PropertySet MF::getProperties() const {
PropertySet opts;
opts.Set( "tol", props.tol );
opts.Set( "maxiter", props.maxiter );
opts.Set( "verbose", props.verbose );
opts.Set( "damping", props.damping );
return opts;
}
PropertySet CBP::getProperties() const {
PropertySet opts;
opts.Set( "tol", props.tol );
opts.Set( "maxiter", props.maxiter );
opts.Set( "verbose", props.verbose );
opts.Set( "logdomain", props.logdomain );
opts.Set( "updates", props.updates );
opts.Set( "damping", props.damping );
opts.Set( "inference", props.inference );
return opts;
}
void GI_libDAI::getMarginals(float* marginals,
const int label)
{
// Set some constants
Real tol = 1e-7;
//Real tol = 1e-9;
size_t verb = 0;
//size_t verb = 3;
size_t maxiter = 100;
// Store the constants in a PropertySet object
PropertySet opts;
#if LIBDAI_24
opts.Set("maxiter",maxiter); // Maximum number of iterations
opts.Set("tol",tol); // Tolerance for convergence
opts.Set("verbose",verb); // Verbosity (amount of output generated)
opts.Set("logdomain",false);
opts.Set("inference",string("MAXPROD"));
opts.Set("updates",string("SEQFIX"));
#else
opts.set("maxiter",maxiter); // Maximum number of iterations
opts.set("tol",tol); // Tolerance for convergence
opts.set("verbose",verb); // Verbosity (amount of output generated)
opts.set("logdomain",false);
opts.set("inference",string("MAXPROD"));
opts.set("updates",string("SEQFIX"));
#endif
FactorGraph fg( factors.begin(), factors.end(), vars.begin(), vars.end(), factors.size(), vars.size() );
BP bp(fg, opts);
bp.init();
bp.run();
for(long sid = 0; sid < slice->getNbSupernodes(); sid++) {
Factor f = bp.belief(fg.var(sid));
marginals[sid] = f[label];
}
}
PropertySet ExactInf::getProperties() const {
PropertySet opts;
opts.Set( "verbose", props.verbose );
return opts;
}
/// Main function
int main( int argc, char *argv[] ) {
try {
// Variables for storing command line arguments
size_t seed;
size_t states = 2;
string type;
size_t d, N, K, k, j, n1, n2, n3, prime;
bool periodic = false;
FactorType ft;
LDPCType ldpc;
Real beta, sigma_w, sigma_th, mean_w, mean_th, noise;
// Declare the supported options.
po::options_description opts("General command line options");
opts.add_options()
("help", "produce help message")
("seed", po::value<size_t>(&seed), "random number seed (tries to read from /dev/urandom if not specified)")
("states", po::value<size_t>(&states), "number of states of each variable (default=2 for binary variables)")
;
// Graph structure options
po::options_description opts_graph("Options for specifying graph structure");
opts_graph.add_options()
("type", po::value<string>(&type), "factor graph type (one of 'FULL', 'DREG', 'LOOP', 'TREE', 'GRID', 'GRID3D', 'HOI', 'LDPC')")
("d", po::value<size_t>(&d), "variable connectivity (only for type=='DREG');\n\t<d><N> should be even")
("N", po::value<size_t>(&N), "number of variables (not for type=='GRID','GRID3D')")
("n1", po::value<size_t>(&n1), "width of grid (only for type=='GRID','GRID3D')")
("n2", po::value<size_t>(&n2), "height of grid (only for type=='GRID','GRID3D')")
("n3", po::value<size_t>(&n3), "length of grid (only for type=='GRID3D')")
("periodic", po::value<bool>(&periodic), "periodic grid? (only for type=='GRID','GRID3D'; default=0)")
("K", po::value<size_t>(&K), "number of factors (only for type=='HOI','LDPC')")
("k", po::value<size_t>(&k), "number of variables per factor (only for type=='HOI','LDPC')")
;
// Factor options
po::options_description opts_factors("Options for specifying factors");
opts_factors.add_options()
("factors", po::value<FactorType>(&ft), "factor type (one of 'EXPGAUSS','POTTS','ISING')")
