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Merge branch 'develop' into feature/g2o-vertices

release/4.3a0
Varun Agrawal 2020-07-24 02:32:25 -05:00
parent 7d27431572 905be415fa
commit f4c8e7580c
84 changed files with 2326 additions and 1205 deletions
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2
.travis.python.sh
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@ -34,7 +34,7 @@ cmake $CURRDIR -DCMAKE_BUILD_TYPE=Release \
make -j$(nproc) install
cd $CURRDIR/../gtsam_install/cython
cd cython
sudo $PYTHON setup.py install

17
CMakeLists.txt
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@ -10,7 +10,7 @@ endif()
# Set the version number for the library
set (GTSAM_VERSION_MAJOR 4)
set (GTSAM_VERSION_MINOR 0)
set (GTSAM_VERSION_PATCH 2)
set (GTSAM_VERSION_PATCH 3)
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}")
@ -68,8 +68,8 @@ if(GTSAM_UNSTABLE_AVAILABLE)
endif()
option(BUILD_SHARED_LIBS "Build shared gtsam library, instead of static" ON)
option(GTSAM_USE_QUATERNIONS "Enable/Disable using an internal Quaternion representation for rotations instead of rotation matrices. If enable, Rot3::EXPMAP is enforced by default." OFF)
option(GTSAM_POSE3_EXPMAP "Enable/Disable using Pose3::EXPMAP as the default mode. If disabled, Pose3::FIRST_ORDER will be used." OFF)
option(GTSAM_ROT3_EXPMAP "Ignore if GTSAM_USE_QUATERNIONS is OFF (Rot3::EXPMAP by default). Otherwise, enable Rot3::EXPMAP, or if disabled, use Rot3::CAYLEY." OFF)
option(GTSAM_POSE3_EXPMAP "Enable/Disable using Pose3::EXPMAP as the default mode. If disabled, Pose3::FIRST_ORDER will be used." ON)
option(GTSAM_ROT3_EXPMAP "Ignore if GTSAM_USE_QUATERNIONS is OFF (Rot3::EXPMAP by default). Otherwise, enable Rot3::EXPMAP, or if disabled, use Rot3::CAYLEY." ON)
option(GTSAM_ENABLE_CONSISTENCY_CHECKS "Enable/Disable expensive consistency checks" OFF)
option(GTSAM_WITH_TBB "Use Intel Threaded Building Blocks (TBB) if available" ON)
option(GTSAM_WITH_EIGEN_MKL "Eigen will use Intel MKL if available" OFF)
@ -454,12 +454,11 @@ endif()
if (GTSAM_INSTALL_CYTHON_TOOLBOX)
set(GTSAM_INSTALL_CYTHON_TOOLBOX 1)
# Set up cache options
set(GTSAM_CYTHON_INSTALL_PATH "" CACHE PATH "Cython toolbox destination, blank defaults to CMAKE_INSTALL_PREFIX/cython")
if(NOT GTSAM_CYTHON_INSTALL_PATH)
set(GTSAM_CYTHON_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/cython")
endif()
# Cython install path appended with Build type (e.g. cython, cythonDebug, etc).
# This does not override custom values set from the command line
set(GTSAM_CYTHON_INSTALL_PATH "${PROJECT_BINARY_DIR}/cython${GTSAM_BUILD_TAG}" CACHE PATH "Cython toolbox destination, blank defaults to PROJECT_BINARY_DIR/cython<GTSAM_BUILD_TAG>")
set(GTSAM_EIGENCY_INSTALL_PATH ${GTSAM_CYTHON_INSTALL_PATH}/gtsam_eigency)
add_subdirectory(cython)
add_subdirectory(cython ${GTSAM_CYTHON_INSTALL_PATH})
else()
set(GTSAM_INSTALL_CYTHON_TOOLBOX 0) # This will go into config.h
endif()
@ -607,7 +606,7 @@ endif()
message(STATUS "Cython toolbox flags ")
print_config_flag(${GTSAM_INSTALL_CYTHON_TOOLBOX} "Install Cython toolbox ")
if(GTSAM_INSTALL_CYTHON_TOOLBOX)
message(STATUS " Python version : ${GTSAM_PYTHON_VERSION}")
message(STATUS " Python version : ${GTSAM_PYTHON_VERSION}")
endif()
message(STATUS "===============================================================")

4
GTSAM-Concepts.md
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@ -72,9 +72,9 @@ A Lie group is both a manifold *and* a group. Hence, a LIE_GROUP type should imp
However, we now also need to be able to evaluate the derivatives of compose and inverse.
Hence, we have the following extra valid static functions defined in the struct `gtsam::traits<T>`:
* `r = traits<T>::Compose(p,q,Hq,Hp)`
* `r = traits<T>::Compose(p,q,Hp,Hq)`
* `q = traits<T>::Inverse(p,Hp)`
* `r = traits<T>::Between(p,q,Hq,H2p)`
* `r = traits<T>::Between(p,q,Hp,Hq)`
where above the *H* arguments stand for optional Jacobian arguments.
That makes it possible to create factors implementing priors (PriorFactor) or relations between two instances of a Lie group type (BetweenFactor).

2
appveyor.yml
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@ -1,5 +1,5 @@
# version format
version: 4.0.2-{branch}-build{build}
version: 4.0.3-{branch}-build{build}
os: Visual Studio 2019

5
cmake/GtsamBuildTypes.cmake
View File

@ -1,3 +1,8 @@
# Set cmake policy to recognize the AppleClang compiler
# independently from the Clang compiler.
if(POLICY CMP0025)
cmake_policy(SET CMP0025 NEW)
endif()
# function: list_append_cache(var [new_values ...])
# Like "list(APPEND ...)" but working for CACHE variables.

101
cmake/GtsamCythonWrap.cmake
View File

@ -41,9 +41,8 @@ execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
function(wrap_and_install_library_cython interface_header extra_imports install_path libs dependencies)
# Paths for generated files
get_filename_component(module_name "${interface_header}" NAME_WE)
set(generated_files_path "${PROJECT_BINARY_DIR}/cython/${module_name}")
set(generated_files_path "${install_path}")
wrap_library_cython("${interface_header}" "${generated_files_path}" "${extra_imports}" "${libs}" "${dependencies}")
install_cython_wrapped_library("${interface_header}" "${generated_files_path}" "${install_path}")
endfunction()
function(set_up_required_cython_packages)
@ -138,6 +137,10 @@ function(cythonize target pyx_file output_lib_we output_dir include_dirs libs in
target_link_libraries(${target} "${libs}")
endif()
add_dependencies(${target} ${target}_pyx2cpp)
if(TARGET ${python_install_target})
add_dependencies(${python_install_target} ${target})
endif()
endfunction()
# Internal function that wraps a library and compiles the wrapper
@ -150,9 +153,12 @@ function(wrap_library_cython interface_header generated_files_path extra_imports
get_filename_component(module_name "${interface_header}" NAME_WE)
# Wrap module to Cython pyx
message(STATUS "Cython wrapper generating ${module_name}.pyx")
message(STATUS "Cython wrapper generating ${generated_files_path}/${module_name}.pyx")
set(generated_pyx "${generated_files_path}/${module_name}.pyx")
file(MAKE_DIRECTORY "${generated_files_path}")
if(NOT EXISTS ${generated_files_path})
file(MAKE_DIRECTORY "${generated_files_path}")
endif()
add_custom_command(
OUTPUT ${generated_pyx}
DEPENDS ${interface_header} wrap
@ -175,46 +181,6 @@ function(wrap_library_cython interface_header generated_files_path extra_imports
COMMAND cmake -E remove_directory ${generated_files_path})
endfunction()
# Internal function that installs a wrap toolbox
function(install_cython_wrapped_library interface_header generated_files_path install_path)
get_filename_component(module_name "${interface_header}" NAME_WE)
# NOTE: only installs .pxd and .pyx and binary files (not .cpp) - the trailing slash on the directory name
# here prevents creating the top-level module name directory in the destination.
# Split up filename to strip trailing '/' in GTSAM_CYTHON_INSTALL_PATH/subdirectory if there is one
get_filename_component(location "${install_path}" PATH)
get_filename_component(name "${install_path}" NAME)
message(STATUS "Installing Cython Toolbox to ${location}${GTSAM_BUILD_TAG}/${name}") #${GTSAM_CYTHON_INSTALL_PATH}"
if(GTSAM_BUILD_TYPE_POSTFIXES)
foreach(build_type ${CMAKE_CONFIGURATION_TYPES})
string(TOUPPER "${build_type}" build_type_upper)
if(${build_type_upper} STREQUAL "RELEASE")
set(build_type_tag "") # Don't create release mode tag on installed directory
else()
set(build_type_tag "${build_type}")
endif()
install(DIRECTORY "${generated_files_path}/" DESTINATION "${location}${build_type_tag}/${name}"
CONFIGURATIONS "${build_type}"
PATTERN "build" EXCLUDE
PATTERN "CMakeFiles" EXCLUDE
PATTERN "Makefile" EXCLUDE
PATTERN "*.cmake" EXCLUDE
PATTERN "*.cpp" EXCLUDE
PATTERN "*.py" EXCLUDE)
endforeach()
else()
install(DIRECTORY "${generated_files_path}/" DESTINATION ${install_path}
PATTERN "build" EXCLUDE
PATTERN "CMakeFiles" EXCLUDE
PATTERN "Makefile" EXCLUDE
PATTERN "*.cmake" EXCLUDE
PATTERN "*.cpp" EXCLUDE
PATTERN "*.py" EXCLUDE)
endif()
endfunction()
# Helper function to install Cython scripts and handle multiple build types where the scripts
# should be installed to all build type toolboxes
#
@ -232,50 +198,7 @@ function(install_cython_scripts source_directory dest_directory patterns)
foreach(pattern ${patterns})
list(APPEND patterns_args PATTERN "${pattern}")
endforeach()
if(GTSAM_BUILD_TYPE_POSTFIXES)
foreach(build_type ${CMAKE_CONFIGURATION_TYPES})
string(TOUPPER "${build_type}" build_type_upper)
if(${build_type_upper} STREQUAL "RELEASE")
set(build_type_tag "") # Don't create release mode tag on installed directory
else()
set(build_type_tag "${build_type}")
endif()
# Split up filename to strip trailing '/' in GTSAM_CYTHON_INSTALL_PATH if there is one
get_filename_component(location "${dest_directory}" PATH)
get_filename_component(name "${dest_directory}" NAME)
install(DIRECTORY "${source_directory}" DESTINATION "${location}/${name}${build_type_tag}" CONFIGURATIONS "${build_type}"
file(COPY "${source_directory}" DESTINATION "${dest_directory}"
FILES_MATCHING ${patterns_args} PATTERN "${exclude_patterns}" EXCLUDE)
endforeach()
else()
install(DIRECTORY "${source_directory}" DESTINATION "${dest_directory}" FILES_MATCHING ${patterns_args} PATTERN "${exclude_patterns}" EXCLUDE)
endif()
endfunction()
# Helper function to install specific files and handle multiple build types where the scripts
# should be installed to all build type toolboxes
#
# Arguments:
# source_files: The source files to be installed.
# dest_directory: The destination directory to install to.
function(install_cython_files source_files dest_directory)
if(GTSAM_BUILD_TYPE_POSTFIXES)
foreach(build_type ${CMAKE_CONFIGURATION_TYPES})
string(TOUPPER "${build_type}" build_type_upper)
if(${build_type_upper} STREQUAL "RELEASE")
set(build_type_tag "") # Don't create release mode tag on installed directory
else()
set(build_type_tag "${build_type}")
endif()
# Split up filename to strip trailing '/' in GTSAM_CYTHON_INSTALL_PATH if there is one
get_filename_component(location "${dest_directory}" PATH)
get_filename_component(name "${dest_directory}" NAME)
install(FILES "${source_files}" DESTINATION "${location}/${name}${build_type_tag}" CONFIGURATIONS "${build_type}")
endforeach()
else()
install(FILES "${source_files}" DESTINATION "${dest_directory}")
endif()
endfunction()

33
cython/CMakeLists.txt
View File

@ -3,16 +3,32 @@ include(GtsamCythonWrap)
# Create the cython toolbox for the gtsam library
if (GTSAM_INSTALL_CYTHON_TOOLBOX)
# Add the new make target command
set(python_install_target python-install)
add_custom_target(${python_install_target}
COMMAND ${PYTHON_EXECUTABLE} ${GTSAM_CYTHON_INSTALL_PATH}/setup.py install
WORKING_DIRECTORY ${GTSAM_CYTHON_INSTALL_PATH})
# build and include the eigency version of eigency
add_subdirectory(gtsam_eigency)
include_directories(${PROJECT_BINARY_DIR}/cython/gtsam_eigency)
include_directories(${GTSAM_EIGENCY_INSTALL_PATH})
# Fix for error "C1128: number of sections exceeded object file format limit"
if(MSVC)
add_compile_options(/bigobj)
endif()
# wrap gtsam
# First set up all the package related files.
# This also ensures the below wrap operations work correctly.
set(CYTHON_INSTALL_REQUIREMENTS_FILE "${PROJECT_SOURCE_DIR}/cython/requirements.txt")
# Install the custom-generated __init__.py
# This makes the cython (sub-)directories into python packages, so gtsam can be found while wrapping gtsam_unstable
configure_file(${PROJECT_SOURCE_DIR}/cython/gtsam/__init__.py ${GTSAM_CYTHON_INSTALL_PATH}/gtsam/__init__.py COPYONLY)
configure_file(${PROJECT_SOURCE_DIR}/cython/gtsam_unstable/__init__.py ${GTSAM_CYTHON_INSTALL_PATH}/gtsam_unstable/__init__.py COPYONLY)
configure_file(${PROJECT_SOURCE_DIR}/cython/setup.py.in ${GTSAM_CYTHON_INSTALL_PATH}/setup.py)
# Wrap gtsam
add_custom_target(gtsam_header DEPENDS "../gtsam.h")
wrap_and_install_library_cython("../gtsam.h" # interface_header
"" # extra imports
@ -20,8 +36,9 @@ if (GTSAM_INSTALL_CYTHON_TOOLBOX)
gtsam # library to link with
"wrap;cythonize_eigency;gtsam;gtsam_header" # dependencies which need to be built before wrapping
)
add_dependencies(${python_install_target} gtsam gtsam_header)
# wrap gtsam_unstable
# Wrap gtsam_unstable
if(GTSAM_BUILD_UNSTABLE)
add_custom_target(gtsam_unstable_header DEPENDS "../gtsam_unstable/gtsam_unstable.h")
wrap_and_install_library_cython("../gtsam_unstable/gtsam_unstable.h" # interface_header
@ -30,17 +47,9 @@ if (GTSAM_INSTALL_CYTHON_TOOLBOX)
gtsam_unstable # library to link with
"gtsam_unstable;gtsam_unstable_header;cythonize_gtsam" # dependencies to be built before wrapping
)
add_dependencies(${python_install_target} gtsam_unstable gtsam_unstable_header)
endif()
file(READ "${PROJECT_SOURCE_DIR}/cython/requirements.txt" CYTHON_INSTALL_REQUIREMENTS)
file(READ "${PROJECT_SOURCE_DIR}/README.md" README_CONTENTS)
# Install the custom-generated __init__.py
# This is to make the build/cython/gtsam folder a python package, so gtsam can be found while wrapping gtsam_unstable
configure_file(${PROJECT_SOURCE_DIR}/cython/gtsam/__init__.py ${PROJECT_BINARY_DIR}/cython/gtsam/__init__.py COPYONLY)
configure_file(${PROJECT_SOURCE_DIR}/cython/gtsam_unstable/__init__.py ${PROJECT_BINARY_DIR}/cython/gtsam_unstable/__init__.py COPYONLY)
configure_file(${PROJECT_SOURCE_DIR}/cython/setup.py.in ${PROJECT_BINARY_DIR}/cython/setup.py)
install_cython_files("${PROJECT_BINARY_DIR}/cython/setup.py" "${GTSAM_CYTHON_INSTALL_PATH}")
# install scripts and tests
install_cython_scripts("${PROJECT_SOURCE_DIR}/cython/gtsam" "${GTSAM_CYTHON_INSTALL_PATH}" "*.py")
install_cython_scripts("${PROJECT_SOURCE_DIR}/cython/gtsam_unstable" "${GTSAM_CYTHON_INSTALL_PATH}" "*.py")

