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 ParityAggrLit::index(Grounder *g, Groundable *gr, VarSet &bound)
{
(void)g;
if(assign_)
{
VarSet vars;
VarVec bind;
lower_->vars(vars);
std::set_difference(vars.begin(), vars.end(), bound.begin(), bound.end(), std::back_insert_iterator<VarVec>(bind));
if(bind.size() > 0)
{
bound.insert(bind.begin(), bind.end());
return;
}
}
gr->instantiator()->append(new MatchIndex(this));
}
Index *IncLit::index(Grounder *, Formula *, VarSet &bound)
{
VarSet vars;
VarVec bind;
var_->vars(vars);
std::set_difference(vars.begin(), vars.end(), bound.begin(), bound.end(), std::back_insert_iterator<VarVec>(bind));
bound.insert(bind.begin(), bind.end());
return new IncIndex(this);
}
Factor LC::belief (const VarSet &ns) const {
if( ns.size() == 0 )
return Factor();
else if( ns.size() == 1 )
return beliefV( findVar( *(ns.begin()) ) );
else {
DAI_THROW(BELIEF_NOT_AVAILABLE);
return Factor();
}
}
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 );
}
}
Factor ExactInf::belief( const VarSet &ns ) const {
if( ns.size() == 0 )
return Factor();
else if( ns.size() == 1 ) {
return beliefV( findVar( *(ns.begin()) ) );
} else {
size_t I;
for( I = 0; I < nrFactors(); I++ )
if( factor(I).vars() >> ns )
break;
if( I == nrFactors() )
DAI_THROW(BELIEF_NOT_AVAILABLE);
return beliefF(I).marginal(ns);
}
}
std::vector<std::map<size_t, int> > AnyPositionCnfCompress::createCounts(size_t &gndFactor, VarSet &superVarSet) {
// create zero entries for each position
map<long, map<size_t, int> > countMap;
foreach (const dai::BipartiteGraph::Neighbor &tmpVar, _cfg.nbF(gndFactor)) {
Var liftedVar = _varRepr[_varColorVec[tmpVar]];
size_t pos = find(_cfg.factor(gndFactor).sigma().begin(), _cfg.factor(gndFactor).sigma().end(), tmpVar.iter) - _cfg.factor(gndFactor).sigma().begin();
countMap[liftedVar.label()][pos] = 0;
}
vector<map<size_t, int> > counts;
size_t posCount;
size_t negCount;
for (vector<Var>::const_iterator iter = superVarSet.begin(); iter < superVarSet.end(); iter++) {
posCount = 0;
negCount = 0;
foreach(const dai::BipartiteGraph::Neighbor tmpFac, _cfg.nbV(_cfg.findVar(*iter))) {
if (_facRepr[_facColorVec[tmpFac]] == gndFactor) {
size_t pos = find(_cfg.factor(tmpFac).sigma().begin(), _cfg.factor(tmpFac).sigma().end(), tmpFac.dual) - _cfg.factor(tmpFac).sigma().begin();
double res = log(_cfg.factor(tmpFac).states() - _zeroStates[tmpFac]) / log(2);
size_t nrPosLiterals = size_t(res);
bool sign = (pos < nrPosLiterals);
if (sign) {
posCount++;
} else {
negCount++;
}
}
}
map<size_t, int>::iterator posIter=countMap[iter->label()].begin();
if (posCount > 0) {
posIter->second = posCount;
}
if (negCount > 0) {
for (size_t j=0;j<posCount; j++, posIter++) {}
posIter->second = negCount;
}
counts.push_back(countMap[iter->label()]);
}
return counts;
}
std::vector<std::map<size_t, int> > CompressInterface::createCounts(size_t &gndFactor, VarSet &superVarSet) {
// create zero entries for each position
map<long, map<size_t, int> > countMap;
foreach (const dai::BipartiteGraph::Neighbor &tmpVar, _cfg.nbF(gndFactor)) {
Var liftedVar = _varRepr[_varColorVec[tmpVar]];
size_t pos = find(_cfg.factor(gndFactor).sigma().begin(), _cfg.factor(gndFactor).sigma().end(), tmpVar.iter) - _cfg.factor(gndFactor).sigma().begin();
countMap[liftedVar.label()][pos] = 0;
}
vector<map<size_t, int> > counts;
for (vector<Var>::const_iterator iter = superVarSet.begin(); iter < superVarSet.end(); iter++) {
foreach(const dai::BipartiteGraph::Neighbor gndFac, _cfg.nbV(_cfg.findVar(*iter))) {
if (_facRepr[_facColorVec[gndFac]] == gndFactor) {
size_t pos = find(_cfg.factor(gndFac).sigma().begin(), _cfg.factor(gndFac).sigma().end(), gndFac.dual) - _cfg.factor(gndFac).sigma().begin();
countMap[iter->label()][pos]++;
}
}
counts.push_back(countMap[iter->label()]);
}
return counts;
}
/* Convert cell vector of Matlab sets to vector<VarSet> */
vector<VarSet> mx2VarSets(const mxArray *vs, const FactorGraph &fg, long verbose, vector<Permute> &perms) {
vector<VarSet> varsets;
int n1 = mxGetM(vs);
int n2 = mxGetN(vs);
if( n2 != 1 && n1 != 1 )
mexErrMsgTxt("varsets should be a Nx1 or 1xN cell matrix.");
size_t nr_vs = n1;
if( n1 == 1 )
nr_vs = n2;
// interpret vs, linear cell array of varsets
varsets.reserve( nr_vs );
perms.clear();
perms.reserve( nr_vs );
