Inline lambda

gtsam/hybrid/HybridGaussianConditional.cpp

@@ -288,59 +288,6 @@ std::set<DiscreteKey> DiscreteKeysAsSet(const DiscreteKeys &discreteKeys) {
   return s;
 }
 
-/* ************************************************************************* */
-std::function<GaussianConditional::shared_ptr(
-    const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
-HybridGaussianConditional::prunerFunc(const DecisionTreeFactor &discreteProbs) {
-  // Get the discrete keys as sets for the decision tree
-  // and the hybrid gaussian conditional.
-  auto discreteProbsKeySet = DiscreteKeysAsSet(discreteProbs.discreteKeys());
-  auto hybridGaussianCondKeySet = DiscreteKeysAsSet(this->discreteKeys());
-
-  auto pruner = [discreteProbs, discreteProbsKeySet, hybridGaussianCondKeySet](
-                    const Assignment<Key> &choices,
-                    const GaussianConditional::shared_ptr &conditional)
-      -> GaussianConditional::shared_ptr {
-    // typecast so we can use this to get probability value
-    const DiscreteValues values(choices);
-
-    // Case where the hybrid gaussian conditional has the same
-    // discrete keys as the decision tree.
-    if (hybridGaussianCondKeySet == discreteProbsKeySet) {
-      if (discreteProbs(values) == 0.0) {
-        // empty aka null pointer
-        std::shared_ptr<GaussianConditional> null;
-        return null;
-      } else {
-        return conditional;
-      }
-    } else {
-      std::vector<DiscreteKey> set_diff;
-      std::set_difference(
-          discreteProbsKeySet.begin(), discreteProbsKeySet.end(),
-          hybridGaussianCondKeySet.begin(), hybridGaussianCondKeySet.end(),
-          std::back_inserter(set_diff));
-
-      const std::vector<DiscreteValues> assignments =
-          DiscreteValues::CartesianProduct(set_diff);
-      for (const DiscreteValues &assignment : assignments) {
-        DiscreteValues augmented_values(values);
-        augmented_values.insert(assignment);
-
-        // If any one of the sub-branches are non-zero,
-        // we need this conditional.
-        if (discreteProbs(augmented_values) > 0.0) {
-          return conditional;
-        }
-      }
-      // If we are here, it means that all the sub-branches are 0,
-      // so we prune.
-      return nullptr;
-    }
-  };
-  return pruner;
-}
-
 /* *******************************************************************************/
 HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
     const DecisionTreeFactor &discreteProbs) const {
@@ -358,14 +305,10 @@ HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
     // Case where the hybrid gaussian conditional has the same
     // discrete keys as the decision tree.
     if (hybridGaussianCondKeySet == discreteProbsKeySet) {
-      if (discreteProbs(values) == 0.0) {
+      return (discreteProbs(values) == 0.0) ? nullptr : conditional;
-        // empty aka null pointer
-        std::shared_ptr<GaussianConditional> null;
-        return null;
-      } else {
-        return conditional;
-      }
     } else {
+      // TODO(Frank): It might be faster to "choose" based on values
+      // and then check whether the resulting tree has non-nullptrs.
       std::vector<DiscreteKey> set_diff;
       std::set_difference(
           discreteProbsKeySet.begin(), discreteProbsKeySet.end(),
@@ -384,8 +327,7 @@ HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
           return conditional;
         }
       }
-      // If we are here, it means that all the sub-branches are 0,
+      // If we are here, it means that all the sub-branches are 0, so we prune.
-      // so we prune.
       return nullptr;
     }
   };

gtsam/hybrid/HybridGaussianConditional.h

@@ -243,17 +243,6 @@ class GTSAM_EXPORT HybridGaussianConditional
   /// Convert to a DecisionTree of Gaussian factor graphs.
   GaussianFactorGraphTree asGaussianFactorGraphTree() const;
 
-  /**
-   * @brief Get the pruner function from discrete probabilities.
-   *
-   * @param discreteProbs The probabilities of only discrete keys.
-   * @return std::function<GaussianConditional::shared_ptr(
-   * const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
-   */
-  std::function<GaussianConditional::shared_ptr(
-      const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
-  prunerFunc(const DecisionTreeFactor &prunedProbabilities);
-
   /// Check whether `given` has values for all frontal keys.
   bool allFrontalsGiven(const VectorValues &given) const;