134
cython/README.md
View File

@ -1,43 +1,51 @@
# Python Wrapper
This is the Cython/Python wrapper around the GTSAM C++ library.
This is the Python wrapper around the GTSAM C++ library. We use Cython to generate the bindings to the underlying C++ code.
## Requirements
- If you want to build the GTSAM python library for a specific python version (eg 3.6),
use the `-DGTSAM_PYTHON_VERSION=3.6` option when running `cmake` otherwise the default interpreter will be used.
- If the interpreter is inside an environment (such as an anaconda environment or virtualenv environment),
then the environment should be active while building GTSAM.
- This wrapper needs `Cython(>=0.25.2)`, `backports_abc(>=0.5)`, and `numpy(>=1.11.0)`. These can be installed as follows:
```bash
pip install -r <gtsam_folder>/cython/requirements.txt
```
- For compatibility with GTSAM's Eigen version, it contains its own cloned version of [Eigency](https://github.com/wouterboomsma/eigency.git),
named `gtsam_eigency`, to interface between C++'s Eigen and Python's numpy.
## Install
- if you want to build the gtsam python library for a specific python version (eg 2.7), use the `-DGTSAM_PYTHON_VERSION=2.7` option when running `cmake` otherwise the default interpreter will be used.
- If the interpreter is inside an environment (such as an anaconda environment or virtualenv environment) then the environment should be active while building gtsam.
- This wrapper needs Cython(>=0.25.2), backports_abc>=0.5, and numpy. These can be installed as follows:
- Run cmake with the `GTSAM_INSTALL_CYTHON_TOOLBOX` cmake flag enabled to configure building the wrapper. The wrapped module will be built and copied to the directory defined by `GTSAM_CYTHON_INSTALL_PATH`, which is by default `<PROJECT_BINARY_DIR>/cython` in Release mode and `<PROJECT_BINARY_DIR>/cython<CMAKE_BUILD_TYPE>` for other modes.
```bash
pip install -r <gtsam_folder>/cython/requirements.txt
```
- Build GTSAM and the wrapper with `make`.
- For compatibility with gtsam's Eigen version, it contains its own cloned version of [Eigency](https://github.com/wouterboomsma/eigency.git),
named **gtsam_eigency**, to interface between C++'s Eigen and Python's numpy.
- To install, simply run `make python-install`.
- The same command can be used to install into a virtual environment if it is active.
- **NOTE**: if you don't want GTSAM to install to a system directory such as `/usr/local`, pass `-DCMAKE_INSTALL_PREFIX="./install"` to cmake to install GTSAM to a subdirectory of the build directory.
- Build and install gtsam using cmake with `GTSAM_INSTALL_CYTHON_TOOLBOX` enabled.
The wrapped module will be installed to `GTSAM_CYTHON_INSTALL_PATH`, which is
by default: `<your CMAKE_INSTALL_PREFIX>/cython`
- To use the library without installing system-wide: modify your `PYTHONPATH` to include the `GTSAM_CYTHON_INSTALL_PATH`:
```bash
export PYTHONPATH=$PYTHONPATH:<GTSAM_CYTHON_INSTALL_PATH>
```
- To install system-wide: run `make install` then navigate to `GTSAM_CYTHON_INSTALL_PATH` and run `python setup.py install`
- (the same command can be used to install into a virtual environment if it is active)
- note: if you don't want gtsam to install to a system directory such as `/usr/local`, pass `-DCMAKE_INSTALL_PREFIX="./install"` to cmake to install gtsam to a subdirectory of the build directory.
- if you run `setup.py` from the build directory rather than the installation directory, the script will warn you with the message: `setup.py is being run from an unexpected location`.
Before `make install` is run, not all the components of the package have been copied across, so running `setup.py` from the build directory would result in an incomplete package.
- You can also directly run `make python-install` without running `make`, and it will compile all the dependencies accordingly.
## Unit Tests
The Cython toolbox also has a small set of unit tests located in the
test directory. To run them:
```bash
cd <your GTSAM_CYTHON_INSTALL_PATH>
python -m unittest discover
```
```bash
cd <GTSAM_CYTHON_INSTALL_PATH>
python -m unittest discover
```
## Utils
TODO
## Examples
TODO
## Writing Your Own Scripts
@ -46,79 +54,63 @@ See the tests for examples.
### Some Important Notes:
- Vector/Matrix:
+ GTSAM expects double-precision floating point vectors and matrices.
Hence, you should pass numpy matrices with dtype=float, or 'float64'.
+ Also, GTSAM expects *column-major* matrices, unlike the default storage
scheme in numpy. Hence, you should pass column-major matrices to gtsam using
- GTSAM expects double-precision floating point vectors and matrices.
Hence, you should pass numpy matrices with `dtype=float`, or `float64`.
- Also, GTSAM expects _column-major_ matrices, unlike the default storage
scheme in numpy. Hence, you should pass column-major matrices to GTSAM using
the flag order='F'. And you always get column-major matrices back.
For more details, see: https://github.com/wouterboomsma/eigency#storage-layout---why-arrays-are-sometimes-transposed
+ Passing row-major matrices of different dtype, e.g. 'int', will also work
For more details, see [this link](https://github.com/wouterboomsma/eigency#storage-layout---why-arrays-are-sometimes-transposed).
- Passing row-major matrices of different dtype, e.g. `int`, will also work
as the wrapper converts them to column-major and dtype float for you,
using numpy.array.astype(float, order='F', copy=False).
However, this will result a copy if your matrix is not in the expected type
and storage order.
- Inner namespace: Classes in inner namespace will be prefixed by <innerNamespace>_ in Python.
Examples: noiseModel_Gaussian, noiseModel_mEstimator_Tukey
- Inner namespace: Classes in inner namespace will be prefixed by <innerNamespace>\_ in Python.
Examples: `noiseModel_Gaussian`, `noiseModel_mEstimator_Tukey`
- Casting from a base class to a derive class must be done explicitly.
Examples:
```Python
noiseBase = factor.noiseModel()
noiseGaussian = dynamic_cast_noiseModel_Gaussian_noiseModel_Base(noiseBase)
```
## Wrapping Your Own Project That Uses GTSAM
Examples:
- Set PYTHONPATH to include ${GTSAM_CYTHON_INSTALL_PATH}
+ so that it can find gtsam Cython header: gtsam/gtsam.pxd
```python
noiseBase = factor.noiseModel()
noiseGaussian = dynamic_cast_noiseModel_Gaussian_noiseModel_Base(noiseBase)
```
- In your CMakeList.txt
```cmake
find_package(GTSAM REQUIRED) # Make sure gtsam's install folder is in your PATH
set(CMAKE_MODULE_PATH "${CMAKE_MODULE_PATH}" "${GTSAM_DIR}/../GTSAMCMakeTools")
## Wrapping Custom GTSAM-based Project
# Wrap
include(GtsamCythonWrap)
include_directories(${GTSAM_EIGENCY_INSTALL_PATH})
wrap_and_install_library_cython("your_project_interface.h"
"from gtsam.gtsam cimport *" # extra import of gtsam/gtsam.pxd Cython header
"your_install_path"
"libraries_to_link_with_the_cython_module"
"dependencies_which_need_to_be_built_before_the_wrapper"
)
#Optional: install_cython_scripts and install_cython_files. See GtsamCythonWrap.cmake.
```
Please refer to the template project and the corresponding tutorial available [here](https://github.com/borglab/GTSAM-project-python).
## KNOWN ISSUES
- Doesn't work with python3 installed from homebrew
- size-related issue: can only wrap up to a certain number of classes: up to mEstimator!
- Guess: 64 vs 32b? disutils Compiler flags?
- Bug with Cython 0.24: instantiated factor classes return FastVector<size_t> for keys(), which can't be casted to FastVector<Key>
- Upgrading to 0.25 solves the problem
- Need default constructor and default copy constructor for almost every classes... :(
- support these constructors by default and declare "delete" for special classes?
- Doesn't work with python3 installed from homebrew
- size-related issue: can only wrap up to a certain number of classes: up to mEstimator!
- Guess: 64 vs 32b? disutils Compiler flags?
- Bug with Cython 0.24: instantiated factor classes return FastVector<size_t> for keys(), which can't be casted to FastVector<Key>
- Upgrading to 0.25 solves the problem
- Need default constructor and default copy constructor for almost every classes... :(
- support these constructors by default and declare "delete" for special classes?
### TODO
- [ ] allow duplication of parent' functions in child classes. Not allowed for now due to conflicts in Cython.
- [ ] a common header for boost shared_ptr? (Or wait until everything is switched to std::shared_ptr in gtsam?)
- [ ] a common header for boost shared_ptr? (Or wait until everything is switched to std::shared_ptr in GTSAM?)
- [ ] inner namespaces ==> inner packages?
- [ ] Wrap fixed-size Matrices/Vectors?
### Completed/Cancelled:
- [x] Fix Python tests: don't use " import <package> * ": Bad style!!! (18-03-17 19:50)
- [x] Fix Python tests: don't use " import <package> \* ": Bad style!!! (18-03-17 19:50)
- [x] Unit tests for cython wrappers @done (18-03-17 18:45) -- simply compare generated files
- [x] Wrap unstable @done (18-03-17 15:30)
- [x] Unify cython/gtsam.h and the original gtsam.h @done (18-03-17 15:30)
- [x] Unify cython/GTSAM.h and the original GTSAM.h @done (18-03-17 15:30)
- [x] 18-03-17: manage to unify the two versions by removing std container stubs from the matlab version,and keeping KeyList/KeyVector/KeySet as in the matlab version. Probably Cython 0.25 fixes the casting problem.
- [x] 06-03-17: manage to remove the requirements for default and copy constructors
- [ ] 25-11-16: Try to unify but failed. Main reasons are: Key/size_t, std containers, KeyVector/KeyList/KeySet. Matlab doesn't need to know about Key, but I can't make Cython to ignore Key as it couldn't cast KeyVector, i.e. FastVector<Key>, to FastVector<size_t>.
- [ ] Marginal and JointMarginal: revert changes @failed (17-03-17 11:00) -- Cython does need a default constructor! It produces cpp code like this: ```gtsam::JointMarginal __pyx_t_1;``` Users don't have to wrap this constructor, however.
- [ ] Marginal and JointMarginal: revert changes @failed (17-03-17 11:00) -- Cython does need a default constructor! It produces cpp code like this: `GTSAM::JointMarginal __pyx_t_1;` Users don't have to wrap this constructor, however.
- [x] Convert input numpy Matrix/Vector to float dtype and storage order 'F' automatically, cannot crash! @done (15-03-17 13:00)
- [x] Remove requirements.txt - Frank: don't bother with only 2 packages and a special case for eigency! @done (08-03-17 10:30)
- [x] CMake install script @done (25-11-16 02:30)
@ -132,7 +124,7 @@ wrap_and_install_library_cython("your_project_interface.h"
- [x] Casting from parent and grandparents @done (16-11-16 17:00)
- [x] Allow overloading constructors. The current solution is annoying!!! @done (16-11-16 17:00)
- [x] Support "print obj" @done (16-11-16 17:00)
- [x] methods for FastVector: at, [], ... @done (16-11-16 17:00)
- [x] methods for FastVector: at, [], ... @done (16-11-16 17:00)
- [x] Cython: Key and size_t: traits<size_t> doesn't exist @done (16-09-12 18:34)
- [x] KeyVector, KeyList, KeySet... @done (16-09-13 17:19)
- [x] [Nice to have] parse typedef @done (16-09-13 17:19)

13
cython/gtsam/tests/test_NonlinearOptimizer.py
View File

@ -17,7 +17,8 @@ import unittest
import gtsam
from gtsam import (DoglegOptimizer, DoglegParams, GaussNewtonOptimizer,
GaussNewtonParams, LevenbergMarquardtOptimizer,
LevenbergMarquardtParams, NonlinearFactorGraph, Ordering,
LevenbergMarquardtParams, PCGSolverParameters,
DummyPreconditionerParameters, NonlinearFactorGraph, Ordering,
Point2, PriorFactorPoint2, Values)
from gtsam.utils.test_case import GtsamTestCase
@ -61,6 +62,16 @@ class TestScenario(GtsamTestCase):
fg, initial_values, lmParams).optimize()
self.assertAlmostEqual(0, fg.error(actual2))
# Levenberg-Marquardt
lmParams = LevenbergMarquardtParams.CeresDefaults()
lmParams.setLinearSolverType("ITERATIVE")
cgParams = PCGSolverParameters()
cgParams.setPreconditionerParams(DummyPreconditionerParameters())
lmParams.setIterativeParams(cgParams)
actual2 = LevenbergMarquardtOptimizer(
fg, initial_values, lmParams).optimize()
self.assertAlmostEqual(0, fg.error(actual2))
# Dogleg
dlParams = DoglegParams()
dlParams.setOrdering(ordering)

15
cython/gtsam/tests/test_logging_optimizer.py
View File

@ -43,6 +43,11 @@ class TestOptimizeComet(GtsamTestCase):
self.optimizer = gtsam.GaussNewtonOptimizer(
graph, initial, self.params)
self.lmparams = gtsam.LevenbergMarquardtParams()
self.lmoptimizer = gtsam.LevenbergMarquardtOptimizer(
graph, initial, self.lmparams
)
# setup output capture
self.capturedOutput = StringIO()
sys.stdout = self.capturedOutput
@ -65,6 +70,16 @@ class TestOptimizeComet(GtsamTestCase):
actual = self.optimizer.values()
self.gtsamAssertEquals(actual.atRot3(KEY), self.expected, tol=1e-6)
def test_lm_simple_printing(self):
"""Make sure we are properly terminating LM"""
def hook(_, error):
print(error)
gtsam_optimize(self.lmoptimizer, self.lmparams, hook)
actual = self.lmoptimizer.values()
self.gtsamAssertEquals(actual.atRot3(KEY), self.expected, tol=1e-6)
@unittest.skip("Not a test we want run every time, as needs comet.ml account")
def test_comet(self):
"""Test with a comet hook."""

4
cython/gtsam/utils/logging_optimizer.py
View File

@ -46,5 +46,7 @@ def gtsam_optimize(optimizer,
def check_convergence(optimizer, current_error, new_error):
return (optimizer.iterations() >= params.getMaxIterations()) or (
gtsam.checkConvergence(params.getRelativeErrorTol(), params.getAbsoluteErrorTol(), params.getErrorTol(),
current_error, new_error))
current_error, new_error)) or (
isinstance(optimizer, gtsam.LevenbergMarquardtOptimizer) and optimizer.lambda_() > params.getlambdaUpperBound())
optimize(optimizer, check_convergence, hook)
return optimizer.values()

21
cython/gtsam_eigency/CMakeLists.txt
View File

@ -4,11 +4,11 @@ include(GtsamCythonWrap)
# so that the cython-generated header "conversions_api.h" can be found when cythonizing eigency's core
# and eigency's cython pxd headers can be found when cythonizing gtsam
file(COPY "." DESTINATION ".")
set(OUTPUT_DIR "${PROJECT_BINARY_DIR}/cython/gtsam_eigency")
set(OUTPUT_DIR "${GTSAM_CYTHON_INSTALL_PATH}/gtsam_eigency")
set(EIGENCY_INCLUDE_DIR ${OUTPUT_DIR})
# This is to make the build/cython/gtsam_eigency folder a python package
configure_file(__init__.py.in ${PROJECT_BINARY_DIR}/cython/gtsam_eigency/__init__.py)
configure_file(__init__.py.in ${OUTPUT_DIR}/__init__.py)
# include eigency headers
include_directories(${EIGENCY_INCLUDE_DIR})
@ -16,8 +16,8 @@ include_directories(${EIGENCY_INCLUDE_DIR})
# Cythonize and build eigency
message(STATUS "Cythonize and build eigency")
# Important trick: use "../gtsam_eigency/conversions.pyx" to let cython know that the conversions module is
# a part of the gtsam_eigency package and generate the function call import_gtsam_igency__conversions()
# in conversions_api.h correctly!!!
# a part of the gtsam_eigency package and generate the function call import_gtsam_eigency__conversions()
# in conversions_api.h correctly!
cythonize(cythonize_eigency_conversions "../gtsam_eigency/conversions.pyx" "conversions"
"${OUTPUT_DIR}" "${EIGENCY_INCLUDE_DIR}" "" "" "")
cythonize(cythonize_eigency_core "../gtsam_eigency/core.pyx" "core"
@ -37,13 +37,6 @@ add_dependencies(cythonize_eigency_core cythonize_eigency_conversions)
add_custom_target(cythonize_eigency)
add_dependencies(cythonize_eigency cythonize_eigency_conversions cythonize_eigency_core)
# install
install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
DESTINATION "${GTSAM_CYTHON_INSTALL_PATH}${GTSAM_BUILD_TAG}"
PATTERN "CMakeLists.txt" EXCLUDE
PATTERN "__init__.py.in" EXCLUDE)
install(TARGETS cythonize_eigency_core cythonize_eigency_conversions
DESTINATION "${GTSAM_CYTHON_INSTALL_PATH}${GTSAM_BUILD_TAG}/gtsam_eigency")
install(FILES ${OUTPUT_DIR}/conversions_api.h DESTINATION ${GTSAM_CYTHON_INSTALL_PATH}${GTSAM_BUILD_TAG}/gtsam_eigency)
configure_file(__init__.py.in ${OUTPUT_DIR}/__init__.py)
install(FILES ${OUTPUT_DIR}/__init__.py DESTINATION ${GTSAM_CYTHON_INSTALL_PATH}${GTSAM_BUILD_TAG}/gtsam_eigency)
if(TARGET ${python_install_target})
add_dependencies(${python_install_target} cythonize_eigency)
endif()

27
cython/setup.py.in
View File

@ -7,6 +7,22 @@ except ImportError:
packages = find_packages()
package_data = {
package:
[f for f in os.listdir(package.replace('.', os.path.sep)) if os.path.splitext(f)[1] in ('.so', '.pyd')]
for package in packages
}
cython_install_requirements = open("${CYTHON_INSTALL_REQUIREMENTS_FILE}").readlines()
install_requires = [line.strip() \
for line in cython_install_requirements \
if len(line.strip()) > 0 and not line.strip().startswith('#')
]
# Cleaner to read in the contents rather than copy them over.
readme_contents = open("${PROJECT_SOURCE_DIR}/README.md").read()
setup(
name='gtsam',
description='Georgia Tech Smoothing And Mapping library',
@ -16,7 +32,7 @@ setup(
author_email='frank.dellaert@gtsam.org',
license='Simplified BSD license',
keywords='slam sam robotics localization mapping optimization',
long_description='''${README_CONTENTS}''',
long_description=readme_contents,
long_description_content_type='text/markdown',
python_requires='>=2.7',
# https://pypi.org/pypi?%3Aaction=list_classifiers
@ -34,11 +50,6 @@ setup(
],
packages=packages,
package_data={package:
[f for f in os.listdir(package.replace('.', os.path.sep)) if os.path.splitext(f)[1] in ('.so', '.pyd')]
for package in packages
},
install_requires=[line.strip() for line in '''
${CYTHON_INSTALL_REQUIREMENTS}
'''.splitlines() if len(line.strip()) > 0 and not line.strip().startswith('#')]
package_data=package_data,
install_requires=install_requires
)

BIN
doc/robust.pdf Normal file
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21
docker/README.md Normal file
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@ -0,0 +1,21 @@
# Instructions
Build all docker images, in order:
```bash
(cd ubuntu-boost-tbb && ./build.sh)
(cd ubuntu-gtsam && ./build.sh)
(cd ubuntu-gtsam-python && ./build.sh)
(cd ubuntu-gtsam-python-vnc && ./build.sh)
```
Then launch with:
docker run -p 5900:5900 dellaert/ubuntu-gtsam-python-vnc:bionic
Then open a remote VNC X client, for example:
sudo apt-get install tigervnc-viewer
xtigervncviewer :5900

18
docker/ubuntu-boost-tbb-eigen3/Dockerfile
View File

@ -1,18 +0,0 @@
# Get the base Ubuntu image from Docker Hub
FROM ubuntu:bionic
# Update apps on the base image
RUN apt-get -y update && apt-get install -y
# Install C++
RUN apt-get -y install build-essential
# Install boost and cmake
RUN apt-get -y install libboost-all-dev cmake
# Install TBB
RUN apt-get -y install libtbb-dev
# Install latest Eigen
RUN apt-get install -y libeigen3-dev

19
docker/ubuntu-boost-tbb/Dockerfile Normal file
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@ -0,0 +1,19 @@
# Basic Ubuntu 18.04 image with Boost and TBB installed. To be used for building further downstream packages.
# Get the base Ubuntu image from Docker Hub
FROM ubuntu:bionic
# Disable GUI prompts
ENV DEBIAN_FRONTEND noninteractive
# Update apps on the base image
RUN apt-get -y update && apt-get -y install
# Install C++
RUN apt-get -y install build-essential apt-utils
# Install boost and cmake
RUN apt-get -y install libboost-all-dev cmake
# Install TBB
RUN apt-get -y install libtbb-dev

3
docker/ubuntu-boost-tbb/build.sh Executable file
View File

@ -0,0 +1,3 @@
# Build command for Docker image
# TODO(dellaert): use docker compose and/or cmake
docker build --no-cache -t dellaert/ubuntu-boost-tbb:bionic .

20
docker/ubuntu-gtsam-python-vnc/Dockerfile Normal file
View File

@ -0,0 +1,20 @@
# This GTSAM image connects to the host X-server via VNC to provide a Graphical User Interface for interaction.
# Get the base Ubuntu/GTSAM image from Docker Hub
FROM dellaert/ubuntu-gtsam-python:bionic
# Things needed to get a python GUI
ENV DEBIAN_FRONTEND noninteractive
RUN apt install -y python-tk
RUN python3 -m pip install matplotlib
# Install a VNC X-server, Frame buffer, and windows manager
RUN apt install -y x11vnc xvfb fluxbox
# Finally, install wmctrl needed for bootstrap script
RUN apt install -y wmctrl
# Copy bootstrap script and make sure it runs
COPY bootstrap.sh /
CMD '/bootstrap.sh'

111
docker/ubuntu-gtsam-python-vnc/bootstrap.sh Executable file
View File

@ -0,0 +1,111 @@
#!/bin/bash
# Based on: http://www.richud.com/wiki/Ubuntu_Fluxbox_GUI_with_x11vnc_and_Xvfb
main() {
log_i "Starting xvfb virtual display..."
launch_xvfb
log_i "Starting window manager..."
launch_window_manager
log_i "Starting VNC server..."
run_vnc_server
}
launch_xvfb() {
local xvfbLockFilePath="/tmp/.X1-lock"
if [ -f "${xvfbLockFilePath}" ]
then
log_i "Removing xvfb lock file '${xvfbLockFilePath}'..."
if ! rm -v "${xvfbLockFilePath}"
then
log_e "Failed to remove xvfb lock file"
exit 1
fi
fi
# Set defaults if the user did not specify envs.
export DISPLAY=${XVFB_DISPLAY:-:1}
local screen=${XVFB_SCREEN:-0}
local resolution=${XVFB_RESOLUTION:-1280x960x24}
local timeout=${XVFB_TIMEOUT:-5}
# Start and wait for either Xvfb to be fully up or we hit the timeout.
Xvfb ${DISPLAY} -screen ${screen} ${resolution} &
local loopCount=0
until xdpyinfo -display ${DISPLAY} > /dev/null 2>&1
do
loopCount=$((loopCount+1))
sleep 1
if [ ${loopCount} -gt ${timeout} ]
then
log_e "xvfb failed to start"
exit 1
fi
done
}
launch_window_manager() {
local timeout=${XVFB_TIMEOUT:-5}
# Start and wait for either fluxbox to be fully up or we hit the timeout.
fluxbox &
local loopCount=0
until wmctrl -m > /dev/null 2>&1
do
loopCount=$((loopCount+1))
sleep 1
if [ ${loopCount} -gt ${timeout} ]
then
log_e "fluxbox failed to start"
exit 1
fi
done
}
run_vnc_server() {
local passwordArgument='-nopw'
if [ -n "${VNC_SERVER_PASSWORD}" ]
then
local passwordFilePath="${HOME}/.x11vnc.pass"
if ! x11vnc -storepasswd "${VNC_SERVER_PASSWORD}" "${passwordFilePath}"
then
log_e "Failed to store x11vnc password"
exit 1
fi
passwordArgument=-"-rfbauth ${passwordFilePath}"
log_i "The VNC server will ask for a password"
else
log_w "The VNC server will NOT ask for a password"
fi
x11vnc -ncache 10 -ncache_cr -display ${DISPLAY} -forever ${passwordArgument} &
wait $!
}
log_i() {
log "[INFO] ${@}"
}
log_w() {
log "[WARN] ${@}"
}
log_e() {
log "[ERROR] ${@}"
}
log() {
echo "[$(date '+%Y-%m-%d %H:%M:%S')] ${@}"
}
control_c() {
echo ""
exit
}
trap control_c SIGINT SIGTERM SIGHUP
main
exit

4
docker/ubuntu-gtsam-python-vnc/build.sh Executable file
View File

@ -0,0 +1,4 @@
# Build command for Docker image
# TODO(dellaert): use docker compose and/or cmake
# Needs to be run in docker/ubuntu-gtsam-python-vnc directory
docker build -t dellaert/ubuntu-gtsam-python-vnc:bionic .

