27 changed files with 148 additions and 328 deletions
--- a/.github/workflows/build-cibw.yml
+++ b/.github/workflows/build-cibw.yml
@ -1,7 +1,7 @@
 # This workflow builds the Python wheels using cibuildwheel and uploads them to TestPyPI.
 # It can be triggered on push to the develop branch or manually via Github Actions.

-name: Build Wheels for Develop
+name: Build Wheels (cibuildwheel)

 on:
  push:
--- a/.github/workflows/prod-cibw.yml
+++ b/.github/workflows/prod-cibw.yml
@ -1,188 +0,0 @@
-# This workflow builds the Python wheels using cibuildwheel and uploads them to TestPyPI.
-# It can be triggered on push to the develop branch or manually via Github Actions.
-
-name: Build Wheels for Release
-
-on:
-  release:
-    types: [published]
-  workflow_dispatch:
-
-jobs:
-  build_wheels:
-    name: Build Wheels
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          # Linux x86_64
-          - os: ubuntu-latest
-            python_version: "3.10"
-            cibw_python_version: 310
-            platform_id: manylinux_x86_64
-            manylinux_image: manylinux2014
-          - os: ubuntu-latest
-            python_version: "3.11"
-            cibw_python_version: 311
-            platform_id: manylinux_x86_64
-            manylinux_image: manylinux2014
-          - os: ubuntu-latest
-            python_version: "3.12"
-            cibw_python_version: 312
-            platform_id: manylinux_x86_64
-            manylinux_image: manylinux2014
-          - os: ubuntu-latest
-            python_version: "3.13"
-            cibw_python_version: 313
-            platform_id: manylinux_x86_64
-            manylinux_image: manylinux2014
-
-          # Linux aarch64
-          - os: ubuntu-24.04-arm
-            python_version: "3.10"
-            cibw_python_version: 310
-            platform_id: manylinux_aarch64
-            manylinux_image: manylinux2014
-          - os: ubuntu-24.04-arm
-            python_version: "3.11"
-            cibw_python_version: 311
-            platform_id: manylinux_aarch64
-            manylinux_image: manylinux2014
-          - os: ubuntu-24.04-arm
-            python_version: "3.12"
-            cibw_python_version: 312
-            platform_id: manylinux_aarch64
-            manylinux_image: manylinux2014
-          - os: ubuntu-24.04-arm
-            python_version: "3.13"
-            cibw_python_version: 313
-            platform_id: manylinux_aarch64
-            manylinux_image: manylinux2014
-
-          # MacOS x86_64
-          - os: macos-13
-            python_version: "3.10"
-            cibw_python_version: 310
-            platform_id: macosx_x86_64
-          - os: macos-13
-            python_version: "3.11"
-            cibw_python_version: 311
-            platform_id: macosx_x86_64
-          - os: macos-13
-            python_version: "3.12"
-            cibw_python_version: 312
-            platform_id: macosx_x86_64
-          - os: macos-13
-            python_version: "3.13"
-            cibw_python_version: 313
-            platform_id: macosx_x86_64
-
-          # MacOS arm64
-          - os: macos-14
-            python_version: "3.10"
-            cibw_python_version: 310
-            platform_id: macosx_arm64
-          - os: macos-14
-            python_version: "3.11"
-            cibw_python_version: 311
-            platform_id: macosx_arm64
-          - os: macos-14
-            python_version: "3.12"
-            cibw_python_version: 312
-            platform_id: macosx_arm64
-          - os: macos-14
-            python_version: "3.13"
-            cibw_python_version: 313
-            platform_id: macosx_arm64
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.sha }}
-
-      - name: Set up Python ${{ matrix.python_version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-
-      - name: Install Dependencies
-        run: |
-          python3 -m pip install -r python/dev_requirements.txt
-          if [ "$RUNNER_OS" == "Linux" ]; then
-            sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev ninja-build
-          elif [ "$RUNNER_OS" == "macOS" ]; then
-            brew install boost ninja python-setuptools
-          else
-            echo "$RUNNER_OS not supported"
-            exit 1
-          fi
-
-      # We first build the Python wrapper module on the host machine. This is done because cibuildwheel
-      # expects a setup.py file to be present in the project directory.
-      #
-      # The Python wrapper module is then rebuilt within the cibuildwheel container before building
-      # the wheels to ensure platform compatibility.
-      - name: Run CMake
-        run: |
-          cmake . -B build -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }}
-
-      # If on macOS, we previously installed boost using homebrew for the first build.
-      # We need to uninstall it before building the wheels with cibuildwheel, which will
-      # install boost from source.
-      - name: Uninstall Boost (MacOS)
-        if: runner.os == 'macOS'
-        run: |
-          brew uninstall boost
-
-      - name: Build and test wheels
-        env:
-          # Generate the platform identifier. See https://cibuildwheel.pypa.io/en/stable/options/#build-skip.
-          CIBW_BUILD: cp${{ matrix.cibw_python_version }}-${{ matrix.platform_id }}
-          CIBW_MANYLINUX_X86_64_IMAGE: ${{ matrix.manylinux_image }}
-          CIBW_MANYLINUX_AARCH64_IMAGE: ${{ matrix.manylinux_image }}
-          CIBW_ARCHS: all
-
-          # Set the minimum required MacOS version for the wheels.
-          MACOSX_DEPLOYMENT_TARGET: 10.15
-
-          # Set DYLD_LIBRARY_PATH to REPAIR_LIBRARY_PATH, which is set in cibw_before_all.sh. REPAIR_LIBRARY_PATH
-          # simply appends BOOST_LIBRARYDIR to the path, which is required during during link-time repair.
-          CIBW_REPAIR_WHEEL_COMMAND_MACOS: DYLD_LIBRARY_PATH=$REPAIR_LIBRARY_PATH delocate-wheel --require-archs {delocate_archs} -w {dest_dir} -v {wheel}
-
-          # Use build instead of pip wheel to build the wheels. This is recommended by PyPA.
-          # See https://cibuildwheel.pypa.io/en/stable/options/#build-frontend.
-          CIBW_BUILD_FRONTEND: "build"
-          CIBW_BEFORE_ALL: bash .github/scripts/python_wheels/cibw_before_all.sh ${{ matrix.python_version }} {project}
-
-          CIBW_BUILD_VERBOSITY: 1
-
-        run: bash .github/scripts/python_wheels/build_wheels.sh
-
-      - name: Store artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: cibw-wheels-cp${{ matrix.cibw_python_version }}-${{ matrix.platform_id }}
-          path: wheelhouse/*.whl
-
-  upload_all:
-    name: Upload All
-    needs: build_wheels
-    runs-on: ubuntu-latest
-    permissions:
-      id-token: write
-    steps:
-      - name: Download Artifacts
-        uses: actions/download-artifact@v4
-        with:
-          path: dist/
-          merge-multiple: true
-
-      - name: Publish to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          verbose: true
-          packages-dir: dist/
-          repository-url: https://test.pypi.org/legacy/
--- a/README.md
+++ b/README.md
@ -55,9 +55,6 @@ Optional prerequisites - used automatically if findable by CMake:

