Finish 4.2a0

Author: dellaert

.github/workflows/build-windows.yml

@@ -26,7 +26,11 @@ jobs:
             windows-2019-cl,
           ]
 
-        build_type: [Debug, Release]
+        build_type: [
+            Debug,
+            #TODO(Varun) The release build takes over 2.5 hours, need to figure out why.
+            # Release
+          ]
         build_unstable: [ON]
         include:
           #TODO This build fails, need to understand why.
@@ -90,13 +94,18 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v2
 
-      - name: Build
+      - name: Configuration
         run: |
           cmake -E remove_directory build
           cmake -B build -S . -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF -DBOOST_ROOT="${env:BOOST_ROOT}" -DBOOST_INCLUDEDIR="${env:BOOST_ROOT}\boost\include" -DBOOST_LIBRARYDIR="${env:BOOST_ROOT}\lib"
-          cmake --build build --config ${{ matrix.build_type }} --target gtsam
-          cmake --build build --config ${{ matrix.build_type }} --target gtsam_unstable
-          cmake --build build --config ${{ matrix.build_type }} --target wrap
-          cmake --build build --config ${{ matrix.build_type }} --target check.base
-          cmake --build build --config ${{ matrix.build_type }} --target check.base_unstable
-          cmake --build build --config ${{ matrix.build_type }} --target check.linear
+
+      - name: Build
+        run: |
+          # Since Visual Studio is a multi-generator, we need to use --config
+          # https://stackoverflow.com/a/24470998/1236990
+          cmake --build build -j 4 --config ${{ matrix.build_type }} --target gtsam
+          cmake --build build -j 4 --config ${{ matrix.build_type }} --target gtsam_unstable
+          cmake --build build -j 4 --config ${{ matrix.build_type }} --target wrap
+          cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.base
+          cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.base_unstable
+          cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.linear

CMakeLists.txt

@@ -9,12 +9,18 @@ endif()
 
 # Set the version number for the library
 set (GTSAM_VERSION_MAJOR 4)
-set (GTSAM_VERSION_MINOR 1)
-set (GTSAM_VERSION_PATCH 1)
+set (GTSAM_VERSION_MINOR 2)
+set (GTSAM_VERSION_PATCH 0)
+set (GTSAM_PRERELEASE_VERSION "a0")
 math (EXPR GTSAM_VERSION_NUMERIC "10000 * ${GTSAM_VERSION_MAJOR} + 100 * ${GTSAM_VERSION_MINOR} + ${GTSAM_VERSION_PATCH}")
-set (GTSAM_VERSION_STRING "${GTSAM_VERSION_MAJOR}.${GTSAM_VERSION_MINOR}.${GTSAM_VERSION_PATCH}")
 
-set (CMAKE_PROJECT_VERSION ${GTSAM_VERSION_STRING})
+if (${GTSAM_VERSION_PATCH} EQUAL 0)
+    set (GTSAM_VERSION_STRING "${GTSAM_VERSION_MAJOR}.${GTSAM_VERSION_MINOR}${GTSAM_PRERELEASE_VERSION}")
+else()
+    set (GTSAM_VERSION_STRING "${GTSAM_VERSION_MAJOR}.${GTSAM_VERSION_MINOR}.${GTSAM_VERSION_PATCH}${GTSAM_PRERELEASE_VERSION}")
+endif()
+message(STATUS "GTSAM Version: ${GTSAM_VERSION_STRING}")
+
 set (CMAKE_PROJECT_VERSION_MAJOR ${GTSAM_VERSION_MAJOR})
 set (CMAKE_PROJECT_VERSION_MINOR ${GTSAM_VERSION_MINOR})
 set (CMAKE_PROJECT_VERSION_PATCH ${GTSAM_VERSION_PATCH})
@@ -87,6 +93,13 @@ if(GTSAM_BUILD_PYTHON OR GTSAM_INSTALL_MATLAB_TOOLBOX)
         CACHE STRING "The Python version to use for wrapping")
     # Set the include directory for matlab.h
     set(GTWRAP_INCLUDE_NAME "wrap")
+
+    # Copy matlab.h to the correct folder.
+    configure_file(${PROJECT_SOURCE_DIR}/wrap/matlab.h
+               ${PROJECT_BINARY_DIR}/wrap/matlab.h COPYONLY)
+    # Add the include directories so that matlab.h can be found
+    include_directories("${PROJECT_BINARY_DIR}" "${GTSAM_EIGEN_INCLUDE_FOR_BUILD}")
+
     add_subdirectory(wrap)
     list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/wrap/cmake")
 endif()