("beta", po::value<Real>(&beta), "inverse temperature (ignored for factors=='ISING')")
("mean_w", po::value<Real>(&mean_w), "mean of pairwise interactions w_{ij} (only for factors=='ISING')")
("mean_th", po::value<Real>(&mean_th), "mean of unary interactions th_i (only for factors=='ISING')")
("sigma_w", po::value<Real>(&sigma_w), "stddev of pairwise interactions w_{ij} (only for factors=='ISING')")
("sigma_th", po::value<Real>(&sigma_th), "stddev of unary interactions th_i (only for factors=='ISING'")
;
// LDPC options
po::options_description opts_ldpc("Options for specifying LDPC code factor graphs");
opts_ldpc.add_options()
("ldpc", po::value<LDPCType>(&ldpc), "type of LDPC code (one of 'SMALL','GROUP','RANDOM')")
("j", po::value<size_t>(&j), "number of parity checks per bit (only for type=='LDPC')")
("noise", po::value<Real>(&noise), "bitflip probability for binary symmetric channel (only for type=='LDPC')")
("prime", po::value<size_t>(&prime), "prime number for construction of LDPC code (only for type=='LDPC' with ldpc='GROUP'))")
;
// All options
opts.add(opts_graph).add(opts_factors).add(opts_ldpc);
// Parse command line arguments
po::variables_map vm;
po::store(po::parse_command_line(argc, argv, opts), vm);
po::notify(vm);
// Display help message if necessary
if( vm.count("help") || !vm.count("type") ) {
cout << "This program is part of libDAI - http://www.libdai.org/" << endl << endl;
cout << "Usage: ./createfg [options]" << endl << endl;
cout << "Creates a factor graph according to the specified options." << endl << endl;
cout << endl << opts << endl;
cout << "The following factor graph types with pairwise interactions can be created:" << endl;
cout << "\t'FULL': fully connected graph of <N> variables" << endl;
cout << "\t'DREG': random regular graph of <N> variables where each variable is connected with <d> others" << endl;
cout << "\t'LOOP': a single loop of <N> variables" << endl;
cout << "\t'TREE': random tree-structured (acyclic, connected) graph of <N> variables" << endl;
cout << "\t'GRID': 2D grid of <n1>x<n2> variables" << endl;
cout << "\t'GRID3D': 3D grid of <n1>x<n2>x<n3> variables" << endl;
cout << "The following higher-order interactions factor graphs can be created:" << endl;
cout << "\t'HOI': random factor graph consisting of <N> variables and <K> factors," << endl;
cout << "\t each factor being an interaction of <k> variables." << endl;
cout << "The following LDPC code factor graphs can be created:" << endl;
cout << "\t'LDPC': simulates LDPC decoding problem, using an LDPC code of <N> bits and <K>" << endl;
cout << "\t parity checks, with <k> bits per check and <j> checks per bit, transmitted" << endl;
cout << "\t on a binary symmetric channel with probability <noise> of flipping a bit." << endl;
cout << "\t The transmitted codeword has all bits set to zero. The argument 'ldpc'" << endl;
cout << "\t determines how the LDPC code is constructed: either using a group structure," << endl;
cout << "\t or randomly, or a fixed small code with (N,K,k,j) = (4,4,3,3)." << endl << endl;
cout << "For all types except type=='LDPC', the factors have to be specified as well." << endl << endl;
cout << "EXPGAUSS factors (the default) are created by drawing all log-factor entries" << endl;
cout << "independently from a Gaussian with mean 0 and standard deviation <beta>." << endl << endl;
cout << "In case of pairwise interactions, one can also choose POTTS factors, for which" << endl;
cout << "the log-factors are simply delta functions multiplied by the strength <beta>." << endl << endl;
