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#ifndef OPENGM_ASTAR_HXX
#define OPENGM_ASTAR_HXX
#include <cmath>
#include <vector>
#include <list>
#include <set>
#include <iostream>
#include <algorithm>
#include <iostream>
#include <functional>
#include "opengm/opengm.hxx"
#include "opengm/graphicalmodel/graphicalmodel.hxx"
#include <opengm/graphicalmodel/graphicalmodel_manipulator.hxx>
#include "opengm/inference/inference.hxx"
#include "opengm/inference/messagepassing/messagepassing.hxx"
#include "opengm/inference/visitors/visitors.hxx"
namespace opengm {
/// \cond HIDDEN_SYMBOLS
// node of the search tree for the a-star search
template<class FactorType> struct AStarNode {
typename std::vector<typename FactorType::LabelType> conf;
typename FactorType::ValueType value;
};
/*
template<class AStar, bool Verbose=false>
class AStarVisitor {
public:
typedef AStar astar_type;
typedef typename astar_type::ValueType ValueType;
AStarVisitor();
void operator()(const astar_type&, const std::vector<size_t>& conf, const size_t heapsize, const ValueType& bound1, const ValueType& bound2, const double& runtime);
private:
size_t step_;
};
*/
/// \endcond
/// \brief A star search algorithm
///
/// Kappes, J. H. "Inference on Highly-Connected Discrete Graphical Models with Applications to Visual Object Recognition". Ph.D. Thesis 2011
/// Bergtholdt, M. & Kappes, J. H. & Schnoerr, C.: "Learning of Graphical Models and Efficient Inference for Object Class Recognition", DAGM 2006
/// Bergtholdt, M. & Kappes, J. H. & Schmidt, S. & Schnoerr, C.: "A Study of Parts-Based Object Class Detection Using Complete Graphs" IJCV 2010
///
/// Within this implementation of the AStar-Algo the user has to set the
/// the node order and a acyclic graph for the calculation of the heuristic.
/// A good choice for both is critical for good performance!
/// A heuristic which select these parameters automatically will be added in the next version
///
/// The AStar-Algo transform the problem into a shortest path problem in an exponentially large graph.
/// Due to the problem structure, this graph can be represented implicitly!
/// To find the shortest path we perform a best first search and use a admissable tree-based heuristic
/// to underestimate the cost to a goal node. This lower bound allows us to reduce the search to an
/// manageable subspace of the exponentially large search-space.
///
/// \ingroup inference
template<class GM,class ACC>
class AStar : public Inference<GM,ACC>
{
public:
///graphical model type
typedef GM GraphicalModelType;
///accumulation type
typedef ACC AccumulationType;
OPENGM_GM_TYPE_TYPEDEFS;
/// configuration vector type
typedef typename std::vector<LabelType> ConfVec ;
/// configuration iterator
typedef typename ConfVec::iterator ConfVecIt;
/// visitor
typedef opengm::visitors::VerboseVisitor<AStar<GM, ACC> > VerboseVisitorType;
typedef opengm::visitors::TimingVisitor<AStar<GM, ACC> > TimingVisitorType;
typedef opengm::visitors::EmptyVisitor<AStar<GM, ACC> > EmptyVisitorType;
template<class _GM>
struct RebindGm{
typedef AStar<_GM, ACC> type;
};
template<class _GM,class _ACC>
struct RebindGmAndAcc{
typedef AStar<_GM, _ACC > type;
};
enum Heuristic{
DEFAULT_HEURISTIC = 0,
FAST_HEURISTIC = 1,
STANDARD_HEURISTIC = 2
};
struct Parameter {
Parameter()
{
maxHeapSize_ = 3000000;
numberOfOpt_ = 1;
objectiveBound_ = AccumulationType::template neutral<ValueType>();
heuristic_ = Parameter::DEFAULTHEURISTIC;
};