README.md

@@ -2,9 +2,9 @@
 
 **Important Note**
 
-As of August 1 2020, the `develop` branch is officially in "Pre 4.1" mode, and features deprecated in 4.0 have been removed. Please use the last [4.0.3 release](https://github.com/borglab/gtsam/releases/tag/4.0.3) if you need those features. 
+As of Dec 2021, the `develop` branch is officially in "Pre 4.2" mode. A great new feature we will be adding in 4.2 is *hybrid inference* a la DCSLAM (Kevin Doherty et al) and we envision several API-breaking changes will happen in the discrete folder.
 
-However, most are easily converted and can be tracked down (in 4.0.3) by disabling the cmake flag `GTSAM_ALLOW_DEPRECATED_SINCE_V4`.
+In addition, features deprecated in 4.1 will be removed. Please use the last [4.1.1 release](https://github.com/borglab/gtsam/releases/tag/4.1.1) if you need those features. However, most (not all, unfortunately) are easily converted and can be tracked down (in 4.1.1) by disabling the cmake flag `GTSAM_ALLOW_DEPRECATED_SINCE_V41`.
 
 ## What is GTSAM?
 

examples/DiscreteBayesNetExample.cpp

@@ -56,8 +56,8 @@ int main(int argc, char **argv) {
   DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
 
   // solve
-  DiscreteFactor::sharedValues mpe = chordal->optimize();
-  GTSAM_PRINT(*mpe);
+  auto mpe = chordal->optimize();
+  GTSAM_PRINT(mpe);
 
   // We can also build a Bayes tree (directed junction tree).
   // The elimination order above will do fine:
@@ -70,14 +70,14 @@ int main(int argc, char **argv) {
 
   // solve again, now with evidence
   DiscreteBayesNet::shared_ptr chordal2 = fg.eliminateSequential(ordering);
-  DiscreteFactor::sharedValues mpe2 = chordal2->optimize();
-  GTSAM_PRINT(*mpe2);
+  auto mpe2 = chordal2->optimize();
+  GTSAM_PRINT(mpe2);
 
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
-    DiscreteFactor::sharedValues sample = chordal2->sample();
-    GTSAM_PRINT(*sample);
+    auto sample = chordal2->sample();
+    GTSAM_PRINT(sample);
   }
   return 0;
 }

examples/DiscreteBayesNet_FG.cpp

@@ -33,11 +33,11 @@ using namespace gtsam;
 int main(int argc, char **argv) {
   // Define keys and a print function
   Key C(1), S(2), R(3), W(4);
-  auto print = [=](DiscreteFactor::sharedValues values) {
-    cout << boolalpha << "Cloudy = " << static_cast<bool>((*values)[C])
-         << "  Sprinkler = " << static_cast<bool>((*values)[S])
-         << "  Rain = " << boolalpha << static_cast<bool>((*values)[R])
-         << "  WetGrass = " << static_cast<bool>((*values)[W]) << endl;
+  auto print = [=](const DiscreteFactor::Values& values) {
+    cout << boolalpha << "Cloudy = " << static_cast<bool>(values.at(C))
+         << "  Sprinkler = " << static_cast<bool>(values.at(S))
+         << "  Rain = " << boolalpha << static_cast<bool>(values.at(R))
+         << "  WetGrass = " << static_cast<bool>(values.at(W)) << endl;
   };
 
