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use default RNG object for mt19937_64 everywhere

release/4.3a0
Varun Agrawal 2025-05-16 23:59:27 -04:00
parent bc7646f82f
commit 995bedb4ca
3 changed files with 9 additions and 25 deletions
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12
gtsam/linear/GaussianBayesNet.cpp
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@ -26,9 +26,6 @@
using namespace std; using namespace std;
using namespace gtsam; using namespace gtsam;
// In Wrappers we have no access to this so have a default ready
static std::mt19937_64 kRandomNumberGenerator(42);
namespace gtsam { namespace gtsam {
// Instantiate base class // Instantiate base class
@ -76,15 +73,6 @@ namespace gtsam {
return result; return result;
} }
/* ************************************************************************ */
VectorValues GaussianBayesNet::sample() const {
return sample(&kRandomNumberGenerator);
}
VectorValues GaussianBayesNet::sample(const VectorValues& given) const {
return sample(given, &kRandomNumberGenerator);
}
/* ************************************************************************ */ /* ************************************************************************ */
VectorValues GaussianBayesNet::optimizeGradientSearch() const VectorValues GaussianBayesNet::optimizeGradientSearch() const
{ {

10
gtsam/linear/GaussianBayesNet.h
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@ -131,7 +131,7 @@ namespace gtsam {
* std::mt19937_64 rng(42); * std::mt19937_64 rng(42);
* auto sample = gbn.sample(&rng); * auto sample = gbn.sample(&rng);
*/ */
VectorValues sample(std::mt19937_64* rng) const; VectorValues sample(std::mt19937_64* rng = nullptr) const;
/** /**
* Sample from an incomplete BayesNet, given missing variables * Sample from an incomplete BayesNet, given missing variables
@ -140,13 +140,7 @@ namespace gtsam {
* VectorValues given = ...; * VectorValues given = ...;
* auto sample = gbn.sample(given, &rng); * auto sample = gbn.sample(given, &rng);
*/ */
VectorValues sample(const VectorValues& given, std::mt19937_64* rng) const; VectorValues sample(const VectorValues& given, std::mt19937_64* rng = nullptr) const;
/// Sample using ancestral sampling, use default rng
VectorValues sample() const;
/// Sample from an incomplete BayesNet, use default rng
VectorValues sample(const VectorValues& given) const;
/** /**
* Return ordering corresponding to a topological sort. * Return ordering corresponding to a topological sort.

12
gtsam/linear/linear.i
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@ -560,8 +560,9 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
const gtsam::VectorValues& frontalValues) const; const gtsam::VectorValues& frontalValues) const;
gtsam::JacobianFactor* likelihood(gtsam::Vector frontal) const; gtsam::JacobianFactor* likelihood(gtsam::Vector frontal) const;
gtsam::VectorValues sample(std::mt19937_64@ rng = nullptr) const; gtsam::VectorValues sample(std::mt19937_64 @rng = nullptr) const;
gtsam::VectorValues sample(const gtsam::VectorValues& parents, std::mt19937_64@ rng = nullptr) const; gtsam::VectorValues sample(const gtsam::VectorValues& parents,
std::mt19937_64 @rng = nullptr) const;
// Advanced Interface // Advanced Interface
gtsam::VectorValues solveOtherRHS(const gtsam::VectorValues& parents, gtsam::VectorValues solveOtherRHS(const gtsam::VectorValues& parents,
@ -627,9 +628,10 @@ virtual class GaussianBayesNet {
gtsam::VectorValues optimize() const; gtsam::VectorValues optimize() const;
gtsam::VectorValues optimize(const gtsam::VectorValues& given) const; gtsam::VectorValues optimize(const gtsam::VectorValues& given) const;
gtsam::VectorValues optimizeGradientSearch() const; gtsam::VectorValues optimizeGradientSearch() const;
gtsam::VectorValues sample(const gtsam::VectorValues& given) const; gtsam::VectorValues sample(const gtsam::VectorValues& given,
gtsam::VectorValues sample() const; std::mt19937_64 @rng = nullptr) const;
gtsam::VectorValues sample(std::mt19937_64 @rng = nullptr) const;
gtsam::VectorValues backSubstitute(const gtsam::VectorValues& gx) const; gtsam::VectorValues backSubstitute(const gtsam::VectorValues& gx) const;
gtsam::VectorValues backSubstituteTranspose(const gtsam::VectorValues& gx) const; gtsam::VectorValues backSubstituteTranspose(const gtsam::VectorValues& gx) const;