Document methods, refactor pruning a tiny bit.

gtsam/hybrid/HybridBayesNet.cpp

@@ -141,8 +141,8 @@ std::function<double(const Assignment<Key> &, double)> prunerFunc(
 
 /* ************************************************************************* */
 void HybridBayesNet::updateDiscreteConditionals(
-    const DecisionTreeFactor::shared_ptr &prunedDecisionTree) {
-  KeyVector prunedTreeKeys = prunedDecisionTree->keys();
+    const DecisionTreeFactor &prunedDecisionTree) {
+  KeyVector prunedTreeKeys = prunedDecisionTree.keys();
 
   // Loop with index since we need it later.
   for (size_t i = 0; i < this->size(); i++) {
@@ -154,7 +154,7 @@ void HybridBayesNet::updateDiscreteConditionals(
       auto discreteTree =
           boost::dynamic_pointer_cast<DecisionTreeFactor::ADT>(discrete);
       DecisionTreeFactor::ADT prunedDiscreteTree =
-          discreteTree->apply(prunerFunc(*prunedDecisionTree, *conditional));
+          discreteTree->apply(prunerFunc(prunedDecisionTree, *conditional));
 
       // Create the new (hybrid) conditional
       KeyVector frontals(discrete->frontals().begin(),
@@ -173,9 +173,7 @@ void HybridBayesNet::updateDiscreteConditionals(
 HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
   // Get the decision tree of only the discrete keys
   auto discreteConditionals = this->discreteConditionals();
-  const DecisionTreeFactor::shared_ptr decisionTree =
-      boost::make_shared<DecisionTreeFactor>(
-          discreteConditionals->prune(maxNrLeaves));
+  const auto decisionTree = discreteConditionals->prune(maxNrLeaves);
 
   this->updateDiscreteConditionals(decisionTree);
 
@@ -194,7 +192,7 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
     if (auto gm = conditional->asMixture()) {
       // Make a copy of the Gaussian mixture and prune it!
       auto prunedGaussianMixture = boost::make_shared<GaussianMixture>(*gm);
-      prunedGaussianMixture->prune(*decisionTree);  // imperative :-(
+      prunedGaussianMixture->prune(decisionTree);  // imperative :-(
 
       // Type-erase and add to the pruned Bayes Net fragment.
       prunedBayesNetFragment.push_back(prunedGaussianMixture);

gtsam/hybrid/HybridBayesNet.h

@@ -51,33 +51,51 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   /// @{
 
   /// GTSAM-style printing
-  void print(
-      const std::string &s = "",
-      const KeyFormatter &formatter = DefaultKeyFormatter) const override;
+  void print(const std::string &s = "", const KeyFormatter &formatter =
+                                            DefaultKeyFormatter) const override;
 
   /// GTSAM-style equals
-  bool equals(const This& fg, double tol = 1e-9) const;
-  
+  bool equals(const This &fg, double tol = 1e-9) const;
+
   /// @}
   /// @name Standard Interface
   /// @{
 
-  /// Add HybridConditional to Bayes Net
-  using Base::emplace_shared;
+  /**
+   * @brief Add a hybrid conditional using a shared_ptr.
+   *
+   * This is the "native" push back, as this class stores hybrid conditionals.
+   */
+  void push_back(boost::shared_ptr<HybridConditional> conditional) {
+    factors_.push_back(conditional);
+  }
 
-  /// Add a conditional directly using a pointer.
+  /**
+   * Preferred: add a conditional directly using a pointer.
+   *
+   * Examples:
+   *   hbn.emplace_back(new GaussianMixture(...)));
+   *   hbn.emplace_back(new GaussianConditional(...)));
+   *   hbn.emplace_back(new DiscreteConditional(...)));
+   */
   template <class Conditional>
   void emplace_back(Conditional *conditional) {
     factors_.push_back(boost::make_shared<HybridConditional>(
         boost::shared_ptr<Conditional>(conditional)));
   }
 
-  /// Add a conditional directly using a shared_ptr.
-  void push_back(boost::shared_ptr<HybridConditional> conditional) {
-    factors_.push_back(conditional);
-  }
-
-  /// Add a conditional directly using implicit conversion.
+  /**
+   * Add a conditional using a shared_ptr, using implicit conversion to
+   * a HybridConditional.
+   *
+   * This is useful when you create a conditional shared pointer as you need it
+   * somewhere else.
+   *
+   * Example:
+   *   auto shared_ptr_to_a_conditional =
+   *     boost::make_shared<GaussianMixture>(...);
+   *  hbn.push_back(shared_ptr_to_a_conditional);
+   */
   void push_back(HybridConditional &&conditional) {
     factors_.push_back(
         boost::make_shared<HybridConditional>(std::move(conditional)));
@@ -214,8 +232,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    *
    * @param prunedDecisionTree
    */
-  void updateDiscreteConditionals(
-      const DecisionTreeFactor::shared_ptr &prunedDecisionTree);
+  void updateDiscreteConditionals(const DecisionTreeFactor &prunedDecisionTree);
 
   /** Serialization function */
   friend class boost::serialization::access;

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -93,8 +93,7 @@ TEST(HybridBayesNet, evaluateHybrid) {
 
   // Create hybrid Bayes net.
   HybridBayesNet bayesNet;
-  bayesNet.push_back(GaussianConditional::sharedMeanAndStddev(
-      X(0), 2 * I_1x1, X(1), Vector1(-4.0), 5.0));
+  bayesNet.push_back(continuousConditional);
   bayesNet.emplace_back(
       new GaussianMixture({X(1)}, {}, {Asia}, {conditional0, conditional1}));
   bayesNet.emplace_back(new DiscreteConditional(Asia, "99/1"));