/** \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() );
}
Beispiel #2
0
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);
}
Beispiel #3
0
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() );
}
Beispiel #4
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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 );
    }
}