 GTSAM 4 introduces several new features, most notably Expressions and a Python toolbox. It also introduces traits, a C++ technique that allows optimizing with non-GTSAM types. That opens the door to retiring geometric types such as Point2 and Point3 to pure Eigen types, which we also do. A significant change which will not trigger a compile error is that zero-initializing of Point2 and Point3 is deprecated, so please be aware that this might render functions using their default constructor incorrect.

- There is a flag `GTSAM_ALLOW_DEPRECATED_SINCE_V43` for newly deprecated methods since the 4.3 release, which is on by default, allowing anyone to just pull version 4.3 and compile.
-
-
 ## Wrappers

 We provide support for [MATLAB](matlab/README.md) and [Python](python/README.md) wrappers for GTSAM. Please refer to the linked documents for more details.
--- a/cmake/HandleBoost.cmake
+++ b/cmake/HandleBoost.cmake
@ -6,88 +6,51 @@
 # Boost_NO_SYSTEM_PATHS: set to true to keep the find script from ignoring BOOST_ROOT

 if(MSVC)
-    set(Boost_USE_STATIC_LIBS ON)
+    # By default, boost only builds static libraries on windows
+    set(Boost_USE_STATIC_LIBS ON)  # only find static libs
+    # If we ever reset above on windows and, ...
+    # If we use Boost shared libs, disable auto linking.
+    # Some libraries, at least Boost Program Options, rely on this to export DLL symbols.
    if(NOT Boost_USE_STATIC_LIBS)
        list_append_cache(GTSAM_COMPILE_DEFINITIONS_PUBLIC BOOST_ALL_NO_LIB BOOST_ALL_DYN_LINK)
    endif()
-    if(MSVC_VERSION LESS 1910)
-        list_append_cache(GTSAM_COMPILE_OPTIONS_PRIVATE -Zm295)
+    # Virtual memory range for PCH exceeded on VS2015
+    if(MSVC_VERSION LESS 1910) # older than VS2017
+      list_append_cache(GTSAM_COMPILE_OPTIONS_PRIVATE -Zm295)
    endif()
 endif()

