Make prune functional

gtsam/hybrid/HybridGaussianConditional.cpp

@@ -342,13 +342,57 @@ HybridGaussianConditional::prunerFunc(const DecisionTreeFactor &discreteProbs) {
 }
 
 /* *******************************************************************************/
-void HybridGaussianConditional::prune(const DecisionTreeFactor &discreteProbs) {
+HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
+    const DecisionTreeFactor &discreteProbs) const {
+  auto discreteProbsKeySet = DiscreteKeysAsSet(discreteProbs.discreteKeys());
+  auto hybridGaussianCondKeySet = DiscreteKeysAsSet(this->discreteKeys());
+
   // Functional which loops over all assignments and create a set of
   // GaussianConditionals
-  auto pruner = prunerFunc(discreteProbs);
+  auto pruner = [&](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;
+    }
+  };
 
   auto pruned_conditionals = conditionals_.apply(pruner);
-  conditionals_.root_ = pruned_conditionals.root_;
+  return std::make_shared<HybridGaussianConditional>(discreteKeys(),
+                                                     pruned_conditionals);
 }
 
 /* *******************************************************************************/

gtsam/hybrid/HybridGaussianConditional.h

@@ -225,8 +225,10 @@ class GTSAM_EXPORT HybridGaussianConditional
    * `discreteProbs`.
    *
    * @param discreteProbs A pruned set of probabilities for the discrete keys.
+   * @return Shared pointer to possibly a pruned HybridGaussianConditional
    */
-  void prune(const DecisionTreeFactor &discreteProbs);
+  HybridGaussianConditional::shared_ptr prune(
+      const DecisionTreeFactor &discreteProbs) const;
 
   /// @}