template<class P>
Parameter(const P & p )
: maxHeapSize_(p.maxHeapSize_),
numberOfOpt_(p.numberOfOpt_),
objectiveBound_(p.objectiveBound_),
nodeOrder_(p.nodeOrder_),
treeFactorIds_(p.treeFactorIds_){
}
/// \brief add tree factor id
/// \param id factor id
void addTreeFactorId(size_t id)
{ treeFactorIds_.push_back(id); }
/// DEFAULTHEURISTIC ;
static const size_t DEFAULTHEURISTIC = 0;
/// FASTHEURISTIC
static const size_t FASTHEURISTIC = 1;
/// STANDARDHEURISTIC
static const size_t STANDARDHEURISTIC = 2;
/// maxHeapSize_ maximum size of the heap
size_t maxHeapSize_;
/// number od N-best solutions that should be found
size_t numberOfOpt_;
/// objective bound
ValueType objectiveBound_;
/// heuritstic
///
/// DEFAULTHEURISTIC = 0;
/// FASTHEURISTIC = 1
/// STANDARDHEURISTIC = 2
size_t heuristic_;
std::vector<IndexType> nodeOrder_;
std::vector<size_t> treeFactorIds_;
};
AStar(const GM& gm, Parameter para = Parameter());
virtual std::string name() const {return "AStar";}
const GraphicalModelType& graphicalModel() const;
virtual InferenceTermination infer();
virtual void reset();
template<class VisitorType> InferenceTermination infer(VisitorType& vistitor);
ValueType bound()const {return belowBound_;}
ValueType value()const;
virtual InferenceTermination marginal(const size_t,IndependentFactorType& out)const {return UNKNOWN;}
virtual InferenceTermination factorMarginal(const size_t, IndependentFactorType& out)const {return UNKNOWN;}
virtual InferenceTermination arg(std::vector<LabelType>& v, const size_t = 1)const;
virtual InferenceTermination args(std::vector< std::vector<LabelType> >& v)const;
private:
const GM& gm_;
Parameter parameter_;
// remeber passed parameter in parameterInitial_
// to reset astar
Parameter parameterInitial_;
std::vector<AStarNode<IndependentFactorType> > array_;
std::vector<size_t> numStates_;
size_t numNodes_;
std::vector<IndependentFactorType> treeFactor_;
std::vector<IndependentFactorType> optimizedFactor_;
std::vector<ConfVec > optConf_;
std::vector<bool> isTreeFactor_;
ValueType aboveBound_;
ValueType belowBound_;
template<class VisitorType> void expand(VisitorType& vistitor);
std::vector<ValueType> fastHeuristic(ConfVec conf);
inline static bool comp1(const AStarNode<IndependentFactorType>& a, const AStarNode<IndependentFactorType>& b)
{return AccumulationType::ibop(a.value,b.value);};
inline static bool comp2(const AStarNode<IndependentFactorType>& a, const AStarNode<IndependentFactorType>& b)
{ return AccumulationType::bop(a.value,b.value);};
inline static ValueType better(ValueType a, ValueType b) {return AccumulationType::op(a,b);};
inline static ValueType wrose(ValueType a, ValueType b) {return AccumulationType::iop(a,b);};
};
//*******************
//** Impelentation **
//*******************
/// \brief constructor
/// \param gm graphical model
/// \param para AStar parameter
template<class GM, class ACC >
AStar<GM,ACC>
::AStar
(
const GM& gm,
Parameter para
):gm_(gm)
{
parameterInitial_=para;
parameter_ = para;
if( parameter_.heuristic_ == Parameter::DEFAULTHEURISTIC) {
if(gm_.factorOrder()<=2)
parameter_.heuristic_ = Parameter::FASTHEURISTIC;
else
parameter_.heuristic_ = Parameter::STANDARDHEURISTIC;
}
OPENGM_ASSERT(parameter_.heuristic_ == Parameter::FASTHEURISTIC || parameter_.heuristic_ == Parameter::STANDARDHEURISTIC);
ACC::ineutral(belowBound_);
ACC::neutral(aboveBound_);
//Set variables
isTreeFactor_.resize(gm_.numberOfFactors());
numStates_.resize(gm_.numberOfVariables());