-# ---- 关键修改：强制使用 FindBoost 模块模式，而不是 CONFIG 模式 ----
-set(Boost_NO_BOOST_CMAKE ON CACHE BOOL "Use FindBoost module mode" FORCE)

-# 允许多线程库（照旧）
-set(Boost_USE_MULTITHREADED ON)
-
-# 最低版本
+# Store these in variables so they are automatically replicated in GTSAMConfig.cmake and such.
 set(BOOST_FIND_MINIMUM_VERSION 1.65)
+set(BOOST_FIND_MINIMUM_COMPONENTS serialization system filesystem thread program_options date_time timer chrono regex)

-# ---- 关键修改：按版本决定是否需要 system 组件 ----
-set(BOOST_FIND_MINIMUM_COMPONENTS
-        serialization filesystem thread program_options date_time timer chrono regex
-)
+find_package(Boost ${BOOST_FIND_MINIMUM_VERSION} COMPONENTS ${BOOST_FIND_MINIMUM_COMPONENTS} REQUIRED)

-# 只有旧版 Boost (< 1.69) 需要 system；>=1.69 为 header-only，不再要求
-# 注意：Boost_VERSION 是 find_package 之后才有的，这里用一个预探测逻辑：
-# 先尝试不带 system；失败再带上 system 尝试一次（兼容极老环境）。
-# 也可以用工具链变量提前注入 Boost_VERSION，这里采用两段式最稳妥。
-
-# 第一次尝试：不含 system（适配 Boost>=1.69）
-message(STATUS "Trying Boost without 'system' component first (>=1.69 header-only)...")
-set(_boost_try_components ${BOOST_FIND_MINIMUM_COMPONENTS})
-set(_boost_found FALSE)
-find_package(Boost ${BOOST_FIND_MINIMUM_VERSION} COMPONENTS ${_boost_try_components} QUIET)
-if(Boost_FOUND)
-    set(_boost_found TRUE)
-else()
-    # 第二次尝试：加入 system（兼容 Boost<1.69）
-    message(STATUS "Retrying Boost with 'system' component for older Boost (<1.69)...")
-    list(APPEND _boost_try_components system)
-    find_package(Boost ${BOOST_FIND_MINIMUM_VERSION} COMPONENTS ${_boost_try_components} REQUIRED)
+# Required components
+if(NOT Boost_SERIALIZATION_LIBRARY OR NOT Boost_SYSTEM_LIBRARY OR NOT Boost_FILESYSTEM_LIBRARY OR
+    NOT Boost_THREAD_LIBRARY OR NOT Boost_DATE_TIME_LIBRARY)
+  message(FATAL_ERROR "Missing required Boost components >= v1.65, please install/upgrade Boost or configure your search paths.")
 endif()

-# 记录最终组件集合
-set(BOOST_FIND_MINIMUM_COMPONENTS ${_boost_try_components})
-
-# ---- 必要组件检查（按最终集合判断），不要无条件检查 Boost_SYSTEM_LIBRARY ----
-set(_missing_required FALSE)
-foreach(_comp IN LISTS BOOST_FIND_MINIMUM_COMPONENTS)
-    string(TOUPPER "${_comp}" _COMP_UP)
-    if(NOT DEFINED "Boost_${_COMP_UP}_LIBRARY" AND NOT TARGET "Boost::${_comp}")
-        # 某些组件（如 header-only）可能没有 *_LIBRARY 变量，但会有导入目标或仅头文件
-        # 对 header-only 的 system（>=1.69）这里不会要求
-        message(STATUS "Note: Boost component '${_comp}' has no explicit *_LIBRARY var; relying on imported target or headers.")
-    endif()
-endforeach()
-
-# ---- 计时接口选择，与原逻辑一致 ----
 option(GTSAM_DISABLE_NEW_TIMERS "Disables using Boost.chrono for timing" OFF)
-
-# ---- 链接库列表（不再无条件包含 Boost::system）----
+# Allow for not using the timer libraries on boost < 1.48 (GTSAM timing code falls back to old timer library)
 set(GTSAM_BOOST_LIBRARIES
-        Boost::serialization
-        Boost::filesystem
-        Boost::thread
-        Boost::date_time
-        Boost::regex
+  Boost::serialization
+  Boost::system
+  Boost::filesystem
+  Boost::thread
+  Boost::date_time
+  Boost::regex
 )
-
-# 如果最终组件包含 chrono/timer，则追加
-if(TARGET Boost::chrono)
-    list(APPEND GTSAM_BOOST_LIBRARIES Boost::chrono)
-endif()
-if(TARGET Boost::timer)
-    list(APPEND GTSAM_BOOST_LIBRARIES Boost::timer)
-endif()
-
-# 仅当真的找了 system（即老版本 Boost）时才链接 Boost::system
-if("system" IN_LIST BOOST_FIND_MINIMUM_COMPONENTS AND TARGET Boost::system)
-    list(APPEND GTSAM_BOOST_LIBRARIES Boost::system)
-endif()
-
 if (GTSAM_DISABLE_NEW_TIMERS)
    message("WARNING:  GTSAM timing instrumentation manually disabled")
    list_append_cache(GTSAM_COMPILE_DEFINITIONS_PUBLIC DGTSAM_DISABLE_NEW_TIMERS)
+else()
+    if(Boost_TIMER_LIBRARY)
+      list(APPEND GTSAM_BOOST_LIBRARIES Boost::timer Boost::chrono)
+    else()
+      list(APPEND GTSAM_BOOST_LIBRARIES rt) # When using the header-only boost timer library, need -lrt
+      message("WARNING:  GTSAM timing instrumentation will use the older, less accurate, Boost timer library because boost older than 1.48 was found.")
+    endif()
 endif()
--- a/gtsam/base/utilities.h
+++ b/gtsam/base/utilities.h
@ -1,17 +1,10 @@
 #pragma once

