make sample methods DRY in HybridBayesNet and GaussianConditional

gtsam/hybrid/HybridBayesNet.cpp

@@ -202,16 +202,6 @@ HybridValues HybridBayesNet::sample(std::mt19937_64 *rng) const {
   return sample(given, rng);
 }
 
-/* ************************************************************************* */
-HybridValues HybridBayesNet::sample(const HybridValues &given) const {
-  return sample(given, &kRandomNumberGenerator);
-}
-
-/* ************************************************************************* */
-HybridValues HybridBayesNet::sample() const {
-  return sample(&kRandomNumberGenerator);
-}
-
 /* ************************************************************************* */
 AlgebraicDecisionTree<Key> HybridBayesNet::errorTree(
     const VectorValues &continuousValues) const {

gtsam/hybrid/HybridBayesNet.h

@@ -181,10 +181,11 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    *   auto sample = bn.sample(given, &rng);
    *
    * @param given Values of missing variables.
-   * @param rng The pseudo-random number generator.
+   * @param rng The optional pseudo-random number generator.
    * @return HybridValues
    */
-  HybridValues sample(const HybridValues &given, std::mt19937_64 *rng) const;
+  HybridValues sample(const HybridValues &given,
+                      std::mt19937_64 *rng = nullptr) const;
 
   /**
    * @brief Sample using ancestral sampling.
@@ -193,25 +194,10 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    *   std::mt19937_64 rng(42);
    *   auto sample = bn.sample(&rng);
    *
-   * @param rng The pseudo-random number generator.
+   * @param rng The optional pseudo-random number generator.
    * @return HybridValues
    */
-  HybridValues sample(std::mt19937_64 *rng) const;
+  HybridValues sample(std::mt19937_64 *rng = nullptr) const;
-
-  /**
-   * @brief Sample from an incomplete BayesNet, use default rng.
-   *
-   * @param given Values of missing variables.
-   * @return HybridValues
-   */
-  HybridValues sample(const HybridValues &given) const;
-
-  /**
-   * @brief Sample using ancestral sampling, use default rng.
-   *
-   * @return HybridValues
-   */
-  HybridValues sample() const;
 
   /**
    * @brief Prune the Bayes Net such that we have at most maxNrLeaves leaves.

gtsam/linear/GaussianConditional.cpp

@@ -347,6 +347,10 @@ namespace gtsam {
 
     VectorValues solution = solve(parentsValues);
     Key key = firstFrontalKey();
+
+    // Check if rng is nullptr, then assign default
+    rng = (rng == nullptr) ? &kRandomNumberGenerator : rng;
+
     // The vector of sigma values for sampling.
     // If no model, initialize sigmas to 1, else to model sigmas
     const Vector& sigmas = (!model_) ? Vector::Ones(rows()) : model_->sigmas();
@@ -359,16 +363,7 @@ namespace gtsam {
       throw std::invalid_argument(
           "sample() can only be invoked on no-parent prior");
     VectorValues values;
-    return sample(values);
+    return sample(values, rng);
-  }
-
-  /* ************************************************************************ */
-  VectorValues GaussianConditional::sample() const {
-    return sample(&kRandomNumberGenerator);
-  }
-
-  VectorValues GaussianConditional::sample(const VectorValues& given) const {
-    return sample(given, &kRandomNumberGenerator);
   }
 
   /* ************************************************************************ */

gtsam/linear/GaussianConditional.h

@@ -217,7 +217,7 @@ namespace gtsam {
      *   std::mt19937_64 rng(42);
      *   auto sample = gc.sample(&rng);
      */
-    VectorValues sample(std::mt19937_64* rng) const;
+    VectorValues sample(std::mt19937_64* rng = nullptr) const;
 
     /**
      * Sample from conditional, given missing variables
@@ -227,13 +227,7 @@ namespace gtsam {
      *   auto sample = gc.sample(given, &rng);
      */
     VectorValues sample(const VectorValues& parentsValues,
-                        std::mt19937_64* rng) const;
+                        std::mt19937_64* rng = nullptr) const;
-
-    /// Sample, use default rng
-    VectorValues sample() const;
-
-    /// Sample with given values, use default rng
-    VectorValues sample(const VectorValues& parentsValues) const;
 
     /// @}
     /// @name Linear algebra.