Make all sampling methods take random number generator

gtsam/linear/GaussianBayesNet.cpp

@@ -26,6 +26,9 @@
 using namespace std;
 using namespace gtsam;
 
+// In Wrappers we have no access to this so have a default ready
+static std::mt19937_64 kRandomNumberGenerator(42);
+
 namespace gtsam {
 
   // Instantiate base class
@@ -56,20 +59,30 @@ namespace gtsam {
   }
 
   /* ************************************************************************ */
-  VectorValues GaussianBayesNet::sample() const {
+  VectorValues GaussianBayesNet::sample(std::mt19937_64* rng) const {
     VectorValues result;  // no missing variables -> create an empty vector
-    return sample(result);
+    return sample(result, rng);
   }
 
-  VectorValues GaussianBayesNet::sample(VectorValues result) const {
+  VectorValues GaussianBayesNet::sample(VectorValues result,
+                                        std::mt19937_64* rng) const {
     // sample each node in reverse topological sort order (parents first)
     for (auto cg : boost::adaptors::reverse(*this)) {
-      const VectorValues sampled = cg->sample(result);
+      const VectorValues sampled = cg->sample(result, rng);
       result.insert(sampled);
     }
     return result;
   }
 
+  /* ************************************************************************ */
+  VectorValues GaussianBayesNet::sample() const {
+    return sample(&kRandomNumberGenerator);
+  }
+
+  VectorValues GaussianBayesNet::sample(VectorValues given) const {
+    return sample(given, &kRandomNumberGenerator);
+  }
+
   /* ************************************************************************ */
   VectorValues GaussianBayesNet::optimizeGradientSearch() const
   {

gtsam/linear/GaussianBayesNet.h

@@ -95,10 +95,27 @@ namespace gtsam {
     /// Version of optimize for incomplete BayesNet, given missing variables
     VectorValues optimize(const VectorValues given) const;
 
-    /// Sample using ancestral sampling
+    /**
+     * Sample using ancestral sampling
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   auto sample = gbn.sample(&rng);
+     */
+    VectorValues sample(std::mt19937_64* rng) const;
+
+    /**
+     * Sample from an incomplete BayesNet, given missing variables
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   VectorValues given = ...;
+     *   auto sample = gbn.sample(given, &rng);
+     */
+    VectorValues sample(VectorValues given, std::mt19937_64* rng) const;
+
+    /// Sample using ancestral sampling, use default rng
     VectorValues sample() const;
 
-    /// Sample from an incomplete BayesNet, given missing variables
+    /// Sample from an incomplete BayesNet, use default rng
     VectorValues sample(VectorValues given) const;
 
     /**

gtsam/linear/GaussianConditional.cpp

@@ -35,6 +35,9 @@
 #include <list>
 #include <string>
 
+// In Wrappers we have no access to this so have a default ready
+static std::mt19937_64 kRandomNumberGenerator(42);
+
 using namespace std;
 
 namespace gtsam {
@@ -222,8 +225,8 @@ namespace gtsam {
   }
 
   /* ************************************************************************ */
-  VectorValues GaussianConditional::sample(
-      const VectorValues& parentsValues) const {
+  VectorValues GaussianConditional::sample(const VectorValues& parentsValues,
+                                           std::mt19937_64* rng) const {
     if (nrFrontals() != 1) {
       throw std::invalid_argument(
           "GaussianConditional::sample can only be called on single variable "
@@ -235,13 +238,13 @@ namespace gtsam {
           "model was specified at construction.");
     }
     VectorValues solution = solve(parentsValues);
-    Sampler sampler(model_);
     Key key = firstFrontalKey();
-    solution[key] += sampler.sample();
+    const Vector& sigmas = model_->sigmas();
+    solution[key] += Sampler::sampleDiagonal(sigmas, rng);
     return solution;
   }
 
