Fix examples

examples/DiscreteBayesNetExample.cpp

@@ -53,10 +53,9 @@ int main(int argc, char **argv) {
   // Create solver and eliminate
   Ordering ordering;
   ordering += Key(0), Key(1), Key(2), Key(3), Key(4), Key(5), Key(6), Key(7);
-  DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
 
   // solve
-  auto mpe = chordal->optimize();
+  auto mpe = fg.optimize();
   GTSAM_PRINT(mpe);
 
   // We can also build a Bayes tree (directed junction tree).
@@ -69,14 +68,14 @@ int main(int argc, char **argv) {
   fg.add(Dyspnea, "0 1");
 
   // solve again, now with evidence
-  DiscreteBayesNet::shared_ptr chordal2 = fg.eliminateSequential(ordering);
-  auto mpe2 = chordal2->optimize();
+  auto mpe2 = fg.optimize();
   GTSAM_PRINT(mpe2);
 
   // We can also sample from it
+  DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
-    auto sample = chordal2->sample();
+    auto sample = chordal->sample();
     GTSAM_PRINT(sample);
   }
   return 0;

examples/DiscreteBayesNet_FG.cpp

@@ -85,7 +85,7 @@ int main(int argc, char **argv) {
         }
 
   // "Most Probable Explanation", i.e., configuration with largest value
-  auto mpe = graph.eliminateSequential()->optimize();
+  auto mpe = graph.optimize();
   cout << "\nMost Probable Explanation (MPE):" << endl;
   print(mpe);
 
@@ -96,8 +96,7 @@ int main(int argc, char **argv) {
   graph.add(Cloudy, "1 0");
 
   // solve again, now with evidence
-  DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  auto mpe_with_evidence = chordal->optimize();
+  auto mpe_with_evidence = graph.optimize();
 
   cout << "\nMPE given C=0:" << endl;
   print(mpe_with_evidence);
@@ -110,7 +109,8 @@ int main(int argc, char **argv) {
   cout << "\nP(W=1|C=0):" << marginals.marginalProbabilities(WetGrass)[1]
        << endl;
 
-  // We can also sample from it
+  // We can also sample from the eliminated graph
+  DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
     auto sample = chordal->sample();

examples/HMMExample.cpp

@@ -59,16 +59,16 @@ int main(int argc, char **argv) {
   // Convert to factor graph
   DiscreteFactorGraph factorGraph(hmm);
 
+  // Do max-prodcut
+  auto mpe = factorGraph.optimize();
+  GTSAM_PRINT(mpe);
+
   // Create solver and eliminate
   // This will create a DAG ordered with arrow of time reversed
   DiscreteBayesNet::shared_ptr chordal =
       factorGraph.eliminateSequential(ordering);
   chordal->print("Eliminated");
 
-  // solve
-  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++) {

examples/UGM_chain.cpp

@@ -68,9 +68,8 @@ int main(int argc, char** argv) {
        << graph.size() << " factors (Unary+Edge).";
 
   // "Decoding", i.e., configuration with largest value
-  // We use sequential variable elimination
-  DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  auto optimalDecoding = chordal->optimize();
+  // Uses max-product.
+  auto optimalDecoding = graph.optimize();
   optimalDecoding.print("\nMost Probable Explanation (optimalDecoding)\n");
 
   // "Inference" Computing marginals for each node

examples/UGM_small.cpp

@@ -61,9 +61,8 @@ 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();
-  auto optimalDecoding = chordal->optimize();
+  // Uses max-product
+  auto optimalDecoding = graph.optimize();
   GTSAM_PRINT(optimalDecoding);
 
   // "Inference" Computing marginals

gtsam_unstable/discrete/examples/schedulingExample.cpp

@@ -165,11 +165,11 @@ void solveStaged(size_t addMutex = 2) {
       root->print(""/*scheduler.studentName(s)*/);
 
     // solve root node only
-    DiscreteValues values;
-    size_t bestSlot = root->solve(values);
+    size_t bestSlot = root->argmax();
 
     // get corresponding count
     DiscreteKey dkey = scheduler.studentKey(6 - s);
+    DiscreteValues values;
     values[dkey.first] = bestSlot;
     size_t count = (*root)(values);
 
@@ -319,11 +319,11 @@ void accomodateStudent() {
   //  GTSAM_PRINT(*chordal);
 
   // solve root node only
-  DiscreteValues values;
-  size_t bestSlot = root->solve(values);
+  size_t bestSlot = root->argmax();
 
   // get corresponding count
   DiscreteKey dkey = scheduler.studentKey(0);
+  DiscreteValues values;
   values[dkey.first] = bestSlot;
   size_t count = (*root)(values);
   cout << boost::format("%s = %d (%d), count = %d") % scheduler.studentName(0)

gtsam_unstable/discrete/examples/schedulingQuals12.cpp

@@ -190,11 +190,11 @@ void solveStaged(size_t addMutex = 2) {
       root->print(""/*scheduler.studentName(s)*/);
 
     // solve root node only
-    DiscreteValues values;
-    size_t bestSlot = root->solve(values);
+    size_t bestSlot = root->argmax();
 
     // get corresponding count
     DiscreteKey dkey = scheduler.studentKey(NRSTUDENTS - 1 - s);
+    DiscreteValues values;
     values[dkey.first] = bestSlot;
     size_t count = (*root)(values);
 

gtsam_unstable/discrete/examples/schedulingQuals13.cpp

@@ -212,11 +212,11 @@ void solveStaged(size_t addMutex = 2) {
       root->print(""/*scheduler.studentName(s)*/);
 
     // solve root node only
-    DiscreteValues values;
-    size_t bestSlot = root->solve(values);
+    size_t bestSlot = root->argmax();
 
     // get corresponding count
     DiscreteKey dkey = scheduler.studentKey(NRSTUDENTS - 1 - s);
+    DiscreteValues values;
     values[dkey.first] = bestSlot;
     double count = (*root)(values);