5
docker/ubuntu-gtsam-python-vnc/vnc.sh Executable file
View File

@ -0,0 +1,5 @@
# After running this script, connect VNC client to 0.0.0.0:5900
docker run -it \
--workdir="/usr/src/gtsam" \
-p 5900:5900 \
dellaert/ubuntu-gtsam-python-vnc:bionic

31
docker/ubuntu-gtsam-python/Dockerfile Normal file
View File

@ -0,0 +1,31 @@
# GTSAM Ubuntu image with Python wrapper support.
# Get the base Ubuntu/GTSAM image from Docker Hub
FROM dellaert/ubuntu-gtsam:bionic
# Install pip
RUN apt-get install -y python3-pip python3-dev
# Install python wrapper requirements
RUN python3 -m pip install -U -r /usr/src/gtsam/cython/requirements.txt
# Run cmake again, now with cython toolbox on
WORKDIR /usr/src/gtsam/build
RUN cmake \
-DCMAKE_BUILD_TYPE=Release \
-DGTSAM_WITH_EIGEN_MKL=OFF \
-DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \
-DGTSAM_BUILD_TIMING_ALWAYS=OFF \
-DGTSAM_BUILD_TESTS=OFF \
-DGTSAM_INSTALL_CYTHON_TOOLBOX=ON \
-DGTSAM_PYTHON_VERSION=3\
..
# Build again, as ubuntu-gtsam image cleaned
RUN make -j4 install && make clean
# Needed to run python wrapper:
RUN echo 'export PYTHONPATH=/usr/local/cython/:$PYTHONPATH' >> /root/.bashrc
# Run bash
CMD ["bash"]

3
docker/ubuntu-gtsam-python/build.sh Executable file
View File

@ -0,0 +1,3 @@
# Build command for Docker image
# TODO(dellaert): use docker compose and/or cmake
docker build --no-cache -t dellaert/ubuntu-gtsam-python:bionic .

36
docker/ubuntu-gtsam/Dockerfile Normal file
View File

@ -0,0 +1,36 @@
# Ubuntu image with GTSAM installed. Configured with Boost and TBB support.
# Get the base Ubuntu image from Docker Hub
FROM dellaert/ubuntu-boost-tbb:bionic
# Install git
RUN apt-get update && \
apt-get install -y git
# Install compiler
RUN apt-get install -y build-essential
# Clone GTSAM (develop branch)
WORKDIR /usr/src/
RUN git clone --single-branch --branch develop https://github.com/borglab/gtsam.git
# Change to build directory. Will be created automatically.
WORKDIR /usr/src/gtsam/build
# Run cmake
RUN cmake \
-DCMAKE_BUILD_TYPE=Release \
-DGTSAM_WITH_EIGEN_MKL=OFF \
-DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \
-DGTSAM_BUILD_TIMING_ALWAYS=OFF \
-DGTSAM_BUILD_TESTS=OFF \
-DGTSAM_INSTALL_CYTHON_TOOLBOX=OFF \
..
# Build
RUN make -j4 install && make clean
# Needed to link with GTSAM
RUN echo 'export LD_LIBRARY_PATH=/usr/local/lib:LD_LIBRARY_PATH' >> /root/.bashrc
# Run bash
CMD ["bash"]

3
docker/ubuntu-gtsam/build.sh Executable file
View File

@ -0,0 +1,3 @@
# Build command for Docker image
# TODO(dellaert): use docker compose and/or cmake
docker build --no-cache -t dellaert/ubuntu-gtsam:bionic .

83
examples/DiscreteBayesNetExample.cpp Normal file
View File

@ -0,0 +1,83 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file DiscreteBayesNetExample.cpp
* @brief Discrete Bayes Net example with famous Asia Bayes Network
* @author Frank Dellaert
* @date JULY 10, 2020
*/
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteMarginals.h>
#include <gtsam/inference/BayesNet.h>
#include <iomanip>
using namespace std;
using namespace gtsam;
int main(int argc, char **argv) {
DiscreteBayesNet asia;
DiscreteKey Asia(0, 2), Smoking(4, 2), Tuberculosis(3, 2), LungCancer(6, 2),
Bronchitis(7, 2), Either(5, 2), XRay(2, 2), Dyspnea(1, 2);
asia.add(Asia % "99/1");
asia.add(Smoking % "50/50");
asia.add(Tuberculosis | Asia = "99/1 95/5");
asia.add(LungCancer | Smoking = "99/1 90/10");
asia.add(Bronchitis | Smoking = "70/30 40/60");
asia.add((Either | Tuberculosis, LungCancer) = "F T T T");
asia.add(XRay | Either = "95/5 2/98");
asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
// print
vector<string> pretty = {"Asia", "Dyspnea", "XRay", "Tuberculosis",
"Smoking", "Either", "LungCancer", "Bronchitis"};
auto formatter = [pretty](Key key) { return pretty[key]; };
asia.print("Asia", formatter);
// Convert to factor graph
DiscreteFactorGraph fg(asia);
// Create solver and eliminate
Ordering ordering;
ordering += Key(0), Key(1), Key(2), Key(3), Key(4), Key(5), Key(6), Key(7);
DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
// solve
DiscreteFactor::sharedValues mpe = chordal->optimize();
GTSAM_PRINT(*mpe);
// We can also build a Bayes tree (directed junction tree).
// The elimination order above will do fine:
auto bayesTree = fg.eliminateMultifrontal(ordering);
bayesTree->print("bayesTree", formatter);
// add evidence, we were in Asia and we have dyspnea
fg.add(Asia, "0 1");
fg.add(Dyspnea, "0 1");
// solve again, now with evidence
DiscreteBayesNet::shared_ptr chordal2 = fg.eliminateSequential(ordering);
DiscreteFactor::sharedValues 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);
}
return 0;
}

2
examples/DiscreteBayesNet_FG.cpp
View File

@ -10,7 +10,7 @@
* -------------------------------------------------------------------------- */
/**
* @file DiscreteBayesNet_graph.cpp
* @file DiscreteBayesNet_FG.cpp
* @brief Discrete Bayes Net example using Factor Graphs
* @author Abhijit
* @date Jun 4, 2012

94
examples/HMMExample.cpp Normal file
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@ -0,0 +1,94 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2020, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file DiscreteBayesNetExample.cpp
* @brief Hidden Markov Model example, discrete.
* @author Frank Dellaert
* @date July 12, 2020
*/
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteMarginals.h>
#include <gtsam/inference/BayesNet.h>
#include <iomanip>
#include <sstream>
using namespace std;
using namespace gtsam;
int main(int argc, char **argv) {
const int nrNodes = 4;
const size_t nrStates = 3;
// Define variables as well as ordering
Ordering ordering;
vector<DiscreteKey> keys;
for (int k = 0; k < nrNodes; k++) {
DiscreteKey key_i(k, nrStates);
keys.push_back(key_i);
ordering.emplace_back(k);
}
// Create HMM as a DiscreteBayesNet
DiscreteBayesNet hmm;
// Define backbone
const string transition = "8/1/1 1/8/1 1/1/8";
for (int k = 1; k < nrNodes; k++) {
hmm.add(keys[k] | keys[k - 1] = transition);
}
// Add some measurements, not needed for all time steps!
hmm.add(keys[0] % "7/2/1");
hmm.add(keys[1] % "1/9/0");
hmm.add(keys.back() % "5/4/1");
// print
hmm.print("HMM");
// Convert to factor graph
DiscreteFactorGraph factorGraph(hmm);
// Create solver and eliminate
// This will create a DAG ordered with arrow of time reversed
DiscreteBayesNet::shared_ptr chordal =
factorGraph.eliminateSequential(ordering);
chordal->print("Eliminated");
// solve
DiscreteFactor::sharedValues 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);
}
// Or compute the marginals. This re-eliminates the FG into a Bayes tree
cout << "\nComputing Node Marginals .." << endl;
DiscreteMarginals marginals(factorGraph);
for (int k = 0; k < nrNodes; k++) {
Vector margProbs = marginals.marginalProbabilities(keys[k]);
stringstream ss;
ss << "marginal " << k;
print(margProbs, ss.str());
}
// TODO(frank): put in the glue to have DiscreteMarginals produce *arbitrary*
// joints efficiently, by the Bayes tree shortcut magic. All the code is there
// but it's not yet connected.
return 0;
}

22
examples/IMUKittiExampleGPS.cpp
View File

@ -163,7 +163,7 @@ int main(int argc, char* argv[]) {
vector<GpsMeasurement> gps_measurements;
loadKittiData(kitti_calibration, imu_measurements, gps_measurements);
Vector6 BodyP = (Vector(6) << kitti_calibration.body_ptx, kitti_calibration.body_pty, kitti_calibration.body_ptz,
Vector6 BodyP = (Vector6() << kitti_calibration.body_ptx, kitti_calibration.body_pty, kitti_calibration.body_ptz,
kitti_calibration.body_prx, kitti_calibration.body_pry, kitti_calibration.body_prz)
.finished();
auto body_T_imu = Pose3::Expmap(BodyP);
@ -173,28 +173,29 @@ int main(int argc, char* argv[]) {
}
// Configure different variables
double t_offset = gps_measurements[0].time;
// double t_offset = gps_measurements[0].time;
size_t first_gps_pose = 1;
size_t gps_skip = 10; // Skip this many GPS measurements each time
double g = 9.8;
auto w_coriolis = Vector3(); // zero vector
auto w_coriolis = Vector3::Zero(); // zero vector
// Configure noise models
auto noise_model_gps = noiseModel::Diagonal::Precisions((Vector(6) << Vector3::Constant(0),
auto noise_model_gps = noiseModel::Diagonal::Precisions((Vector6() << Vector3::Constant(0),
Vector3::Constant(1.0/0.07))
.finished());
// Set initial conditions for the estimated trajectory
// initial pose is the reference frame (navigation frame)
auto current_pose_global = Pose3(Rot3(), gps_measurements[first_gps_pose].position);
auto current_velocity_global = Vector3(); // the vehicle is stationary at the beginning at position 0,0,0
// the vehicle is stationary at the beginning at position 0,0,0
Vector3 current_velocity_global = Vector3::Zero();
auto current_bias = imuBias::ConstantBias(); // init with zero bias
auto sigma_init_x = noiseModel::Diagonal::Precisions((Vector(6) << Vector3::Constant(0),
auto sigma_init_x = noiseModel::Diagonal::Precisions((Vector6() << Vector3::Constant(0),
Vector3::Constant(1.0))
.finished());
auto sigma_init_v = noiseModel::Diagonal::Sigmas(Vector3::Constant(1000.0));
auto sigma_init_b = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.100),
auto sigma_init_b = noiseModel::Diagonal::Sigmas((Vector6() << Vector3::Constant(0.100),
Vector3::Constant(5.00e-05))
.finished());
@ -270,7 +271,7 @@ int main(int argc, char* argv[]) {
previous_bias_key, *current_summarized_measurement);
// Bias evolution as given in the IMU metadata
auto sigma_between_b = noiseModel::Diagonal::Sigmas((Vector(6) <<
auto sigma_between_b = noiseModel::Diagonal::Sigmas((Vector6() <<
Vector3::Constant(sqrt(included_imu_measurement_count) * kitti_calibration.accelerometer_bias_sigma),
Vector3::Constant(sqrt(included_imu_measurement_count) * kitti_calibration.gyroscope_bias_sigma))
.finished());
@ -342,6 +343,11 @@ int main(int argc, char* argv[]) {
auto pose_quat = pose.rotation().toQuaternion();
auto gps = gps_measurements[i].position;
cout << "State at #" << i << endl;
cout << "Pose:" << endl << pose << endl;
cout << "Velocity:" << endl << velocity << endl;
cout << "Bias:" << endl << bias << endl;
fprintf(fp_out, "%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n",
gps_measurements[i].time,
pose.x(), pose.y(), pose.z(),

35
examples/ImuFactorsExample.cpp
View File

@ -35,22 +35,28 @@
* optional arguments:
* data_csv_path path to the CSV file with the IMU data.
* -c use CombinedImuFactor
* Note: Define USE_LM to use Levenberg Marquardt Optimizer
* By default ISAM2 is used
*/
// GTSAM related includes.
#include <gtsam/inference/Symbol.h>
#include <gtsam/navigation/CombinedImuFactor.h>
#include <gtsam/navigation/GPSFactor.h>
#include <gtsam/navigation/ImuFactor.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/inference/Symbol.h>
#include <cstring>
#include <fstream>
#include <iostream>
// Uncomment the following to use Levenberg Marquardt Optimizer
// #define USE_LM
using namespace gtsam;
using namespace std;
@ -64,6 +70,17 @@ static const char use_combined_imu_flag[3] = "-c";
int main(int argc, char* argv[]) {
string data_filename;
bool use_combined_imu = false;
#ifndef USE_LM
printf("Using ISAM2\n");
ISAM2Params parameters;
parameters.relinearizeThreshold = 0.01;
parameters.relinearizeSkip = 1;
ISAM2 isam2(parameters);
#else
printf("Using Levenberg Marquardt Optimizer\n");
#endif
if (argc < 2) {
printf("using default CSV file\n");
data_filename = findExampleDataFile("imuAndGPSdata.csv");
@ -252,9 +269,19 @@ int main(int argc, char* argv[]) {
initial_values.insert(V(correction_count), prop_state.v());
initial_values.insert(B(correction_count), prev_bias);
Values result;
#ifdef USE_LM
LevenbergMarquardtOptimizer optimizer(*graph, initial_values);
Values result = optimizer.optimize();
result = optimizer.optimize();
#else
isam2.update(*graph, initial_values);
isam2.update();
result = isam2.calculateEstimate();
// reset the graph
graph->resize(0);
initial_values.clear();
#endif
// Overwrite the beginning of the preintegration for the next step.
prev_state = NavState(result.at<Pose3>(X(correction_count)),
result.at<Vector3>(V(correction_count)));

2
examples/SFMExample.cpp
View File

@ -109,7 +109,7 @@ int main(int argc, char* argv[]) {
Symbol('x', i), corrupted_pose);
}
for (size_t j = 0; j < points.size(); ++j) {
auto corrupted_point = points[j] + Point3(-0.25, 0.20, 0.15);
Point3 corrupted_point = points[j] + Point3(-0.25, 0.20, 0.15);
initialEstimate.insert<Point3>(Symbol('l', j), corrupted_point);
}
initialEstimate.print("Initial Estimates:\n");

34
gtsam.h
View File

@ -1459,7 +1459,7 @@ virtual class Null: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Fair: gtsam::noiseModel::mEstimator::Base {
@ -1470,7 +1470,7 @@ virtual class Fair: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Huber: gtsam::noiseModel::mEstimator::Base {
@ -1481,7 +1481,7 @@ virtual class Huber: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
@ -1492,7 +1492,7 @@ virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
@ -1503,7 +1503,7 @@ virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
@ -1514,7 +1514,7 @@ virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
@ -1525,7 +1525,7 @@ virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class DCS: gtsam::noiseModel::mEstimator::Base {
@ -1536,7 +1536,7 @@ virtual class DCS: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
@ -1547,7 +1547,7 @@ virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
}///\namespace mEstimator
@ -1938,6 +1938,22 @@ virtual class ConjugateGradientParameters : gtsam::IterativeOptimizationParamete
void print() const;
};
#include <gtsam/linear/Preconditioner.h>
virtual class PreconditionerParameters {
PreconditionerParameters();
};
virtual class DummyPreconditionerParameters : gtsam::PreconditionerParameters {
DummyPreconditionerParameters();
};
#include <gtsam/linear/PCGSolver.h>
virtual class PCGSolverParameters : gtsam::ConjugateGradientParameters {
PCGSolverParameters();
void print(string s);
void setPreconditionerParams(gtsam::PreconditionerParameters* preconditioner);
};
#include <gtsam/linear/SubgraphSolver.h>
virtual class SubgraphSolverParameters : gtsam::ConjugateGradientParameters {
SubgraphSolverParameters();

12
gtsam/CMakeLists.txt
View File

@ -15,7 +15,7 @@ set (gtsam_subdirs
sfm
slam
smart
navigation
navigation
)
set(gtsam_srcs)
@ -186,11 +186,17 @@ install(
list(APPEND GTSAM_EXPORTED_TARGETS gtsam)
set(GTSAM_EXPORTED_TARGETS "${GTSAM_EXPORTED_TARGETS}" PARENT_SCOPE)
# make sure that ccolamd compiles even in face of warnings
# Make sure that ccolamd compiles even in face of warnings
# and suppress all warnings from 3rd party code if Release build
if(WIN32)
set_source_files_properties(${3rdparty_srcs} PROPERTIES COMPILE_FLAGS "-w")
set_source_files_properties(${3rdparty_srcs} PROPERTIES COMPILE_FLAGS "/w")
else()
if("${CMAKE_BUILD_TYPE}" STREQUAL "Release")
# Suppress all warnings from 3rd party sources.
set_source_files_properties(${3rdparty_srcs} PROPERTIES COMPILE_FLAGS "-w")
else()
set_source_files_properties(${3rdparty_srcs} PROPERTIES COMPILE_FLAGS "-Wno-error")
endif()
endif()
# Create the matlab toolbox for the gtsam library

61
gtsam/base/Matrix.h
View File

@ -548,17 +548,47 @@ GTSAM_EXPORT Vector columnNormSquare(const Matrix &A);
namespace boost {
namespace serialization {
/**
* Ref. https://stackoverflow.com/questions/18382457/eigen-and-boostserialize/22903063#22903063
*
* Eigen supports calling resize() on both static and dynamic matrices.
* This allows for a uniform API, with resize having no effect if the static matrix
* is already the correct size.
* https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html#TutorialMatrixSizesResizing
*
* We use all the Matrix template parameters to ensure wide compatibility.
*
* eigen_typekit in ROS uses the same code
* http://docs.ros.org/lunar/api/eigen_typekit/html/eigen__mqueue_8cpp_source.html
*/
// split version - sends sizes ahead
template<class Archive>
void save(Archive & ar, const gtsam::Matrix & m, unsigned int /*version*/) {
template<class Archive,
typename Scalar_,
int Rows_,
int Cols_,
int Ops_,
int MaxRows_,
int MaxCols_>
void save(Archive & ar,
const Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_> & m,
const unsigned int /*version*/) {
const size_t rows = m.rows(), cols = m.cols();
ar << BOOST_SERIALIZATION_NVP(rows);
ar << BOOST_SERIALIZATION_NVP(cols);
ar << make_nvp("data", make_array(m.data(), m.size()));
}
template<class Archive>
void load(Archive & ar, gtsam::Matrix & m, unsigned int /*version*/) {
template<class Archive,
typename Scalar_,
int Rows_,
int Cols_,
int Ops_,
int MaxRows_,
int MaxCols_>
void load(Archive & ar,
Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_> & m,
const unsigned int /*version*/) {
size_t rows, cols;
ar >> BOOST_SERIALIZATION_NVP(rows);
ar >> BOOST_SERIALIZATION_NVP(cols);
@ -566,8 +596,25 @@ namespace boost {
ar >> make_nvp("data", make_array(m.data(), m.size()));
}
// templated version of BOOST_SERIALIZATION_SPLIT_FREE(Eigen::Matrix);
template<class Archive,
typename Scalar_,
int Rows_,
int Cols_,
int Ops_,
int MaxRows_,
int MaxCols_>
void serialize(Archive & ar,
Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_> & m,
const unsigned int version) {
split_free(ar, m, version);
}
// specialized to Matrix for MATLAB wrapper
template <class Archive>
void serialize(Archive& ar, gtsam::Matrix& m, const unsigned int version) {
split_free(ar, m, version);
}
} // namespace serialization
} // namespace boost
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Matrix);

3
gtsam/discrete/DiscreteBayesNet.h
View File

@ -20,13 +20,14 @@
#include <vector>
#include <map>
#include <boost/shared_ptr.hpp>
#include <gtsam/inference/BayesNet.h>
#include <gtsam/inference/FactorGraph.h>
#include <gtsam/discrete/DiscreteConditional.h>
namespace gtsam {
/** A Bayes net made from linear-Discrete densities */
class GTSAM_EXPORT DiscreteBayesNet: public FactorGraph<DiscreteConditional>
class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional>
{
public:

23
gtsam/discrete/DiscreteBayesTree.cpp
View File

@ -29,13 +29,32 @@ namespace gtsam {
template class BayesTreeCliqueBase<DiscreteBayesTreeClique, DiscreteFactorGraph>;
template class BayesTree<DiscreteBayesTreeClique>;
/* ************************************************************************* */
double DiscreteBayesTreeClique::evaluate(
const DiscreteConditional::Values& values) const {
// evaluate all conditionals and multiply
double result = (*conditional_)(values);
for (const auto& child : children) {
result *= child->evaluate(values);
}
return result;
}
/* ************************************************************************* */
bool DiscreteBayesTree::equals(const This& other, double tol) const
{
bool DiscreteBayesTree::equals(const This& other, double tol) const {
return Base::equals(other, tol);
}
/* ************************************************************************* */
double DiscreteBayesTree::evaluate(
const DiscreteConditional::Values& values) const {
double result = 1.0;
for (const auto& root : roots_) {
result *= root->evaluate(values);
}
return result;
}
} // \namespace gtsam