   // We assume binary state variables
@@ -85,7 +85,7 @@ int main(int argc, char **argv) {
         }
 
   // "Most Probable Explanation", i.e., configuration with largest value
-  DiscreteFactor::sharedValues mpe = graph.eliminateSequential()->optimize();
+  auto mpe = graph.eliminateSequential()->optimize();
   cout << "\nMost Probable Explanation (MPE):" << endl;
   print(mpe);
 
@@ -97,7 +97,7 @@ int main(int argc, char **argv) {
 
   // solve again, now with evidence
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  DiscreteFactor::sharedValues mpe_with_evidence = chordal->optimize();
+  auto mpe_with_evidence = chordal->optimize();
 
   cout << "\nMPE given C=0:" << endl;
   print(mpe_with_evidence);
@@ -113,7 +113,7 @@ int main(int argc, char **argv) {
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
-    DiscreteFactor::sharedValues sample = chordal->sample();
+    auto sample = chordal->sample();
     print(sample);
   }
   return 0;

examples/FisheyeExample.cpp

@@ -122,8 +122,7 @@ int main(int argc, char *argv[]) {
   std::cout << "initial error=" << graph.error(initialEstimate) << std::endl;
   std::cout << "final error=" << graph.error(result) << std::endl;
 
-  std::ofstream os("examples/vio_batch.dot");
-  graph.saveGraph(os, result);
+  graph.saveGraph("examples/vio_batch.dot", result);
 
   return 0;
 }

examples/HMMExample.cpp

@@ -66,14 +66,14 @@ int main(int argc, char **argv) {
   chordal->print("Eliminated");
 
   // solve
-  DiscreteFactor::sharedValues mpe = chordal->optimize();
-  GTSAM_PRINT(*mpe);
+  auto mpe = chordal->optimize();
+  GTSAM_PRINT(mpe);
 
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t k = 0; k < 10; k++) {
-    DiscreteFactor::sharedValues sample = chordal->sample();
-    GTSAM_PRINT(*sample);
+    auto sample = chordal->sample();
+    GTSAM_PRINT(sample);
   }
 
   // Or compute the marginals. This re-eliminates the FG into a Bayes tree

examples/Pose2SLAMExample_graphviz.cpp

@@ -60,11 +60,10 @@ int main(int argc, char** argv) {
 
   // save factor graph as graphviz dot file
   // Render to PDF using "fdp Pose2SLAMExample.dot -Tpdf > graph.pdf"
-  ofstream os("Pose2SLAMExample.dot");
-  graph.saveGraph(os, result);
+  graph.saveGraph("Pose2SLAMExample.dot", result);
 
   // Also print out to console
-  graph.saveGraph(cout, result);
+  graph.dot(cout, result);
 
   return 0;
 }

examples/UGM_chain.cpp

@@ -70,8 +70,8 @@ int main(int argc, char** argv) {
   // "Decoding", i.e., configuration with largest value
   // We use sequential variable elimination
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  DiscreteFactor::sharedValues optimalDecoding = chordal->optimize();
-  optimalDecoding->print("\nMost Probable Explanation (optimalDecoding)\n");
+  auto optimalDecoding = chordal->optimize();
+  optimalDecoding.print("\nMost Probable Explanation (optimalDecoding)\n");
 
   // "Inference" Computing marginals for each node
   // Here we'll make use of DiscreteMarginals class, which makes use of

examples/UGM_small.cpp

@@ -63,8 +63,8 @@ int main(int argc, char** argv) {
   // "Decoding", i.e., configuration with largest value (MPE)
   // We use sequential variable elimination
   DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  DiscreteFactor::sharedValues optimalDecoding = chordal->optimize();
-  optimalDecoding->print("\noptimalDecoding");
+  auto optimalDecoding = chordal->optimize();
+  GTSAM_PRINT(optimalDecoding);
 
   // "Inference" Computing marginals
   cout << "\nComputing Node Marginals .." << endl;