void SharedParameters::collectSufficientStatistics( InfAlg &alg ) { for( std::map< FactorIndex, Permute >::iterator i = _perms.begin(); i != _perms.end(); ++i ) { Permute &perm = i->second; VarSet &vs = _varsets[i->first]; Factor b = alg.belief(vs); Prob p( b.nrStates(), 0.0 ); for( size_t entry = 0; entry < b.nrStates(); ++entry ) p.set( entry, b[perm.convertLinearIndex(entry)] ); // apply inverse permutation _estimation->addSufficientStatistics( p ); } }
void BruteForceOptMatching::outputSingleFactorValues( const ConnectedFactorGraph& graph) { // output factor values for (int j = 0; j < graph.factors.size(); j++) { Factor fac = graph.factors[j]; if (fac.vars().size() != 1) continue; cout << "singvals for var " << fac.vars().front().label() << " :\n"; for (int k = 0; k < fac.nrStates(); k++) { cout << fac.get(k) << " "; } cout << "\n"; } }
void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray*prhs[] ) { // Check for proper number of arguments if( ((nrhs < NR_IN) || (nrhs > NR_IN + NR_IN_OPT)) || ((nlhs < NR_OUT) || (nlhs > NR_OUT + NR_OUT_OPT)) ) { mexErrMsgTxt("Usage: [logZ,q,qv,qf,qmap,margs] = dai_jtree(psi,varsets,opts)\n\n" "\n" "INPUT: psi = linear cell array containing the factors\n" " (psi{i} should be a structure with a Member field\n" " and a P field).\n" " varsets = linear cell array containing varsets for which marginals\n" " are requested.\n" " opts = string of options.\n" "\n" "OUTPUT: logZ = logarithm of the partition sum.\n" " q = linear cell array containing all calculated marginals.\n" " qv = linear cell array containing all variable marginals.\n" " qf = linear cell array containing all factor marginals.\n" " qmap = linear array containing the MAP state.\n" " margs = linear cell array containing all requested marginals.\n"); } // Get psi and construct factorgraph vector<Factor> factors = mx2Factors(PSI_IN, 0); FactorGraph fg(factors); // Get varsets vector<Permute> perms; vector<VarSet> varsets = mx2VarSets(VARSETS_IN,fg,0,perms); // Get options string char *opts; size_t buflen = mxGetN( OPTS_IN ) + 1; opts = (char *)mxCalloc( buflen, sizeof(char) ); mxGetString( OPTS_IN, opts, buflen ); // Convert to options object props stringstream ss; ss << opts; PropertySet props; ss >> props; // Construct InfAlg object, init and run JTree jt = JTree( fg, props ); jt.init(); jt.run(); // Save logZ double logZ = NAN; logZ = jt.logZ(); // Hand over results to MATLAB LOGZ_OUT = mxCreateDoubleMatrix(1,1,mxREAL); *(mxGetPr(LOGZ_OUT)) = logZ; Q_OUT = Factors2mx(jt.beliefs()); if( nlhs >= 3 ) { vector<Factor> qv; qv.reserve( fg.nrVars() ); for( size_t i = 0; i < fg.nrVars(); i++ ) qv.push_back( jt.belief( fg.var(i) ) ); QV_OUT = Factors2mx( qv ); } if( nlhs >= 4 ) { vector<Factor> qf; qf.reserve( fg.nrFactors() ); for( size_t I = 0; I < fg.nrFactors(); I++ ) qf.push_back( jt.belief( fg.factor(I).vars() ) ); QF_OUT = Factors2mx( qf ); } if( nlhs >= 5 ) { std::vector<size_t> map_state; bool supported = true; try { map_state = jt.findMaximum(); } catch( Exception &e ) { if( e.getCode() == Exception::NOT_IMPLEMENTED ) supported = false; else throw; } if( supported ) { QMAP_OUT = mxCreateNumericMatrix(map_state.size(), 1, mxUINT32_CLASS, mxREAL); uint32_T* qmap_p = reinterpret_cast<uint32_T *>(mxGetPr(QMAP_OUT)); for (size_t n = 0; n < map_state.size(); ++n) qmap_p[n] = map_state[n]; } else { mexErrMsgTxt("Calculating a MAP state is not supported by this inference algorithm."); } } if( nlhs >= 6 ) { vector<Factor> margs; margs.reserve( varsets.size() ); for( size_t s = 0; s < varsets.size(); s++ ) { Factor marg; jt.init(); jt.run(); marg = jt.calcMarginal( varsets[s] ); // permute entries of marg Factor margperm = marg; for( size_t li = 0; li < marg.nrStates(); li++ ) margperm.set( li, marg[perms[s].convertLinearIndex(li)] ); margs.push_back( margperm ); } MARGS_OUT = Factors2mx( margs ); } return; }