cout << "For pairwise interactions and binary variables, one can also use ISING factors." << endl;
cout << "Here variables x1...xN are assumed to be +1/-1--valued, and unary interactions" << endl;
cout << "are of the form exp(th*xi) with th drawn from a Gaussian distribution with mean" << endl;
cout << "<mean_th> and standard deviation <sigma_th>, and pairwise interactions are of the" << endl;
cout << "form exp(w*xi*xj) with w drawn from a Gaussian distribution with mean <mean_w>" << endl;
cout << "and standard deviation <sigma_w>." << endl;
return 1;
}
// Set default number of states
if( !vm.count("states") )
states = 2;
// Set default factor type
if( !vm.count("factors") )
ft = FactorType::EXPGAUSS;
// Check validness of factor type
if( ft == FactorType::POTTS )
if( type == HOI_TYPE )
throw "For factors=='POTTS', interactions should be pairwise (type!='HOI')";
if( ft == FactorType::ISING )
if( ((states != 2) || (type == HOI_TYPE)) )
throw "For factors=='ISING', variables should be binary (states==2) and interactions should be pairwise (type!='HOI')";
// Read random seed
if( !vm.count("seed") ) {
ifstream infile;
bool success;
infile.open( "/dev/urandom" );
success = infile.is_open();
if( success ) {
infile.read( (char *)&seed, sizeof(size_t) / sizeof(char) );
success = infile.good();
infile.close();
}
if( !success )
throw "Please specify random number seed.";
}
rnd_seed( seed );
// Set default periodicity
if( !vm.count("periodic") )
periodic = false;
// Store some options in a PropertySet object
PropertySet options;
if( vm.count("mean_th") )
options.Set("mean_th", mean_th);
if( vm.count("sigma_th") )
options.Set("sigma_th", sigma_th);
if( vm.count("mean_w") )
options.Set("mean_w", mean_w);
if( vm.count("sigma_w") )
options.Set("sigma_w", sigma_w);
if( vm.count("beta") )
options.Set("beta", beta);
// Output some comments
cout << "# Factor graph made by " << argv[0] << endl;
cout << "# type = " << type << endl;
cout << "# states = " << states << endl;
// The factor graph to be constructed
FactorGraph fg;
#define NEED_ARG(name, desc) do { if(!vm.count(name)) throw "Please specify " desc " with --" name; } while(0);
if( type == FULL_TYPE || type == DREG_TYPE || type == LOOP_TYPE || type == TREE_TYPE || type == GRID_TYPE || type == GRID3D_TYPE ) {
// Pairwise interactions
// Check command line options
if( type == GRID_TYPE ) {
NEED_ARG("n1", "width of grid");
NEED_ARG("n2", "height of grid");
N = n1 * n2;
} else if( type == GRID3D_TYPE ) {
NEED_ARG("n1", "width of grid");
NEED_ARG("n2", "height of grid");
NEED_ARG("n3", "depth of grid");
N = n1 * n2 * n3;
} else
NEED_ARG("N", "number of variables");
if( states > 2 || ft == FactorType::POTTS ) {
NEED_ARG("beta", "stddev of log-factor entries");
} else {
NEED_ARG("mean_w", "mean of pairwise interactions");
NEED_ARG("mean_th", "mean of unary interactions");
NEED_ARG("sigma_w", "stddev of pairwise interactions");
NEED_ARG("sigma_th", "stddev of unary interactions");
}
if( type == DREG_TYPE )
NEED_ARG("d", "connectivity (number of neighboring variables of each variable)");
// Build pairwise interaction graph
GraphAL G;
if( type == FULL_TYPE )
G = createGraphFull( N );
else if( type == DREG_TYPE )
G = createGraphRegular( N, d );
else if( type == LOOP_TYPE )
G = createGraphLoop( N );
else if( type == TREE_TYPE )
G = createGraphTree( N );
else if( type == GRID_TYPE )
G = createGraphGrid( n1, n2, periodic );
else if( type == GRID3D_TYPE )
G = createGraphGrid3D( n1, n2, n3, periodic );
// Construct factor graph from pairwise interaction graph
fg = createFG( G, ft, states, options );