-#include <gtsam/dllexport.h>
-
-#include <iostream>
-#include <random>
-#include <sstream>
 #include <string>
+#include <iostream>
+#include <sstream>

-/**
- * @brief Global default pseudo-random number generator object.
- * In wrappers we can access std::mt19937_64 via gtsam.MT19937
- */
-static std::mt19937_64 kRandomNumberGenerator(42);
+#include <gtsam/dllexport.h>

 namespace gtsam {
 /**
@ -35,7 +28,7 @@ private:
  std::streambuf* coutBuffer_;
 };

-}  // namespace gtsam
+}

 namespace gtsam {
 // Adapted from https://stackoverflow.com/a/32223343/9151520
--- a/gtsam/discrete/DiscreteConditional.cpp
+++ b/gtsam/discrete/DiscreteConditional.cpp
@ -18,7 +18,6 @@

 #include <gtsam/base/Testable.h>
 #include <gtsam/base/debug.h>
-#include <gtsam/base/utilities.h>
 #include <gtsam/discrete/DiscreteConditional.h>
 #include <gtsam/discrete/Ring.h>
 #include <gtsam/discrete/Signature.h>
--- a/gtsam/discrete/DiscreteConditional.h
+++ b/gtsam/discrete/DiscreteConditional.h
@ -27,6 +27,9 @@
 #include <string>
 #include <vector>

+// In wrappers we can access std::mt19937_64 via gtsam.MT19937
+static std::mt19937_64 kRandomNumberGenerator(42);
+
 namespace gtsam {

 /**
--- a/gtsam/discrete/DiscreteFactorGraph.h
+++ b/gtsam/discrete/DiscreteFactorGraph.h
@ -127,9 +127,6 @@ class GTSAM_EXPORT DiscreteFactorGraph
  template <class DERIVED_FACTOR>
  DiscreteFactorGraph(const FactorGraph<DERIVED_FACTOR>& graph) : Base(graph) {}

-  /// Destructor
-  virtual ~DiscreteFactorGraph() {}
-
  /// @name Testable
  /// @{

--- a/gtsam/discrete/TableDistribution.cpp
+++ b/gtsam/discrete/TableDistribution.cpp
@ -17,7 +17,6 @@

 #include <gtsam/base/Testable.h>
 #include <gtsam/base/debug.h>
-#include <gtsam/base/utilities.h>
 #include <gtsam/discrete/Ring.h>
 #include <gtsam/discrete/Signature.h>
 #include <gtsam/discrete/TableDistribution.h>
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@ -202,6 +202,16 @@ 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 {
--- a/gtsam/hybrid/HybridBayesNet.h
+++ b/gtsam/hybrid/HybridBayesNet.h
@ -181,11 +181,10 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   *   auto sample = bn.sample(given, &rng);
   *
   * @param given Values of missing variables.
-   * @param rng The optional pseudo-random number generator.
+   * @param rng The pseudo-random number generator.
   * @return HybridValues
   */
-  HybridValues sample(const HybridValues &given,
-                      std::mt19937_64 *rng = nullptr) const;
+  HybridValues sample(const HybridValues &given, std::mt19937_64 *rng) const;