84
gtsam/discrete/DiscreteBayesTree.h
View File

@ -11,7 +11,8 @@
/**
* @file DiscreteBayesTree.h
* @brief Discrete Bayes Tree, the result of eliminating a DiscreteJunctionTree
* @brief Discrete Bayes Tree, the result of eliminating a
* DiscreteJunctionTree
* @brief DiscreteBayesTree
* @author Frank Dellaert
* @author Richard Roberts
@ -22,45 +23,62 @@
#include <gtsam/discrete/DiscreteBayesNet.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/inference/BayesTree.h>
#include <gtsam/inference/Conditional.h>
#include <gtsam/inference/BayesTreeCliqueBase.h>
#include <string>
namespace gtsam {
// Forward declarations
class DiscreteConditional;
class VectorValues;
// Forward declarations
class DiscreteConditional;
class VectorValues;
/* ************************************************************************* */
/** A clique in a DiscreteBayesTree */
class GTSAM_EXPORT DiscreteBayesTreeClique :
public BayesTreeCliqueBase<DiscreteBayesTreeClique, DiscreteFactorGraph>
{
public:
typedef DiscreteBayesTreeClique This;
typedef BayesTreeCliqueBase<DiscreteBayesTreeClique, DiscreteFactorGraph> Base;
typedef boost::shared_ptr<This> shared_ptr;
typedef boost::weak_ptr<This> weak_ptr;
DiscreteBayesTreeClique() {}
DiscreteBayesTreeClique(const boost::shared_ptr<DiscreteConditional>& conditional) : Base(conditional) {}
};
/* ************************************************************************* */
/** A clique in a DiscreteBayesTree */
class GTSAM_EXPORT DiscreteBayesTreeClique
: public BayesTreeCliqueBase<DiscreteBayesTreeClique, DiscreteFactorGraph> {
public:
typedef DiscreteBayesTreeClique This;
typedef BayesTreeCliqueBase<DiscreteBayesTreeClique, DiscreteFactorGraph>
Base;
typedef boost::shared_ptr<This> shared_ptr;
typedef boost::weak_ptr<This> weak_ptr;
DiscreteBayesTreeClique() {}
DiscreteBayesTreeClique(
const boost::shared_ptr<DiscreteConditional>& conditional)
: Base(conditional) {}
/* ************************************************************************* */
/** A Bayes tree representing a Discrete density */
class GTSAM_EXPORT DiscreteBayesTree :
public BayesTree<DiscreteBayesTreeClique>
{
private:
typedef BayesTree<DiscreteBayesTreeClique> Base;
/// print index signature only
void printSignature(
const std::string& s = "Clique: ",
const KeyFormatter& formatter = DefaultKeyFormatter) const {
conditional_->printSignature(s, formatter);
}
public:
typedef DiscreteBayesTree This;
typedef boost::shared_ptr<This> shared_ptr;
//** evaluate conditional probability of subtree for given Values */
double evaluate(const DiscreteConditional::Values& values) const;
};
/** Default constructor, creates an empty Bayes tree */
DiscreteBayesTree() {}
/* ************************************************************************* */
/** A Bayes tree representing a Discrete density */
class GTSAM_EXPORT DiscreteBayesTree
: public BayesTree<DiscreteBayesTreeClique> {
private:
typedef BayesTree<DiscreteBayesTreeClique> Base;
/** Check equality */
bool equals(const This& other, double tol = 1e-9) const;
};
public:
typedef DiscreteBayesTree This;
typedef boost::shared_ptr<This> shared_ptr;
}
/** Default constructor, creates an empty Bayes tree */
DiscreteBayesTree() {}
/** Check equality */
bool equals(const This& other, double tol = 1e-9) const;
//** evaluate probability for given Values */
double evaluate(const DiscreteConditional::Values& values) const;
};
} // namespace gtsam

78
gtsam/discrete/DiscreteConditional.cpp
View File

@ -27,6 +27,7 @@
#include <algorithm>
#include <random>
#include <stdexcept>
#include <string>
#include <vector>
using namespace std;
@ -61,16 +62,26 @@ DiscreteConditional::DiscreteConditional(const DecisionTreeFactor& joint,
}
/* ******************************************************************************** */
DiscreteConditional::DiscreteConditional(const Signature& signature) :
BaseFactor(signature.discreteKeysParentsFirst(), signature.cpt()), BaseConditional(
1) {
}
DiscreteConditional::DiscreteConditional(const Signature& signature)
: BaseFactor(signature.discreteKeys(), signature.cpt()),
BaseConditional(1) {}
/* ******************************************************************************** */
void DiscreteConditional::print(const std::string& s,
const KeyFormatter& formatter) const {
std::cout << s << std::endl;
Potentials::print(s);
void DiscreteConditional::print(const string& s,
const KeyFormatter& formatter) const {
cout << s << " P( ";
for (const_iterator it = beginFrontals(); it != endFrontals(); ++it) {
cout << formatter(*it) << " ";
}
if (nrParents()) {
cout << "| ";
for (const_iterator it = beginParents(); it != endParents(); ++it) {
cout << formatter(*it) << " ";
}
}
cout << ")";
Potentials::print("");
cout << endl;
}
/* ******************************************************************************** */
@ -173,55 +184,28 @@ size_t DiscreteConditional::solve(const Values& parentsValues) const {
/* ******************************************************************************** */
size_t DiscreteConditional::sample(const Values& parentsValues) const {
static mt19937 rng(2); // random number generator
bool debug = ISDEBUG("DiscreteConditional::sample");
static mt19937 rng(2); // random number generator
// Get the correct conditional density
ADT pFS = choose(parentsValues); // P(F|S=parentsValues)
if (debug)
GTSAM_PRINT(pFS);
ADT pFS = choose(parentsValues); // P(F|S=parentsValues)
// get cumulative distribution function (cdf)
// TODO, only works for one key now, seems horribly slow this way
// TODO(Duy): only works for one key now, seems horribly slow this way
assert(nrFrontals() == 1);
Key j = (firstFrontalKey());
size_t nj = cardinality(j);
vector<double> cdf(nj);
Key key = firstFrontalKey();
size_t nj = cardinality(key);
vector<double> p(nj);
Values frontals;
double sum = 0;
for (size_t value = 0; value < nj; value++) {
frontals[j] = value;
double pValueS = pFS(frontals); // P(F=value|S=parentsValues)
sum += pValueS; // accumulate
if (debug)
cout << sum << " ";
if (pValueS == 1) {
if (debug)
cout << "--> " << value << endl;
return value; // shortcut exit
frontals[key] = value;
p[value] = pFS(frontals); // P(F=value|S=parentsValues)
if (p[value] == 1.0) {
return value; // shortcut exit
}
cdf[value] = sum;
}
// inspired by http://www.boost.org/doc/libs/1_46_1/doc/html/boost_random/tutorial.html
uniform_real_distribution<double> dist(0, cdf.back());
size_t sampled = lower_bound(cdf.begin(), cdf.end(), dist(rng)) - cdf.begin();
if (debug)
cout << "-> " << sampled << endl;
return sampled;
return 0;
std::discrete_distribution<size_t> distribution(p.begin(), p.end());
return distribution(rng);
}
/* ******************************************************************************** */
//void DiscreteConditional::permuteWithInverse(
// const Permutation& inversePermutation) {
// IndexConditionalOrdered::permuteWithInverse(inversePermutation);
// Potentials::permuteWithInverse(inversePermutation);
//}
/* ******************************************************************************** */
}// namespace

9
gtsam/discrete/DiscreteConditional.h
View File

@ -24,6 +24,8 @@
#include <boost/shared_ptr.hpp>
#include <boost/make_shared.hpp>
#include <string>
namespace gtsam {
/**
@ -92,6 +94,13 @@ public:
/// @name Standard Interface
/// @{
/// print index signature only
void printSignature(
const std::string& s = "Discrete Conditional: ",
const KeyFormatter& formatter = DefaultKeyFormatter) const {
static_cast<const BaseConditional*>(this)->print(s, formatter);
}
/// Evaluate, just look up in AlgebraicDecisonTree
virtual double operator()(const Values& values) const {
return Potentials::operator()(values);

68
gtsam/discrete/Potentials.cpp
View File

@ -15,50 +15,52 @@
* @author Frank Dellaert
*/
#include <gtsam/discrete/Potentials.h>
#include <gtsam/discrete/DecisionTree-inl.h>
#include <gtsam/discrete/Potentials.h>
#include <boost/format.hpp>
#include <string>
using namespace std;
namespace gtsam {
// explicit instantiation
template class DecisionTree<Key, double> ;
template class AlgebraicDecisionTree<Key> ;
// explicit instantiation
template class DecisionTree<Key, double>;
template class AlgebraicDecisionTree<Key>;
/* ************************************************************************* */
double Potentials::safe_div(const double& a, const double& b) {
// cout << boost::format("%g / %g = %g\n") % a % b % ((a == 0) ? 0 : (a / b));
// The use for safe_div is when we divide the product factor by the sum factor.
// If the product or sum is zero, we accord zero probability to the event.
return (a == 0 || b == 0) ? 0 : (a / b);
}
/* ************************************************************************* */
double Potentials::safe_div(const double& a, const double& b) {
// cout << boost::format("%g / %g = %g\n") % a % b % ((a == 0) ? 0 : (a / b));
// The use for safe_div is when we divide the product factor by the sum
// factor. If the product or sum is zero, we accord zero probability to the
// event.
return (a == 0 || b == 0) ? 0 : (a / b);
}
/* ******************************************************************************** */
Potentials::Potentials() :
ADT(1.0) {
}
/* ********************************************************************************
*/
Potentials::Potentials() : ADT(1.0) {}
/* ******************************************************************************** */
Potentials::Potentials(const DiscreteKeys& keys, const ADT& decisionTree) :
ADT(decisionTree), cardinalities_(keys.cardinalities()) {
}
/* ********************************************************************************
*/
Potentials::Potentials(const DiscreteKeys& keys, const ADT& decisionTree)
: ADT(decisionTree), cardinalities_(keys.cardinalities()) {}
/* ************************************************************************* */
bool Potentials::equals(const Potentials& other, double tol) const {
return ADT::equals(other, tol);
}
/* ************************************************************************* */
bool Potentials::equals(const Potentials& other, double tol) const {
return ADT::equals(other, tol);
}
/* ************************************************************************* */
void Potentials::print(const string& s,
const KeyFormatter& formatter) const {
cout << s << "\n Cardinalities: ";
for(const DiscreteKey& key: cardinalities_)
cout << formatter(key.first) << "=" << formatter(key.second) << " ";
cout << endl;
ADT::print(" ");
}
/* ************************************************************************* */
void Potentials::print(const string& s, const KeyFormatter& formatter) const {
cout << s << "\n Cardinalities: {";
for (const DiscreteKey& key : cardinalities_)
cout << formatter(key.first) << ":" << key.second << ", ";
cout << "}" << endl;
ADT::print(" ");
}
//
// /* ************************************************************************* */
// template<class P>
@ -95,4 +97,4 @@ namespace gtsam {
/* ************************************************************************* */
} // namespace gtsam
} // namespace gtsam

18
gtsam/discrete/Signature.cpp
View File

@ -122,28 +122,30 @@ namespace gtsam {
key_(key) {
}
DiscreteKeys Signature::discreteKeysParentsFirst() const {
DiscreteKeys Signature::discreteKeys() const {
DiscreteKeys keys;
for(const DiscreteKey& key: parents_)
keys.push_back(key);
keys.push_back(key_);
for (const DiscreteKey& key : parents_) keys.push_back(key);
return keys;
}
KeyVector Signature::indices() const {
KeyVector js;
js.push_back(key_.first);
for(const DiscreteKey& key: parents_)
js.push_back(key.first);
for (const DiscreteKey& key : parents_) js.push_back(key.first);
return js;
}
vector<double> Signature::cpt() const {
vector<double> cpt;
if (table_) {
for(const Row& row: *table_)
for(const double& x: row)
cpt.push_back(x);
const size_t nrStates = table_->at(0).size();
for (size_t j = 0; j < nrStates; j++) {
for (const Row& row : *table_) {
assert(row.size() == nrStates);
cpt.push_back(row[j]);
}
}
}
return cpt;
}

4
gtsam/discrete/Signature.h
View File

@ -86,8 +86,8 @@ namespace gtsam {
return parents_;
}
/** All keys, with variable key last */
DiscreteKeys discreteKeysParentsFirst() const;
/** All keys, with variable key first */
DiscreteKeys discreteKeys() const;
/** All key indices, with variable key first */
KeyVector indices() const;

7
gtsam/discrete/tests/testAlgebraicDecisionTree.cpp
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@ -132,7 +132,7 @@ TEST(ADT, example3)
/** Convert Signature into CPT */
ADT create(const Signature& signature) {
ADT p(signature.discreteKeysParentsFirst(), signature.cpt());
ADT p(signature.discreteKeys(), signature.cpt());
static size_t count = 0;
const DiscreteKey& key = signature.key();
string dotfile = (boost::format("CPT-%03d-%d") % ++count % key.first).str();
@ -181,19 +181,20 @@ TEST(ADT, joint)
dot(joint, "Asia-ASTLBEX");
joint = apply(joint, pD, &mul);
dot(joint, "Asia-ASTLBEXD");
EXPECT_LONGS_EQUAL(346, (long)muls);
EXPECT_LONGS_EQUAL(346, muls);
gttoc_(asiaJoint);
tictoc_getNode(asiaJointNode, asiaJoint);
elapsed = asiaJointNode->secs() + asiaJointNode->wall();
tictoc_reset_();
printCounts("Asia joint");
// Form P(A,S,T,L) = P(A) P(S) P(T|A) P(L|S)
ADT pASTL = pA;
pASTL = apply(pASTL, pS, &mul);
pASTL = apply(pASTL, pT, &mul);
pASTL = apply(pASTL, pL, &mul);
// test combine
// test combine to check that P(A) = \sum_{S,T,L} P(A,S,T,L)
ADT fAa = pASTL.combine(L, &add_).combine(T, &add_).combine(S, &add_);
EXPECT(assert_equal(pA, fAa));
ADT fAb = pASTL.combine(S, &add_).combine(T, &add_).combine(L, &add_);

126
gtsam/discrete/tests/testDiscreteBayesNet.cpp
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@ -18,110 +18,135 @@
#include <gtsam/discrete/DiscreteBayesNet.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/base/Testable.h>
#include <gtsam/discrete/DiscreteMarginals.h>
#include <gtsam/base/debug.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/Vector.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/std/map.hpp>
#include <boost/assign/list_inserter.hpp>
#include <boost/assign/std/map.hpp>
using namespace boost::assign;
#include <iostream>
#include <string>
#include <vector>
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST(DiscreteBayesNet, Asia)
{
TEST(DiscreteBayesNet, bayesNet) {
DiscreteBayesNet bayesNet;
DiscreteKey Parent(0, 2), Child(1, 2);
auto prior = boost::make_shared<DiscreteConditional>(Parent % "6/4");
CHECK(assert_equal(Potentials::ADT({Parent}, "0.6 0.4"),
(Potentials::ADT)*prior));
bayesNet.push_back(prior);
auto conditional =
boost::make_shared<DiscreteConditional>(Child | Parent = "7/3 8/2");
EXPECT_LONGS_EQUAL(1, *(conditional->beginFrontals()));
Potentials::ADT expected(Child & Parent, "0.7 0.8 0.3 0.2");
CHECK(assert_equal(expected, (Potentials::ADT)*conditional));
bayesNet.push_back(conditional);
DiscreteFactorGraph fg(bayesNet);
LONGS_EQUAL(2, fg.back()->size());
// Check the marginals
const double expectedMarginal[2]{0.4, 0.6 * 0.3 + 0.4 * 0.2};
DiscreteMarginals marginals(fg);
for (size_t j = 0; j < 2; j++) {
Vector FT = marginals.marginalProbabilities(DiscreteKey(j, 2));
EXPECT_DOUBLES_EQUAL(expectedMarginal[j], FT[1], 1e-3);
EXPECT_DOUBLES_EQUAL(FT[0], 1.0 - FT[1], 1e-9);
}
}
/* ************************************************************************* */
TEST(DiscreteBayesNet, Asia) {
DiscreteBayesNet asia;
// DiscreteKey A("Asia"), S("Smoking"), T("Tuberculosis"), L("LungCancer"), B(
// "Bronchitis"), E("Either"), X("XRay"), D("Dyspnoea");
DiscreteKey A(0,2), S(4,2), T(3,2), L(6,2), B(7,2), E(5,2), X(2,2), D(1,2);
DiscreteKey Asia(0, 2), Smoking(4, 2), Tuberculosis(3, 2), LungCancer(6, 2),
Bronchitis(7, 2), Either(5, 2), XRay(2, 2), Dyspnea(1, 2);
// TODO: make a version that doesn't use the parser
asia.add(A % "99/1");
asia.add(S % "50/50");
asia.add(Asia % "99/1");
asia.add(Smoking % "50/50");
asia.add(T | A = "99/1 95/5");
asia.add(L | S = "99/1 90/10");
asia.add(B | S = "70/30 40/60");
asia.add(Tuberculosis | Asia = "99/1 95/5");
asia.add(LungCancer | Smoking = "99/1 90/10");
asia.add(Bronchitis | Smoking = "70/30 40/60");
asia.add((E | T, L) = "F T T T");
asia.add((Either | Tuberculosis, LungCancer) = "F T T T");
asia.add(X | E = "95/5 2/98");
// next lines are same as asia.add((D | E, B) = "9/1 2/8 3/7 1/9");
DiscreteConditional::shared_ptr actual =
boost::make_shared<DiscreteConditional>((D | E, B) = "9/1 2/8 3/7 1/9");
asia.push_back(actual);
// GTSAM_PRINT(asia);
asia.add(XRay | Either = "95/5 2/98");
asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
// Convert to factor graph
DiscreteFactorGraph fg(asia);
// GTSAM_PRINT(fg);
LONGS_EQUAL(3,fg.back()->size());
Potentials::ADT expected(B & D & E, "0.9 0.3 0.1 0.7 0.2 0.1 0.8 0.9");
CHECK(assert_equal(expected,(Potentials::ADT)*actual));
LONGS_EQUAL(3, fg.back()->size());
// Check the marginals we know (of the parent-less nodes)
DiscreteMarginals marginals(fg);
Vector2 va(0.99, 0.01), vs(0.5, 0.5);
EXPECT(assert_equal(va, marginals.marginalProbabilities(Asia)));
EXPECT(assert_equal(vs, marginals.marginalProbabilities(Smoking)));
// Create solver and eliminate
Ordering ordering;
ordering += Key(0),Key(1),Key(2),Key(3),Key(4),Key(5),Key(6),Key(7);
ordering += Key(0), Key(1), Key(2), Key(3), Key(4), Key(5), Key(6), Key(7);
DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
// GTSAM_PRINT(*chordal);
DiscreteConditional expected2(B % "11/9");
CHECK(assert_equal(expected2,*chordal->back()));
DiscreteConditional expected2(Bronchitis % "11/9");
EXPECT(assert_equal(expected2, *chordal->back()));
// solve
DiscreteFactor::sharedValues actualMPE = chordal->optimize();
DiscreteFactor::Values expectedMPE;
insert(expectedMPE)(A.first, 0)(D.first, 0)(X.first, 0)(T.first, 0)(S.first,
0)(E.first, 0)(L.first, 0)(B.first, 0);
insert(expectedMPE)(Asia.first, 0)(Dyspnea.first, 0)(XRay.first, 0)(
Tuberculosis.first, 0)(Smoking.first, 0)(Either.first, 0)(
LungCancer.first, 0)(Bronchitis.first, 0);
EXPECT(assert_equal(expectedMPE, *actualMPE));
// add evidence, we were in Asia and we have Dispnoea
fg.add(A, "0 1");
fg.add(D, "0 1");
// fg.product().dot("fg");
// add evidence, we were in Asia and we have dyspnea
fg.add(Asia, "0 1");
fg.add(Dyspnea, "0 1");
// solve again, now with evidence
DiscreteBayesNet::shared_ptr chordal2 = fg.eliminateSequential(ordering);
// GTSAM_PRINT(*chordal2);
DiscreteFactor::sharedValues actualMPE2 = chordal2->optimize();
DiscreteFactor::Values expectedMPE2;
insert(expectedMPE2)(A.first, 1)(D.first, 1)(X.first, 0)(T.first, 0)(S.first,
1)(E.first, 0)(L.first, 0)(B.first, 1);
insert(expectedMPE2)(Asia.first, 1)(Dyspnea.first, 1)(XRay.first, 0)(
Tuberculosis.first, 0)(Smoking.first, 1)(Either.first, 0)(
LungCancer.first, 0)(Bronchitis.first, 1);
EXPECT(assert_equal(expectedMPE2, *actualMPE2));
// now sample from it
DiscreteFactor::Values expectedSample;
SETDEBUG("DiscreteConditional::sample", false);
insert(expectedSample)(A.first, 1)(D.first, 1)(X.first, 1)(T.first, 0)(
S.first, 1)(E.first, 1)(L.first, 1)(B.first, 0);
insert(expectedSample)(Asia.first, 1)(Dyspnea.first, 1)(XRay.first, 1)(
Tuberculosis.first, 0)(Smoking.first, 1)(Either.first, 1)(
LungCancer.first, 1)(Bronchitis.first, 0);
DiscreteFactor::sharedValues actualSample = chordal2->sample();
EXPECT(assert_equal(expectedSample, *actualSample));
}
/* ************************************************************************* */
TEST_UNSAFE(DiscreteBayesNet, Sugar)
{
DiscreteKey T(0,2), L(1,2), E(2,2), D(3,2), C(8,3), S(7,2);
TEST_UNSAFE(DiscreteBayesNet, Sugar) {
DiscreteKey T(0, 2), L(1, 2), E(2, 2), C(8, 3), S(7, 2);
DiscreteBayesNet bn;
// test some mistakes
// add(bn, D);
// add(bn, D | E);
// add(bn, D | E = "blah");
// try logic
bn.add((E | T, L) = "OR");
bn.add((E | T, L) = "AND");
// // try multivalued
bn.add(C % "1/1/2");
bn.add(C | S = "1/1/2 5/2/3");
// try multivalued
bn.add(C % "1/1/2");
bn.add(C | S = "1/1/2 5/2/3");
}
/* ************************************************************************* */
@ -130,4 +155,3 @@ int main() {
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

