Exemplo n.º 1
0
// 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_();
}
Exemplo n.º 2
0
/* ************************************************************************* */
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);
  }
}
Exemplo n.º 3
0
// 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 !!

}