add pruning support for HybridNonlinearFactor

gtsam/hybrid/HybridNonlinearFactor.cpp

@@ -16,6 +16,7 @@
  * @date   Sep 12, 2024
  */
 
+#include <gtsam/discrete/DecisionTreeFactor.h>
 #include <gtsam/hybrid/HybridNonlinearFactor.h>
 #include <gtsam/linear/NoiseModel.h>
 #include <gtsam/nonlinear/NonlinearFactor.h>
@@ -202,4 +203,34 @@ std::shared_ptr<HybridGaussianFactor> HybridNonlinearFactor::linearize(
                                                 linearized_factors);
 }
 
+/* *******************************************************************************/
+HybridNonlinearFactor::shared_ptr HybridNonlinearFactor::prune(
+    const DecisionTreeFactor& discreteProbs) const {
+  // Find keys in discreteProbs.keys() but not in this->keys():
+  std::set<Key> mine(this->keys().begin(), this->keys().end());
+  std::set<Key> theirs(discreteProbs.keys().begin(),
+                       discreteProbs.keys().end());
+  std::vector<Key> diff;
+  std::set_difference(theirs.begin(), theirs.end(), mine.begin(), mine.end(),
+                      std::back_inserter(diff));
+
+  // Find maximum probability value for every combination of our keys.
+  Ordering keys(diff);
+  auto max = discreteProbs.max(keys);
+
+  // Check the max value for every combination of our keys.
+  // If the max value is 0.0, we can prune the corresponding conditional.
+  auto pruner =
+      [&](const Assignment<Key>& choices,
+          const NonlinearFactorValuePair& pair) -> NonlinearFactorValuePair {
+    if (max->evaluate(choices) == 0.0)
+      return {nullptr, std::numeric_limits<double>::infinity()};
+    else
+      return pair;
+  };
+
+  FactorValuePairs prunedFactors = factors().apply(pruner);
+  return std::make_shared<HybridNonlinearFactor>(discreteKeys(), prunedFactors);
+}
+
 }  // namespace gtsam

gtsam/hybrid/HybridNonlinearFactor.h

@@ -166,6 +166,9 @@ class GTSAM_EXPORT HybridNonlinearFactor : public HybridFactor {
 
   /// @}
 
+  /// Getter for NonlinearFactor decision tree
+  const FactorValuePairs& factors() const { return factors_; }
+
   /// Linearize specific nonlinear factors based on the assignment in
   /// discreteValues.
   GaussianFactor::shared_ptr linearize(
@@ -176,6 +179,10 @@ class GTSAM_EXPORT HybridNonlinearFactor : public HybridFactor {
   std::shared_ptr<HybridGaussianFactor> linearize(
       const Values& continuousValues) const;
 
+  /// Prune this factor based on the discrete probabilities.
+  HybridNonlinearFactor::shared_ptr prune(
+      const DecisionTreeFactor& discreteProbs) const;
+
  private:
   /// Helper struct to assist private constructor below.
   struct ConstructorHelper;