Fixed all examples

examples/DiscreteBayesNetExample.cpp

@@ -57,7 +57,7 @@ int main(int argc, char **argv) {
 
   // solve
   auto mpe = chordal->optimize();
-  GTSAM_PRINT(*mpe);
+  GTSAM_PRINT(mpe);
 
   // We can also build a Bayes tree (directed junction tree).
   // The elimination order above will do fine:
@@ -71,13 +71,13 @@ int main(int argc, char **argv) {
   // solve again, now with evidence
   DiscreteBayesNet::shared_ptr chordal2 = fg.eliminateSequential(ordering);
   auto mpe2 = chordal2->optimize();
-  GTSAM_PRINT(*mpe2);
+  GTSAM_PRINT(mpe2);
 
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
     auto sample = chordal2->sample();
-    GTSAM_PRINT(*sample);
+    GTSAM_PRINT(sample);
   }
   return 0;
 }

examples/DiscreteBayesNet_FG.cpp

@@ -34,10 +34,10 @@ int main(int argc, char **argv) {
   // Define keys and a print function
   Key C(1), S(2), R(3), W(4);
   auto print = [=](const DiscreteFactor::Values& values) {
-    cout << boolalpha << "Cloudy = " << static_cast<bool>(values[C])
-         << "  Sprinkler = " << static_cast<bool>(values[S])
-         << "  Rain = " << boolalpha << static_cast<bool>(values[R])
-         << "  WetGrass = " << static_cast<bool>(values[W]) << endl;
+    cout << boolalpha << "Cloudy = " << static_cast<bool>(values.at(C))
+         << "  Sprinkler = " << static_cast<bool>(values.at(S))
+         << "  Rain = " << boolalpha << static_cast<bool>(values.at(R))
+         << "  WetGrass = " << static_cast<bool>(values.at(W)) << endl;
   };
 
   // We assume binary state variables

examples/HMMExample.cpp

@@ -67,13 +67,13 @@ int main(int argc, char **argv) {
 
   // solve
   auto mpe = chordal->optimize();
-  GTSAM_PRINT(*mpe);
+  GTSAM_PRINT(mpe);
 
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t k = 0; k < 10; k++) {
     auto sample = chordal->sample();
-    GTSAM_PRINT(*sample);
+    GTSAM_PRINT(sample);
   }
 
   // Or compute the marginals. This re-eliminates the FG into a Bayes tree

examples/UGM_chain.cpp

@@ -71,7 +71,7 @@ int main(int argc, char** argv) {
   // We use sequential variable elimination
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
   auto optimalDecoding = chordal->optimize();
-  optimalDecoding->print("\nMost Probable Explanation (optimalDecoding)\n");
+  optimalDecoding.print("\nMost Probable Explanation (optimalDecoding)\n");
 
   // "Inference" Computing marginals for each node
   // Here we'll make use of DiscreteMarginals class, which makes use of

examples/UGM_small.cpp

@@ -64,7 +64,7 @@ int main(int argc, char** argv) {
   // We use sequential variable elimination
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
   auto optimalDecoding = chordal->optimize();
-  optimalDecoding->print("\noptimalDecoding");
+  GTSAM_PRINT(optimalDecoding);
 
   // "Inference" Computing marginals
   cout << "\nComputing Node Marginals .." << endl;

gtsam_unstable/discrete/examples/schedulingExample.cpp

@@ -225,7 +225,7 @@ void sampleSolutions() {
   // now, sample schedules
   for (size_t n = 0; n < 500; n++) {
     vector<size_t> stats(19, 0);
-    vector<Scheduler::sharedValues> samples;
+    vector<Scheduler::Values> samples;
     for (size_t i = 0; i < 7; i++) {
       samples.push_back(samplers[i]->sample());
       schedulers[i].accumulateStats(samples[i], stats);

gtsam_unstable/discrete/examples/schedulingQuals12.cpp

@@ -234,7 +234,7 @@ void sampleSolutions() {
   // now, sample schedules
   for (size_t n = 0; n < 500; n++) {
     vector<size_t> stats(19, 0);
-    vector<Scheduler::sharedValues> samples;
+    vector<Scheduler::Values> samples;
     for (size_t i = 0; i < NRSTUDENTS; i++) {
       samples.push_back(samplers[i]->sample());
       schedulers[i].accumulateStats(samples[i], stats);

gtsam_unstable/discrete/examples/schedulingQuals13.cpp

@@ -259,7 +259,7 @@ void sampleSolutions() {
   // now, sample schedules
   for (size_t n = 0; n < 10000; n++) {
     vector<size_t> stats(nrFaculty, 0);
-    vector<Scheduler::sharedValues> samples;
+    vector<Scheduler::Values> samples;
     for (size_t i = 0; i < NRSTUDENTS; i++) {
       samples.push_back(samplers[i]->sample());
       schedulers[i].accumulateStats(samples[i], stats);