482
gtsam/discrete/tests/testDiscreteBayesTree.cpp
View File

@ -1,261 +1,228 @@
///* ----------------------------------------------------------------------------
//
// * GTSAM Copyright 2010, Georgia Tech Research Corporation,
// * Atlanta, Georgia 30332-0415
// * All Rights Reserved
// * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
//
// * See LICENSE for the license information
//
// * -------------------------------------------------------------------------- */
//
///*
// * @file testDiscreteBayesTree.cpp
// * @date sept 15, 2012
// * @author Frank Dellaert
// */
//
//#include <gtsam/discrete/DiscreteBayesNet.h>
//#include <gtsam/discrete/DiscreteBayesTree.h>
//#include <gtsam/discrete/DiscreteFactorGraph.h>
//
//#include <boost/assign/std/vector.hpp>
//using namespace boost::assign;
//
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2020, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/*
* @file testDiscreteBayesTree.cpp
* @date sept 15, 2012
* @author Frank Dellaert
*/
#include <gtsam/base/Vector.h>
#include <gtsam/discrete/DiscreteBayesNet.h>
#include <gtsam/discrete/DiscreteBayesTree.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/inference/BayesNet.h>
#include <boost/assign/std/vector.hpp>
using namespace boost::assign;
#include <CppUnitLite/TestHarness.h>
//
//using namespace std;
//using namespace gtsam;
//
//static bool debug = false;
//
///**
// * Custom clique class to debug shortcuts
// */
////class Clique: public BayesTreeCliqueBaseOrdered<Clique, DiscreteConditional> {
////
////protected:
////
////public:
////
//// typedef BayesTreeCliqueBaseOrdered<Clique, DiscreteConditional> Base;
//// typedef boost::shared_ptr<Clique> shared_ptr;
////
//// // Constructors
//// Clique() {
//// }
//// Clique(const DiscreteConditional::shared_ptr& conditional) :
//// Base(conditional) {
//// }
//// Clique(
//// const std::pair<DiscreteConditional::shared_ptr,
//// DiscreteConditional::FactorType::shared_ptr>& result) :
//// Base(result) {
//// }
////
//// /// print index signature only
//// void printSignature(const std::string& s = "Clique: ",
//// const KeyFormatter& indexFormatter = DefaultKeyFormatter) const {
//// ((IndexConditionalOrdered::shared_ptr) conditional_)->print(s, indexFormatter);
//// }
////
//// /// evaluate value of sub-tree
//// double evaluate(const DiscreteConditional::Values & values) {
//// double result = (*(this->conditional_))(values);
//// // evaluate all children and multiply into result
//// for(boost::shared_ptr<Clique> c: children_)
//// result *= c->evaluate(values);
//// return result;
//// }
////
////};
//
////typedef BayesTreeOrdered<DiscreteConditional, Clique> DiscreteBayesTree;
////
/////* ************************************************************************* */
////double evaluate(const DiscreteBayesTree& tree,
//// const DiscreteConditional::Values & values) {
//// return tree.root()->evaluate(values);
////}
//
///* ************************************************************************* */
//
//TEST_UNSAFE( DiscreteBayesTree, thinTree ) {
//
// const int nrNodes = 15;
// const size_t nrStates = 2;
//
// // define variables
// vector<DiscreteKey> key;
// for (int i = 0; i < nrNodes; i++) {
// DiscreteKey key_i(i, nrStates);
// key.push_back(key_i);
// }
//
// // create a thin-tree Bayesnet, a la Jean-Guillaume
// DiscreteBayesNet bayesNet;
// bayesNet.add(key[14] % "1/3");
//
// bayesNet.add(key[13] | key[14] = "1/3 3/1");
// bayesNet.add(key[12] | key[14] = "3/1 3/1");
//
// bayesNet.add((key[11] | key[13], key[14]) = "1/4 2/3 3/2 4/1");
// bayesNet.add((key[10] | key[13], key[14]) = "1/4 3/2 2/3 4/1");
// bayesNet.add((key[9] | key[12], key[14]) = "4/1 2/3 F 1/4");
// bayesNet.add((key[8] | key[12], key[14]) = "T 1/4 3/2 4/1");
//
// bayesNet.add((key[7] | key[11], key[13]) = "1/4 2/3 3/2 4/1");
// bayesNet.add((key[6] | key[11], key[13]) = "1/4 3/2 2/3 4/1");
// bayesNet.add((key[5] | key[10], key[13]) = "4/1 2/3 3/2 1/4");
// bayesNet.add((key[4] | key[10], key[13]) = "2/3 1/4 3/2 4/1");
//
// bayesNet.add((key[3] | key[9], key[12]) = "1/4 2/3 3/2 4/1");
// bayesNet.add((key[2] | key[9], key[12]) = "1/4 8/2 2/3 4/1");
// bayesNet.add((key[1] | key[8], key[12]) = "4/1 2/3 3/2 1/4");
// bayesNet.add((key[0] | key[8], key[12]) = "2/3 1/4 3/2 4/1");
//
//// if (debug) {
//// GTSAM_PRINT(bayesNet);
//// bayesNet.saveGraph("/tmp/discreteBayesNet.dot");
//// }
//
// // create a BayesTree out of a Bayes net
// DiscreteBayesTree bayesTree(bayesNet);
// if (debug) {
// GTSAM_PRINT(bayesTree);
// bayesTree.saveGraph("/tmp/discreteBayesTree.dot");
// }
//
// // Check whether BN and BT give the same answer on all configurations
// // Also calculate all some marginals
// Vector marginals = zero(15);
// double joint_12_14 = 0, joint_9_12_14 = 0, joint_8_12_14 = 0, joint_8_12 = 0,
// joint82 = 0, joint12 = 0, joint24 = 0, joint45 = 0, joint46 = 0,
// joint_4_11 = 0;
// vector<DiscreteFactor::Values> allPosbValues = cartesianProduct(
// key[0] & key[1] & key[2] & key[3] & key[4] & key[5] & key[6] & key[7]
// & key[8] & key[9] & key[10] & key[11] & key[12] & key[13] & key[14]);
// for (size_t i = 0; i < allPosbValues.size(); ++i) {
// DiscreteFactor::Values x = allPosbValues[i];
// double expected = evaluate(bayesNet, x);
// double actual = evaluate(bayesTree, x);
// DOUBLES_EQUAL(expected, actual, 1e-9);
// // collect marginals
// for (size_t i = 0; i < 15; i++)
// if (x[i])
// marginals[i] += actual;
// // calculate shortcut 8 and 0
// if (x[12] && x[14])
// joint_12_14 += actual;
// if (x[9] && x[12] & x[14])
// joint_9_12_14 += actual;
// if (x[8] && x[12] & x[14])
// joint_8_12_14 += actual;
// if (x[8] && x[12])
// joint_8_12 += actual;
// if (x[8] && x[2])
// joint82 += actual;
// if (x[1] && x[2])
// joint12 += actual;
// if (x[2] && x[4])
// joint24 += actual;
// if (x[4] && x[5])
// joint45 += actual;
// if (x[4] && x[6])
// joint46 += actual;
// if (x[4] && x[11])
// joint_4_11 += actual;
// }
// DiscreteFactor::Values all1 = allPosbValues.back();
//
// Clique::shared_ptr R = bayesTree.root();
//
// // check separator marginal P(S0)
// Clique::shared_ptr c = bayesTree[0];
// DiscreteFactorGraph separatorMarginal0 = c->separatorMarginal(R,
// EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_8_12, separatorMarginal0(all1), 1e-9);
//
// // check separator marginal P(S9), should be P(14)
// c = bayesTree[9];
// DiscreteFactorGraph separatorMarginal9 = c->separatorMarginal(R,
// EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(marginals[14], separatorMarginal9(all1), 1e-9);
//
// // check separator marginal of root, should be empty
// c = bayesTree[11];
// DiscreteFactorGraph separatorMarginal11 = c->separatorMarginal(R,
// EliminateDiscrete);
// EXPECT_LONGS_EQUAL(0, separatorMarginal11.size());
//
// // check shortcut P(S9||R) to root
// c = bayesTree[9];
// DiscreteBayesNet shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_LONGS_EQUAL(0, shortcut.size());
//
// // check shortcut P(S8||R) to root
// c = bayesTree[8];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_12_14/marginals[14], evaluate(shortcut,all1),
// 1e-9);
//
// // check shortcut P(S2||R) to root
// c = bayesTree[2];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_9_12_14/marginals[14], evaluate(shortcut,all1),
// 1e-9);
//
// // check shortcut P(S0||R) to root
// c = bayesTree[0];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_8_12_14/marginals[14], evaluate(shortcut,all1),
// 1e-9);
//
// // calculate all shortcuts to root
// DiscreteBayesTree::Nodes cliques = bayesTree.nodes();
// for(Clique::shared_ptr c: cliques) {
// DiscreteBayesNet shortcut = c->shortcut(R, EliminateDiscrete);
// if (debug) {
// c->printSignature();
// shortcut.print("shortcut:");
// }
// }
//
// // Check all marginals
// DiscreteFactor::shared_ptr marginalFactor;
// for (size_t i = 0; i < 15; i++) {
// marginalFactor = bayesTree.marginalFactor(i, EliminateDiscrete);
// double actual = (*marginalFactor)(all1);
// EXPECT_DOUBLES_EQUAL(marginals[i], actual, 1e-9);
// }
//
// DiscreteBayesNet::shared_ptr actualJoint;
//
// // Check joint P(8,2) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(8, 2, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint82, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(1,2) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(1, 2, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint12, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(2,4)
// actualJoint = bayesTree.jointBayesNet(2, 4, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint24, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(4,5) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(4, 5, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint46, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(4,6) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(4, 6, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint46, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(4,11)
// actualJoint = bayesTree.jointBayesNet(4, 11, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_4_11, evaluate(*actualJoint,all1), 1e-9);
//
//}
#include <vector>
using namespace std;
using namespace gtsam;
static bool debug = false;
/* ************************************************************************* */
TEST_UNSAFE(DiscreteBayesTree, ThinTree) {
const int nrNodes = 15;
const size_t nrStates = 2;
// define variables
vector<DiscreteKey> key;
for (int i = 0; i < nrNodes; i++) {
DiscreteKey key_i(i, nrStates);
key.push_back(key_i);
}
// create a thin-tree Bayesnet, a la Jean-Guillaume
DiscreteBayesNet bayesNet;
bayesNet.add(key[14] % "1/3");
bayesNet.add(key[13] | key[14] = "1/3 3/1");
bayesNet.add(key[12] | key[14] = "3/1 3/1");
bayesNet.add((key[11] | key[13], key[14]) = "1/4 2/3 3/2 4/1");
bayesNet.add((key[10] | key[13], key[14]) = "1/4 3/2 2/3 4/1");
bayesNet.add((key[9] | key[12], key[14]) = "4/1 2/3 F 1/4");
bayesNet.add((key[8] | key[12], key[14]) = "T 1/4 3/2 4/1");
bayesNet.add((key[7] | key[11], key[13]) = "1/4 2/3 3/2 4/1");
bayesNet.add((key[6] | key[11], key[13]) = "1/4 3/2 2/3 4/1");
bayesNet.add((key[5] | key[10], key[13]) = "4/1 2/3 3/2 1/4");
bayesNet.add((key[4] | key[10], key[13]) = "2/3 1/4 3/2 4/1");
bayesNet.add((key[3] | key[9], key[12]) = "1/4 2/3 3/2 4/1");
bayesNet.add((key[2] | key[9], key[12]) = "1/4 8/2 2/3 4/1");
bayesNet.add((key[1] | key[8], key[12]) = "4/1 2/3 3/2 1/4");
bayesNet.add((key[0] | key[8], key[12]) = "2/3 1/4 3/2 4/1");
if (debug) {
GTSAM_PRINT(bayesNet);
bayesNet.saveGraph("/tmp/discreteBayesNet.dot");
}
// create a BayesTree out of a Bayes net
auto bayesTree = DiscreteFactorGraph(bayesNet).eliminateMultifrontal();
if (debug) {
GTSAM_PRINT(*bayesTree);
bayesTree->saveGraph("/tmp/discreteBayesTree.dot");
}
// Check frontals and parents
for (size_t i : {13, 14, 9, 3, 2, 8, 1, 0, 10, 5, 4}) {
auto clique_i = (*bayesTree)[i];
EXPECT_LONGS_EQUAL(i, *(clique_i->conditional_->beginFrontals()));
}
auto R = bayesTree->roots().front();
// Check whether BN and BT give the same answer on all configurations
vector<DiscreteFactor::Values> allPosbValues = cartesianProduct(
key[0] & key[1] & key[2] & key[3] & key[4] & key[5] & key[6] & key[7] &
key[8] & key[9] & key[10] & key[11] & key[12] & key[13] & key[14]);
for (size_t i = 0; i < allPosbValues.size(); ++i) {
DiscreteFactor::Values x = allPosbValues[i];
double expected = bayesNet.evaluate(x);
double actual = bayesTree->evaluate(x);
DOUBLES_EQUAL(expected, actual, 1e-9);
}
// Calculate all some marginals for Values==all1
Vector marginals = Vector::Zero(15);
double joint_12_14 = 0, joint_9_12_14 = 0, joint_8_12_14 = 0, joint_8_12 = 0,
joint82 = 0, joint12 = 0, joint24 = 0, joint45 = 0, joint46 = 0,
joint_4_11 = 0, joint_11_13 = 0, joint_11_13_14 = 0,
joint_11_12_13_14 = 0, joint_9_11_12_13 = 0, joint_8_11_12_13 = 0;
for (size_t i = 0; i < allPosbValues.size(); ++i) {
DiscreteFactor::Values x = allPosbValues[i];
double px = bayesTree->evaluate(x);
for (size_t i = 0; i < 15; i++)
if (x[i]) marginals[i] += px;
if (x[12] && x[14]) {
joint_12_14 += px;
if (x[9]) joint_9_12_14 += px;
if (x[8]) joint_8_12_14 += px;
}
if (x[8] && x[12]) joint_8_12 += px;
if (x[2]) {
if (x[8]) joint82 += px;
if (x[1]) joint12 += px;
}
if (x[4]) {
if (x[2]) joint24 += px;
if (x[5]) joint45 += px;
if (x[6]) joint46 += px;
if (x[11]) joint_4_11 += px;
}
if (x[11] && x[13]) {
joint_11_13 += px;
if (x[8] && x[12]) joint_8_11_12_13 += px;
if (x[9] && x[12]) joint_9_11_12_13 += px;
if (x[14]) {
joint_11_13_14 += px;
if (x[12]) {
joint_11_12_13_14 += px;
}
}
}
}
DiscreteFactor::Values all1 = allPosbValues.back();
// check separator marginal P(S0)
auto clique = (*bayesTree)[0];
DiscreteFactorGraph separatorMarginal0 =
clique->separatorMarginal(EliminateDiscrete);
DOUBLES_EQUAL(joint_8_12, separatorMarginal0(all1), 1e-9);
// check separator marginal P(S9), should be P(14)
clique = (*bayesTree)[9];
DiscreteFactorGraph separatorMarginal9 =
clique->separatorMarginal(EliminateDiscrete);
DOUBLES_EQUAL(marginals[14], separatorMarginal9(all1), 1e-9);
// check separator marginal of root, should be empty
clique = (*bayesTree)[11];
DiscreteFactorGraph separatorMarginal11 =
clique->separatorMarginal(EliminateDiscrete);
LONGS_EQUAL(0, separatorMarginal11.size());
// check shortcut P(S9||R) to root
clique = (*bayesTree)[9];
DiscreteBayesNet shortcut = clique->shortcut(R, EliminateDiscrete);
LONGS_EQUAL(1, shortcut.size());
DOUBLES_EQUAL(joint_11_13_14 / joint_11_13, shortcut.evaluate(all1), 1e-9);
// check shortcut P(S8||R) to root
clique = (*bayesTree)[8];
shortcut = clique->shortcut(R, EliminateDiscrete);
DOUBLES_EQUAL(joint_11_12_13_14 / joint_11_13, shortcut.evaluate(all1), 1e-9);
// check shortcut P(S2||R) to root
clique = (*bayesTree)[2];
shortcut = clique->shortcut(R, EliminateDiscrete);
DOUBLES_EQUAL(joint_9_11_12_13 / joint_11_13, shortcut.evaluate(all1), 1e-9);
// check shortcut P(S0||R) to root
clique = (*bayesTree)[0];
shortcut = clique->shortcut(R, EliminateDiscrete);
DOUBLES_EQUAL(joint_8_11_12_13 / joint_11_13, shortcut.evaluate(all1), 1e-9);
// calculate all shortcuts to root
DiscreteBayesTree::Nodes cliques = bayesTree->nodes();
for (auto clique : cliques) {
DiscreteBayesNet shortcut = clique.second->shortcut(R, EliminateDiscrete);
if (debug) {
clique.second->conditional_->printSignature();
shortcut.print("shortcut:");
}
}
// Check all marginals
DiscreteFactor::shared_ptr marginalFactor;
for (size_t i = 0; i < 15; i++) {
marginalFactor = bayesTree->marginalFactor(i, EliminateDiscrete);
double actual = (*marginalFactor)(all1);
DOUBLES_EQUAL(marginals[i], actual, 1e-9);
}
DiscreteBayesNet::shared_ptr actualJoint;
// Check joint P(8, 2)
actualJoint = bayesTree->jointBayesNet(8, 2, EliminateDiscrete);
DOUBLES_EQUAL(joint82, actualJoint->evaluate(all1), 1e-9);
// Check joint P(1, 2)
actualJoint = bayesTree->jointBayesNet(1, 2, EliminateDiscrete);
DOUBLES_EQUAL(joint12, actualJoint->evaluate(all1), 1e-9);
// Check joint P(2, 4)
actualJoint = bayesTree->jointBayesNet(2, 4, EliminateDiscrete);
DOUBLES_EQUAL(joint24, actualJoint->evaluate(all1), 1e-9);
// Check joint P(4, 5)
actualJoint = bayesTree->jointBayesNet(4, 5, EliminateDiscrete);
DOUBLES_EQUAL(joint45, actualJoint->evaluate(all1), 1e-9);
// Check joint P(4, 6)
actualJoint = bayesTree->jointBayesNet(4, 6, EliminateDiscrete);
DOUBLES_EQUAL(joint46, actualJoint->evaluate(all1), 1e-9);
// Check joint P(4, 11)
actualJoint = bayesTree->jointBayesNet(4, 11, EliminateDiscrete);
DOUBLES_EQUAL(joint_4_11, actualJoint->evaluate(all1), 1e-9);
}
/* ************************************************************************* */
int main() {
@ -263,4 +230,3 @@ int main() {
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

