feat:修正boost高版本问题，不再使用system包

Add production workflow for `cibuildwheel`

.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 (cibuildwheel)
+name: Build Wheels for Develop
 
 on:
   push:

.github/workflows/prod-cibw.yml

@@ -0,0 +1,188 @@
+# 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/

README.md

@@ -55,6 +55,9 @@ 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.

cmake/HandleBoost.cmake

@@ -6,51 +6,88 @@
 # Boost_NO_SYSTEM_PATHS: set to true to keep the find script from ignoring BOOST_ROOT
 
 if(MSVC)
-    # 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.
+    set(Boost_USE_STATIC_LIBS ON)
     if(NOT Boost_USE_STATIC_LIBS)
         list_append_cache(GTSAM_COMPILE_DEFINITIONS_PUBLIC BOOST_ALL_NO_LIB BOOST_ALL_DYN_LINK)
     endif()
-    # Virtual memory range for PCH exceeded on VS2015
-    if(MSVC_VERSION LESS 1910) # older than VS2017
+    if(MSVC_VERSION LESS 1910)
         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)
 
-# Store these in variables so they are automatically replicated in GTSAMConfig.cmake and such.
+# 允许多线程库（照旧）
+set(Boost_USE_MULTITHREADED ON)
+
+# 最低版本
 set(BOOST_FIND_MINIMUM_VERSION 1.65)
-set(BOOST_FIND_MINIMUM_COMPONENTS serialization system filesystem thread program_options date_time timer chrono regex)
 
-find_package(Boost ${BOOST_FIND_MINIMUM_VERSION} COMPONENTS ${BOOST_FIND_MINIMUM_COMPONENTS} REQUIRED)
+# ---- 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
+)
 
-# 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.")
+# 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)
 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)
-# Allow for not using the timer libraries on boost < 1.48 (GTSAM timing code falls back to old timer library)
+
+# ---- 链接库列表（不再无条件包含 Boost::system）----
 set(GTSAM_BOOST_LIBRARIES
         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()

gtsam/base/utilities.h

@@ -1,11 +1,18 @@
 #pragma once
 
-#include <string>
-#include <iostream>
-#include <sstream>
-
 #include <gtsam/dllexport.h>
 
+#include <iostream>
+#include <random>
+#include <sstream>
+#include <string>
+
+/**
+ * @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);
+
 namespace gtsam {
 /**
  * For Python __str__().
@@ -28,7 +35,7 @@ private:
   std::streambuf* coutBuffer_;
 };
 
-}
+}  // namespace gtsam
 
 namespace gtsam {
 // Adapted from https://stackoverflow.com/a/32223343/9151520

gtsam/discrete/DiscreteConditional.cpp

@@ -18,6 +18,7 @@
 
 #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>

gtsam/discrete/DiscreteConditional.h

@@ -27,9 +27,6 @@
 #include <string>
 #include <vector>
 
-// In wrappers we can access std::mt19937_64 via gtsam.MT19937
-static std::mt19937_64 kRandomNumberGenerator(42);
-
 namespace gtsam {
 
 /**

gtsam/discrete/DiscreteFactorGraph.h

@@ -127,6 +127,9 @@ class GTSAM_EXPORT DiscreteFactorGraph
   template <class DERIVED_FACTOR>
   DiscreteFactorGraph(const FactorGraph<DERIVED_FACTOR>& graph) : Base(graph) {}
 
+  /// Destructor
+  virtual ~DiscreteFactorGraph() {}
+
   /// @name Testable
   /// @{
 

gtsam/discrete/TableDistribution.cpp

@@ -17,6 +17,7 @@
 
 #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>

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;
-
-  /**
-   * @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;
+  HybridValues sample(std::mt19937_64 *rng = nullptr) const;
 
   /**
    * @brief Prune the Bayes Net such that we have at most maxNrLeaves leaves.

