PrunerFunc helper function

gtsam/hybrid/HybridBayesNet.cpp

@@ -49,6 +49,38 @@ DecisionTreeFactor::shared_ptr HybridBayesNet::discreteConditionals() const {
   return boost::make_shared<DecisionTreeFactor>(dtFactor);
 }
 
+/**
+ * @brief Helper function to get the pruner functional.
+ *
+ * @param probDecisionTree The probability decision tree of only discrete keys.
+ * @param discreteFactorKeySet Set of DiscreteKeys in probDecisionTree.
+ * Pre-computed for efficiency.
+ * @param gaussianMixtureKeySet Set of DiscreteKeys in the GaussianMixture.
+ * @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::shared_ptr &probDecisionTree,
+           const std::set<DiscreteKey> &discreteFactorKeySet,
+           const std::set<DiscreteKey> &gaussianMixtureKeySet) {
+  auto pruner = [&](const Assignment<Key> &choices,
+                    const GaussianConditional::shared_ptr &conditional)
+      -> GaussianConditional::shared_ptr {
+    // typecast so we can use this to get probability value
+    DiscreteValues values(choices);
+
+    if ((*probDecisionTree)(values) == 0.0) {
+      // empty aka null pointer
+      boost::shared_ptr<GaussianConditional> null;
+      return null;
+    } else {
+      return conditional;
+    }
+  };
+  return pruner;
+}
+
 /* ************************************************************************* */
 HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
   // Get the decision tree of only the discrete keys
@@ -57,6 +89,8 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
       boost::make_shared<DecisionTreeFactor>(
           discreteConditionals->prune(maxNrLeaves));
 
+  auto discreteFactorKeySet = DiscreteKeysAsSet(discreteFactor->discreteKeys());
+
   /* To Prune, we visitWith every leaf in the GaussianMixture.
    * For each leaf, using the assignment we can check the discrete decision tree
    * for 0.0 probability, then just set the leaf to a nullptr.
@@ -66,23 +100,6 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
 
   HybridBayesNet prunedBayesNetFragment;
 
-  // Functional which loops over all assignments and create a set of
-  // GaussianConditionals
-  auto pruner = [&](const Assignment<Key> &choices,
-                    const GaussianConditional::shared_ptr &conditional)
-      -> GaussianConditional::shared_ptr {
-    // typecast so we can use this to get probability value
-    DiscreteValues values(choices);
-
-    if ((*discreteFactor)(values) == 0.0) {
-      // empty aka null pointer
-      boost::shared_ptr<GaussianConditional> null;
-      return null;
-    } else {
-      return conditional;
-    }
-  };
-
   // Go through all the conditionals in the
   // Bayes Net and prune them as per discreteFactor.
   for (size_t i = 0; i < this->size(); i++) {
@@ -92,17 +109,19 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
         boost::dynamic_pointer_cast<GaussianMixture>(conditional->inner());
 
     if (gaussianMixture) {
-      // We may have mixtures with less discrete keys than discreteFactor so we
+      // We may have mixtures with less discrete keys than discreteFactor so
-      // skip those since the label assignment does not exist.
+      // we skip those since the label assignment does not exist.
       auto gmKeySet = DiscreteKeysAsSet(gaussianMixture->discreteKeys());
-      auto dfKeySet = DiscreteKeysAsSet(discreteFactor->discreteKeys());
+      if (gmKeySet != discreteFactorKeySet) {
-      if (gmKeySet != dfKeySet) {
         // Add the gaussianMixture which doesn't have to be pruned.
         prunedBayesNetFragment.push_back(
             boost::make_shared<HybridConditional>(gaussianMixture));
         continue;
       }
 
+      // Get the pruner function.
+      auto pruner = PrunerFunc(discreteFactor, discreteFactorKeySet, gmKeySet);
+
       // Run the pruning to get a new, pruned tree
       GaussianMixture::Conditionals prunedTree =
           gaussianMixture->conditionals().apply(pruner);
@@ -173,7 +192,7 @@ GaussianBayesNet HybridBayesNet::choose(
   return gbn;
 }
 
-/* *******************************************************************************/
+/* ************************************************************************* */
 HybridValues HybridBayesNet::optimize() const {
   // Solve for the MPE
   DiscreteBayesNet discrete_bn;
@@ -190,7 +209,7 @@ HybridValues HybridBayesNet::optimize() const {
   return HybridValues(mpe, gbn.optimize());
 }
 
-/* *******************************************************************************/
+/* ************************************************************************* */
 VectorValues HybridBayesNet::optimize(const DiscreteValues &assignment) const {
   GaussianBayesNet gbn = this->choose(assignment);
   return gbn.optimize();