/* ************************************************************************* */
void runLargeExample() {
Scheduler scheduler = largeExample();
scheduler.print();
// BUILD THE GRAPH !
size_t addMutex = 2;
scheduler.buildGraph(addMutex);
// Do brute force product and output that to file
if (scheduler.nrStudents() == 1) { // otherwise too slow
DecisionTreeFactor product = scheduler.product();
product.dot("scheduling-large", false);
}
// Do exact inference
// SETDEBUG("timing-verbose", true);
SETDEBUG("DiscreteConditional::DiscreteConditional", true);
gttic(large);
DiscreteFactor::sharedValues MPE = scheduler.optimalAssignment();
gttoc(large);
tictoc_finishedIteration();
tictoc_print();
scheduler.printAssignment(MPE);
}
/* ************************************************************************* */
std::pair<DiscreteConditional::shared_ptr, DecisionTreeFactor::shared_ptr> //
EliminateDiscrete(const DiscreteFactorGraph& factors, const Ordering& frontalKeys) {
// PRODUCT: multiply all factors
gttic(product);
DecisionTreeFactor product;
BOOST_FOREACH(const DiscreteFactor::shared_ptr& factor, factors)
product = (*factor) * product;
gttoc(product);
// sum out frontals, this is the factor on the separator
gttic(sum);
DecisionTreeFactor::shared_ptr sum = product.sum(frontalKeys);
gttoc(sum);
// Ordering keys for the conditional so that frontalKeys are really in front
Ordering orderedKeys;
orderedKeys.insert(orderedKeys.end(), frontalKeys.begin(), frontalKeys.end());
orderedKeys.insert(orderedKeys.end(), sum->keys().begin(), sum->keys().end());
// now divide product/sum to get conditional
gttic(divide);
DiscreteConditional::shared_ptr cond(new DiscreteConditional(product, *sum, orderedKeys));
gttoc(divide);
return std::make_pair(cond, sum);
}
/* ************************************************************************* */
void runLargeExample() {
Scheduler scheduler = largeExample();
scheduler.print();
// BUILD THE GRAPH !
size_t addMutex = 3;
// SETDEBUG("Scheduler::buildGraph", true);
scheduler.buildGraph(addMutex);
// Do brute force product and output that to file
if (scheduler.nrStudents() == 1) { // otherwise too slow
DecisionTreeFactor product = scheduler.product();
product.dot("scheduling-large", false);
}
// Do exact inference
// SETDEBUG("timing-verbose", true);
SETDEBUG("DiscreteConditional::DiscreteConditional", true);
#define SAMPLE
#ifdef SAMPLE
tic(2, "large");
DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
toc(2, "large");
tictoc_finishedIteration();
tictoc_print();
for (size_t i=0;i<100;i++) {
DiscreteFactor::sharedValues assignment = sample(*chordal);
vector<size_t> stats(scheduler.nrFaculty());
scheduler.accumulateStats(assignment, stats);
size_t max = *max_element(stats.begin(), stats.end());
size_t min = *min_element(stats.begin(), stats.end());
size_t nz = count_if(stats.begin(), stats.end(), NonZero);
// cout << min << ", " << max << ", " << nz << endl;
if (nz >= 13 && min >=1 && max <= 4) {
cout << "======================================================\n";
scheduler.printAssignment(assignment);
}
}
#else
tic(2, "large");
DiscreteFactor::sharedValues MPE = scheduler.optimalAssignment();
toc(2, "large");
tictoc_finishedIteration();
tictoc_print();
scheduler.printAssignment(MPE);
#endif
}
/* ************************************************************************* */
std::pair<DiscreteConditional::shared_ptr, DecisionTreeFactor::shared_ptr> //
EliminateDiscrete(const FactorGraph<DiscreteFactor>& factors, size_t num) {
// PRODUCT: multiply all factors
gttic(product);
DecisionTreeFactor product;
BOOST_FOREACH(const DiscreteFactor::shared_ptr& factor, factors)
product = (*factor) * product;
gttoc(product);
// sum out frontals, this is the factor on the separator
gttic(sum);
DecisionTreeFactor::shared_ptr sum = product.sum(num);
gttoc(sum);
// now divide product/sum to get conditional
gttic(divide);
DiscreteConditional::shared_ptr cond(new DiscreteConditional(product, *sum));
gttoc(divide);
return std::make_pair(cond, sum);
}
