reduce the diff even more

gtsam/hybrid/GaussianMixture.cpp

@@ -145,8 +145,6 @@ void GaussianMixture::print(const std::string &s,
     std::cout << "(" << formatter(dk.first) << ", " << dk.second << "), ";
   }
   std::cout << "\n";
-  std::cout << " logNormalizationConstant: " << logConstant_ << "\n"
-            << std::endl;
   conditionals_.print(
       "", [&](Key k) { return formatter(k); },
       [&](const GaussianConditional::shared_ptr &gf) -> std::string {

gtsam/hybrid/HybridBayesNet.cpp

@@ -221,7 +221,6 @@ GaussianBayesNet HybridBayesNet::choose(
 HybridValues HybridBayesNet::optimize() const {
   // Collect all the discrete factors to compute MPE
   DiscreteBayesNet discrete_bn;
-
   for (auto &&conditional : *this) {
     if (conditional->isDiscrete()) {
       discrete_bn.push_back(conditional->asDiscrete());

gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp

@@ -675,7 +675,6 @@ factor 6:  P( m1 | m0 ):
 size: 3
 conditional 0: Hybrid  P( x0 | x1 m0)
  Discrete Keys = (m0, 2), 
-
  Choice(m0) 
  0 Leaf p(x0 | x1)
   R = [ 10.0499 ]
@@ -691,7 +690,6 @@ conditional 0: Hybrid  P( x0 | x1 m0)
 
 conditional 1: Hybrid  P( x1 | x2 m0 m1)
  Discrete Keys = (m0, 2), (m1, 2), 
-
  Choice(m1) 
  0 Choice(m0) 
  0 0 Leaf p(x1 | x2)
@@ -721,7 +719,6 @@ conditional 1: Hybrid  P( x1 | x2 m0 m1)
 
 conditional 2: Hybrid  P( x2 | m0 m1)
  Discrete Keys = (m0, 2), (m1, 2), 
-
  Choice(m1) 
  0 Choice(m0) 
  0 0 Leaf p(x2)