  /**
   * @brief Sample using ancestral sampling.
@ -194,10 +193,25 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   *   std::mt19937_64 rng(42);
   *   auto sample = bn.sample(&rng);
   *
-   * @param rng The optional pseudo-random number generator.
+   * @param rng The pseudo-random number generator.
   * @return HybridValues
   */
-  HybridValues sample(std::mt19937_64 *rng = nullptr) const;
+  HybridValues sample(std::mt19937_64 *rng) 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.
--- a/gtsam/hybrid/hybrid.i
+++ b/gtsam/hybrid/hybrid.i
@ -158,8 +158,10 @@ class HybridBayesNet {
  gtsam::HybridValues optimize() const;
  gtsam::VectorValues optimize(const gtsam::DiscreteValues& assignment) const;

-  gtsam::HybridValues sample(const gtsam::HybridValues& given, std::mt19937_64@ rng = nullptr) const;
-  gtsam::HybridValues sample(std::mt19937_64@ rng = nullptr) const;
+  gtsam::HybridValues sample(const gtsam::HybridValues& given, std::mt19937_64@ rng) const;
+  gtsam::HybridValues sample(std::mt19937_64@ rng) const;
+  gtsam::HybridValues sample(const gtsam::HybridValues& given) const;
+  gtsam::HybridValues sample() const;

  void print(string s = "HybridBayesNet\n",
             const gtsam::KeyFormatter& keyFormatter =
--- a/gtsam/inference/Key.cpp
+++ b/gtsam/inference/Key.cpp
@ -28,7 +28,6 @@ namespace gtsam {

 /// Assign default key formatter
 KeyFormatter DefaultKeyFormatter = &_defaultKeyFormatter;
-KeyFormatter MultiRobotKeyFormatter = &_multirobotKeyFormatter;

 /* ************************************************************************* */
 string _defaultKeyFormatter(Key key) {
--- a/gtsam/inference/Key.h
+++ b/gtsam/inference/Key.h
@ -57,7 +57,8 @@ GTSAM_EXPORT std::string _multirobotKeyFormatter(gtsam::Key key);
 /// formatter in print functions.
 ///
 /// Checks for LabeledSymbol, Symbol and then plain keys, in order.
-extern GTSAM_EXPORT KeyFormatter MultiRobotKeyFormatter; 
+static const gtsam::KeyFormatter MultiRobotKeyFormatter =
+    &_multirobotKeyFormatter;

 /// To use the key_formatter on Keys, they must be wrapped in a StreamedKey.
 struct StreamedKey {
--- a/gtsam/linear/GaussianBayesNet.cpp
+++ b/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
@ -73,6 +76,15 @@ namespace gtsam {
    return result;
  }

+  /* ************************************************************************ */
+  VectorValues GaussianBayesNet::sample() const {
+    return sample(&kRandomNumberGenerator);
+  }
+
+  VectorValues GaussianBayesNet::sample(const VectorValues& given) const {
+    return sample(given, &kRandomNumberGenerator);
+  }
+
  /* ************************************************************************ */
  VectorValues GaussianBayesNet::optimizeGradientSearch() const
  {
--- a/gtsam/linear/GaussianBayesNet.h
+++ b/gtsam/linear/GaussianBayesNet.h
@ -131,7 +131,7 @@ namespace gtsam {
     *   std::mt19937_64 rng(42);
     *   auto sample = gbn.sample(&rng);
     */
-    VectorValues sample(std::mt19937_64* rng = nullptr) const;
+    VectorValues sample(std::mt19937_64* rng) const;

    /**
     * Sample from an incomplete BayesNet, given missing variables
@ -140,7 +140,13 @@ namespace gtsam {
     *   VectorValues given = ...;
     *   auto sample = gbn.sample(given, &rng);
     */
-    VectorValues sample(const VectorValues& given, std::mt19937_64* rng = nullptr) const;
+    VectorValues sample(const VectorValues& given, std::mt19937_64* rng) 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.
--- a/gtsam/linear/GaussianConditional.cpp
+++ b/gtsam/linear/GaussianConditional.cpp
@ -15,12 +15,11 @@
 * @author Christian Potthast, Frank Dellaert
 */

-#include <gtsam/base/utilities.h>
-#include <gtsam/hybrid/HybridValues.h>
 #include <gtsam/linear/GaussianConditional.h>
 #include <gtsam/linear/Sampler.h>
 #include <gtsam/linear/VectorValues.h>
 #include <gtsam/linear/linearExceptions.h>
+#include <gtsam/hybrid/HybridValues.h>

 #ifdef __GNUC__
 #pragma GCC diagnostic push
@ -35,6 +34,9 @@
 #include <string>
 #include <cmath>