int main( int argc, char *argv[] ) { if ( argc != 3 ) { cout << "Usage: " << argv[0] << " <filename.fg> [map|pd]" << endl << endl; cout << "Reads factor graph <filename.fg> and runs" << endl; cout << "map: Junction tree MAP" << endl; cout << "pd : LBP and posterior decoding" << endl << endl; return 1; } else { // Redirect cerr to inf.log ofstream errlog("inf.log"); //streambuf* orig_cerr = cerr.rdbuf(); cerr.rdbuf(errlog.rdbuf()); // Read FactorGraph from the file specified by the first command line argument FactorGraph fg; fg.ReadFromFile(argv[1]); // Set some constants size_t maxiter = 10000; Real tol = 1e-9; size_t verb = 1; // Store the constants in a PropertySet object PropertySet opts; opts.set("maxiter",maxiter); // Maximum number of iterations opts.set("tol",tol); // Tolerance for convergence opts.set("verbose",verb); // Verbosity (amount of output generated) if (strcmp(argv[2], "map") == 0) { // Construct another JTree (junction tree) object that is used to calculate // the joint configuration of variables that has maximum probability (MAP state) JTree jtmap( fg, opts("updates",string("HUGIN"))("inference",string("MAXPROD")) ); // Initialize junction tree algorithm jtmap.init(); // Run junction tree algorithm jtmap.run(); // Calculate joint state of all variables that has maximum probability vector<size_t> jtmapstate = jtmap.findMaximum(); /* // Report exact MAP variable marginals cout << "Exact MAP variable marginals:" << endl; for( size_t i = 0; i < fg.nrVars(); i++ ) cout << jtmap.belief(fg.var(i)) << endl; */ // Report exact MAP joint state cerr << "Exact MAP state (log score = " << fg.logScore( jtmapstate ) << "):" << endl; cout << fg.nrVars() << endl; for( size_t i = 0; i < jtmapstate.size(); i++ ) cout << fg.var(i).label() << " " << jtmapstate[i] + 1 << endl; // +1 because in MATLAB assignments start at 1 } else if (strcmp(argv[2], "pd") == 0) { // Construct a BP (belief propagation) object from the FactorGraph fg // using the parameters specified by opts and two additional properties, // specifying the type of updates the BP algorithm should perform and // whether they should be done in the real or in the logdomain BP bp(fg, opts("updates",string("SEQMAX"))("logdomain",true)); // Initialize belief propagation algorithm bp.init(); // Run belief propagation algorithm bp.run(); // Report variable marginals for fg, calculated by the belief propagation algorithm cerr << "LBP posterior decoding (highest prob assignment in marginal):" << endl; cout << fg.nrVars() << endl; for( size_t i = 0; i < fg.nrVars(); i++ ) {// iterate over all variables in fg //cout << bp.belief(fg.var(i)) << endl; // display the belief of bp for that variable Factor marginal = bp.belief(fg.var(i)); Real maxprob = marginal.max(); for (size_t j = 0; j < marginal.nrStates(); j++) { if (marginal[j] == maxprob) { cout << fg.var(i).label() << " " << j + 1 << endl; // +1 because in MATLAB assignments start at 1 } } } } else { cerr << "Invalid inference algorithm specified." << endl; return 1; } } return 0; }