numNodes_ = gm_.numberOfVariables();
for(size_t i=0; i<numNodes_;++i)
numStates_[i] = gm_.numberOfLabels(i);
//Check nodeOrder
if(parameter_.nodeOrder_.size()==0) {
parameter_.nodeOrder_.resize(numNodes_);
std::vector<std::pair<IndexType,IndexType> > tmp(numNodes_,std::pair<IndexType,IndexType>());
for(size_t i=0; i<numNodes_; ++i){
tmp[i].first = gm_.numberOfFactors(i);
tmp[i].second = i;
}
std::sort(tmp.begin(),tmp.end());
for(size_t i=0; i<numNodes_; ++i){
parameter_.nodeOrder_[i] = tmp[numNodes_-i-1].second;
//parameter_.nodeOrder_[i] = i;
}
}
if(parameter_.nodeOrder_.size()!=numNodes_)
throw RuntimeError("The node order does not fit to the model.");
OPENGM_ASSERT(std::set<size_t>(parameter_.nodeOrder_.begin(), parameter_.nodeOrder_.end()).size()==numNodes_);
for(size_t i=0;i<numNodes_; ++i) {
OPENGM_ASSERT(parameter_.nodeOrder_[i] < numNodes_);
OPENGM_ASSERT(parameter_.nodeOrder_[i] >= 0);
}
//Check FactorIds
if(parameter_.treeFactorIds_.size()==0) {
//Select tree factors
for(size_t i=0; i<gm_.numberOfFactors(); ++i) {
if((gm_[i].numberOfVariables()==2) &&
(gm_[i].variableIndex(0)==parameter_.nodeOrder_.back() || gm_[i].variableIndex(1)==parameter_.nodeOrder_.back())
)
parameter_.addTreeFactorId(i);
}
}
for(size_t i=0; i<parameter_.treeFactorIds_.size(); ++i)
OPENGM_ASSERT(gm_.numberOfFactors() > parameter_.treeFactorIds_[i]);
//compute optimized factors
optimizedFactor_.resize(gm_.numberOfFactors());
for(size_t i=0; i<gm_.numberOfFactors(); ++i) {
if(gm_[i].numberOfVariables()<=1) continue;
std::vector<size_t> index(gm_[i].numberOfVariables());
gm_[i].variableIndices(index.begin());
optimizedFactor_[i].assign(gm_ ,index.end()-1, index.end());
opengm::accumulate<ACC>(gm[i],index.begin()+1,index.end(),optimizedFactor_[i]);
//gm_[i].template accumulate<ACC>(index.begin()+1,index.end(),optimizedFactor_[i]);
OPENGM_ASSERT(optimizedFactor_[i].numberOfVariables() == 1);
OPENGM_ASSERT(optimizedFactor_[i].variableIndex(0) == index[0]);
}
//PUSH EMPTY CONFIGURATION TO HEAP
AStarNode<IndependentFactorType> a;
a.conf.resize(0);
a.value = 0;
array_.push_back(a);
make_heap(array_.begin(), array_.end(), comp1);
//Check if maximal order is smaller equal 2, otherwise fall back to naive computation of heuristic
if(parameter_.heuristic_ == parameter_.FASTHEURISTIC) {
for(size_t i=0; i<parameter_.treeFactorIds_.size(); ++i) {
if(gm_[parameter_.treeFactorIds_[i]].numberOfVariables() > 2) {
throw RuntimeError("The heuristic includes factor of order > 2.");
}
}
}
//Init treefactor structure
treeFactor_.clear();
for(size_t i=0; i<gm_.numberOfFactors(); ++i)
isTreeFactor_[i] = false;
for(size_t i=0; i<parameter_.treeFactorIds_.size(); ++i) {
int factorId = parameter_.treeFactorIds_[i];
isTreeFactor_[factorId] = true;
treeFactor_.push_back(gm_[factorId]);
}
}
/// \brief reset
///
/// \warning reset assumes that the structure of
/// the graphical model has not changed
///
/// TODO
template<class GM, class ACC >
void
AStar<GM,ACC>::reset()
{
///todo
}
template <class GM, class ACC>
InferenceTermination
AStar<GM,ACC>::infer()
{
EmptyVisitorType v;
return infer(v);
}
/// \brief inference with visitor
/// \param visitor visitor
template<class GM, class ACC>
template<class VisitorType>
InferenceTermination AStar<GM,ACC>::infer(VisitorType& visitor)
{
size_t exitFlag=0;
optConf_.resize(0);
visitor.begin(*this);
while(array_.size()>0 && exitFlag==0) {
if(parameter_.numberOfOpt_ == optConf_.size()) {