gtsam/hybrid/hybrid.i

@@ -158,10 +158,8 @@ 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) const;
-  gtsam::HybridValues sample(std::mt19937_64@ rng) const;
-  gtsam::HybridValues sample(const gtsam::HybridValues& given) const;
-  gtsam::HybridValues sample() const;
+  gtsam::HybridValues sample(const gtsam::HybridValues& given, std::mt19937_64@ rng = nullptr) const;
+  gtsam::HybridValues sample(std::mt19937_64@ rng = nullptr) const;
 
   void print(string s = "HybridBayesNet\n",
              const gtsam::KeyFormatter& keyFormatter =

gtsam/inference/Key.cpp

@@ -28,6 +28,7 @@ namespace gtsam {
 
 /// Assign default key formatter
 KeyFormatter DefaultKeyFormatter = &_defaultKeyFormatter;
+KeyFormatter MultiRobotKeyFormatter = &_multirobotKeyFormatter;
 
 /* ************************************************************************* */
 string _defaultKeyFormatter(Key key) {

gtsam/inference/Key.h

@@ -57,8 +57,7 @@ GTSAM_EXPORT std::string _multirobotKeyFormatter(gtsam::Key key);
 /// formatter in print functions.
 ///
 /// Checks for LabeledSymbol, Symbol and then plain keys, in order.
-static const gtsam::KeyFormatter MultiRobotKeyFormatter =
-    &_multirobotKeyFormatter;
+extern GTSAM_EXPORT KeyFormatter MultiRobotKeyFormatter; 
 
 /// To use the key_formatter on Keys, they must be wrapped in a StreamedKey.
 struct StreamedKey {

gtsam/linear/GaussianBayesNet.cpp

@@ -26,9 +26,6 @@
 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
@@ -76,15 +73,6 @@ 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
   {

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) const;
+    VectorValues sample(std::mt19937_64* rng = nullptr) const;
 
     /**
      * Sample from an incomplete BayesNet, given missing variables
@@ -140,13 +140,7 @@ namespace gtsam {
      *   VectorValues given = ...;
      *   auto sample = gbn.sample(given, &rng);
      */
-    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;
+    VectorValues sample(const VectorValues& given, std::mt19937_64* rng = nullptr) const;
 
     /**
      * Return ordering corresponding to a topological sort.

gtsam/linear/GaussianConditional.cpp

@@ -15,11 +15,12 @@
  * @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
@@ -34,9 +35,6 @@
 #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 {
@@ -347,6 +345,10 @@ 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 +361,7 @@ namespace gtsam {
       throw std::invalid_argument(
           "sample() can only be invoked on no-parent prior");
     VectorValues values;
-    return sample(values);
-  }
-
-  /* ************************************************************************ */
-  VectorValues GaussianConditional::sample() const {
-    return sample(&kRandomNumberGenerator);
-  }
-
-  VectorValues GaussianConditional::sample(const VectorValues& given) const {
-    return sample(given, &kRandomNumberGenerator);
+    return sample(values, rng);
   }
 
   /* ************************************************************************ */

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;
-
-    /// Sample, use default rng
-    VectorValues sample() const;
-
-    /// Sample with given values, use default rng
-    VectorValues sample(const VectorValues& parentsValues) const;
+                        std::mt19937_64* rng = nullptr) const;
 
     /// @}
     /// @name Linear algebra.

gtsam/linear/linear.i

@@ -560,10 +560,9 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
       const gtsam::VectorValues& frontalValues) const;
   gtsam::JacobianFactor* likelihood(gtsam::Vector frontal) 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;
+  gtsam::VectorValues sample(std::mt19937_64 @rng = nullptr) const;
+  gtsam::VectorValues sample(const gtsam::VectorValues& parents,
+                             std::mt19937_64 @rng = nullptr) const;
 