// Output some additional comments
if( type == GRID_TYPE || type == GRID3D_TYPE ) {
cout << "# n1 = " << n1 << endl;
cout << "# n2 = " << n2 << endl;
if( type == GRID3D_TYPE )
cout << "# n3 = " << n3 << endl;
}
if( type == DREG_TYPE )
cout << "# d = " << d << endl;
cout << "# options = " << options << endl;
} else if( type == HOI_TYPE ) {
// Higher order interactions
// Check command line arguments
NEED_ARG("N", "number of variables");
NEED_ARG("K", "number of factors");
NEED_ARG("k", "number of variables per factor");
NEED_ARG("beta", "stddev of log-factor entries");
// Create higher-order interactions factor graph
do {
fg = createHOIFG( N, K, k, beta );
} while( !fg.isConnected() );
// Output some additional comments
cout << "# K = " << K << endl;
cout << "# k = " << k << endl;
cout << "# beta = " << beta << endl;
} else if( type == LDPC_TYPE ) {
// LDPC codes
// Check command line arguments
NEED_ARG("ldpc", "type of LDPC code");
NEED_ARG("noise", "bitflip probability for binary symmetric channel");
// Check more command line arguments (seperately for each LDPC type)
if( ldpc == LDPCType::RANDOM ) {
NEED_ARG("N", "number of variables");
NEED_ARG("K", "number of factors");
NEED_ARG("k", "number of variables per factor");
NEED_ARG("j", "number of parity checks per bit");
if( N * j != K * k )
throw "Parameters should satisfy N * j == K * k";
} else if( ldpc == LDPCType::GROUP ) {
NEED_ARG("prime", "prime number");
NEED_ARG("k", "number of variables per factor");
NEED_ARG("j", "number of parity checks per bit");
if( !isPrime(prime) )
throw "Parameter <prime> should be prime";
if( !((prime-1) % j == 0 ) )
throw "Parameters should satisfy (prime-1) % j == 0";
if( !((prime-1) % k == 0 ) )
throw "Parameters should satisfy (prime-1) % k == 0";
N = prime * k;
K = prime * j;
} else if( ldpc == LDPCType::SMALL ) {
N = 4;
K = 4;
j = 3;
k = 3;
}
// Output some additional comments
cout << "# N = " << N << endl;
cout << "# K = " << K << endl;
cout << "# j = " << j << endl;
cout << "# k = " << k << endl;
if( ldpc == LDPCType::GROUP )
cout << "# prime = " << prime << endl;
cout << "# noise = " << noise << endl;
// Construct likelihood and paritycheck factors
Real likelihood[4] = {1.0 - noise, noise, noise, 1.0 - noise};
Real *paritycheck = new Real[1 << k];
createParityCheck(paritycheck, k, 0.0);
// Create LDPC structure
BipartiteGraph ldpcG;
bool regular;
do {
if( ldpc == LDPCType::GROUP )
ldpcG = createGroupStructuredLDPCGraph( prime, j, k );
else if( ldpc == LDPCType::RANDOM )
ldpcG = createRandomBipartiteGraph( N, K, j, k );
else if( ldpc == LDPCType::SMALL )
ldpcG = createSmallLDPCGraph();
regular = true;
for( size_t i = 0; i < N; i++ )
if( ldpcG.nb1(i).size() != j )
regular = false;
for( size_t I = 0; I < K; I++ )
if( ldpcG.nb2(I).size() != k )
regular = false;
} while( !regular && !ldpcG.isConnected() );
// Convert to FactorGraph
vector<Factor> factors;
for( size_t I = 0; I < K; I++ ) {
VarSet vs;
for( size_t _i = 0; _i < k; _i++ ) {
size_t i = ldpcG.nb2(I)[_i];
vs |= Var( i, 2 );
}
factors.push_back( Factor( vs, paritycheck ) );
}
delete paritycheck;
// Generate noise vector
vector<char> noisebits(N,0);
size_t bitflips = 0;
for( size_t i = 0; i < N; i++ ) {
if( rnd_uniform() < noise ) {
noisebits[i] = 1;
bitflips++;
}
}
cout << "# bitflips = " << bitflips << endl;
// Simulate transmission of all-zero codeword
vector<char> input(N,0);
vector<char> output(N,0);
for( size_t i = 0; i < N; i++ )
output[i] = (input[i] + noisebits[i]) & 1;
// Add likelihoods
for( size_t i = 0; i < N; i++ )
factors.push_back( Factor(Var(i,2), likelihood + output[i]*2) );