-  VectorValues GaussianConditional::sample() const {
+  VectorValues GaussianConditional::sample(std::mt19937_64* rng) const {
     if (nrParents() != 0)
       throw std::invalid_argument(
           "sample() can only be invoked on no-parent prior");
@@ -249,6 +252,15 @@ namespace gtsam {
     return sample(values);
   }
 
+  /* ************************************************************************ */
+  VectorValues GaussianConditional::sample() const {
+    return sample(&kRandomNumberGenerator);
+  }
+
+  VectorValues GaussianConditional::sample(const VectorValues& given) const {
+    return sample(given, &kRandomNumberGenerator);
+  }
+
   /* ************************************************************************ */
 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
   void GTSAM_DEPRECATED

gtsam/linear/GaussianConditional.h

@@ -26,6 +26,8 @@
 #include <gtsam/inference/Conditional.h>
 #include <gtsam/linear/VectorValues.h>
 
+#include <random> // for std::mt19937_64 
+
 namespace gtsam {
 
   /**
@@ -150,15 +152,29 @@ namespace gtsam {
     void solveTransposeInPlace(VectorValues& gy) const;
 
     /**
-     * sample
-     * @param parentsValues Known values of the parents
-     * @return sample from conditional
+     * Sample from conditional, zero parent version
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   auto sample = gbn.sample(&rng);
      */
-    VectorValues sample(const VectorValues& parentsValues) const;
+    VectorValues sample(std::mt19937_64* rng) const;
 
-    /// Zero parent version.
+    /**
+     * Sample from conditional, given missing variables
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   VectorValues given = ...;
+     *   auto sample = gbn.sample(given, &rng);
+     */
+    VectorValues sample(const VectorValues& parentsValues,
+                        std::mt19937_64* rng) const;
+
+    /// Sample, use default rng
     VectorValues sample() const;
 
+    /// Sample with given values, use default rng
+    VectorValues sample(const VectorValues& parentsValues) const;
+
     /// @}
 
 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42

gtsam/linear/tests/testGaussianBayesNet.cpp

@@ -155,6 +155,14 @@ TEST(GaussianBayesNet, sample) {
   EXPECT_LONGS_EQUAL(2, actual.size());
   EXPECT(assert_equal(mean, actual[X(1)], 50 * sigma));
   EXPECT(assert_equal(A1 * mean + b, actual[X(0)], 50 * sigma));
+
+  // Use a specific random generator
+  std::mt19937_64 rng(4242);
+  auto actual3 = gbn.sample(&rng);
+  EXPECT_LONGS_EQUAL(2, actual.size());
+  // regression:
+  EXPECT(assert_equal(Vector2(20.0129382, 40.0039798), actual[X(1)], 1e-5));
+  EXPECT(assert_equal(Vector2(110.032083, 230.039811), actual[X(0)], 1e-5));
 }
 
 /* ************************************************************************* */

gtsam/linear/tests/testGaussianConditional.cpp

@@ -352,14 +352,21 @@ TEST(GaussianConditional, sample) {
   EXPECT_LONGS_EQUAL(1, actual1.size());
   EXPECT(assert_equal(b, actual1[X(0)], 50 * sigma));
 
-  VectorValues values;
-  values.insert(X(1), x1);
+  VectorValues given;
+  given.insert(X(1), x1);
 
   auto conditional =
       GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), b, sigma);
-  auto actual2 = conditional.sample(values);
+  auto actual2 = conditional.sample(given);
   EXPECT_LONGS_EQUAL(1, actual2.size());
   EXPECT(assert_equal(A1 * x1 + b, actual2[X(0)], 50 * sigma));
+
+  // Use a specific random generator
+  std::mt19937_64 rng(4242);
+  auto actual3 = conditional.sample(given, &rng);
+  EXPECT_LONGS_EQUAL(1, actual2.size());
+  // regression:
+  EXPECT(assert_equal(Vector2(31.0111856, 64.9850775), actual2[X(0)], 1e-5));
 }
 
 /* ************************************************************************* */