+// In wrappers we can access std::mt19937_64 via gtsam.MT19937
+static std::mt19937_64 kRandomNumberGenerator(42);
+
 using namespace std;

 namespace gtsam {
@ -345,10 +347,6 @@ 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();
@ -361,7 +359,16 @@ namespace gtsam {
      throw std::invalid_argument(
          "sample() can only be invoked on no-parent prior");
    VectorValues values;
-    return sample(values, rng);
+    return sample(values);
+  }
+
+  /* ************************************************************************ */
+  VectorValues GaussianConditional::sample() const {
+    return sample(&kRandomNumberGenerator);
+  }
+
+  VectorValues GaussianConditional::sample(const VectorValues& given) const {
+    return sample(given, &kRandomNumberGenerator);
  }

  /* ************************************************************************ */
--- a/gtsam/linear/GaussianConditional.h
+++ b/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 = nullptr) const;
+    VectorValues sample(std::mt19937_64* rng) const;

    /**
     * Sample from conditional, given missing variables
@ -227,7 +227,13 @@ namespace gtsam {
     *   auto sample = gc.sample(given, &rng);
     */
    VectorValues sample(const VectorValues& parentsValues,
-                        std::mt19937_64* rng = nullptr) const;
+                        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;

    /// @}
    /// @name Linear algebra.
--- a/gtsam/linear/linear.i
+++ b/gtsam/linear/linear.i
@ -560,9 +560,10 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
      const gtsam::VectorValues& frontalValues) const;
  gtsam::JacobianFactor* likelihood(gtsam::Vector frontal) 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(std::mt19937_64@ rng) const;
+  gtsam::VectorValues sample(const gtsam::VectorValues& parents, std::mt19937_64@ rng) const;
+  gtsam::VectorValues sample() const;
+  gtsam::VectorValues sample(const gtsam::VectorValues& parents) const;

  // Advanced Interface
  gtsam::VectorValues solveOtherRHS(const gtsam::VectorValues& parents,
@ -628,10 +629,9 @@ virtual class GaussianBayesNet {
  gtsam::VectorValues optimize() const;
  gtsam::VectorValues optimize(const gtsam::VectorValues& given) const;
  gtsam::VectorValues optimizeGradientSearch() const;
-
-  gtsam::VectorValues sample(const gtsam::VectorValues& given,
-                             std::mt19937_64 @rng = nullptr) const;
-  gtsam::VectorValues sample(std::mt19937_64 @rng = nullptr) const;
+  
+  gtsam::VectorValues sample(const gtsam::VectorValues& given) const;
+  gtsam::VectorValues sample() const;
  gtsam::VectorValues backSubstitute(const gtsam::VectorValues& gx) const;
  gtsam::VectorValues backSubstituteTranspose(const gtsam::VectorValues& gx) const;

--- a/gtsam/linear/tests/testGaussianConditional.cpp
+++ b/gtsam/linear/tests/testGaussianConditional.cpp
@ -441,7 +441,7 @@ TEST(GaussianConditional, likelihood) {

 /* ************************************************************************* */
 // Test sampling
-TEST(GaussianConditional, Sample) {
+TEST(GaussianConditional, sample) {
  Matrix A1 = (Matrix(2, 2) << 1., 2., 3., 4.).finished();
  const Vector2 b(20, 40), x1(3, 4);
  const double sigma = 0.01;
@ -465,10 +465,8 @@ TEST(GaussianConditional, Sample) {
  auto actual3 = conditional.sample(given, &rng);
  EXPECT_LONGS_EQUAL(1, actual2.size());
  // regressions
-#if __APPLE__
-EXPECT(assert_equal(Vector2(31.0111856, 64.9850775), actual2[X(0)], 1e-5));
-#elif _WIN32
-  EXPECT(assert_equal(Vector2(30.995317, 64.9943165), actual2[X(0)], 1e-5));
+#if __APPLE__ || _WIN32
+  EXPECT(assert_equal(Vector2(31.0111856, 64.9850775), actual2[X(0)], 1e-5));
 #elif __linux__
  EXPECT(assert_equal(Vector2(30.9809331, 64.9927588), actual2[X(0)], 1e-5));
 #endif
--- a/gtsam/nonlinear/Values-inl.h
+++ b/gtsam/nonlinear/Values-inl.h
@ -27,6 +27,8 @@
 #include <utility>
 #include <gtsam/nonlinear/Values.h>

+#include <gtsam/nonlinear/Values.h> // Only so Eclipse finds class definition
+
 namespace gtsam {


