Correction to docs and lambdas

gtsam/hybrid/GaussianMixture.cpp

@@ -299,19 +299,19 @@ void GaussianMixture::prune(const DecisionTreeFactor &decisionTree) {
 /* *******************************************************************************/
 AlgebraicDecisionTree<Key> GaussianMixture::logProbability(
     const VectorValues &continuousValues) const {
-  // functor to calculate to double logProbability value from
+  // functor to calculate (double) logProbability value from
   // GaussianConditional.
-  auto errorFunc =
+  auto probFunc =
       [continuousValues](const GaussianConditional::shared_ptr &conditional) {
         if (conditional) {
           return conditional->logProbability(continuousValues);
         } else {
-          // Return arbitrarily large logProbability if conditional is null
+          // Return arbitrarily small logProbability if conditional is null
           // Conditional is null if it is pruned out.
-          return 1e50;
+          return -1e20;
         }
       };
-  return DecisionTree<Key, double>(conditionals_, errorFunc);
+  return DecisionTree<Key, double>(conditionals_, probFunc);
 }
 
 /* *******************************************************************************/

gtsam/hybrid/HybridBayesNet.cpp

@@ -76,13 +76,16 @@ std::function<double(const Assignment<Key> &, double)> prunerFunc(
   auto pruner = [prunedDecisionTree, decisionTreeKeySet, conditionalKeySet](
                     const Assignment<Key> &choices,
                     double probability) -> double {
+    // This corresponds to 0 probability
+    double pruned_prob = 0.0;
+
     // typecast so we can use this to get probability value
     DiscreteValues values(choices);
     // Case where the Gaussian mixture has the same
     // discrete keys as the decision tree.
     if (conditionalKeySet == decisionTreeKeySet) {
       if (prunedDecisionTree(values) == 0) {
-        return 0.0;
+        return pruned_prob;
       } else {
         return probability;
       }
@@ -133,7 +136,7 @@ std::function<double(const Assignment<Key> &, double)> prunerFunc(
       }
       // If we are here, it means that all the sub-branches are 0,
       // so we prune.
-      return 0.0;
+      return pruned_prob;
     }
   };
   return pruner;

gtsam/hybrid/HybridNonlinearISAM.cpp

@@ -39,6 +39,7 @@ void HybridNonlinearISAM::update(const HybridNonlinearFactorGraph& newFactors,
   if (newFactors.size() > 0) {
     // Reorder and relinearize every reorderInterval updates
     if (reorderInterval_ > 0 && ++reorderCounter_ >= reorderInterval_) {
+      // TODO(Varun) Relinearization doesn't take into account pruning
       reorder_relinearize();
       reorderCounter_ = 0;
     }