   // Advanced Interface
   gtsam::VectorValues solveOtherRHS(const gtsam::VectorValues& parents,
@@ -630,8 +629,9 @@ virtual class GaussianBayesNet {
   gtsam::VectorValues optimize(const gtsam::VectorValues& given) const;
   gtsam::VectorValues optimizeGradientSearch() const;
 
-  gtsam::VectorValues sample(const gtsam::VectorValues& given) const;
-  gtsam::VectorValues sample() 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 backSubstitute(const gtsam::VectorValues& gx) const;
   gtsam::VectorValues backSubstituteTranspose(const gtsam::VectorValues& gx) const;
 

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;
@@ -465,8 +465,10 @@ TEST(GaussianConditional, sample) {
   auto actual3 = conditional.sample(given, &rng);
   EXPECT_LONGS_EQUAL(1, actual2.size());
   // regressions
-#if __APPLE__ || _WIN32
+#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));
 #elif __linux__
   EXPECT(assert_equal(Vector2(30.9809331, 64.9927588), actual2[X(0)], 1e-5));
 #endif

gtsam/nonlinear/Values-inl.h

@@ -27,8 +27,6 @@
 #include <utility>
 #include <gtsam/nonlinear/Values.h>
 
-#include <gtsam/nonlinear/Values.h> // Only so Eclipse finds class definition
-
 namespace gtsam {
 
 

gtsam/sfm/SfmTrack.h

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

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 kRandomNumberGenerator(42);
+static std::mt19937 kPRNG(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(kRandomNumberGenerator);
+  return initializeRandomly(kPRNG);
 }
 
 /* ************************************************************************* */
@@ -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, kRandomNumberGenerator);
+  return initializeRandomlyAt(p, kPRNG);
 }
 
 /* ************************************************************************* */

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 kRandomNumberGenerator(42);
+static std::mt19937 kPRNG(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,
-                            &kRandomNumberGenerator, initialValues);
+                            &kPRNG, initialValues);
 }
 
 Values TranslationRecovery::run(

gtsam/sfm/tests/testShonanAveraging.cpp

@@ -45,7 +45,7 @@ ShonanAveraging3 fromExampleName(
 
 static const ShonanAveraging3 kShonan = fromExampleName("toyExample.g2o");
 
-static std::mt19937 kRandomNumberGenerator(42);
+static std::mt19937 kPRNG(42);
 
 /* ************************************************************************* */
 TEST(ShonanAveraging3, checkConstructor) {
@@ -78,7 +78,7 @@ TEST(ShonanAveraging3, buildGraphAt) {
 
 /* ************************************************************************* */
 TEST(ShonanAveraging3, checkOptimality) {
-  const Values randomRotations = kShonan.initializeRandomly(kRandomNumberGenerator);
+  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
   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(kRandomNumberGenerator);
+  initial = subgraphShonan.initializeRandomly(kPRNG);
 
   // 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(kRandomNumberGenerator);
+  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
   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(kRandomNumberGenerator);
+  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
   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(kRandomNumberGenerator);
+  const Values randomRotations = kShonan.initializeRandomly(kPRNG);
   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(kRandomNumberGenerator); 
+  auto initial = kShonan.initializeRandomly(kPRNG); 
   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(kRandomNumberGenerator); 
+  auto initial = shonan.initializeRandomly(kPRNG); 
   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(kRandomNumberGenerator); 
+  auto initial = shonan.initializeRandomly(kPRNG); 
   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(kRandomNumberGenerator); 
+  auto initial = shonan.initializeRandomly(kPRNG); 
   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(kRandomNumberGenerator);
+  auto initial = shonan.initializeRandomly(kPRNG);
   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!

package.xml

@@ -23,7 +23,6 @@
 
   <buildtool_depend>cmake</buildtool_depend>
 
-  <depend>boost</depend>
   <depend>eigen</depend>
   <depend>tbb</depend>
 

python/gtsam/preamble/inference.h

@@ -10,3 +10,5 @@
  * 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>