void BP::construct() {
// create edge properties
_edges.clear();
_edges.reserve( nrVars() );
_edge2lut.clear();
if( props.updates == Properties::UpdateType::SEQMAX )
_edge2lut.reserve( nrVars() );
for( size_t i = 0; i < nrVars(); ++i ) {
_edges.push_back( vector<EdgeProp>() );
_edges[i].reserve( nbV(i).size() );
if( props.updates == Properties::UpdateType::SEQMAX ) {
_edge2lut.push_back( vector<LutType::iterator>() );
_edge2lut[i].reserve( nbV(i).size() );
}
foreach( const Neighbor &I, nbV(i) ) {
EdgeProp newEP;
newEP.message = Prob( var(i).states() );
newEP.newMessage = Prob( var(i).states() );
if( DAI_BP_FAST ) {
newEP.index.reserve( factor(I).nrStates() );
for( IndexFor k( var(i), factor(I).vars() ); k.valid(); ++k )
newEP.index.push_back( k );
}
newEP.residual = 0.0;
_edges[i].push_back( newEP );
if( props.updates == Properties::UpdateType::SEQMAX )
_edge2lut[i].push_back( _lut.insert( make_pair( newEP.residual, make_pair( i, _edges[i].size() - 1 ))) );
}
}
Factor LC::NewPancake (size_t i, size_t _I, bool & hasNaNs) {
size_t I = nbV(i)[_I];
Factor piet = _pancakes[i];
// recalculate _pancake[i]
VarSet Ivars = factor(I).vars();
Factor A_I;
for( VarSet::const_iterator k = Ivars.begin(); k != Ivars.end(); k++ )
if( var(i) != *k )
A_I *= (_pancakes[findVar(*k)] * factor(I).inverse()).marginal( Ivars / var(i), false );
if( Ivars.size() > 1 )
A_I ^= (1.0 / (Ivars.size() - 1));
Factor A_Ii = (_pancakes[i] * factor(I).inverse() * _phis[i][_I].inverse()).marginal( Ivars / var(i), false );
Factor quot = A_I / A_Ii;
if( props.damping != 0.0 )
quot = (quot^(1.0 - props.damping)) * (_phis[i][_I]^props.damping);
piet *= quot / _phis[i][_I].normalized();
_phis[i][_I] = quot.normalized();
piet.normalize();
if( piet.hasNaNs() ) {
cerr << name() << "::NewPancake(" << i << ", " << _I << "): has NaNs!" << endl;
hasNaNs = true;
}
return piet;
}
void CBP::construct() {
// prepare datastructures for compression
for (size_t i=0; i<nrVars(); i++) {
_gndVarToSuperVar[i] = i;
}
for (size_t i=0; i<nrFactors(); i++) {
_gndFacToSuperFac[i] = i;
}
// create edge properties
_edges.clear();
_edges.reserve( nrVars() );
for( size_t i = 0; i < nrVars(); ++i ) {
_edges.push_back( vector<EdgeProp>() );
_edges[i].reserve( nbV(i).size() );
foreach( const Neighbor &I, nbV(i) ) {
EdgeProp newEP;
size_t edgeCount = factor(I).counts()[I.dual].size();
newEP.message = vector<Prob>(edgeCount,Prob( var(i).states() ));
newEP.newMessage = vector<Prob>(edgeCount,Prob( var(i).states() ));
newEP.count = vector<int>(edgeCount);
newEP.index = vector<ind_t>(edgeCount);
newEP.nrPos = edgeCount;
// simulate orginal varSet with possibly more variables, must ensure that the number of variables is equal to number of ground variables
VarSet gndVarSet;
size_t varCount = 0;
foreach(const Neighbor &tmpVar, nbF(I)) {
for(map<size_t, int>::const_iterator iter=factor(I).counts()[tmpVar.iter].begin(); iter!=factor(I).counts()[tmpVar.iter].end();iter++) {
gndVarSet |= Var(varCount, var(tmpVar).states());
varCount++;
}
}
varCount = 0;
foreach(const Neighbor &tmpVar, nbF(I)) {
size_t pos=0;
for(map<size_t, int>::const_iterator iter=factor(I).counts()[tmpVar.iter].begin(); iter!=factor(I).counts()[tmpVar.iter].end();iter++,pos++) {
if (tmpVar == i) {
// assumes that counts are iterated in increases order of positions
size_t sortedPos = factor(I).sigma()[(*iter).first];
newEP.count[pos] = (*iter).second;
newEP.index[pos].reserve( factor(I).states() );
for( IndexFor k( Var(sortedPos, var(i).states()), gndVarSet ); k >= 0; ++k ) {
newEP.index[pos].push_back( k );
}
}
varCount++;
}
}
_edges[i].push_back( newEP );
}
LC::LC( const FactorGraph & fg, const PropertySet &opts ) : DAIAlgFG(fg), _pancakes(), _cavitydists(), _phis(), _beliefs(), _maxdiff(0.0), _iters(0), props() {
setProperties( opts );
// create pancakes
_pancakes.resize( nrVars() );
// create cavitydists
for( size_t i=0; i < nrVars(); i++ )
_cavitydists.push_back(Factor( delta(i) ));
// create phis
_phis.reserve( nrVars() );
for( size_t i = 0; i < nrVars(); i++ ) {
_phis.push_back( vector<Factor>() );
_phis[i].reserve( nbV(i).size() );
foreach( const Neighbor &I, nbV(i) )
_phis[i].push_back( Factor( factor(I).vars() / var(i) ) );
}
// create beliefs
_beliefs.reserve( nrVars() );
for( size_t i=0; i < nrVars(); i++ )
_beliefs.push_back(Factor(var(i)));
}
