/lambda.hpp>
namespace gtsam {
using namespace std;
/* ************************************************************************* */
template <class FG>
JunctionTree<FG>::JunctionTree(const FG& fg) {
tic("JT 1 constructor");
// Symbolic factorization: GaussianFactorGraph -> SymbolicFactorGraph
// -> SymbolicBayesNet -> SymbolicBayesTree
tic("JT 1.1 symbolic elimination");
SymbolicBayesNet::shared_ptr sbn = SymbolicSequentialSolver(fg).eliminate();
// SymbolicFactorGraph sfg(fg);
// SymbolicBayesNet::shared_ptr sbn_orig = Inference::Eliminate(sfg);
// assert(assert_equal(*sbn, *sbn_orig));
toc("JT 1.1 symbolic elimination");
tic("JT 1.2 symbolic BayesTree");
SymbolicBayesTree sbt(*sbn);
toc("JT 1.2 symbolic BayesTree");
// distribute factors
tic("JT 1.3 distributeFactors");
this->root_ = distributeFactors(fg, sbt.root());
toc("JT 1.3 distributeFactors");
toc("JT 1 constructor");
}
/* ************************************************************************* */
template<class FG>
typename JunctionTree<FG>::sharedClique JunctionTree<FG>::distributeFactors(
const FG& fg, const typename SymbolicBayesTree::sharedClique& bayesClique) {
// Build "target" index. This is an index for each variable of the factors
// that involve this variable as their *lowest-ordered* variable. For each
// factor, it is the lowest-ordered variable of that factor that pulls the
// factor into elimination, after which all of the information in the
// factor is contained in the eliminated factors that are passed up the
// tree as elimination continues.
// Two stages - first build an array of the lowest-ordered variable in each
// factor and find the last variable to be eliminated.
vector<Index> lowestOrdered(fg.size());
Index maxVar = 0;
for(size_t i=0; i<fg.size(); ++i)
if(fg[i]) {
typename FG::Factor::const_iterator min = std::min_element(fg[i]->begin(), fg[i]->end());
if(min == fg[i]->end())
lowestOrdered[i] = numeric_limits<Index>::max();
else {
lowestOrdered[i] = *min;
maxVar = std::max(maxVar, *min);
}
}
// Now add each factor to the list corresponding to its lowest-ordered
// variable.
vector<list<size_t, boost::fast_pool_allocator<size_t> > > targets(maxVar+1);
for(size_t i=0; i<lowestOrdered.size(); ++i)
if(lowestOrdered[i] != numeric_limits<Index>::max())
targets[lowestOrdered[i]].push_back(i);
// Now call the recursive distributeFactors
return distributeFactors(fg, targets, bayesClique);
}
/* ************************************************************************* */
template<class FG>
typename JunctionTree<FG>::sharedClique JunctionTree<FG>::distributeFactors(const FG& fg,
const std::vector<std::list<size_t,boost::fast_pool_allocator<size_t> > >& targets,
const SymbolicBayesTree::sharedClique& bayesClique) {
if(bayesClique) {
// create a new clique in the junction tree
list<Index> frontals = bayesClique->ordering();
sharedClique clique(new Clique(frontals.begin(), frontals.end(), bayesClique->separator_.begin(), bayesClique->separator_.end()));
// count the factors for this cluster to pre-allocate space
{
size_t nFactors = 0;
BOOST_FOREACH(const Index frontal, clique->frontal) {
// There may be less variables in "targets" than there really are if
// some of the highest-numbered variables do not pull in any factors.
if(frontal < targets.size())
nFactors += targets[frontal].size(); }
clique->reserve(nFactors);
}
// add the factors to this cluster
BOOST_FOREACH(const Index frontal, clique->frontal) {
if(frontal < targets.size()) {
BOOST_FOREACH(const size_t factorI, targets[frontal]) {
clique->push_back(fg[factorI]); } } }
// recursively call the children
BOOST_FOREACH(const typename SymbolicBayesTree::sharedClique bayesChild, bayesClique->children()) {
sharedClique child = distributeFactors(fg, targets, bayesChild);
clique->addChild(child);
child->parent() = clique;
}
return clique;
} else
return sharedClique();
}
/* ************************************************************************* */
template <class FG>
pair<typename JunctionTree<FG>::BayesTree::sharedClique, typename FG::sharedFactor>
JunctionTree<FG>::eliminateOneClique(const boost::shared_ptr<const Clique>& current) const {
FG fg; // factor graph will be assembled from local factors and marginalized children
fg.reserve(current->size() + current->children().size());
fg.push_back(*current); // add the local factors
// receive the factors from the child and its clique point
list<typename BayesTree::sharedClique> children;
BOOST_FOREACH(const boost::shared_ptr<const Clique>& child, current->children()) {
pair<typename BayesTree::sharedClique, typename FG::sharedFactor> tree_factor(
eliminateOneClique(child));
children.push_back(tree_factor.first);
fg.push_back(tree_factor.second);
}
// eliminate the combined factors
// warning: fg is being eliminated in-place and will contain marginal afterwards
tic("JT 2.1 VariableSlots");
VariableSlots variableSlots(fg);
toc("JT 2.1 VariableSlots");
#ifndef NDEBUG
// Debug check that the keys found in the factors match the frontal and
// separator keys of the clique.
list<Index> allKeys;
allKeys.insert(allKeys.end(), current->frontal.begin(), current->frontal.end());
allKeys.insert(allKeys.end(), current->separator.begin(), current->separator.end());
vector<Index> varslotsKeys(variableSlots.size());
std::transform(variableSlots.begin(), variableSlots.end(), varslotsKeys.begin(),
boost::lambda::bind(&VariableSlots::iterator::value_type::first, boost::lambda::_1));
assert(std::equal(allKeys.begin(), allKeys.end(), varslotsKeys.begin()));
#endif
// Now that we know which factors and variables, and where variables
// come from and go to, create and eliminate the new joint factor.
tic("JT 2.2 Combine");
typename FG::sharedFactor jointFactor = FG::Factor::Combine(fg, variableSlots);
toc("JT 2.2 Combine");
tic("JT 2.3 Eliminate");
typename FG::bayesnet_type::shared_ptr fragment = jointFactor->eliminate(current->frontal.size());
toc("JT 2.3 Eliminate");
assert(std::equal(jointFactor->begin(), jointFactor->end(), current->separator.begin()));
tic("JT 2.4 Update tree");
// create a new clique corresponding the combined factors
typename BayesTree::sharedClique new_clique(new typename BayesTree::Clique(*fragment));
new_clique->children_ = children;
BOOST_FOREACH(typename BayesTree::sharedClique& childRoot, children)
childRoot->parent_ = new_clique;
new_clique->cachedFactor() = jointFactor;
toc("JT 2.4 Update tree");
return make_pair(new_clique, jointFactor);
}
/* ************************************************************************* */
template <class FG>
typename JunctionTree<FG>::BayesTree::sharedClique JunctionTree<FG>::eliminate() const {
if(this->root()) {
tic("JT 2 eliminate");
pair<typename BayesTree::sharedClique, typename FG::sharedFactor> ret = this->eliminateOneClique(this->root());
if (ret.second->size() != 0)
throw runtime_error("JuntionTree::eliminate: elimination failed because of factors left over!");
toc("JT 2 eliminate");
return ret.first;
} else
return typename BayesTree::sharedClique();
}
} //namespace gtsam