Make HybridBayesNet testable and add serialization

gtsam/hybrid/HybridBayesNet.h

@@ -18,6 +18,7 @@
 #pragma once
 
 #include <gtsam/discrete/DecisionTreeFactor.h>
+#include <gtsam/global_includes.h>
 #include <gtsam/hybrid/HybridConditional.h>
 #include <gtsam/hybrid/HybridValues.h>
 #include <gtsam/inference/BayesNet.h>
@@ -37,12 +38,31 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   using shared_ptr = boost::shared_ptr<HybridBayesNet>;
   using sharedConditional = boost::shared_ptr<ConditionalType>;
 
+  /// @name Standard Constructors
+  /// @{
+
   /** Construct empty bayes net */
   HybridBayesNet() = default;
 
-  /// Prune the Hybrid Bayes Net given the discrete decision tree.
+  /// @}
-  HybridBayesNet prune(
+  /// @name Testable
-      const DecisionTreeFactor::shared_ptr &discreteFactor) const;
+  /// @{
+
+  /** Check equality */
+  bool equals(const This &bn, double tol = 1e-9) const {
+    return Base::equals(bn, tol);
+  }
+
+  /// print graph
+  void print(
+      const std::string &s = "",
+      const KeyFormatter &formatter = DefaultKeyFormatter) const override {
+    Base::print(s, formatter);
+  }
+
+  /// @}
+  /// @name Standard Interface
+  /// @{
 
   /// Add HybridConditional to Bayes Net
   using Base::add;
@@ -71,9 +91,13 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    */
   GaussianBayesNet choose(const DiscreteValues &assignment) const;
 
-  /// Solve the HybridBayesNet by back-substitution.
+  /**
-  /// TODO(Shangjie) do we need to create a HybridGaussianBayesNet class, and
+   * @brief Solve the HybridBayesNet by first computing the MPE of all the
-  /// put this method there?
+   * discrete variables and then optimizing the continuous variables based on
+   * the MPE assignment.
+   *
+   * @return HybridValues
+   */
   HybridValues optimize() const;
 
   /**
@@ -84,6 +108,24 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    * @return Values
    */
   VectorValues optimize(const DiscreteValues &assignment) const;
+
+  /// Prune the Hybrid Bayes Net given the discrete decision tree.
+  HybridBayesNet prune(
+      const DecisionTreeFactor::shared_ptr &discreteFactor) const;
+
+  /// @}
+
+ private:
+  /** Serialization function */
+  friend class boost::serialization::access;
+  template <class ARCHIVE>
+  void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
+    ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
+  }
 };
 
+/// traits
+template <>
+struct traits<HybridBayesNet> : public Testable<HybridBayesNet> {};
+
 }  // namespace gtsam

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -18,6 +18,7 @@
  * @date    December 2021
  */
 
+#include <gtsam/base/serializationTestHelpers.h>
 #include <gtsam/hybrid/HybridBayesNet.h>
 #include <gtsam/nonlinear/NonlinearFactorGraph.h>
 
@@ -28,6 +29,8 @@
 
 using namespace std;
 using namespace gtsam;
+using namespace gtsam::serializationTestHelpers;
+
 using noiseModel::Isotropic;
 using symbol_shorthand::M;
 using symbol_shorthand::X;
@@ -146,6 +149,18 @@ TEST(HybridBayesNet, Optimize) {
   EXPECT(assert_equal(expectedValues, delta.continuous(), 1e-5));
 }
 
+/* ****************************************************************************/
+// Test HybridBayesNet serialization.
+TEST(HybridBayesNet, Serialization) {
+  Switching s(4);
+  Ordering ordering = s.linearizedFactorGraph.getHybridOrdering();
+  HybridBayesNet hbn = *(s.linearizedFactorGraph.eliminateSequential(ordering));
+
+  EXPECT(equalsObj<HybridBayesNet>(hbn));
+  EXPECT(equalsXML<HybridBayesNet>(hbn));
+  EXPECT(equalsBinary<HybridBayesNet>(hbn));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;