visitor.end(*this);
return NORMAL;
}
while(array_.front().conf.size() < numNodes_ && exitFlag==0) {
expand(visitor);
belowBound_ = array_.front().value;
exitFlag = visitor(*this);
//visitor(*this, array_.front().conf, array_.size(), array_.front().value, globalBound_, time);
}
if(array_.front().conf.size()>=numNodes_){
ValueType value = array_.front().value;
belowBound_ = value;
std::vector<LabelType> conf(numNodes_);
for(size_t n=0; n<numNodes_; ++n) {
conf[parameter_.nodeOrder_[n]] = array_.front().conf[n];
}
optConf_.push_back(conf);
visitor(*this);
if(ACC::bop(parameter_.objectiveBound_, value)) {
visitor.end(*this);
return NORMAL;
}
}
pop_heap(array_.begin(), array_.end(), comp1); //greater<FactorType,Accumulation>);
array_.pop_back();
}
visitor.end(*this);
return UNKNOWN;
}
template<class GM, class ACC>
typename GM::ValueType AStar<GM, ACC>::value() const
{
if(optConf_.size()>=1){
return gm_.evaluate(optConf_[0]);
}
else{
return ACC::template neutral<ValueType>();
}
}
template<class GM, class ACC>
InferenceTermination AStar<GM, ACC>
::arg(ConfVec& conf, const size_t n)const
{
if(n>optConf_.size()) {
conf.resize(gm_.numberOfVariables(),0);
return UNKNOWN;
}
//conf.resize(opt_conf[n-1].size());
conf=optConf_[n-1];
return NORMAL;
}
/// \brief args
/// \param[out]conf state vectors
///
///get the inference solutions
template<class GM, class ACC>
InferenceTermination AStar<GM,ACC>
::args(std::vector<std::vector<typename AStar<GM,ACC>::LabelType> >& conf)const
{
conf=optConf_;
return NORMAL;
}
template<class GM, class ACC>
template<class VisitorType>
void AStar<GM, ACC>::expand(VisitorType& visitor)
{
//CHECK HEAP SIZE
if(array_.size()>parameter_.maxHeapSize_*0.99) {
partial_sort(array_.begin(), array_.begin()+(int)(parameter_.maxHeapSize_/2), array_.end(), comp2);
array_.resize((int)(parameter_.maxHeapSize_/2));
}
//GET HEAP HEAD
AStarNode<IndependentFactorType> a = array_.front();
size_t subconfsize = a.conf.size();
//REMOVE HEAD FROM HEAP
OPENGM_ASSERT(array_.size() > 0);
pop_heap(array_.begin(), array_.end(), comp1); //greater<FactorType,Accumulation>);
array_.pop_back();
if( parameter_.heuristic_ == parameter_.STANDARDHEURISTIC) {
//BUILD GRAPHICAL MODEL FOR HEURISTC CALCULATION
typedef typename opengm::DiscreteSpace<IndexType, LabelType> MSpaceType;
typedef typename meta::TypeListGenerator< ExplicitFunction<ValueType,IndexType,LabelType>, ViewFixVariablesFunction<GM>, ViewFunction<GM>, ConstantFunction<ValueType, IndexType, LabelType> >::type MFunctionTypeList;
typedef GraphicalModel<ValueType, typename GM::OperatorType, MFunctionTypeList, MSpaceType> MGM;
IndexType numberOfVariables = 0;
std::vector<IndexType> varMap(gm_.numberOfVariables(),0);
std::vector<LabelType> fixVariableLabel(gm_.numberOfVariables(),0);
std::vector<bool> fixVariable(gm_.numberOfVariables(),false);
for(size_t i =0; i<subconfsize ; ++i) {
fixVariableLabel[parameter_.nodeOrder_[i]] = a.conf[i];
fixVariable[parameter_.nodeOrder_[i]] = true;
}
for(IndexType var=0; var<gm_.numberOfVariables();++var){
if(fixVariable[var]==false){
varMap[var] = numberOfVariables++;
}
}
std::vector<LabelType> shape(numberOfVariables,0);
for(IndexType var=0; var<gm_.numberOfVariables();++var){
if(fixVariable[var]==false){
shape[varMap[var]] = gm_.numberOfLabels(var);
}
}
MSpaceType space(shape.begin(),shape.end());
MGM mgm(space);
std::vector<PositionAndLabel<IndexType,LabelType> > fixedVars;
std::vector<IndexType> MVars;
ValueType constant;