// Construct Factor Graph
fg = FactorGraph( factors );
} else
throw "Invalid type";
// Output additional comments
cout << "# N = " << fg.nrVars() << endl;
cout << "# seed = " << seed << endl;
// Output factor graph
cout << fg;
} catch( const char *e ) {
/// Display error message
cerr << "Error: " << e << endl;
return 1;
}
return 0;
}
/**
* Run BP on a given factor graph
* @param nodeLabelsBP node inferred by BP
*/
double GI_libDAI::run(labelType* inferredLabels,
int id,
size_t maxiter,
labelType* nodeLabelsGroundTruth,
bool computeEnergyAtEachIteration,
double* _loss)
{
double energy = 0;
double loss = 0;
string paramMSRC;
Config::Instance()->getParameter("msrc", paramMSRC);
bool useMSRC = paramMSRC.c_str()[0] == '1';
bool replaceVoidMSRC = false;
if(useMSRC) {
Config::Instance()->getParameter("msrc_replace_void", paramMSRC);
replaceVoidMSRC = paramMSRC.c_str()[0] == '1';
}
// Set some constants
Real tol = 1e-7;
//Real tol = 1e-9;
size_t verb = 0;
//size_t verb = 3;
// Store the constants in a PropertySet object
PropertySet opts;
#if LIBDAI_24
opts.Set("maxiter",maxiter); // Maximum number of iterations
opts.Set("tol",tol); // Tolerance for convergence
opts.Set("verbose",verb); // Verbosity (amount of output generated)
opts.Set("logdomain",false);
opts.Set("inference",string("MAXPROD"));
opts.Set("updates",string("SEQFIX"));
#else
opts.set("maxiter",maxiter); // Maximum number of iterations
opts.set("tol",tol); // Tolerance for convergence
opts.set("verbose",verb); // Verbosity (amount of output generated)
opts.set("logdomain",false);
opts.set("inference",string("MAXPROD"));
opts.set("updates",string("SEQFIX"));
#endif
FactorGraph fg( factors.begin(), factors.end(), vars.begin(), vars.end(), factors.size(), vars.size() );
// 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("SEQFIX"))("logdomain",true));
//BP bp(fg, opts("updates",string("SEQFIX"))("logdomain",false));
//BP bp(fg, opts("updates",string("SEQFIX"))("logdomain",false)("inference",string("MAXPROD")));
BP bp(fg, opts);
// Initialize belief propagation algorithm
bp.init();
vector<std::size_t> labels;
// Run belief propagation algorithm
if(computeEnergyAtEachIteration) {
#if OUTPUT_ENERGY
// one file per example
stringstream sEnergyMaxFile;
sEnergyMaxFile << "x_" << id;
sEnergyMaxFile << "_energyBPMax.txt";
ofstream ofsEnergyMax(sEnergyMaxFile.str().c_str(),ios::app);
#endif
int i = maxiter;
//for(int i = 1; i <= (int)maxiter; i+=10) // loop to see how energy evolves
{
//INFERENCE_PRINT("[gi_libDAI] BP Iteration %d\n", i);
#if LIBDAI_24
opts.Set("maxiter",(size_t)i); // Maximum number of iterations
#else
opts.set("maxiter",(size_t)i); // Maximum number of iterations
#endif
bp.setProperties(opts);
//Real maxDiff = bp.run();
bp.run();
labels = bp.findMaximum();
if(replaceVoidMSRC) {
if(lossPerLabel == 0) {
copyLabels_MSRC(labels, inferredLabels, bp, fg);
} else {
copy(labels.begin(),labels.end(),inferredLabels);
}
} else {
copy(labels.begin(),labels.end(),inferredLabels);
}
energy = GraphInference::computeEnergy(inferredLabels);
#if OUTPUT_ENERGY
ofsEnergyMax << energy << " " << loss << endl;
#endif
//if( maxDiff < tol )
// break;
}
#if OUTPUT_ENERGY
ofsEnergyMax.close();
#endif
} else {
bp.run();
labels = bp.findMaximum();
if(replaceVoidMSRC) {
if(lossPerLabel == 0) {
copyLabels_MSRC(labels,
inferredLabels,
bp,
fg);
} else {
copy(labels.begin(),labels.end(),inferredLabels);
}
} else {
copy(labels.begin(),labels.end(),inferredLabels);
}
energy = GraphInference::computeEnergy(inferredLabels);
}
if(_loss) {
*_loss = loss;
}
return energy;
}