--- a/gtsam/sfm/SfmTrack.h
+++ b/gtsam/sfm/SfmTrack.h
@ -81,12 +81,13 @@ struct GTSAM_EXPORT SfmTrack2d {
   * @returns boolean result of the validation.
   */
  bool hasUniqueCameras() const {
-    std::vector<size_t> cameraIndices;
+    std::vector<int> track_cam_indices;
    for (auto& measurement : measurements) {
-      cameraIndices.emplace_back(measurement.first);
+      track_cam_indices.emplace_back(measurement.first);
    }
-    auto i = std::adjacent_find(cameraIndices.begin(), cameraIndices.end());
-    bool all_cameras_unique = (i == cameraIndices.end());
+    auto i =
+        std::adjacent_find(track_cam_indices.begin(), track_cam_indices.end());
+    bool all_cameras_unique = (i == track_cam_indices.end());
    return all_cameras_unique;
  }

--- a/gtsam/sfm/ShonanAveraging.cpp
+++ b/gtsam/sfm/ShonanAveraging.cpp
@ -42,7 +42,7 @@
 namespace gtsam {

 // In Wrappers we have no access to this so have a default ready
-static std::mt19937 kPRNG(42);
+static std::mt19937 kRandomNumberGenerator(42);

 using Sparse = Eigen::SparseMatrix<double>;

@ -869,7 +869,7 @@ Values ShonanAveraging<d>::initializeRandomly(std::mt19937 &rng) const {
 /* ************************************************************************* */
 template <size_t d>
 Values ShonanAveraging<d>::initializeRandomly() const {
-  return initializeRandomly(kPRNG);
+  return initializeRandomly(kRandomNumberGenerator);
 }

 /* ************************************************************************* */
@ -883,7 +883,7 @@ Values ShonanAveraging<d>::initializeRandomlyAt(size_t p,
 /* ************************************************************************* */
 template <size_t d>
 Values ShonanAveraging<d>::initializeRandomlyAt(size_t p) const {
-  return initializeRandomlyAt(p, kPRNG);
+  return initializeRandomlyAt(p, kRandomNumberGenerator);
 }

 /* ************************************************************************* */
--- a/gtsam/sfm/TranslationRecovery.cpp
+++ b/gtsam/sfm/TranslationRecovery.cpp
@ -39,7 +39,7 @@ using namespace gtsam;
 using namespace std;

 // In Wrappers we have no access to this so have a default ready.
-static std::mt19937 kPRNG(42);
+static std::mt19937 kRandomNumberGenerator(42);

 // Some relative translations may be zero. We treat nodes that have a zero
 // relativeTranslation as a single node.
@ -185,7 +185,7 @@ Values TranslationRecovery::initializeRandomly(
    const std::vector<BinaryMeasurement<Point3>> &betweenTranslations,
    const Values &initialValues) const {
  return initializeRandomly(relativeTranslations, betweenTranslations,
-                            &kPRNG, initialValues);
+                            &kRandomNumberGenerator, initialValues);
 }

 Values TranslationRecovery::run(
--- a/gtsam/sfm/tests/testShonanAveraging.cpp
+++ b/gtsam/sfm/tests/testShonanAveraging.cpp
@ -45,7 +45,7 @@ ShonanAveraging3 fromExampleName(

 static const ShonanAveraging3 kShonan = fromExampleName("toyExample.g2o");

-static std::mt19937 kPRNG(42);
+static std::mt19937 kRandomNumberGenerator(42);

 /* ************************************************************************* */
 TEST(ShonanAveraging3, checkConstructor) {
@ -78,7 +78,7 @@ TEST(ShonanAveraging3, buildGraphAt) {

 /* ************************************************************************* */
 TEST(ShonanAveraging3, checkOptimality) {
-  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
+  const Values randomRotations = kShonan.initializeRandomly(kRandomNumberGenerator);
  Values random = ShonanAveraging3::LiftTo<Rot3>(4, randomRotations);  // lift to 4!
  auto Lambda = kShonan.computeLambda(random);
  EXPECT_LONGS_EQUAL(15, Lambda.rows());
@ -106,7 +106,7 @@ TEST(ShonanAveraging3, checkSubgraph) {

  // Create initial random estimation
  Values initial;
-  initial = subgraphShonan.initializeRandomly(kPRNG);
+  initial = subgraphShonan.initializeRandomly(kRandomNumberGenerator);

  // Run Shonan with SUBGRAPH solver
  auto result = subgraphShonan.run(initial, 3, 3);
@ -115,7 +115,7 @@ TEST(ShonanAveraging3, checkSubgraph) {

 /* ************************************************************************* */
 TEST(ShonanAveraging3, tryOptimizingAt3) {
-  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
+  const Values randomRotations = kShonan.initializeRandomly(kRandomNumberGenerator);
  Values initial = ShonanAveraging3::LiftTo<Rot3>(3, randomRotations);  // convert to SOn
  EXPECT(!kShonan.checkOptimality(initial));
  const Values result = kShonan.tryOptimizingAt(3, initial);
@ -130,7 +130,7 @@ TEST(ShonanAveraging3, tryOptimizingAt3) {

