Fixed all examples

examples/DiscreteBayesNetExample.cpp

@@ -56,8 +56,8 @@ int main(int argc, char **argv) {
   DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
 
   // solve
-  autompe = chordal->optimize();
-  GTSAM_PRINT(*mpe);
+  auto mpe = chordal->optimize();
+  GTSAM_PRINT(mpe);
 
   // We can also build a Bayes tree (directed junction tree).
   // The elimination order above will do fine:
@@ -70,14 +70,14 @@ int main(int argc, char **argv) {
 
   // solve again, now with evidence
   DiscreteBayesNet::shared_ptr chordal2 = fg.eliminateSequential(ordering);
-  autompe2 = chordal2->optimize();
-  GTSAM_PRINT(*mpe2);
+  auto mpe2 = chordal2->optimize();
+  GTSAM_PRINT(mpe2);
 
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
-    autosample = chordal2->sample();
-    GTSAM_PRINT(*sample);
+    auto sample = chordal2->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
@@ -85,7 +85,7 @@ int main(int argc, char **argv) {
         }
 
   // "Most Probable Explanation", i.e., configuration with largest value
-  autompe = graph.eliminateSequential()->optimize();
+  auto mpe = graph.eliminateSequential()->optimize();
   cout << "\nMost Probable Explanation (MPE):" << endl;
   print(mpe);
 
@@ -97,7 +97,7 @@ int main(int argc, char **argv) {
 
   // solve again, now with evidence
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  autompe_with_evidence = chordal->optimize();
+  auto mpe_with_evidence = chordal->optimize();
 
   cout << "\nMPE given C=0:" << endl;
   print(mpe_with_evidence);
@@ -113,7 +113,7 @@ int main(int argc, char **argv) {
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
-    autosample = chordal->sample();
+    auto sample = chordal->sample();
     print(sample);
   }
   return 0;

examples/HMMExample.cpp

@@ -66,14 +66,14 @@ int main(int argc, char **argv) {
   chordal->print("Eliminated");
 
   // solve
-  autompe = chordal->optimize();
-  GTSAM_PRINT(*mpe);
+  auto mpe = chordal->optimize();
+  GTSAM_PRINT(mpe);
 
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t k = 0; k < 10; k++) {
-    autosample = chordal->sample();
-    GTSAM_PRINT(*sample);
+    auto sample = chordal->sample();
+    GTSAM_PRINT(sample);
   }
 
   // Or compute the marginals. This re-eliminates the FG into a Bayes tree

examples/UGM_chain.cpp

@@ -70,8 +70,8 @@ int main(int argc, char** argv) {
   // "Decoding", i.e., configuration with largest value
   // We use sequential variable elimination
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  autooptimalDecoding = chordal->optimize();
-  optimalDecoding->print("\nMost Probable Explanation (optimalDecoding)\n");
+  auto optimalDecoding = chordal->optimize();
+  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

@@ -63,8 +63,8 @@ int main(int argc, char** argv) {
   // "Decoding", i.e., configuration with largest value (MPE)
   // We use sequential variable elimination
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  autooptimalDecoding = chordal->optimize();
-  optimalDecoding->print("\noptimalDecoding");
+  auto optimalDecoding = chordal->optimize();
+  GTSAM_PRINT(optimalDecoding);
 
   // "Inference" Computing marginals
   cout << "\nComputing Node Marginals .." << endl;

gtsam_unstable/discrete/examples/schedulingExample.cpp

@@ -122,7 +122,7 @@ void runLargeExample() {
   //  SETDEBUG("timing-verbose", true);
   SETDEBUG("DiscreteConditional::DiscreteConditional", true);
   gttic(large);
-  autoMPE = scheduler.optimalAssignment();
+  auto MPE = scheduler.optimalAssignment();
   gttoc(large);
   tictoc_finishedIteration();
   tictoc_print();
@@ -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

@@ -129,7 +129,7 @@ void runLargeExample() {
   tictoc_finishedIteration();
   tictoc_print();
   for (size_t i=0;i<100;i++) {
-    autoassignment = chordal->sample();
+    auto assignment = chordal->sample();
     vector<size_t> stats(scheduler.nrFaculty());
     scheduler.accumulateStats(assignment, stats);
     size_t max = *max_element(stats.begin(), stats.end());
@@ -143,7 +143,7 @@ void runLargeExample() {
   }
 #else
   gttic(large);
-  autoMPE = scheduler.optimalAssignment();
+  auto MPE = scheduler.optimalAssignment();
   gttoc(large);
   tictoc_finishedIteration();
   tictoc_print();
@@ -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

@@ -153,7 +153,7 @@ void runLargeExample() {
   tictoc_finishedIteration();
   tictoc_print();
   for (size_t i=0;i<100;i++) {
-    autoassignment = sample(*chordal);
+    auto assignment = sample(*chordal);
     vector<size_t> stats(scheduler.nrFaculty());
     scheduler.accumulateStats(assignment, stats);
     size_t max = *max_element(stats.begin(), stats.end());
@@ -167,7 +167,7 @@ void runLargeExample() {
   }
 #else
   gttic(large);
-  autoMPE = scheduler.optimalAssignment();
+  auto MPE = scheduler.optimalAssignment();
   gttoc(large);
   tictoc_finishedIteration();
   tictoc_print();
@@ -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);