GM::OperatorType::neutral(constant);
for(IndexType f=0; f<gm_.numberOfFactors();++f){
fixedVars.resize(0);
MVars.resize(0);
for(IndexType i=0; i<gm_[f].numberOfVariables(); ++i){
const IndexType var = gm_[f].variableIndex(i);
if(fixVariable[var]){
fixedVars.push_back(PositionAndLabel<IndexType,LabelType>(i,fixVariableLabel[var]));
}else{
MVars.push_back(varMap[var]);
}
}
if(fixedVars.size()==gm_[f].numberOfVariables()){//all fixed
std::vector<LabelType> fixedStates(gm_[f].numberOfVariables(),0);
for(IndexType i=0; i<gm_[f].numberOfVariables(); ++i){
fixedStates[i]=fixVariableLabel[ gm_[f].variableIndex(i)];
}
GM::OperatorType::op(gm_[f](fixedStates.begin()),constant);
}else{
if(MVars.size()<2 || isTreeFactor_[f]){
const ViewFixVariablesFunction<GM> func(gm_[f], fixedVars);
mgm.addFactor(mgm.addFunction(func),MVars.begin(), MVars.end());
}else{
std::vector<IndexType> variablesIndices(optimizedFactor_[f].numberOfVariables());
for(size_t i=0; i<variablesIndices.size(); ++i)
variablesIndices[i] = varMap[optimizedFactor_[f].variableIndex(i)];
LabelType numberOfLabels = optimizedFactor_[f].numberOfLabels(0);
opengm::ExplicitFunction<ValueType,IndexType,LabelType> func(&numberOfLabels,&numberOfLabels+1,0);
for(LabelType i=0; i<numberOfLabels; ++i)
func(i) = optimizedFactor_[f](i);
mgm.addFactor(mgm.addFunction(func),variablesIndices.begin(),variablesIndices.end() );
OPENGM_ASSERT(mgm[mgm.numberOfFactors()-1].numberOfVariables()==1);
}
}
}
{
LabelType temp;
ConstantFunction<ValueType, IndexType, LabelType> func(&temp, &temp, constant);
mgm.addFactor(mgm.addFunction(func),MVars.begin(), MVars.begin());
}
typedef typename opengm::BeliefPropagationUpdateRules<MGM,ACC> UpdateRules;
typename MessagePassing<MGM, ACC, UpdateRules, opengm::MaxDistance>::Parameter bpPara;
bpPara.isAcyclic_ = opengm::Tribool::False;
bpPara.maximumNumberOfSteps_ = mgm.numberOfVariables();
OPENGM_ASSERT(mgm.isAcyclic());
MessagePassing<MGM, ACC, UpdateRules, opengm::MaxDistance> bp(mgm,bpPara);
try{
bp.infer();//Asynchronous();
}
catch(...) {
throw RuntimeError("bp failed in astar");
}
ACC::op(bp.value(),aboveBound_,aboveBound_);
std::vector<LabelType> conf(mgm.numberOfVariables());
std::vector<IndexType> theVar(1, varMap[parameter_.nodeOrder_[subconfsize]]);
std::vector<LabelType> theLabel(1,0);
a.conf.resize(subconfsize+1);
for(size_t i=0; i<numStates_[parameter_.nodeOrder_[subconfsize]]; ++i) {
a.conf[subconfsize] = i;
theLabel[0] =i;
bp.constrainedOptimum(theVar,theLabel,conf);
a.value = mgm.evaluate(conf);
array_.push_back(a);
push_heap(array_.begin(), array_.end(), comp1); //greater<FactorType,Accumulation>) ;
}
}
if( parameter_.heuristic_ == parameter_.FASTHEURISTIC) {
std::vector<LabelType> conf(subconfsize);
for(size_t i=0;i<subconfsize;++i)
conf[i] = a.conf[i];
std::vector<ValueType> bound = fastHeuristic(conf);
a.conf.resize(subconfsize+1);
for(size_t i=0; i<numStates_[parameter_.nodeOrder_[subconfsize]]; ++i) {
a.conf[subconfsize] = i;
a.value = bound[i];
//if(bound[i]<10) {
array_.push_back(a);
push_heap(array_.begin(), array_.end(), comp1); //greater<FactorType,Accumulation>) ;
//}
}
}
}
template<class GM, class ACC>
std::vector<typename AStar<GM, ACC>::ValueType>
AStar<GM, ACC>::fastHeuristic(typename AStar<GM, ACC>::ConfVec conf)
{
std::list<size_t> factorList;
std::vector<size_t> nodeDegree(numNodes_,0);
std::vector<int> nodeLabel(numNodes_,-1);
std::vector<std::vector<ValueType > > nodeEnergy(numNodes_);