 /* ************************************************************************* */
 TEST(ShonanAveraging3, tryOptimizingAt4) {
-  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
+  const Values randomRotations = kShonan.initializeRandomly(kRandomNumberGenerator);
  Values random = ShonanAveraging3::LiftTo<Rot3>(4, randomRotations);  // lift to 4!
  const Values result = kShonan.tryOptimizingAt(4, random);
  EXPECT(kShonan.checkOptimality(result));
@ -228,7 +228,7 @@ TEST(ShonanAveraging3, CheckWithEigen) {

 /* ************************************************************************* */
 TEST(ShonanAveraging3, initializeWithDescent) {
-  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
+  const Values randomRotations = kShonan.initializeRandomly(kRandomNumberGenerator);
  Values random = ShonanAveraging3::LiftTo<Rot3>(3, randomRotations);
  const Values Qstar3 = kShonan.tryOptimizingAt(3, random);
  Vector minEigenVector;
@ -240,7 +240,7 @@ TEST(ShonanAveraging3, initializeWithDescent) {

 /* ************************************************************************* */
 TEST(ShonanAveraging3, run) {
-  auto initial = kShonan.initializeRandomly(kPRNG); 
+  auto initial = kShonan.initializeRandomly(kRandomNumberGenerator); 
  auto result = kShonan.run(initial, 5);
  EXPECT_DOUBLES_EQUAL(0, kShonan.cost(result.first), 1e-3);
  EXPECT_DOUBLES_EQUAL(-5.427688831332745e-07, result.second,
@ -295,7 +295,7 @@ TEST(ShonanAveraging3, runKlaus) {
  EXPECT(assert_equal(R02, wR0.between(wR2), 0.1));

  // Run Shonan (with prior on first rotation)
-  auto initial = shonan.initializeRandomly(kPRNG); 
+  auto initial = shonan.initializeRandomly(kRandomNumberGenerator); 
  auto result = shonan.run(initial, 5);
  EXPECT_DOUBLES_EQUAL(0, shonan.cost(result.first), 1e-2);
  EXPECT_DOUBLES_EQUAL(-9.2259161494467889e-05, result.second,
@ -323,7 +323,7 @@ TEST(ShonanAveraging3, runKlausKarcher) {
  static const ShonanAveraging3 shonan = fromExampleName("Klaus3.g2o");

  // Run Shonan (with Karcher mean prior)
-  auto initial = shonan.initializeRandomly(kPRNG); 
+  auto initial = shonan.initializeRandomly(kRandomNumberGenerator); 
  auto result = shonan.run(initial, 5);
  EXPECT_DOUBLES_EQUAL(0, shonan.cost(result.first), 1e-2);
  EXPECT_DOUBLES_EQUAL(-1.361402670507772e-05, result.second,
@ -353,7 +353,7 @@ TEST(ShonanAveraging2, noisyToyGraph) {
  // Check graph building
  NonlinearFactorGraph graph = shonan.buildGraphAt(2);
  EXPECT_LONGS_EQUAL(6, graph.size());
-  auto initial = shonan.initializeRandomly(kPRNG); 
+  auto initial = shonan.initializeRandomly(kRandomNumberGenerator); 
  auto result = shonan.run(initial, 2);
  EXPECT_DOUBLES_EQUAL(0.0008211, shonan.cost(result.first), 1e-6);
  EXPECT_DOUBLES_EQUAL(0, result.second, 1e-10); // certificate!
@ -391,7 +391,7 @@ TEST(ShonanAveraging2, noisyToyGraphWithHuber) {
  }

  // test result
-  auto initial = shonan.initializeRandomly(kPRNG);
+  auto initial = shonan.initializeRandomly(kRandomNumberGenerator);
  auto result = shonan.run(initial, 2,2);
  EXPECT_DOUBLES_EQUAL(0.0008211, shonan.cost(result.first), 1e-6);
  EXPECT_DOUBLES_EQUAL(0, result.second, 1e-10); // certificate!
--- a/package.xml
+++ b/package.xml
@ -23,6 +23,7 @@

  <buildtool_depend>cmake</buildtool_depend>

+  <depend>boost</depend>
  <depend>eigen</depend>
  <depend>tbb</depend>

--- a/python/gtsam/preamble/inference.h
+++ b/python/gtsam/preamble/inference.h
@ -10,5 +10,3 @@
 * Without this they will be automatically converted to a Python object, and all
 * mutations on Python side will not be reflected on C++.
 */
-
-#include <pybind11/stl.h>