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76
gtsam/discrete/tests/testDiscreteConditional.cpp
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@ -16,9 +16,9 @@
* @date Feb 14, 2011
*/
#include <boost/make_shared.hpp>
#include <boost/assign/std/map.hpp>
#include <boost/assign/std/vector.hpp>
#include <boost/make_shared.hpp>
using namespace boost::assign;
#include <CppUnitLite/TestHarness.h>
@ -36,6 +36,11 @@ TEST( DiscreteConditional, constructors)
DiscreteConditional::shared_ptr expected1 = //
boost::make_shared<DiscreteConditional>(X | Y = "1/1 2/3 1/4");
EXPECT(expected1);
EXPECT_LONGS_EQUAL(0, *(expected1->beginFrontals()));
EXPECT_LONGS_EQUAL(2, *(expected1->beginParents()));
EXPECT(expected1->endParents() == expected1->end());
EXPECT(expected1->endFrontals() == expected1->beginParents());
DecisionTreeFactor f1(X & Y, "0.5 0.4 0.2 0.5 0.6 0.8");
DiscreteConditional actual1(1, f1);
EXPECT(assert_equal(*expected1, actual1, 1e-9));
@ -43,71 +48,68 @@ TEST( DiscreteConditional, constructors)
DecisionTreeFactor f2(X & Y & Z,
"0.2 0.5 0.3 0.6 0.4 0.7 0.25 0.55 0.35 0.65 0.45 0.75");
DiscreteConditional actual2(1, f2);
DecisionTreeFactor::shared_ptr actual2factor = actual2.toFactor();
// EXPECT(assert_equal(f2, *actual2factor, 1e-9));
EXPECT(assert_equal(f2 / *f2.sum(1), *actual2.toFactor(), 1e-9));
}
/* ************************************************************************* */
TEST( DiscreteConditional, constructors_alt_interface)
{
DiscreteKey X(0, 2), Y(2, 3), Z(1, 2); // watch ordering !
TEST(DiscreteConditional, constructors_alt_interface) {
DiscreteKey X(0, 2), Y(2, 3), Z(1, 2); // watch ordering !
Signature::Table table;
Signature::Row r1, r2, r3;
r1 += 1.0, 1.0; r2 += 2.0, 3.0; r3 += 1.0, 4.0;
r1 += 1.0, 1.0;
r2 += 2.0, 3.0;
r3 += 1.0, 4.0;
table += r1, r2, r3;
DiscreteConditional::shared_ptr expected1 = //
boost::make_shared<DiscreteConditional>(X | Y = table);
EXPECT(expected1);
auto actual1 = boost::make_shared<DiscreteConditional>(X | Y = table);
EXPECT(actual1);
DecisionTreeFactor f1(X & Y, "0.5 0.4 0.2 0.5 0.6 0.8");
DiscreteConditional actual1(1, f1);
EXPECT(assert_equal(*expected1, actual1, 1e-9));
DiscreteConditional expected1(1, f1);
EXPECT(assert_equal(expected1, *actual1, 1e-9));
DecisionTreeFactor f2(X & Y & Z,
"0.2 0.5 0.3 0.6 0.4 0.7 0.25 0.55 0.35 0.65 0.45 0.75");
DecisionTreeFactor f2(
X & Y & Z, "0.2 0.5 0.3 0.6 0.4 0.7 0.25 0.55 0.35 0.65 0.45 0.75");
DiscreteConditional actual2(1, f2);
DecisionTreeFactor::shared_ptr actual2factor = actual2.toFactor();
// EXPECT(assert_equal(f2, *actual2factor, 1e-9));
EXPECT(assert_equal(f2 / *f2.sum(1), *actual2.toFactor(), 1e-9));
}
/* ************************************************************************* */
TEST( DiscreteConditional, constructors2)
{
TEST(DiscreteConditional, constructors2) {
// Declare keys and ordering
DiscreteKey C(0,2), B(1,2);
DecisionTreeFactor expected(C & B, "0.8 0.75 0.2 0.25");
DiscreteKey C(0, 2), B(1, 2);
DecisionTreeFactor actual(C & B, "0.8 0.75 0.2 0.25");
Signature signature((C | B) = "4/1 3/1");
DiscreteConditional actual(signature);
DecisionTreeFactor::shared_ptr actualFactor = actual.toFactor();
EXPECT(assert_equal(expected, *actualFactor));
DiscreteConditional expected(signature);
DecisionTreeFactor::shared_ptr expectedFactor = expected.toFactor();
EXPECT(assert_equal(*expectedFactor, actual));
}
/* ************************************************************************* */
TEST( DiscreteConditional, constructors3)
{
TEST(DiscreteConditional, constructors3) {
// Declare keys and ordering
DiscreteKey C(0,2), B(1,2), A(2,2);
DecisionTreeFactor expected(C & B & A, "0.8 0.5 0.5 0.2 0.2 0.5 0.5 0.8");
DiscreteKey C(0, 2), B(1, 2), A(2, 2);
DecisionTreeFactor actual(C & B & A, "0.8 0.5 0.5 0.2 0.2 0.5 0.5 0.8");
Signature signature((C | B, A) = "4/1 1/1 1/1 1/4");
DiscreteConditional actual(signature);
DecisionTreeFactor::shared_ptr actualFactor = actual.toFactor();
EXPECT(assert_equal(expected, *actualFactor));
DiscreteConditional expected(signature);
DecisionTreeFactor::shared_ptr expectedFactor = expected.toFactor();
EXPECT(assert_equal(*expectedFactor, actual));
}
/* ************************************************************************* */
TEST( DiscreteConditional, Combine) {
TEST(DiscreteConditional, Combine) {
DiscreteKey A(0, 2), B(1, 2);
vector<DiscreteConditional::shared_ptr> c;
c.push_back(boost::make_shared<DiscreteConditional>(A | B = "1/2 2/1"));
c.push_back(boost::make_shared<DiscreteConditional>(B % "1/2"));
DecisionTreeFactor factor(A & B, "0.111111 0.444444 0.222222 0.222222");
DiscreteConditional expected(2, factor);
DiscreteConditional::shared_ptr actual = DiscreteConditional::Combine(
c.begin(), c.end());
EXPECT(assert_equal(expected, *actual,1e-5));
DiscreteConditional actual(2, factor);
auto expected = DiscreteConditional::Combine(c.begin(), c.end());
EXPECT(assert_equal(*expected, actual, 1e-5));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

1
gtsam/discrete/tests/testDiscreteFactorGraph.cpp
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@ -19,6 +19,7 @@
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteEliminationTree.h>
#include <gtsam/discrete/DiscreteBayesTree.h>
#include <gtsam/inference/BayesNet.h>
#include <CppUnitLite/TestHarness.h>

42
gtsam/discrete/tests/testDiscreteMarginals.cpp
View File

@ -146,8 +146,7 @@ TEST_UNSAFE( DiscreteMarginals, truss ) {
/* ************************************************************************* */
// Second truss example with non-trivial factors
TEST_UNSAFE( DiscreteMarginals, truss2 ) {
TEST_UNSAFE(DiscreteMarginals, truss2) {
const int nrNodes = 5;
const size_t nrStates = 2;
@ -160,40 +159,39 @@ TEST_UNSAFE( DiscreteMarginals, truss2 ) {
// create graph and add three truss potentials
DiscreteFactorGraph graph;
graph.add(key[0] & key[2] & key[4],"1 2 3 4 5 6 7 8");
graph.add(key[1] & key[3] & key[4],"1 2 3 4 5 6 7 8");
graph.add(key[2] & key[3] & key[4],"1 2 3 4 5 6 7 8");
graph.add(key[0] & key[2] & key[4], "1 2 3 4 5 6 7 8");
graph.add(key[1] & key[3] & key[4], "1 2 3 4 5 6 7 8");
graph.add(key[2] & key[3] & key[4], "1 2 3 4 5 6 7 8");
// Calculate the marginals by brute force
vector<DiscreteFactor::Values> allPosbValues = cartesianProduct(
key[0] & key[1] & key[2] & key[3] & key[4]);
vector<DiscreteFactor::Values> allPosbValues =
cartesianProduct(key[0] & key[1] & key[2] & key[3] & key[4]);
Vector T = Z_5x1, F = Z_5x1;
for (size_t i = 0; i < allPosbValues.size(); ++i) {
DiscreteFactor::Values x = allPosbValues[i];
double px = graph(x);
for (size_t j=0;j<5;j++)
if (x[j]) T[j]+=px; else F[j]+=px;
// cout << x[0] << " " << x[1] << " "<< x[2] << " " << x[3] << " " << x[4] << " :\t" << px << endl;
for (size_t j = 0; j < 5; j++)
if (x[j])
T[j] += px;
else
F[j] += px;
}
// Check all marginals given by a sequential solver and Marginals
// DiscreteSequentialSolver solver(graph);
// DiscreteSequentialSolver solver(graph);
DiscreteMarginals marginals(graph);
for (size_t j=0;j<5;j++) {
double sum = T[j]+F[j];
T[j]/=sum;
F[j]/=sum;
// // solver
// Vector actualV = solver.marginalProbabilities(key[j]);
// EXPECT(assert_equal((Vector(2) << F[j], T[j]), actualV));
for (size_t j = 0; j < 5; j++) {
double sum = T[j] + F[j];
T[j] /= sum;
F[j] /= sum;
// Marginals
vector<double> table;
table += F[j],T[j];
DecisionTreeFactor expectedM(key[j],table);
table += F[j], T[j];
DecisionTreeFactor expectedM(key[j], table);
DiscreteFactor::shared_ptr actualM = marginals(j);
EXPECT(assert_equal(expectedM, *boost::dynamic_pointer_cast<DecisionTreeFactor>(actualM)));
EXPECT(assert_equal(
expectedM, *boost::dynamic_pointer_cast<DecisionTreeFactor>(actualM)));
}
}

44
gtsam/discrete/tests/testSignature.cpp
View File

@ -11,36 +11,43 @@
/**
* @file testSignature
* @brief Tests focusing on the details of Signatures to evaluate boost compliance
* @brief Tests focusing on the details of Signatures to evaluate boost
* compliance
* @author Alex Cunningham
* @date Sept 19th 2011
*/
#include <boost/assign/std/vector.hpp>
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/discrete/Signature.h>
#include <boost/assign/std/vector.hpp>
#include <vector>
using namespace std;
using namespace gtsam;
using namespace boost::assign;
DiscreteKey X(0,2), Y(1,3), Z(2,2);
DiscreteKey X(0, 2), Y(1, 3), Z(2, 2);
/* ************************************************************************* */
TEST(testSignature, simple_conditional) {
Signature sig(X | Y = "1/1 2/3 1/4");
Signature::Table table = *sig.table();
vector<double> row[3]{{0.5, 0.5}, {0.4, 0.6}, {0.2, 0.8}};
CHECK(row[0] == table[0]);
CHECK(row[1] == table[1]);
CHECK(row[2] == table[2]);
DiscreteKey actKey = sig.key();
LONGS_EQUAL((long)X.first, (long)actKey.first);
LONGS_EQUAL(X.first, actKey.first);
DiscreteKeys actKeys = sig.discreteKeysParentsFirst();
LONGS_EQUAL(2, (long)actKeys.size());
LONGS_EQUAL((long)Y.first, (long)actKeys.front().first);
LONGS_EQUAL((long)X.first, (long)actKeys.back().first);
DiscreteKeys actKeys = sig.discreteKeys();
LONGS_EQUAL(2, actKeys.size());
LONGS_EQUAL(X.first, actKeys.front().first);
LONGS_EQUAL(Y.first, actKeys.back().first);
vector<double> actCpt = sig.cpt();
EXPECT_LONGS_EQUAL(6, (long)actCpt.size());
EXPECT_LONGS_EQUAL(6, actCpt.size());
}
/* ************************************************************************* */
@ -54,17 +61,20 @@ TEST(testSignature, simple_conditional_nonparser) {
Signature sig(X | Y = table);
DiscreteKey actKey = sig.key();
EXPECT_LONGS_EQUAL((long)X.first, (long)actKey.first);
EXPECT_LONGS_EQUAL(X.first, actKey.first);
DiscreteKeys actKeys = sig.discreteKeysParentsFirst();
LONGS_EQUAL(2, (long)actKeys.size());
LONGS_EQUAL((long)Y.first, (long)actKeys.front().first);
LONGS_EQUAL((long)X.first, (long)actKeys.back().first);
DiscreteKeys actKeys = sig.discreteKeys();
LONGS_EQUAL(2, actKeys.size());
LONGS_EQUAL(X.first, actKeys.front().first);
LONGS_EQUAL(Y.first, actKeys.back().first);
vector<double> actCpt = sig.cpt();
EXPECT_LONGS_EQUAL(6, (long)actCpt.size());
EXPECT_LONGS_EQUAL(6, actCpt.size());
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

167
gtsam/geometry/Pose3.cpp
View File

@ -19,8 +19,10 @@
#include <gtsam/geometry/concepts.h>
#include <gtsam/base/concepts.h>
#include <iostream>
#include <cmath>
#include <iostream>
#include <limits>
#include <string>
using namespace std;
@ -36,10 +38,10 @@ Pose3::Pose3(const Pose2& pose2) :
}
/* ************************************************************************* */
Pose3 Pose3::Create(const Rot3& R, const Point3& t, OptionalJacobian<6, 3> H1,
OptionalJacobian<6, 3> H2) {
if (H1) *H1 << I_3x3, Z_3x3;
if (H2) *H2 << Z_3x3, R.transpose();
Pose3 Pose3::Create(const Rot3& R, const Point3& t, OptionalJacobian<6, 3> HR,
OptionalJacobian<6, 3> Ht) {
if (HR) *HR << I_3x3, Z_3x3;
if (Ht) *Ht << Z_3x3, R.transpose();
return Pose3(R, t);
}
@ -72,15 +74,15 @@ Matrix6 Pose3::adjointMap(const Vector6& xi) {
/* ************************************************************************* */
Vector6 Pose3::adjoint(const Vector6& xi, const Vector6& y,
OptionalJacobian<6,6> H) {
if (H) {
H->setZero();
OptionalJacobian<6, 6> Hxi) {
if (Hxi) {
Hxi->setZero();
for (int i = 0; i < 6; ++i) {
Vector6 dxi;
dxi.setZero();
dxi(i) = 1.0;
Matrix6 Gi = adjointMap(dxi);
H->col(i) = Gi * y;
Hxi->col(i) = Gi * y;
}
}
return adjointMap(xi) * y;
@ -88,15 +90,15 @@ Vector6 Pose3::adjoint(const Vector6& xi, const Vector6& y,
/* ************************************************************************* */
Vector6 Pose3::adjointTranspose(const Vector6& xi, const Vector6& y,
OptionalJacobian<6,6> H) {
if (H) {
H->setZero();
OptionalJacobian<6, 6> Hxi) {
if (Hxi) {
Hxi->setZero();
for (int i = 0; i < 6; ++i) {
Vector6 dxi;
dxi.setZero();
dxi(i) = 1.0;
Matrix6 GTi = adjointMap(dxi).transpose();
H->col(i) = GTi * y;
Hxi->col(i) = GTi * y;
}
}
return adjointMap(xi).transpose() * y;
@ -116,8 +118,8 @@ bool Pose3::equals(const Pose3& pose, double tol) const {
/* ************************************************************************* */
/** Modified from Murray94book version (which assumes w and v normalized?) */
Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> H) {
if (H) *H = ExpmapDerivative(xi);
Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi) {
if (Hxi) *Hxi = ExpmapDerivative(xi);
// get angular velocity omega and translational velocity v from twist xi
Vector3 omega(xi(0), xi(1), xi(2)), v(xi(3), xi(4), xi(5));
@ -125,8 +127,8 @@ Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> H) {
Rot3 R = Rot3::Expmap(omega);
double theta2 = omega.dot(omega);
if (theta2 > std::numeric_limits<double>::epsilon()) {
Vector3 t_parallel = omega * omega.dot(v); // translation parallel to axis
Vector3 omega_cross_v = omega.cross(v); // points towards axis
Vector3 t_parallel = omega * omega.dot(v); // translation parallel to axis
Vector3 omega_cross_v = omega.cross(v); // points towards axis
Vector3 t = (omega_cross_v - R * omega_cross_v + t_parallel) / theta2;
return Pose3(R, t);
} else {
@ -135,10 +137,10 @@ Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> H) {
}
/* ************************************************************************* */
Vector6 Pose3::Logmap(const Pose3& p, OptionalJacobian<6, 6> H) {
if (H) *H = LogmapDerivative(p);
const Vector3 w = Rot3::Logmap(p.rotation());
const Vector3 T = p.translation();
Vector6 Pose3::Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose) {
if (Hpose) *Hpose = LogmapDerivative(pose);
const Vector3 w = Rot3::Logmap(pose.rotation());
const Vector3 T = pose.translation();
const double t = w.norm();
if (t < 1e-10) {
Vector6 log;
@ -158,33 +160,33 @@ Vector6 Pose3::Logmap(const Pose3& p, OptionalJacobian<6, 6> H) {
}
/* ************************************************************************* */
Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian H) {
Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian Hxi) {
#ifdef GTSAM_POSE3_EXPMAP
return Expmap(xi, H);
return Expmap(xi, Hxi);
#else
Matrix3 DR;
Rot3 R = Rot3::Retract(xi.head<3>(), H ? &DR : 0);
if (H) {
*H = I_6x6;
H->topLeftCorner<3,3>() = DR;
Rot3 R = Rot3::Retract(xi.head<3>(), Hxi ? &DR : 0);
if (Hxi) {
*Hxi = I_6x6;
Hxi->topLeftCorner<3, 3>() = DR;
}
return Pose3(R, Point3(xi.tail<3>()));
#endif
}
/* ************************************************************************* */
Vector6 Pose3::ChartAtOrigin::Local(const Pose3& T, ChartJacobian H) {
Vector6 Pose3::ChartAtOrigin::Local(const Pose3& pose, ChartJacobian Hpose) {
#ifdef GTSAM_POSE3_EXPMAP
return Logmap(T, H);
return Logmap(pose, Hpose);
#else
Matrix3 DR;
Vector3 omega = Rot3::LocalCoordinates(T.rotation(), H ? &DR : 0);
if (H) {
*H = I_6x6;
H->topLeftCorner<3,3>() = DR;
Vector3 omega = Rot3::LocalCoordinates(pose.rotation(), Hpose ? &DR : 0);
if (Hpose) {
*Hpose = I_6x6;
Hpose->topLeftCorner<3, 3>() = DR;
}
Vector6 xi;
xi << omega, T.translation();
xi << omega, pose.translation();
return xi;
#endif
}
@ -261,16 +263,16 @@ Matrix6 Pose3::LogmapDerivative(const Pose3& pose) {
}
/* ************************************************************************* */
const Point3& Pose3::translation(OptionalJacobian<3, 6> H) const {
if (H) *H << Z_3x3, rotation().matrix();
const Point3& Pose3::translation(OptionalJacobian<3, 6> Hself) const {
if (Hself) *Hself << Z_3x3, rotation().matrix();
return t_;
}
/* ************************************************************************* */
const Rot3& Pose3::rotation(OptionalJacobian<3, 6> H) const {
if (H) {
*H << I_3x3, Z_3x3;
const Rot3& Pose3::rotation(OptionalJacobian<3, 6> Hself) const {
if (Hself) {
*Hself << I_3x3, Z_3x3;
}
return R_;
}
@ -284,9 +286,10 @@ Matrix4 Pose3::matrix() const {
}
/* ************************************************************************* */
Pose3 Pose3::transformPoseFrom(const Pose3& aTb) const {
Pose3 Pose3::transformPoseFrom(const Pose3& aTb, OptionalJacobian<6, 6> Hself,
OptionalJacobian<6, 6> HaTb) const {
const Pose3& wTa = *this;
return wTa * aTb;
return wTa.compose(aTb, Hself, HaTb);
}
/* ************************************************************************* */
@ -299,101 +302,101 @@ Pose3 Pose3::transform_to(const Pose3& pose) const {
#endif
/* ************************************************************************* */
Pose3 Pose3::transformPoseTo(const Pose3& wTb, OptionalJacobian<6, 6> H1,
OptionalJacobian<6, 6> H2) const {
if (H1) *H1 = -wTb.inverse().AdjointMap() * AdjointMap();
if (H2) *H2 = I_6x6;
Pose3 Pose3::transformPoseTo(const Pose3& wTb, OptionalJacobian<6, 6> Hself,
OptionalJacobian<6, 6> HwTb) const {
if (Hself) *Hself = -wTb.inverse().AdjointMap() * AdjointMap();
if (HwTb) *HwTb = I_6x6;
const Pose3& wTa = *this;
return wTa.inverse() * wTb;
}
/* ************************************************************************* */
Point3 Pose3::transformFrom(const Point3& p, OptionalJacobian<3,6> Dpose,
OptionalJacobian<3,3> Dpoint) const {
Point3 Pose3::transformFrom(const Point3& point, OptionalJacobian<3, 6> Hself,
OptionalJacobian<3, 3> Hpoint) const {
// Only get matrix once, to avoid multiple allocations,
// as well as multiple conversions in the Quaternion case
const Matrix3 R = R_.matrix();
if (Dpose) {
Dpose->leftCols<3>() = R * skewSymmetric(-p.x(), -p.y(), -p.z());
Dpose->rightCols<3>() = R;
if (Hself) {
Hself->leftCols<3>() = R * skewSymmetric(-point.x(), -point.y(), -point.z());
Hself->rightCols<3>() = R;
}
if (Dpoint) {
*Dpoint = R;
if (Hpoint) {
*Hpoint = R;
}
return R_ * p + t_;
return R_ * point + t_;
}
/* ************************************************************************* */
Point3 Pose3::transformTo(const Point3& p, OptionalJacobian<3,6> Dpose,
OptionalJacobian<3,3> Dpoint) const {
Point3 Pose3::transformTo(const Point3& point, OptionalJacobian<3, 6> Hself,
OptionalJacobian<3, 3> Hpoint) const {
// Only get transpose once, to avoid multiple allocations,
// as well as multiple conversions in the Quaternion case
const Matrix3 Rt = R_.transpose();
const Point3 q(Rt*(p - t_));
if (Dpose) {
const Point3 q(Rt*(point - t_));
if (Hself) {
const double wx = q.x(), wy = q.y(), wz = q.z();
(*Dpose) <<
(*Hself) <<
0.0, -wz, +wy,-1.0, 0.0, 0.0,
+wz, 0.0, -wx, 0.0,-1.0, 0.0,
-wy, +wx, 0.0, 0.0, 0.0,-1.0;
}
if (Dpoint) {
*Dpoint = Rt;
if (Hpoint) {
*Hpoint = Rt;
}
return q;
}
/* ************************************************************************* */
double Pose3::range(const Point3& point, OptionalJacobian<1, 6> H1,
OptionalJacobian<1, 3> H2) const {
double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself,
OptionalJacobian<1, 3> Hpoint) const {
Matrix36 D_local_pose;
Matrix3 D_local_point;
Point3 local = transformTo(point, H1 ? &D_local_pose : 0, H2 ? &D_local_point : 0);
if (!H1 && !H2) {
Point3 local = transformTo(point, Hself ? &D_local_pose : 0, Hpoint ? &D_local_point : 0);
if (!Hself && !Hpoint) {
return local.norm();
} else {
Matrix13 D_r_local;
const double r = norm3(local, D_r_local);
if (H1) *H1 = D_r_local * D_local_pose;
if (H2) *H2 = D_r_local * D_local_point;
if (Hself) *Hself = D_r_local * D_local_pose;
if (Hpoint) *Hpoint = D_r_local * D_local_point;
return r;
}
}
/* ************************************************************************* */
double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> H1,
OptionalJacobian<1, 6> H2) const {
double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself,
OptionalJacobian<1, 6> Hpose) const {
Matrix13 D_local_point;
double r = range(pose.translation(), H1, H2 ? &D_local_point : 0);
if (H2) *H2 << Matrix13::Zero(), D_local_point * pose.rotation().matrix();
double r = range(pose.translation(), Hself, Hpose ? &D_local_point : 0);
if (Hpose) *Hpose << Matrix13::Zero(), D_local_point * pose.rotation().matrix();
return r;
}
/* ************************************************************************* */
Unit3 Pose3::bearing(const Point3& point, OptionalJacobian<2, 6> H1,
OptionalJacobian<2, 3> H2) const {
Unit3 Pose3::bearing(const Point3& point, OptionalJacobian<2, 6> Hself,
OptionalJacobian<2, 3> Hpoint) const {
Matrix36 D_local_pose;
Matrix3 D_local_point;
Point3 local = transformTo(point, H1 ? &D_local_pose : 0, H2 ? &D_local_point : 0);
if (!H1 && !H2) {
Point3 local = transformTo(point, Hself ? &D_local_pose : 0, Hpoint ? &D_local_point : 0);
if (!Hself && !Hpoint) {
return Unit3(local);
} else {
Matrix23 D_b_local;
Unit3 b = Unit3::FromPoint3(local, D_b_local);
if (H1) *H1 = D_b_local * D_local_pose;
if (H2) *H2 = D_b_local * D_local_point;
if (Hself) *Hself = D_b_local * D_local_pose;
if (Hpoint) *Hpoint = D_b_local * D_local_point;
return b;
}
}
/* ************************************************************************* */
Unit3 Pose3::bearing(const Pose3& pose, OptionalJacobian<2, 6> H1,
OptionalJacobian<2, 6> H2) const {
if (H2) {
H2->setZero();
return bearing(pose.translation(), H1, H2.cols<3>(3));
Unit3 Pose3::bearing(const Pose3& pose, OptionalJacobian<2, 6> Hself,
OptionalJacobian<2, 6> Hpose) const {
if (Hpose) {
Hpose->setZero();
return bearing(pose.translation(), Hself, Hpose.cols<3>(3));
}
return bearing(pose.translation(), H1, boost::none);
return bearing(pose.translation(), Hself, boost::none);
}
/* ************************************************************************* */

