hybrid folder

gtsam/hybrid/HybridGaussianFactorGraph.h

@@ -27,6 +27,8 @@
 #include <gtsam/linear/GaussianFactor.h>
 #include <gtsam/linear/VectorValues.h>
 
+#include <optional>
+
 namespace gtsam {
 
 // Forward declarations

gtsam/hybrid/HybridGaussianISAM.cpp

@@ -71,8 +71,8 @@ Ordering HybridGaussianISAM::GetOrdering(
 void HybridGaussianISAM::updateInternal(
     const HybridGaussianFactorGraph& newFactors,
     HybridBayesTree::Cliques* orphans,
-    const boost::optional<size_t>& maxNrLeaves,
+    const std::optional<size_t>& maxNrLeaves,
-    const boost::optional<Ordering>& ordering,
+    const std::optional<Ordering>& ordering,
     const HybridBayesTree::Eliminate& function) {
   // Remove the contaminated part of the Bayes tree
   BayesNetType bn;
@@ -116,8 +116,8 @@ void HybridGaussianISAM::updateInternal(
 
 /* ************************************************************************* */
 void HybridGaussianISAM::update(const HybridGaussianFactorGraph& newFactors,
-                                const boost::optional<size_t>& maxNrLeaves,
+                                const std::optional<size_t>& maxNrLeaves,
-                                const boost::optional<Ordering>& ordering,
+                                const std::optional<Ordering>& ordering,
                                 const HybridBayesTree::Eliminate& function) {
   Cliques orphans;
   this->updateInternal(newFactors, &orphans, maxNrLeaves, ordering, function);

gtsam/hybrid/HybridGaussianISAM.h

@@ -24,6 +24,8 @@
 #include <gtsam/hybrid/HybridGaussianFactorGraph.h>
 #include <gtsam/inference/ISAM.h>
 
+#include <optional>
+
 namespace gtsam {
 
 /**
@@ -53,8 +55,8 @@ class GTSAM_EXPORT HybridGaussianISAM : public ISAM<HybridBayesTree> {
   void updateInternal(
       const HybridGaussianFactorGraph& newFactors,
       HybridBayesTree::Cliques* orphans,
-      const boost::optional<size_t>& maxNrLeaves = boost::none,
+      const std::optional<size_t>& maxNrLeaves = {},
-      const boost::optional<Ordering>& ordering = boost::none,
+      const std::optional<Ordering>& ordering = {},
       const HybridBayesTree::Eliminate& function =
           HybridBayesTree::EliminationTraitsType::DefaultEliminate);
 
@@ -68,8 +70,8 @@ class GTSAM_EXPORT HybridGaussianISAM : public ISAM<HybridBayesTree> {
    * @param function Elimination function.
    */
   void update(const HybridGaussianFactorGraph& newFactors,
-              const boost::optional<size_t>& maxNrLeaves = boost::none,
+              const std::optional<size_t>& maxNrLeaves = {},
-              const boost::optional<Ordering>& ordering = boost::none,
+              const std::optional<Ordering>& ordering = {},
               const HybridBayesTree::Eliminate& function =
                   HybridBayesTree::EliminationTraitsType::DefaultEliminate);
 

gtsam/hybrid/HybridNonlinearISAM.cpp

@@ -34,8 +34,8 @@ void HybridNonlinearISAM::saveGraph(const string& s,
 /* ************************************************************************* */
 void HybridNonlinearISAM::update(const HybridNonlinearFactorGraph& newFactors,
                                  const Values& initialValues,
-                                 const boost::optional<size_t>& maxNrLeaves,
+                                 const std::optional<size_t>& maxNrLeaves,
-                                 const boost::optional<Ordering>& ordering) {
+                                 const std::optional<Ordering>& ordering) {
   if (newFactors.size() > 0) {
     // Reorder and relinearize every reorderInterval updates
     if (reorderInterval_ > 0 && ++reorderCounter_ >= reorderInterval_) {
@@ -70,7 +70,7 @@ void HybridNonlinearISAM::reorder_relinearize() {
     // Just recreate the whole BayesTree
     // TODO: allow for constrained ordering here
     // TODO: decouple relinearization and reordering to avoid
-    isam_.update(*factors_.linearize(newLinPoint), boost::none, boost::none,
+    isam_.update(*factors_.linearize(newLinPoint), {}, {},
                  eliminationFunction_);
 
     // Update linearization point

gtsam/hybrid/HybridNonlinearISAM.h

@@ -19,6 +19,7 @@
 
 #include <gtsam/hybrid/HybridGaussianISAM.h>
 #include <gtsam/hybrid/HybridNonlinearFactorGraph.h>
+#include <optional>
 
 namespace gtsam {
 /**
@@ -119,8 +120,8 @@ class GTSAM_EXPORT HybridNonlinearISAM {
   /** Add new factors along with their initial linearization points */
   void update(const HybridNonlinearFactorGraph& newFactors,
               const Values& initialValues,
-              const boost::optional<size_t>& maxNrLeaves = boost::none,
+              const std::optional<size_t>& maxNrLeaves = {},
-              const boost::optional<Ordering>& ordering = boost::none);
+              const std::optional<Ordering>& ordering = {});
 
   /** Relinearization and reordering of variables */
   void reorder_relinearize();

gtsam/hybrid/HybridSmoother.cpp

@@ -26,7 +26,7 @@ namespace gtsam {
 /* ************************************************************************* */
 void HybridSmoother::update(HybridGaussianFactorGraph graph,
                             const Ordering &ordering,
-                            boost::optional<size_t> maxNrLeaves) {
+                            std::optional<size_t> maxNrLeaves) {
   // Add the necessary conditionals from the previous timestep(s).
   std::tie(graph, hybridBayesNet_) =
       addConditionals(graph, hybridBayesNet_, ordering);

gtsam/hybrid/HybridSmoother.h

@@ -20,6 +20,8 @@
 #include <gtsam/hybrid/HybridBayesNet.h>
 #include <gtsam/hybrid/HybridGaussianFactorGraph.h>
 
+#include <optional>
+
 namespace gtsam {
 
 class HybridSmoother {
@@ -48,7 +50,7 @@ class HybridSmoother {
    * if applicable
    */
   void update(HybridGaussianFactorGraph graph, const Ordering& ordering,
-              boost::optional<size_t> maxNrLeaves = boost::none);
+              std::optional<size_t> maxNrLeaves = {});
 
   /**
    * @brief Add conditionals from previous timestep as part of liquefication.

gtsam/hybrid/tests/TinyHybridExample.h

@@ -71,7 +71,7 @@ inline HybridBayesNet createHybridBayesNet(size_t num_measurements = 1,
  */
 inline HybridGaussianFactorGraph createHybridGaussianFactorGraph(
     size_t num_measurements = 1,
-    boost::optional<VectorValues> measurements = boost::none,
+    std::optional<VectorValues> measurements = {},
     bool manyModes = false) {
   auto bayesNet = createHybridBayesNet(num_measurements, manyModes);
   if (measurements) {