257 lines
8.0 KiB
C++
257 lines
8.0 KiB
C++
/*
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* schedulingExample.cpp
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* @brief hard scheduling example
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* @date March 25, 2011
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* @author Frank Dellaert
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*/
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#define ENABLE_TIMING
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#define ADD_NO_CACHING
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#define ADD_NO_PRUNING
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#include <gtsam_unstable/discrete/Scheduler.h>
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#include <gtsam/base/debug.h>
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#include <gtsam/base/timing.h>
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#include <algorithm>
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using namespace std;
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using namespace gtsam;
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size_t NRSTUDENTS = 9;
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bool NonZero(size_t i) {
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return i > 0;
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}
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/* ************************************************************************* */
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void addStudent(Scheduler& s, size_t i) {
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switch (i) {
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case 0:
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s.addStudent("Pan, Yunpeng", "Controls", "Perception", "Mechanics", "Eric Johnson");
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break;
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case 1:
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s.addStudent("Sawhney, Rahul", "Controls", "AI", "Perception", "Henrik Christensen");
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break;
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case 2:
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s.addStudent("Akgun, Baris", "Controls", "AI", "HRI", "Andrea Thomaz");
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break;
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case 3:
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s.addStudent("Jiang, Shu", "Controls", "AI", "Perception", "Ron Arkin");
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break;
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case 4:
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s.addStudent("Grice, Phillip", "Controls", "Perception", "HRI", "Charlie Kemp");
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break;
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case 5:
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s.addStudent("Huaman, Ana", "Controls", "AI", "Perception", "Mike Stilman");
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break;
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case 6:
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s.addStudent("Levihn, Martin", "AI", "Autonomy", "Perception", "Mike Stilman");
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break;
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case 7:
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s.addStudent("Nieto, Carlos", "AI", "Autonomy", "Perception", "Henrik Christensen");
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break;
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case 8:
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s.addStudent("Robinette, Paul", "Controls", "AI", "HRI", "Ayanna Howard");
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break;
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}
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}
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/* ************************************************************************* */
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Scheduler largeExample(size_t nrStudents = NRSTUDENTS) {
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string path("../../../gtsam_unstable/discrete/examples/");
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Scheduler s(nrStudents, path + "Doodle2012.csv");
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s.addArea("Harvey Lipkin", "Mechanics");
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s.addArea("Jun Ueda", "Mechanics");
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s.addArea("Patricio Vela", "Controls");
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s.addArea("Magnus Egerstedt", "Controls");
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s.addArea("Jun Ueda", "Controls");
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s.addArea("Panos Tsiotras", "Controls");
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s.addArea("Fumin Zhang", "Controls");
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s.addArea("Henrik Christensen", "Perception");
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s.addArea("Aaron Bobick", "Perception");
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s.addArea("Mike Stilman", "AI");
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// s.addArea("Henrik Christensen", "AI");
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s.addArea("Ayanna Howard", "AI");
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s.addArea("Charles Isbell", "AI");
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s.addArea("Tucker Balch", "AI");
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s.addArea("Ayanna Howard", "Autonomy");
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s.addArea("Charlie Kemp", "Autonomy");
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s.addArea("Tucker Balch", "Autonomy");
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s.addArea("Ron Arkin", "Autonomy");
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s.addArea("Andrea Thomaz", "HRI");
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s.addArea("Karen Feigh", "HRI");
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s.addArea("Charlie Kemp", "HRI");
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// add students
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for (size_t i = 0; i < nrStudents; i++)
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addStudent(s, i);
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return s;
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}
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/* ************************************************************************* */
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void runLargeExample() {
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Scheduler scheduler = largeExample();
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scheduler.print();
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// BUILD THE GRAPH !
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size_t addMutex = 3;
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// SETDEBUG("Scheduler::buildGraph", true);
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scheduler.buildGraph(addMutex);
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// Do brute force product and output that to file
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if (scheduler.nrStudents() == 1) { // otherwise too slow
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DecisionTreeFactor product =
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*std::dynamic_pointer_cast<DecisionTreeFactor>(scheduler.product());
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product.dot("scheduling-large", DefaultKeyFormatter, false);
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}
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// Do exact inference
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// SETDEBUG("timing-verbose", true);
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SETDEBUG("DiscreteConditional::DiscreteConditional", true);
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#define SAMPLE
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#ifdef SAMPLE
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gttic(large);
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DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
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gttoc(large);
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tictoc_finishedIteration();
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tictoc_print();
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for (size_t i=0;i<100;i++) {
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auto assignment = chordal->sample();
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vector<size_t> stats(scheduler.nrFaculty());
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scheduler.accumulateStats(assignment, stats);
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size_t max = *max_element(stats.begin(), stats.end());
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size_t min = *min_element(stats.begin(), stats.end());
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size_t nz = count_if(stats.begin(), stats.end(), NonZero);
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// cout << min << ", " << max << ", " << nz << endl;
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if (nz >= 13 && min >=1 && max <= 4) {
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cout << "======================================================\n";
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scheduler.printAssignment(assignment);
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}
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}
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#else
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gttic(large);
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auto MPE = scheduler.optimize();
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gttoc(large);
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tictoc_finishedIteration();
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tictoc_print();
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scheduler.printAssignment(MPE);
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#endif
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}
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/* ************************************************************************* */
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// Solve a series of relaxed problems for maximum flexibility solution
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void solveStaged(size_t addMutex = 2) {
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// super-hack! just count...