102
gtsam/geometry/Pose3.h
View File

@ -75,8 +75,8 @@ public:
/// Named constructor with derivatives
static Pose3 Create(const Rot3& R, const Point3& t,
OptionalJacobian<6, 3> H1 = boost::none,
OptionalJacobian<6, 3> H2 = boost::none);
OptionalJacobian<6, 3> HR = boost::none,
OptionalJacobian<6, 3> Ht = boost::none);
/**
* Create Pose3 by aligning two point pairs
@ -117,10 +117,10 @@ public:
/// @{
/// Exponential map at identity - create a rotation from canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$
static Pose3 Expmap(const Vector6& xi, OptionalJacobian<6, 6> H = boost::none);
static Pose3 Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi = boost::none);
/// Log map at identity - return the canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$ of this rotation
static Vector6 Logmap(const Pose3& p, OptionalJacobian<6, 6> H = boost::none);
static Vector6 Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose = boost::none);
/**
* Calculate Adjoint map, transforming a twist in the this pose's (i.e, body) frame to the world spatial frame
@ -157,7 +157,7 @@ public:
* Action of the adjointMap on a Lie-algebra vector y, with optional derivatives
*/
static Vector6 adjoint(const Vector6 &xi, const Vector6 &y,
OptionalJacobian<6, 6> = boost::none);
OptionalJacobian<6, 6> Hxi = boost::none);
// temporary fix for wrappers until case issue is resolved
static Matrix6 adjointMap_(const Vector6 &xi) { return adjointMap(xi);}
@ -167,7 +167,7 @@ public:
* The dual version of adjoint action, acting on the dual space of the Lie-algebra vector space.
*/
static Vector6 adjointTranspose(const Vector6& xi, const Vector6& y,
OptionalJacobian<6, 6> H = boost::none);
OptionalJacobian<6, 6> Hxi = boost::none);
/// Derivative of Expmap
static Matrix6 ExpmapDerivative(const Vector6& xi);
@ -177,8 +177,8 @@ public:
// Chart at origin, depends on compile-time flag GTSAM_POSE3_EXPMAP
struct ChartAtOrigin {
static Pose3 Retract(const Vector6& v, ChartJacobian H = boost::none);
static Vector6 Local(const Pose3& r, ChartJacobian H = boost::none);
static Pose3 Retract(const Vector6& xi, ChartJacobian Hxi = boost::none);
static Vector6 Local(const Pose3& pose, ChartJacobian Hpose = boost::none);
};
using LieGroup<Pose3, 6>::inverse; // version with derivative
@ -201,38 +201,38 @@ public:
/**
* @brief takes point in Pose coordinates and transforms it to world coordinates
* @param p point in Pose coordinates
* @param Dpose optional 3*6 Jacobian wrpt this pose
* @param Dpoint optional 3*3 Jacobian wrpt point
* @param point point in Pose coordinates
* @param Hself optional 3*6 Jacobian wrpt this pose
* @param Hpoint optional 3*3 Jacobian wrpt point
* @return point in world coordinates
*/
Point3 transformFrom(const Point3& p, OptionalJacobian<3, 6> Dpose =
boost::none, OptionalJacobian<3, 3> Dpoint = boost::none) const;
Point3 transformFrom(const Point3& point, OptionalJacobian<3, 6> Hself =
boost::none, OptionalJacobian<3, 3> Hpoint = boost::none) const;
/** syntactic sugar for transformFrom */
inline Point3 operator*(const Point3& p) const {
return transformFrom(p);
inline Point3 operator*(const Point3& point) const {
return transformFrom(point);
}
/**
* @brief takes point in world coordinates and transforms it to Pose coordinates
* @param p point in world coordinates
* @param Dpose optional 3*6 Jacobian wrpt this pose
* @param Dpoint optional 3*3 Jacobian wrpt point
* @param point point in world coordinates
* @param Hself optional 3*6 Jacobian wrpt this pose
* @param Hpoint optional 3*3 Jacobian wrpt point
* @return point in Pose coordinates
*/
Point3 transformTo(const Point3& p, OptionalJacobian<3, 6> Dpose =
boost::none, OptionalJacobian<3, 3> Dpoint = boost::none) const;
Point3 transformTo(const Point3& point, OptionalJacobian<3, 6> Hself =
boost::none, OptionalJacobian<3, 3> Hpoint = boost::none) const;
/// @}
/// @name Standard Interface
/// @{
/// get rotation
const Rot3& rotation(OptionalJacobian<3, 6> H = boost::none) const;
const Rot3& rotation(OptionalJacobian<3, 6> Hself = boost::none) const;
/// get translation
const Point3& translation(OptionalJacobian<3, 6> H = boost::none) const;
const Point3& translation(OptionalJacobian<3, 6> Hself = boost::none) const;
/// get x
double x() const {
@ -252,36 +252,44 @@ public:
/** convert to 4*4 matrix */
Matrix4 matrix() const;
/** receives a pose in local coordinates and transforms it to world coordinates */
Pose3 transformPoseFrom(const Pose3& pose) const;
/**
* Assuming self == wTa, takes a pose aTb in local coordinates
* and transforms it to world coordinates wTb = wTa * aTb.
* This is identical to compose.
*/
Pose3 transformPoseFrom(const Pose3& aTb, OptionalJacobian<6, 6> Hself = boost::none,
OptionalJacobian<6, 6> HaTb = boost::none) const;
/** receives a pose in world coordinates and transforms it to local coordinates */
Pose3 transformPoseTo(const Pose3& pose, OptionalJacobian<6, 6> H1 = boost::none,
OptionalJacobian<6, 6> H2 = boost::none) const;
/**
* Assuming self == wTa, takes a pose wTb in world coordinates
* and transforms it to local coordinates aTb = inv(wTa) * wTb
*/
Pose3 transformPoseTo(const Pose3& wTb, OptionalJacobian<6, 6> Hself = boost::none,
OptionalJacobian<6, 6> HwTb = boost::none) const;
/**
* Calculate range to a landmark
* @param point 3D location of landmark
* @return range (double)
*/
double range(const Point3& point, OptionalJacobian<1, 6> H1 = boost::none,
OptionalJacobian<1, 3> H2 = boost::none) const;
double range(const Point3& point, OptionalJacobian<1, 6> Hself = boost::none,
OptionalJacobian<1, 3> Hpoint = boost::none) const;
/**
* Calculate range to another pose
* @param pose Other SO(3) pose
* @return range (double)
*/
double range(const Pose3& pose, OptionalJacobian<1, 6> H1 = boost::none,
OptionalJacobian<1, 6> H2 = boost::none) const;
double range(const Pose3& pose, OptionalJacobian<1, 6> Hself = boost::none,
OptionalJacobian<1, 6> Hpose = boost::none) const;
/**
* Calculate bearing to a landmark
* @param point 3D location of landmark
* @return bearing (Unit3)
*/
Unit3 bearing(const Point3& point, OptionalJacobian<2, 6> H1 = boost::none,
OptionalJacobian<2, 3> H2 = boost::none) const;
Unit3 bearing(const Point3& point, OptionalJacobian<2, 6> Hself = boost::none,
OptionalJacobian<2, 3> Hpoint = boost::none) const;
/**
* Calculate bearing to another pose
@ -289,8 +297,8 @@ public:
* information is ignored.
* @return bearing (Unit3)
*/
Unit3 bearing(const Pose3& pose, OptionalJacobian<2, 6> H1 = boost::none,
OptionalJacobian<2, 6> H2 = boost::none) const;
Unit3 bearing(const Pose3& pose, OptionalJacobian<2, 6> Hself = boost::none,
OptionalJacobian<2, 6> Hpose = boost::none) const;
/// @}
/// @name Advanced Interface
@ -321,20 +329,20 @@ public:
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
Point3 transform_from(const Point3& p,
OptionalJacobian<3, 6> Dpose = boost::none,
OptionalJacobian<3, 3> Dpoint = boost::none) const {
return transformFrom(p, Dpose, Dpoint);
Point3 transform_from(const Point3& point,
OptionalJacobian<3, 6> Hself = boost::none,
OptionalJacobian<3, 3> Hpoint = boost::none) const {
return transformFrom(point, Hself, Hpoint);
}
Point3 transform_to(const Point3& p,
OptionalJacobian<3, 6> Dpose = boost::none,
OptionalJacobian<3, 3> Dpoint = boost::none) const {
return transformTo(p, Dpose, Dpoint);
Point3 transform_to(const Point3& point,
OptionalJacobian<3, 6> Hself = boost::none,
OptionalJacobian<3, 3> Hpoint = boost::none) const {
return transformTo(point, Hself, Hpoint);
}
Pose3 transform_pose_to(const Pose3& pose,
OptionalJacobian<6, 6> H1 = boost::none,
OptionalJacobian<6, 6> H2 = boost::none) const {
return transformPoseTo(pose, H1, H2);
Pose3 transform_pose_to(const Pose3& pose,
OptionalJacobian<6, 6> Hself = boost::none,
OptionalJacobian<6, 6> Hpose = boost::none) const {
return transformPoseTo(pose, Hself, Hpose);
}
/**
* @deprecated: this function is neither here not there. */

2
gtsam/geometry/SOn.h
View File

@ -340,7 +340,7 @@ void serialize(
const unsigned int file_version
) {
Matrix& M = Q.matrix_;
ar& M;
ar& BOOST_SERIALIZATION_NVP(M);
}
/*

23
gtsam/geometry/tests/testPose3.cpp
View File

@ -418,6 +418,29 @@ TEST(Pose3, transform_to_rotate) {
EXPECT(assert_equal(expected, actual, 0.001));
}
/* ************************************************************************* */
// Check transformPoseFrom and its pushforward
Pose3 transformPoseFrom_(const Pose3& wTa, const Pose3& aTb) {
return wTa.transformPoseFrom(aTb);
}
TEST(Pose3, transformPoseFrom)
{
Matrix actual = (T2*T2).matrix();
Matrix expected = T2.matrix()*T2.matrix();
EXPECT(assert_equal(actual, expected, 1e-8));
Matrix H1, H2;
T2.transformPoseFrom(T2, H1, H2);
Matrix numericalH1 = numericalDerivative21(transformPoseFrom_, T2, T2);
EXPECT(assert_equal(numericalH1, H1, 5e-3));
EXPECT(assert_equal(T2.inverse().AdjointMap(), H1, 5e-3));
Matrix numericalH2 = numericalDerivative22(transformPoseFrom_, T2, T2);
EXPECT(assert_equal(numericalH2, H2, 1e-4));
}
/* ************************************************************************* */
TEST(Pose3, transformTo) {
Pose3 transform(Rot3::Rodrigues(0, 0, -1.570796), Point3(2, 4, 0));

4
gtsam/inference/BayesNet.h
View File

@ -69,4 +69,6 @@ namespace gtsam {
void saveGraph(const std::string &s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
};
}
}
#include <gtsam/inference/BayesNet-inst.h>

54
gtsam/inference/BayesTreeCliqueBase-inst.h
View File

@ -136,57 +136,61 @@ namespace gtsam {
}
}
/* ************************************************************************* */
/* *********************************************************************** */
// separator marginal, uses separator marginal of parent recursively
// P(C) = P(F|S) P(S)
/* ************************************************************************* */
template<class DERIVED, class FACTORGRAPH>
/* *********************************************************************** */
template <class DERIVED, class FACTORGRAPH>
typename BayesTreeCliqueBase<DERIVED, FACTORGRAPH>::FactorGraphType
BayesTreeCliqueBase<DERIVED, FACTORGRAPH>::separatorMarginal(Eliminate function) const
{
BayesTreeCliqueBase<DERIVED, FACTORGRAPH>::separatorMarginal(
Eliminate function) const {
gttic(BayesTreeCliqueBase_separatorMarginal);
// Check if the Separator marginal was already calculated
if (!cachedSeparatorMarginal_)
{
if (!cachedSeparatorMarginal_) {
gttic(BayesTreeCliqueBase_separatorMarginal_cachemiss);
// If this is the root, there is no separator
if (parent_.expired() /*(if we're the root)*/)
{
if (parent_.expired() /*(if we're the root)*/) {
// we are root, return empty
FactorGraphType empty;
cachedSeparatorMarginal_ = empty;
}
else
{
} else {
// Flatten recursion in timing outline
gttoc(BayesTreeCliqueBase_separatorMarginal_cachemiss);
gttoc(BayesTreeCliqueBase_separatorMarginal);
// Obtain P(S) = \int P(Cp) = \int P(Fp|Sp) P(Sp)
// initialize P(Cp) with the parent separator marginal
derived_ptr parent(parent_.lock());
gttoc(BayesTreeCliqueBase_separatorMarginal_cachemiss); // Flatten recursion in timing outline
gttoc(BayesTreeCliqueBase_separatorMarginal);
FactorGraphType p_Cp(parent->separatorMarginal(function)); // P(Sp)
FactorGraphType p_Cp(parent->separatorMarginal(function)); // P(Sp)
gttic(BayesTreeCliqueBase_separatorMarginal);
gttic(BayesTreeCliqueBase_separatorMarginal_cachemiss);
// now add the parent conditional
p_Cp += parent->conditional_; // P(Fp|Sp)
p_Cp += parent->conditional_; // P(Fp|Sp)
// The variables we want to keepSet are exactly the ones in S
KeyVector indicesS(this->conditional()->beginParents(), this->conditional()->endParents());
cachedSeparatorMarginal_ = *p_Cp.marginalMultifrontalBayesNet(Ordering(indicesS), function);
KeyVector indicesS(this->conditional()->beginParents(),
this->conditional()->endParents());
auto separatorMarginal =
p_Cp.marginalMultifrontalBayesNet(Ordering(indicesS), function);
cachedSeparatorMarginal_.reset(*separatorMarginal);
}
}
// return the shortcut P(S||B)
return *cachedSeparatorMarginal_; // return the cached version
return *cachedSeparatorMarginal_; // return the cached version
}
/* ************************************************************************* */
// marginal2, uses separator marginal of parent recursively
/* *********************************************************************** */
// marginal2, uses separator marginal of parent
// P(C) = P(F|S) P(S)
/* ************************************************************************* */
template<class DERIVED, class FACTORGRAPH>
/* *********************************************************************** */
template <class DERIVED, class FACTORGRAPH>
typename BayesTreeCliqueBase<DERIVED, FACTORGRAPH>::FactorGraphType
BayesTreeCliqueBase<DERIVED, FACTORGRAPH>::marginal2(Eliminate function) const
{
BayesTreeCliqueBase<DERIVED, FACTORGRAPH>::marginal2(
Eliminate function) const {
gttic(BayesTreeCliqueBase_marginal2);
// initialize with separator marginal P(S)
FactorGraphType p_C = this->separatorMarginal(function);

3
gtsam/inference/Conditional.h
View File

@ -65,6 +65,8 @@ namespace gtsam {
Conditional(size_t nrFrontals) : nrFrontals_(nrFrontals) {}
/// @}
public:
/// @name Testable
/// @{
@ -76,7 +78,6 @@ namespace gtsam {
/// @}
public:
/// @name Standard Interface
/// @{

2
gtsam/linear/JacobianFactor.cpp
View File

@ -515,7 +515,7 @@ Vector JacobianFactor::error_vector(const VectorValues& c) const {
double JacobianFactor::error(const VectorValues& c) const {
Vector e = unweighted_error(c);
// Use the noise model distance function to get the correct error if available.
if (model_) return 0.5 * model_->distance(e);
if (model_) return 0.5 * model_->squaredMahalanobisDistance(e);
return 0.5 * e.dot(e);
}