size_t nextNode = parameter_.nodeOrder_[conf.size()];
for(size_t i=0; i<numNodes_; ++i) {
nodeEnergy[i].resize(numStates_[i]); //the energy passed to node i
for(size_t j=0;j<numStates_[i];++j)
OperatorType::neutral(nodeEnergy[i][j]);
}
for(size_t i=0;i<conf.size();++i) {
nodeLabel[parameter_.nodeOrder_[i]] = conf[i];
}
//First run:
// * add unarry function
// * add pairwise functions with at least one observed node
// * add the approximation for pairwise none-tree edges
for(size_t i=0; i<gm_.numberOfFactors(); ++i) {
const FactorType & f = gm_[i];
size_t nvar = f.numberOfVariables();
//factors depending from 1 variable can be include
if(nvar==1) {
int index = f.variableIndex(0);
if(nodeLabel[index]>=0) {
nodeEnergy[index].resize(1);
//nodeEnergy[index][0] = operatipon(f(nodeLabel[index]), nodeEnergy[index][0]);
LabelType coordinates[]={static_cast<LabelType>(nodeLabel[index])};
OperatorType::op(f(coordinates), nodeEnergy[index][0]);
}
else{
OPENGM_ASSERT(numStates_[index] == nodeEnergy[index].size());
for(size_t j=0;j<numStates_[index];++j) {
//nodeEnergy[index][j] = operation(f(j),nodeEnergy[index][j]);
LabelType coordinates[]={static_cast<LabelType>(j)};
OperatorType::op(f(coordinates),nodeEnergy[index][j]);
}
}
}
if(nvar==2) {
size_t index1 = f.variableIndex(0);
size_t index2 = f.variableIndex(1);
if(nodeLabel[index1]>=0) {
if(nodeLabel[index2]>=0) {
nodeEnergy[index1].resize(1);
//nodeEnergy[index1][0] = operation(f(nodeLabel[index1],nodeLabel[index2]),nodeEnergy[index1][0]);
LabelType coordinates[]={
static_cast<LabelType>(nodeLabel[index1]),
static_cast<LabelType>(nodeLabel[index2])
};
OperatorType::op(f(coordinates),nodeEnergy[index1][0]);
}
else{
OPENGM_ASSERT(numStates_[index2] == nodeEnergy[index2].size());
for(size_t j=0;j<numStates_[index2];++j) {
//nodeEnergy[index2][j] = operation(f(nodeLabel[index1],j), nodeEnergy[index2][j]);
LabelType coordinates[]={
static_cast<LabelType>(nodeLabel[index1]),
static_cast<LabelType>(j)
};
OperatorType::op(f(coordinates), nodeEnergy[index2][j]);
}
}
}
else if(nodeLabel[index2]>=0) {
OPENGM_ASSERT(numStates_[index1] == nodeEnergy[index1].size());
for(size_t j=0;j<numStates_[index1];++j) {
//nodeEnergy[index1][j] = operation(f(j,nodeLabel[index2]),nodeEnergy[index1][j]);
LabelType coordinates[]={
static_cast<LabelType>(j),
static_cast<LabelType>(nodeLabel[index2])
};
OperatorType::op(f(coordinates),nodeEnergy[index1][j]);
}
}
else if(isTreeFactor_[i]) {
factorList.push_front(i);
++nodeDegree[index1];
++nodeDegree[index2];
continue;
}
else{
for(size_t j=0;j<numStates_[index1];++j) {
//nodeEnergy[index1][j] = operation(optimizedFactor_[i](j), nodeEnergy[index1][j]);
LabelType coordinates[]={static_cast<LabelType>(j)};
OperatorType::op(optimizedFactor_[i](coordinates), nodeEnergy[index1][j]);
}
}
}
if(nvar>2) {
bool covered = true;
std::vector<size_t> state(nvar);
for(size_t j=0; j<nvar; ++j) {
if(nodeLabel[f.variableIndex(j)]<0) {
state[j] = nodeLabel[f.variableIndex(j)];
covered = false;
}
}
if(covered)
nodeEnergy[f.variableIndex(0)][0] = f(state.begin());
else{
for(size_t j=0;j<numStates_[f.variableIndex(0)];++j) {
//nodeEnergy[f.variableIndex(0)][j] = operation(optimizedFactor_[i](j), nodeEnergy[f.variableIndex(0)][j]);
LabelType coordinates[]={static_cast<LabelType>(j)};
OperatorType::op(optimizedFactor_[i](coordinates), nodeEnergy[f.variableIndex(0)][j]);
}
}
}
}
nodeDegree[nextNode] += numNodes_;
// Start dynamic programming to solve the treestructured problem.
