// Solve a series of relaxed problems for maximum flexibility solution
void solveStaged(size_t addMutex = 2) {
// super-hack! just count...
bool debug = false;
SETDEBUG("DiscreteConditional::COUNT", true);
SETDEBUG("DiscreteConditional::DiscreteConditional", debug); // progress
// make a vector with slot availability, initially all 1
// Reads file to get count :-)
vector<double> slotsAvailable(largeExample(0).nrTimeSlots(), 1.0);
// now, find optimal value for each student, using relaxed mutex constraints
for (size_t s = 0; s < NRSTUDENTS; s++) {
// add all students first time, then drop last one second time, etc...
Scheduler scheduler = largeExample(NRSTUDENTS - s);
//scheduler.print(str(boost::format("Scheduler %d") % (NRSTUDENTS-s)));
// only allow slots not yet taken
scheduler.setSlotsAvailable(slotsAvailable);
// BUILD THE GRAPH !
scheduler.buildGraph(addMutex);
// Do EXACT INFERENCE
tic_(3,"eliminate");
DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
toc_(3,"eliminate");
// find root node
DiscreteConditional::shared_ptr root = *(chordal->rbegin());
if (debug)
root->print(""/*scheduler.studentName(s)*/);
// solve root node only
Scheduler::Values values;
size_t bestSlot = root->solve(values);
// get corresponding count
DiscreteKey dkey = scheduler.studentKey(NRSTUDENTS - 1 - s);
values[dkey.first] = bestSlot;
size_t count = (*root)(values);
// remove this slot from consideration
slotsAvailable[bestSlot] = 0.0;
cout << boost::format("%s = %d (%d), count = %d") % scheduler.studentName(NRSTUDENTS-1-s)
% scheduler.slotName(bestSlot) % bestSlot % count << endl;
}
tictoc_print_();
}
