/** \param N number of variables
* \param M number of factors
* \param k number of variables that each factor depends on
* \param beta standard-deviation of Gaussian log-factor entries
*/
FactorGraph createHOIFG( size_t N, size_t M, size_t k, Real beta ) {
vector<Var> vars;
vector<Factor> factors;
vars.reserve(N);
for( size_t i = 0; i < N; i++ )
vars.push_back(Var(i,2));
for( size_t I = 0; I < M; I++ ) {
VarSet vars;
while( vars.size() < k ) {
do {
size_t newind = (size_t)(N * rnd_uniform());
Var newvar = Var(newind, 2);
if( !vars.contains( newvar ) ) {
vars |= newvar;
break;
}
} while( 1 );
}
factors.push_back( createFactorExpGauss( vars, beta ) );
}
return FactorGraph( factors.begin(), factors.end(), vars.begin(), vars.end(), factors.size(), vars.size() );
}
CFactorGraph CompressInterface::createCFactorGraph() {
createVarClustering();
createFacClustering();
// create lifted fg here
vector<CFactor> superFacs;
// clusterIdx => facIdx
for (map<size_t,size_t>::iterator facIter = _facRepr.begin(); facIter != _facRepr.end(); facIter++) {
VarSet superVarSet;
foreach (const dai::BipartiteGraph::Neighbor &tmpVar, _cfg.nbF(facIter->second)) {
Var varCluster = _varRepr[_varColorVec[tmpVar]];
if (!superVarSet.contains(varCluster)) {
superVarSet |= Var(varCluster);
}
}
CFactor superFac = CFactor(superVarSet, _cfg.factor(facIter->second).p());
superFac.sigma() = _cfg.factor(facIter->second).sigma();
superFac.position() = _cfg.factor(facIter->second).position();
superFac.counts() = createCounts(facIter->second, superVarSet);
superFacs.push_back(superFac);
}
return CFactorGraph(superFacs);
}
void MakeHOIFG( size_t N, size_t M, size_t k, Real sigma, FactorGraph &fg ) {
vector<Var> vars;
vector<Factor> factors;
vars.reserve(N);
for( size_t i = 0; i < N; i++ )
vars.push_back(Var(i,2));
for( size_t I = 0; I < M; I++ ) {
VarSet vars;
while( vars.size() < k ) {
do {
size_t newind = (size_t)(N * rnd_uniform());
Var newvar = Var(newind, 2);
if( !vars.contains( newvar ) ) {
vars |= newvar;
break;
}
} while( 1 );
}
factors.push_back( RandomFactor( vars, sigma ) );
}
fg = FactorGraph( factors.begin(), factors.end(), vars.begin(), vars.end(), factors.size(), vars.size() );
}
void findLoopClusters( const FactorGraph & fg, std::set<VarSet> &allcl, VarSet newcl, const Var & root, size_t length, VarSet vars ) {
for( VarSet::const_iterator in = vars.begin(); in != vars.end(); in++ ) {
VarSet ind = fg.delta( *in );
if( (newcl.size()) >= 2 && ind.contains( root ) ) {
allcl.insert( newcl | *in );
}
else if( length > 1 )
findLoopClusters( fg, allcl, newcl | *in, root, length - 1, ind / newcl );
}
}