while(factorList.size()>0) {
size_t id = factorList.front();
factorList.pop_front();
const FactorType & f = gm_[id];
size_t index1 = f.variableIndex(0);
size_t index2 = f.variableIndex(1);
typename FactorType::ValueType temp;
OPENGM_ASSERT(index1 < numNodes_);
OPENGM_ASSERT(index2 < numNodes_);
OPENGM_ASSERT(gm_.numberOfLabels(index1) == numStates_[index1]);
OPENGM_ASSERT(gm_.numberOfLabels(index2) == numStates_[index2]);
if(nodeDegree[index1]==1) {
typename FactorType::ValueType min;
OPENGM_ASSERT(numStates_[index2] == nodeEnergy[index2].size());
for(size_t j2=0;j2<numStates_[index2];++j2) {
ACC::neutral(min);
OPENGM_ASSERT(numStates_[index1] == nodeEnergy[index1].size());
for(size_t j1=0;j1<numStates_[index1];++j1) {
LabelType coordinates[]={static_cast<LabelType>(j1),static_cast<LabelType>(j2)};
OperatorType::op(f(coordinates),nodeEnergy[index1][j1],temp);
ACC::op(min,temp,min);
}
//nodeEnergy[index2][j2] = operation(min,nodeEnergy[index2][j2]);
OperatorType::op(min,nodeEnergy[index2][j2]);
}
--nodeDegree[index1];
--nodeDegree[index2];
nodeEnergy[index1].resize(1);
OperatorType::neutral(nodeEnergy[index1][0]);
}
else if(nodeDegree[index2]==1) {
typename FactorType::ValueType min;
OPENGM_ASSERT(numStates_[index1] == nodeEnergy[index1].size());
for(size_t j1=0;j1<numStates_[index1];++j1) {
ACC::neutral(min);
OPENGM_ASSERT(numStates_[index2] == nodeEnergy[index2].size());
for(size_t j2=0;j2<numStates_[index2];++j2) {
LabelType coordinates[]={static_cast<LabelType>(j1),static_cast<LabelType>(j2)};
OperatorType::op(f(coordinates),nodeEnergy[index2][j2],temp);
ACC::op(min,temp,min);
//if(min>f(j1,j2)*node_energy[index2][j2]) min=f(j1,j2)*node_energy[index2][j2];
}
//nodeEnergy[index1][j1] = operation(min,nodeEnergy[index1][j1]);
OperatorType::op(min,nodeEnergy[index1][j1]);
}
--nodeDegree[index1];
--nodeDegree[index2];
nodeEnergy[index2].resize(1);
OperatorType::neutral(nodeEnergy[index2][0]);
}
else{
factorList.push_back(id);
}
}
//Evaluate
ValueType result;
ValueType min;
OperatorType::neutral(result);
std::vector<ValueType > bound;
for(size_t i=0;i<numNodes_;++i) {
if(i==nextNode) continue;
ACC::neutral(min);
for(size_t j=0; j<nodeEnergy[i].size();++j)
ACC::op(min,nodeEnergy[i][j],min);
//result = operation(result,min);
OperatorType::op(min,result);
}
bound.resize(nodeEnergy[nextNode].size());
for(size_t j=0; j<nodeEnergy[nextNode].size();++j) {
//bound[j] = operation(nodeEnergy[nextNode][j],result);
OperatorType::op(nodeEnergy[nextNode][j],result,bound[j]);
}
return bound;
}
template<class GM, class ACC>
inline const typename AStar<GM, ACC>::GraphicalModelType&
AStar<GM, ACC>::graphicalModel() const
{
return gm_;
}
} // namespace opengm
#endif // #ifndef OPENGM_ASTAR_HXX
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