include discrete junction tree for msvc and fix warning

gtsam/hybrid/HybridConditional.cpp

@@ -66,7 +66,7 @@ void HybridConditional::print(const std::string &s,
   if (isDiscrete_) std::cout << "Disc. ";
   if (isHybrid_) std::cout << "Hybr. ";
   std::cout << "P(";
-  int index = 0;
+  size_t index = 0;
   const size_t N = keys().size();
   const size_t contN = N - discreteKeys_.size();
   while (index < N) {

gtsam/hybrid/HybridFactorGraph.cpp

@@ -22,6 +22,7 @@
 #include <gtsam/discrete/Assignment.h>
 #include <gtsam/discrete/DiscreteEliminationTree.h>
 #include <gtsam/discrete/DiscreteFactorGraph.h>
+#include <gtsam/discrete/DiscreteJunctionTree.h>
 #include <gtsam/hybrid/GaussianMixtureConditional.h>
 #include <gtsam/hybrid/GaussianMixtureFactor.h>
 #include <gtsam/hybrid/HybridConditional.h>
@@ -104,9 +105,10 @@ EliminateHybrid(const HybridFactorGraph &factors, const Ordering &frontalKeys) {
   // Because of all these reasons, we need to think very carefully about how to
   // implement the hybrid factors so that we do not get poor performance.
   //
-  // The first thing is how to represent the GaussianMixtureConditional. A very possible
-  // scenario is that the incoming factors will have different levels of
-  // discrete keys. For example, imagine we are going to eliminate the fragment:
+  // The first thing is how to represent the GaussianMixtureConditional. A very
+  // possible scenario is that the incoming factors will have different levels
+  // of discrete keys. For example, imagine we are going to eliminate the
+  // fragment:
   // $\phi(x1,c1,c2)$, $\phi(x1,c2,c3)$, which is perfectly valid. Now we will
   // need to know how to retrieve the corresponding continuous densities for the
   // assi- -gnment (c1,c2,c3) (OR (c2,c3,c1)! note there is NO defined order!).