DiscreteBayesNet::shared_ptr createSampler(size_t i,
size_t slot, vector<Scheduler>& schedulers) {
Scheduler scheduler = largeExample(0); // todo: wrong nr students
addStudent(scheduler, i);
SETDEBUG("Scheduler::buildGraph", false);
scheduler.addStudentSpecificConstraints(0, slot);
DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
// chordal->print(scheduler[i].studentKey(0).name()); // large !
schedulers.push_back(scheduler);
return chordal;
}
// Sample from solution found above and evaluate cost function
DiscreteBayesNet::shared_ptr createSampler(size_t i,
size_t slot, vector<Scheduler>& schedulers) {
Scheduler scheduler = largeExample(1,false);
addStudent(scheduler, i);
cout << " creating sampler for " << scheduler.studentName(0) << endl;
SETDEBUG("Scheduler::buildGraph", false);
// scheduler.print();
scheduler.addStudentSpecificConstraints(0, slot);
DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
schedulers.push_back(scheduler);
return chordal;
}
// 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
gttic_(eliminate);
DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
gttoc_(eliminate);
// find root node
// chordal->back()->print("back: ");
// chordal->front()->print("front: ");
// exit(0);
DiscreteConditional::shared_ptr root = chordal->back();
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_();
}
/* ************************************************************************* */
void accomodateStudent() {
// super-hack! just count...
bool debug = false;
// SETDEBUG("DiscreteConditional::COUNT",true);
SETDEBUG("DiscreteConditional::DiscreteConditional", debug); // progress
Scheduler scheduler = largeExample(0);
// scheduler.addStudent("Victor E", "Autonomy", "HRI", "AI",
// "Henrik Christensen");
scheduler.addStudent("Carlos N", "Perception", "AI", "Autonomy",
"Henrik Christensen");
scheduler.print("scheduler");
// rule out all occupied slots
vector<size_t> slots;
slots += 16, 17, 11, 2, 0, 5, 9, 14;
vector<double> slotsAvailable(scheduler.nrTimeSlots(), 1.0);
BOOST_FOREACH(size_t s, slots)
slotsAvailable[s] = 0;
scheduler.setSlotsAvailable(slotsAvailable);
// BUILD THE GRAPH !
scheduler.buildGraph(1);
// Do EXACT INFERENCE
DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
// find root node
DiscreteConditional::shared_ptr root = chordal->back();
if (debug)
root->print(""/*scheduler.studentName(s)*/);
// GTSAM_PRINT(*chordal);
// solve root node only
Scheduler::Values values;
size_t bestSlot = root->solve(values);
// get corresponding count
DiscreteKey dkey = scheduler.studentKey(0);
values[dkey.first] = bestSlot;
size_t count = (*root)(values);
cout << boost::format("%s = %d (%d), count = %d") % scheduler.studentName(0)
% scheduler.slotName(bestSlot) % bestSlot % count << endl;
// sample schedules
for (size_t n = 0; n < 10; n++) {
Scheduler::sharedValues sample0 = chordal->sample();
scheduler.printAssignment(sample0);
}
}
/* ************************************************************************* */
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
}
// 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 < 7; s++) {
// add all students first time, then drop last one second time, etc...
Scheduler scheduler = largeExample(7 - s);
//scheduler.print(str(boost::format("Scheduler %d") % (7-s)));
// only allow slots not yet taken
scheduler.setSlotsAvailable(slotsAvailable);
// BUILD THE GRAPH !
scheduler.buildGraph(addMutex);
// Do EXACT INFERENCE
gttic_(eliminate);
DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
gttoc_(eliminate);
// find root node
DiscreteConditional::shared_ptr root = chordal->back();
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(6 - 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(6-s)
% scheduler.slotName(bestSlot) % bestSlot % count << endl;
}
tictoc_print_();
// Solution with addMutex = 2: (20 secs)
// TC = Wed 2 (9), count = 96375041778
// AC = Tue 2 (5), count = 4076088090
// SJ = Mon 1 (0), count = 29596704
// TK = Mon 3 (2), count = 755370
// JH = Wed 4 (11), count = 12000
// TH = Fri 2 (17), count = 220
// MN = Fri 1 (16), count = 5
//
// Mutex does make a difference !!
}