Domain MCSat::applyUP(const Domain& d) {
Domain reduced;
try {
reduced = performUnitPropagation(d);
} catch (contradiction& c) {
// rewrite error message
throw contradiction("Contradiction found in MCSat::run() when running unit prop()");
}
// default model is guaranteed to satisfy the facts
Model m = reduced.defaultModel();
// check to make sure hard clauses are satisfied
std::vector<ELSentence> hardClauses;
std::remove_copy_if(reduced.formulas_begin(), reduced.formulas_end(), std::back_inserter(hardClauses), std::not1(IsHardClausePred()));
for (std::vector<ELSentence>::const_iterator it = hardClauses.begin(); it != hardClauses.end(); it++) {
if (!it->fullySatisfied(m, reduced)) {
throw contradiction("Contradiction found in MCSat::run() when verifying hard clauses are satisfied");
}
}
return reduced;
}
void MCSat::run(boost::mt19937& rng) { // TODO: setup using random initial models
if (d_ == 0) {
throw std::logic_error("MCSat::run() - Domain not set");
}
if (sampleStrategy_ == 0) {
throw std::logic_error("MCSat::run() - SampleStrategy not set");
}
samples_.clear();
samples_.reserve(numSamples_);
//std::cout << "initial domain: ";
//d_->printDebugDescription(std::cout);
// first, run unit propagation on our domain to get a new reduced one.
Domain reduced;
if (useUnitPropagation_) {
reduced = MCSat::applyUP(*d_);
} else {
reduced = *d_;
}
//std::cout << "reduced domain: ";
//reduced.printDebugDescription(std::cout);
Model prevModel = (useRandomInitialModels_ ? reduced.randomModel(rng) : reduced.defaultModel());
// do a starting run on the whole problem as our initial sample
//boost::unordered_set<Model> initModels = sampleSat(prevModel, reduced);
//prevModel = *initModels.begin();
Domain prevDomain = reduced;
if (burnInIterations_ == 0) samples_.push_back(prevModel);
unsigned int totalIterations = numSamples_+burnInIterations_;
for (unsigned int iteration = 1; iteration < totalIterations; iteration++) {
std::vector<ELSentence> newSentences;
if ( totalIterations < 20 ||
iteration % (totalIterations / 20) == 0) {
std::cout << (((double)iteration) / ((double) totalIterations))*100 << "% done." << std::endl;
}
sampleStrategy_->sampleSentences(prevModel, reduced, rng, newSentences);
// if (iteration == 1) {
// std::cout << "initial sampled sentences: ";
// std::copy(newSentences.begin(), newSentences.end(), std::ostream_iterator<ELSentence>(std::cout, "\n"));
// std::cout << "initial model:";
// std::cout << prevModel;
// for (Domain::formula_const_iterator it = prevDomain.formulas_begin();
// it != prevDomain.formulas_end();
// it++) {
// std::cout << "formula " << *it << " is satisfied at: " << it->dSatisfied(prevModel, prevDomain) << std::endl;
// }
// std::cin.get();
// }
// make a new domain using new Sentences
Domain curDomain;
curDomain.setMaxInterval(prevDomain.maxInterval());
for (Domain::fact_const_iterator it = prevDomain.facts_begin(); it != prevDomain.facts_end(); it++) {
curDomain.addFact(*it);
}
for (std::vector<ELSentence>::const_iterator it = newSentences.begin(); it != newSentences.end(); it++) {
curDomain.addFormula(*it);
}
curDomain.addAtoms(prevDomain.atoms_begin(), prevDomain.atoms_end());
//
// if (iteration == burnInIterations_ + numSamples_/2) {
// std::cout << "ITERATION: " << iteration << std::endl;
// std::cout << "curDomain";
// curDomain.printDebugDescription(std::cout);
// std::cout << "sampled sentences: ";
// std::copy(newSentences.begin(), newSentences.end(), std::ostream_iterator<ELSentence>(std::cout, "\n"));
// }
boost::unordered_set<Model> curModels = sampleSat(prevModel, curDomain, rng);
assert(!curModels.empty());
// choose a random model
boost::uniform_int<std::size_t> pickModel(0, curModels.size()-1);
boost::unordered_set<Model>::size_type index = pickModel(rng);
boost::unordered_set<Model>::const_iterator it = curModels.begin();
while (index > 0) {
it++;
index--;
}
// add the model
if (iteration >= burnInIterations_) samples_.push_back(*it);
prevModel = *it;
prevDomain = curDomain;
}
}