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bool debug = false;
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SETDEBUG("DiscreteConditional::COUNT", true);
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SETDEBUG("DiscreteConditional::DiscreteConditional", debug); // progress
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// make a vector with slot availability, initially all 1
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// Reads file to get count :-)
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vector<double> slotsAvailable(largeExample(0).nrTimeSlots(), 1.0);
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// now, find optimal value for each student, using relaxed mutex constraints
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for (size_t s = 0; s < NRSTUDENTS; s++) {
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// add all students first time, then drop last one second time, etc...
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Scheduler scheduler = largeExample(NRSTUDENTS - s);
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//scheduler.print(str(boost::format("Scheduler %d") % (NRSTUDENTS-s)));
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// only allow slots not yet taken
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scheduler.setSlotsAvailable(slotsAvailable);
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// BUILD THE GRAPH !
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scheduler.buildGraph(addMutex);
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// Do EXACT INFERENCE
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gttic_(eliminate);
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DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
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gttoc_(eliminate);
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// find root node
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DiscreteConditional::shared_ptr root = chordal->back();
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if (debug)
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root->print(""/*scheduler.studentName(s)*/);
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// solve root node only
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size_t bestSlot = root->argmax();
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// get corresponding count
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DiscreteKey dkey = scheduler.studentKey(NRSTUDENTS - 1 - s);
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DiscreteValues values;
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values[dkey.first] = bestSlot;
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size_t count = (*root)(values);
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// remove this slot from consideration
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slotsAvailable[bestSlot] = 0.0;
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cout << scheduler.studentName(NRSTUDENTS - 1 - s) << " = " <<
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scheduler.slotName(bestSlot) << " (" << bestSlot
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<< "), count = " << count << endl;
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}
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tictoc_print_();
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}
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/* ************************************************************************* */
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// Sample from solution found above and evaluate cost function
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DiscreteBayesNet::shared_ptr createSampler(size_t i,
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size_t slot, vector<Scheduler>& schedulers) {
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Scheduler scheduler = largeExample(0); // todo: wrong nr students
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addStudent(scheduler, i);
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SETDEBUG("Scheduler::buildGraph", false);
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scheduler.addStudentSpecificConstraints(0, slot);
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DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
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schedulers.push_back(scheduler);
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return chordal;
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}
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void sampleSolutions() {
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vector<Scheduler> schedulers;
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vector<DiscreteBayesNet::shared_ptr> samplers(NRSTUDENTS);
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// Given the time-slots, we can create NRSTUDENTS independent samplers
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vector<size_t> slots{3, 20, 2, 6, 5, 11, 1, 4}; // given slots
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for (size_t i = 0; i < NRSTUDENTS; i++)
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samplers[i] = createSampler(i, slots[i], schedulers);
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// now, sample schedules
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for (size_t n = 0; n < 500; n++) {
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vector<size_t> stats(19, 0);
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vector<DiscreteValues> samples;
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for (size_t i = 0; i < NRSTUDENTS; i++) {
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samples.push_back(samplers[i]->sample());
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schedulers[i].accumulateStats(samples[i], stats);
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}
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size_t max = *max_element(stats.begin(), stats.end());
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size_t min = *min_element(stats.begin(), stats.end());
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size_t nz = count_if(stats.begin(), stats.end(), NonZero);
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if (nz >= 15 && max <= 2) {
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cout << "Sampled schedule " << (n + 1) << ", min = " << min
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<< ", nz = " << nz << ", max = " << max << endl;
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for (size_t i = 0; i < NRSTUDENTS; i++) {
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cout << schedulers[i].studentName(0) << " : " << schedulers[i].slotName(
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slots[i]) << endl;
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schedulers[i].printSpecial(samples[i]);
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}
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}
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}
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}
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/* ************************************************************************* */
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int main() {
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// runLargeExample();
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solveStaged(3);
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// sampleSolutions();
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return 0;
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}
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/* ************************************************************************* */
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