for( size_t cellind = 0; cellind < nr_vs; cellind++ ) {
if( verbose >= 3 )
cerr << "reading varset " << cellind << ": " << endl;
mxArray *cell = mxGetCell(vs, cellind);
if( verbose >= 3 )
cerr << " got cell " << endl;
size_t nr_mem = mxGetN(cell);
if( verbose >= 3 )
cerr << " number members: " << nr_mem << endl;
double *members = mxGetPr(cell);
if( verbose >= 3 )
cerr << " got them! " << endl;
// add variables
VarSet vsvars;
if( verbose >= 3 )
cerr << " vars: ";
vector<long> labels(nr_mem,0);
vector<size_t> dims(nr_mem,0);
for( size_t mi = 0; mi < nr_mem; mi++ ) {
labels[mi] = (long)members[mi];
dims[mi] = fg.var(labels[mi]).states();
if( verbose >= 3 )
cerr << labels[mi] << " ";
vsvars.insert( fg.var(labels[mi]) );
}
if( verbose >= 3 )
cerr << endl;
DAI_ASSERT( nr_mem == vsvars.size() );
varsets.push_back(vsvars);
// calculate permutation matrix
vector<size_t> perm(nr_mem,0);
VarSet::iterator j = vsvars.begin();
for( size_t mi = 0; mi < nr_mem; mi++,j++ ) {
long gezocht = j->label();
vector<long>::iterator piet = find(labels.begin(),labels.end(),gezocht);
perm[mi] = piet - labels.begin();
}
if( verbose >= 3 ) {
cerr << endl << " perm: ";
for( vector<size_t>::iterator r=perm.begin(); r!=perm.end(); r++ )
cerr << *r << " ";
cerr << endl;
}
// create Permute object
vector<size_t> di(nr_mem,0);
size_t prod = 1;
for( size_t k = 0; k < nr_mem; k++ ) {
di[k] = dims[k];
prod *= dims[k];
}
Permute permindex( di, perm );
perms.push_back( permindex );
}
if( verbose >= 3 ) {
for(vector<VarSet>::const_iterator I=varsets.begin(); I!=varsets.end(); I++ )
cerr << *I << endl;
}
return( varsets );
}
int main( int argc, char *argv[] ) {
if( argc != 3 ) {
cout << "Usage: " << argv[0] << " <in.fg> <tw>" << endl << endl;
cout << "Reports some characteristics of the .fg network." << endl;
cout << "Also calculates treewidth (which may take some time) unless <tw> == 0." << endl;
return 1;
} else {
// Read factorgraph
FactorGraph fg;
char *infile = argv[1];
int calc_tw = atoi(argv[2]);
fg.ReadFromFile( infile );
cout << "Number of variables: " << fg.nrVars() << endl;
cout << "Number of factors: " << fg.nrFactors() << endl;
cout << "Connected: " << fg.isConnected() << endl;
cout << "Tree: " << fg.isTree() << endl;
cout << "Has short loops: " << hasShortLoops(fg.factors()) << endl;
cout << "Has negatives: " << hasNegatives(fg.factors()) << endl;
cout << "Binary variables? " << fg.isBinary() << endl;
cout << "Pairwise interactions? " << fg.isPairwise() << endl;
if( calc_tw ) {
std::pair<size_t,size_t> tw = treewidth(fg);
cout << "Treewidth: " << tw.first << endl;
cout << "Largest cluster for JTree has " << tw.second << " states " << endl;
}
double stsp = 1.0;
for( size_t i = 0; i < fg.nrVars(); i++ )
stsp *= fg.var(i).states();
cout << "Total state space: " << stsp << endl;
double cavsum_lcbp = 0.0;
double cavsum_lcbp2 = 0.0;
size_t max_Delta_size = 0;
map<size_t,size_t> cavsizes;
for( size_t i = 0; i < fg.nrVars(); i++ ) {
VarSet di = fg.delta(i);
if( cavsizes.count(di.size()) )
cavsizes[di.size()]++;
else
cavsizes[di.size()] = 1;
size_t Ds = fg.Delta(i).nrStates();
if( Ds > max_Delta_size )
max_Delta_size = Ds;
cavsum_lcbp += di.nrStates();
for( VarSet::const_iterator j = di.begin(); j != di.end(); j++ )
cavsum_lcbp2 += j->states();
}
cout << "Maximum pancake has " << max_Delta_size << " states" << endl;
cout << "LCBP with full cavities needs " << cavsum_lcbp << " BP runs" << endl;
cout << "LCBP with only pairinteractions needs " << cavsum_lcbp2 << " BP runs" << endl;
cout << "Cavity sizes: ";
for( map<size_t,size_t>::const_iterator it = cavsizes.begin(); it != cavsizes.end(); it++ )
cout << it->first << "(" << it->second << ") ";
cout << endl;
cout << "Type: " << (fg.isPairwise() ? "pairwise" : "higher order") << " interactions, " << (fg.isBinary() ? "binary" : "nonbinary") << " variables" << endl;
if( fg.isPairwise() ) {
bool girth_reached = false;
size_t loopdepth;
for( loopdepth = 2; loopdepth <= fg.nrVars() && !girth_reached; loopdepth++ ) {
size_t nr_loops = countLoops( fg, loopdepth );
cout << "Loops up to " << loopdepth << " variables: " << nr_loops << endl;
if( nr_loops > 0 )
girth_reached = true;
}
if( girth_reached )
cout << "Girth: " << loopdepth-1 << endl;
else
cout << "Girth: infinity" << endl;
}
return 0;
}
}