74
gtsam/linear/LossFunctions.cpp
View File

@ -137,12 +137,12 @@ Fair::Fair(double c, const ReweightScheme reweight) : Base(reweight), c_(c) {
}
}
double Fair::weight(double error) const {
return 1.0 / (1.0 + std::abs(error) / c_);
double Fair::weight(double distance) const {
return 1.0 / (1.0 + std::abs(distance) / c_);
}
double Fair::residual(double error) const {
const double absError = std::abs(error);
double Fair::loss(double distance) const {
const double absError = std::abs(distance);
const double normalizedError = absError / c_;
const double c_2 = c_ * c_;
return c_2 * (normalizedError - std::log1p(normalizedError));
@ -170,15 +170,15 @@ Huber::Huber(double k, const ReweightScheme reweight) : Base(reweight), k_(k) {
}
}
double Huber::weight(double error) const {
const double absError = std::abs(error);
double Huber::weight(double distance) const {
const double absError = std::abs(distance);
return (absError <= k_) ? (1.0) : (k_ / absError);
}
double Huber::residual(double error) const {
const double absError = std::abs(error);
double Huber::loss(double distance) const {
const double absError = std::abs(distance);
if (absError <= k_) { // |x| <= k
return error*error / 2;
return distance*distance / 2;
} else { // |x| > k
return k_ * (absError - (k_/2));
}
@ -208,12 +208,12 @@ Cauchy::Cauchy(double k, const ReweightScheme reweight) : Base(reweight), k_(k),
}
}
double Cauchy::weight(double error) const {
return ksquared_ / (ksquared_ + error*error);
double Cauchy::weight(double distance) const {
return ksquared_ / (ksquared_ + distance*distance);
}
double Cauchy::residual(double error) const {
const double val = std::log1p(error * error / ksquared_);
double Cauchy::loss(double distance) const {
const double val = std::log1p(distance * distance / ksquared_);
return ksquared_ * val * 0.5;
}
@ -241,18 +241,18 @@ Tukey::Tukey(double c, const ReweightScheme reweight) : Base(reweight), c_(c), c
}
}
double Tukey::weight(double error) const {
if (std::abs(error) <= c_) {
const double one_minus_xc2 = 1.0 - error*error/csquared_;
double Tukey::weight(double distance) const {
if (std::abs(distance) <= c_) {
const double one_minus_xc2 = 1.0 - distance*distance/csquared_;
return one_minus_xc2 * one_minus_xc2;
}
return 0.0;
}
double Tukey::residual(double error) const {
double absError = std::abs(error);
double Tukey::loss(double distance) const {
double absError = std::abs(distance);
if (absError <= c_) {
const double one_minus_xc2 = 1.0 - error*error/csquared_;
const double one_minus_xc2 = 1.0 - distance*distance/csquared_;
const double t = one_minus_xc2*one_minus_xc2*one_minus_xc2;
return csquared_ * (1 - t) / 6.0;
} else {
@ -280,13 +280,13 @@ Tukey::shared_ptr Tukey::Create(double c, const ReweightScheme reweight) {
Welsch::Welsch(double c, const ReweightScheme reweight) : Base(reweight), c_(c), csquared_(c * c) {}
double Welsch::weight(double error) const {
const double xc2 = (error*error)/csquared_;
double Welsch::weight(double distance) const {
const double xc2 = (distance*distance)/csquared_;
return std::exp(-xc2);
}
double Welsch::residual(double error) const {
const double xc2 = (error*error)/csquared_;
double Welsch::loss(double distance) const {
const double xc2 = (distance*distance)/csquared_;
return csquared_ * 0.5 * -std::expm1(-xc2);
}
@ -311,16 +311,16 @@ GemanMcClure::GemanMcClure(double c, const ReweightScheme reweight)
: Base(reweight), c_(c) {
}
double GemanMcClure::weight(double error) const {
double GemanMcClure::weight(double distance) const {
const double c2 = c_*c_;
const double c4 = c2*c2;
const double c2error = c2 + error*error;
const double c2error = c2 + distance*distance;
return c4/(c2error*c2error);
}
double GemanMcClure::residual(double error) const {
double GemanMcClure::loss(double distance) const {
const double c2 = c_*c_;
const double error2 = error*error;
const double error2 = distance*distance;
return 0.5 * (c2 * error2) / (c2 + error2);
}
@ -345,8 +345,8 @@ DCS::DCS(double c, const ReweightScheme reweight)
: Base(reweight), c_(c) {
}
double DCS::weight(double error) const {
const double e2 = error*error;
double DCS::weight(double distance) const {
const double e2 = distance*distance;
if (e2 > c_)
{
const double w = 2.0*c_/(c_ + e2);
@ -356,10 +356,10 @@ double DCS::weight(double error) const {
return 1.0;
}
double DCS::residual(double error) const {
double DCS::loss(double distance) const {
// This is the simplified version of Eq 9 from (Agarwal13icra)
// after you simplify and cancel terms.
const double e2 = error*error;
const double e2 = distance*distance;
const double e4 = e2*e2;
const double c2 = c_*c_;
@ -391,17 +391,17 @@ L2WithDeadZone::L2WithDeadZone(double k, const ReweightScheme reweight)
}
}
double L2WithDeadZone::weight(double error) const {
double L2WithDeadZone::weight(double distance) const {
// note that this code is slightly uglier than residual, because there are three distinct
// cases to handle (left of deadzone, deadzone, right of deadzone) instead of the two
// cases (deadzone, non-deadzone) in residual.
if (std::abs(error) <= k_) return 0.0;
else if (error > k_) return (-k_+error)/error;
else return (k_+error)/error;
if (std::abs(distance) <= k_) return 0.0;
else if (distance > k_) return (-k_+distance)/distance;
else return (k_+distance)/distance;
}
double L2WithDeadZone::residual(double error) const {
const double abs_error = std::abs(error);
double L2WithDeadZone::loss(double distance) const {
const double abs_error = std::abs(distance);
return (abs_error < k_) ? 0.0 : 0.5*(k_-abs_error)*(k_-abs_error);
}

56
gtsam/linear/LossFunctions.h
View File

@ -36,12 +36,12 @@ namespace noiseModel {
* The mEstimator name space contains all robust error functions.
* It mirrors the exposition at
* https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
* which talks about minimizing \sum \rho(r_i), where \rho is a residual function of choice.
* which talks about minimizing \sum \rho(r_i), where \rho is a loss function of choice.
*
* To illustrate, let's consider the least-squares (L2), L1, and Huber estimators as examples:
*
* Name Symbol Least-Squares L1-norm Huber
* Residual \rho(x) 0.5*x^2 |x| 0.5*x^2 if |x|<k, 0.5*k^2 + k|x-k| otherwise
* Loss \rho(x) 0.5*x^2 |x| 0.5*x^2 if |x|<k, 0.5*k^2 + k|x-k| otherwise
* Derivative \phi(x) x sgn(x) x if |x|<k, k sgn(x) otherwise
* Weight w(x)=\phi(x)/x 1 1/|x| 1 if |x|<k, k/|x| otherwise
*
@ -75,27 +75,31 @@ class GTSAM_EXPORT Base {
* the quadratic function for an L2 penalty, the absolute value function for
* an L1 penalty, etc.
*
* TODO(mikebosse): When the residual function has as input the norm of the
* residual vector, then it prevents implementations of asymmeric loss
* TODO(mikebosse): When the loss function has as input the norm of the
* error vector, then it prevents implementations of asymmeric loss
* functions. It would be better for this function to accept the vector and
* internally call the norm if necessary.
*/
virtual double residual(double error) const { return 0; };
virtual double loss(double distance) const { return 0; };
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
virtual double residual(double distance) const { return loss(distance); };
#endif
/*
* This method is responsible for returning the weight function for a given
* amount of error. The weight function is related to the analytic derivative
* of the residual function. See
* of the loss function. See
* https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
* for details. This method is required when optimizing cost functions with
* robust penalties using iteratively re-weighted least squares.
*/
virtual double weight(double error) const = 0;
virtual double weight(double distance) const = 0;
virtual void print(const std::string &s) const = 0;
virtual bool equals(const Base &expected, double tol = 1e-8) const = 0;
double sqrtWeight(double error) const { return std::sqrt(weight(error)); }
double sqrtWeight(double distance) const { return std::sqrt(weight(distance)); }
/** produce a weight vector according to an error vector and the implemented
* robust function */
@ -123,7 +127,7 @@ class GTSAM_EXPORT Base {
}
};
/// Null class is not robust so is a Gaussian ?
/// Null class should behave as Gaussian
class GTSAM_EXPORT Null : public Base {
public:
typedef boost::shared_ptr<Null> shared_ptr;
@ -131,7 +135,7 @@ class GTSAM_EXPORT Null : public Base {
Null(const ReweightScheme reweight = Block) : Base(reweight) {}
~Null() {}
double weight(double /*error*/) const { return 1.0; }
double residual(double error) const { return error; }
double loss(double distance) const { return 0.5 * distance * distance; }
void print(const std::string &s) const;
bool equals(const Base & /*expected*/, double /*tol*/) const { return true; }
static shared_ptr Create();
@ -154,8 +158,8 @@ class GTSAM_EXPORT Fair : public Base {
typedef boost::shared_ptr<Fair> shared_ptr;
Fair(double c = 1.3998, const ReweightScheme reweight = Block);
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double c, const ReweightScheme reweight = Block);
@ -179,8 +183,8 @@ class GTSAM_EXPORT Huber : public Base {
typedef boost::shared_ptr<Huber> shared_ptr;
Huber(double k = 1.345, const ReweightScheme reweight = Block);
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
@ -209,8 +213,8 @@ class GTSAM_EXPORT Cauchy : public Base {
typedef boost::shared_ptr<Cauchy> shared_ptr;
Cauchy(double k = 0.1, const ReweightScheme reweight = Block);
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
@ -234,8 +238,8 @@ class GTSAM_EXPORT Tukey : public Base {
typedef boost::shared_ptr<Tukey> shared_ptr;
Tukey(double c = 4.6851, const ReweightScheme reweight = Block);
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
@ -259,8 +263,8 @@ class GTSAM_EXPORT Welsch : public Base {
typedef boost::shared_ptr<Welsch> shared_ptr;
Welsch(double c = 2.9846, const ReweightScheme reweight = Block);
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
@ -295,8 +299,8 @@ class GTSAM_EXPORT GemanMcClure : public Base {
GemanMcClure(double c = 1.0, const ReweightScheme reweight = Block);
~GemanMcClure() {}
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
@ -325,8 +329,8 @@ class GTSAM_EXPORT DCS : public Base {
DCS(double c = 1.0, const ReweightScheme reweight = Block);
~DCS() {}
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
@ -358,8 +362,8 @@ class GTSAM_EXPORT L2WithDeadZone : public Base {
typedef boost::shared_ptr<L2WithDeadZone> shared_ptr;
L2WithDeadZone(double k = 1.0, const ReweightScheme reweight = Block);
double weight(double error) const override;
double residual(double error) const override;
double weight(double distance) const override;
double loss(double distance) const override;
void print(const std::string &s) const override;
bool equals(const Base &expected, double tol = 1e-8) const override;
static shared_ptr Create(double k, const ReweightScheme reweight = Block);

29
gtsam/linear/NoiseModel.cpp
View File

@ -74,6 +74,13 @@ Vector Base::sigmas() const {
throw("Base::sigmas: sigmas() not implemented for this noise model");
}
/* ************************************************************************* */
double Base::squaredMahalanobisDistance(const Vector& v) const {
// Note: for Diagonal, which does ediv_, will be correct for constraints
Vector w = whiten(v);
return w.dot(w);
}
/* ************************************************************************* */
Gaussian::shared_ptr Gaussian::SqrtInformation(const Matrix& R, bool smart) {
size_t m = R.rows(), n = R.cols();
@ -164,13 +171,6 @@ Vector Gaussian::unwhiten(const Vector& v) const {
return backSubstituteUpper(thisR(), v);
}
/* ************************************************************************* */
double Gaussian::squaredMahalanobisDistance(const Vector& v) const {
// Note: for Diagonal, which does ediv_, will be correct for constraints
Vector w = whiten(v);
return w.dot(w);
}
/* ************************************************************************* */
Matrix Gaussian::Whiten(const Matrix& H) const {
return thisR() * H;
@ -376,8 +376,19 @@ Vector Constrained::whiten(const Vector& v) const {
return c;
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/* ************************************************************************* */
double Constrained::distance(const Vector& v) const {
double Constrained::error(const Vector& v) const {
Vector w = Diagonal::whiten(v); // get noisemodel for constrained elements
for (size_t i=0; i<dim_; ++i) // add mu weights on constrained variables
if (constrained(i)) // whiten makes constrained variables zero
w[i] = v[i] * sqrt(mu_[i]); // TODO: may want to store sqrt rather than rebuild
return 0.5 * w.dot(w);
}
#endif
/* ************************************************************************* */
double Constrained::squaredMahalanobisDistance(const Vector& v) const {
Vector w = Diagonal::whiten(v); // get noisemodel for constrained elements
for (size_t i=0; i<dim_; ++i) // add mu weights on constrained variables
if (constrained(i)) // whiten makes constrained variables zero
@ -663,7 +674,7 @@ void Robust::WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const{
}
Robust::shared_ptr Robust::Create(
const RobustModel::shared_ptr &robust, const NoiseModel::shared_ptr noise){
const RobustModel::shared_ptr &robust, const NoiseModel::shared_ptr noise){
return shared_ptr(new Robust(robust,noise));
}

187
gtsam/linear/NoiseModel.h
View File

@ -90,7 +90,27 @@ namespace gtsam {
/// Unwhiten an error vector.
virtual Vector unwhiten(const Vector& v) const = 0;
virtual double distance(const Vector& v) const = 0;
/// Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v>
virtual double squaredMahalanobisDistance(const Vector& v) const;
/// Mahalanobis distance
virtual double mahalanobisDistance(const Vector& v) const {
return std::sqrt(squaredMahalanobisDistance(v));
}
/// loss function, input is Mahalanobis distance
virtual double loss(const double squared_distance) const {
return 0.5 * squared_distance;
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// calculate the error value given measurement error vector
virtual double error(const Vector& v) const = 0;
virtual double distance(const Vector& v) {
return error(v) * 2;
}
#endif
virtual void WhitenSystem(std::vector<Matrix>& A, Vector& b) const = 0;
virtual void WhitenSystem(Matrix& A, Vector& b) const = 0;
@ -200,39 +220,30 @@ namespace gtsam {
*/
static shared_ptr Covariance(const Matrix& covariance, bool smart = true);
virtual void print(const std::string& name) const;
virtual bool equals(const Base& expected, double tol=1e-9) const;
virtual Vector sigmas() const;
virtual Vector whiten(const Vector& v) const;
virtual Vector unwhiten(const Vector& v) const;
/**
* Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v>
*/
virtual double squaredMahalanobisDistance(const Vector& v) const;
/**
* Mahalanobis distance
*/
virtual double mahalanobisDistance(const Vector& v) const {
return std::sqrt(squaredMahalanobisDistance(v));
}
void print(const std::string& name) const override;
bool equals(const Base& expected, double tol=1e-9) const override;
Vector sigmas() const override;
Vector whiten(const Vector& v) const override;
Vector unwhiten(const Vector& v) const override;
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
virtual double Mahalanobis(const Vector& v) const {
return squaredMahalanobisDistance(v);
}
#endif
inline virtual double distance(const Vector& v) const {
return squaredMahalanobisDistance(v);
/**
* error value 0.5 * v'*R'*R*v
*/
inline double error(const Vector& v) const override {
return 0.5 * squaredMahalanobisDistance(v);
}
#endif
/**
* Multiply a derivative with R (derivative of whiten)
* Equivalent to whitening each column of the input matrix.
*/
virtual Matrix Whiten(const Matrix& H) const;
Matrix Whiten(const Matrix& H) const override;
/**
* In-place version
@ -247,10 +258,10 @@ namespace gtsam {
/**
* Whiten a system, in place as well
*/
virtual void WhitenSystem(std::vector<Matrix>& A, Vector& b) const;
virtual void WhitenSystem(Matrix& A, Vector& b) const;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const;
void WhitenSystem(std::vector<Matrix>& A, Vector& b) const override;
void WhitenSystem(Matrix& A, Vector& b) const override;
void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const override;
void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const override;
/**
* Apply appropriately weighted QR factorization to the system [A b]
@ -335,13 +346,13 @@ namespace gtsam {
return Variances(precisions.array().inverse(), smart);
}
virtual void print(const std::string& name) const;
virtual Vector sigmas() const { return sigmas_; }
virtual Vector whiten(const Vector& v) const;
virtual Vector unwhiten(const Vector& v) const;
virtual Matrix Whiten(const Matrix& H) const;
virtual void WhitenInPlace(Matrix& H) const;
virtual void WhitenInPlace(Eigen::Block<Matrix> H) const;
void print(const std::string& name) const override;
Vector sigmas() const override { return sigmas_; }
Vector whiten(const Vector& v) const override;
Vector unwhiten(const Vector& v) const override;
Matrix Whiten(const Matrix& H) const override;
void WhitenInPlace(Matrix& H) const override;
void WhitenInPlace(Eigen::Block<Matrix> H) const override;
/**
* Return standard deviations (sqrt of diagonal)
@ -363,7 +374,7 @@ namespace gtsam {
/**
* Return R itself, but note that Whiten(H) is cheaper than R*H
*/
virtual Matrix R() const {
Matrix R() const override {
return invsigmas().asDiagonal();
}
@ -417,10 +428,10 @@ namespace gtsam {
typedef boost::shared_ptr<Constrained> shared_ptr;
virtual ~Constrained() {}
~Constrained() {}
/// true if a constrained noise mode, saves slow/clumsy dynamic casting
virtual bool isConstrained() const { return true; }
bool isConstrained() const override { return true; }
/// Return true if a particular dimension is free or constrained
bool constrained(size_t i) const;
@ -472,12 +483,16 @@ namespace gtsam {
return MixedVariances(precisions.array().inverse());
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/**
* The distance function for a constrained noisemodel,
* The error function for a constrained noisemodel,
* for non-constrained versions, uses sigmas, otherwise
* uses the penalty function with mu
*/
virtual double distance(const Vector& v) const;
double error(const Vector& v) const override;
#endif
double squaredMahalanobisDistance(const Vector& v) const override;
/** Fully constrained variations */
static shared_ptr All(size_t dim) {
@ -494,16 +509,16 @@ namespace gtsam {
return shared_ptr(new Constrained(Vector::Constant(dim, mu), Vector::Constant(dim,0)));
}
virtual void print(const std::string& name) const;
void print(const std::string& name) const override;
/// Calculates error vector with weights applied
virtual Vector whiten(const Vector& v) const;
Vector whiten(const Vector& v) const override;
/// Whitening functions will perform partial whitening on rows
/// with a non-zero sigma. Other rows remain untouched.
virtual Matrix Whiten(const Matrix& H) const;
virtual void WhitenInPlace(Matrix& H) const;
virtual void WhitenInPlace(Eigen::Block<Matrix> H) const;
Matrix Whiten(const Matrix& H) const override;
void WhitenInPlace(Matrix& H) const override;
void WhitenInPlace(Eigen::Block<Matrix> H) const override;
/**
* Apply QR factorization to the system [A b], taking into account constraints
@ -514,7 +529,7 @@ namespace gtsam {
* @param Ab is the m*(n+1) augmented system matrix [A b]
* @return diagonal noise model can be all zeros, mixed, or not-constrained
*/
virtual Diagonal::shared_ptr QR(Matrix& Ab) const;
Diagonal::shared_ptr QR(Matrix& Ab) const override;
/**
* Returns a Unit version of a constrained noisemodel in which
@ -576,14 +591,14 @@ namespace gtsam {
return Variance(dim, 1.0/precision, smart);
}
virtual void print(const std::string& name) const;
virtual double squaredMahalanobisDistance(const Vector& v) const;
virtual Vector whiten(const Vector& v) const;
virtual Vector unwhiten(const Vector& v) const;
virtual Matrix Whiten(const Matrix& H) const;
virtual void WhitenInPlace(Matrix& H) const;
virtual void whitenInPlace(Vector& v) const;
virtual void WhitenInPlace(Eigen::Block<Matrix> H) const;
void print(const std::string& name) const override;
double squaredMahalanobisDistance(const Vector& v) const override;
Vector whiten(const Vector& v) const override;
Vector unwhiten(const Vector& v) const override;
Matrix Whiten(const Matrix& H) const override;
void WhitenInPlace(Matrix& H) const override;
void whitenInPlace(Vector& v) const override;
void WhitenInPlace(Eigen::Block<Matrix> H) const override;
/**
* Return standard deviation
@ -616,7 +631,7 @@ namespace gtsam {
typedef boost::shared_ptr<Unit> shared_ptr;
virtual ~Unit() {}
~Unit() {}
/**
* Create a unit covariance noise model
@ -626,19 +641,19 @@ namespace gtsam {
}
/// true if a unit noise model, saves slow/clumsy dynamic casting
virtual bool isUnit() const { return true; }
bool isUnit() const override { return true; }
virtual void print(const std::string& name) const;
virtual double squaredMahalanobisDistance(const Vector& v) const {return v.dot(v); }
virtual Vector whiten(const Vector& v) const { return v; }
virtual Vector unwhiten(const Vector& v) const { return v; }
virtual Matrix Whiten(const Matrix& H) const { return H; }
virtual void WhitenInPlace(Matrix& /*H*/) const {}
virtual void WhitenInPlace(Eigen::Block<Matrix> /*H*/) const {}
virtual void whitenInPlace(Vector& /*v*/) const {}
virtual void unwhitenInPlace(Vector& /*v*/) const {}
virtual void whitenInPlace(Eigen::Block<Vector>& /*v*/) const {}
virtual void unwhitenInPlace(Eigen::Block<Vector>& /*v*/) const {}
void print(const std::string& name) const override;
double squaredMahalanobisDistance(const Vector& v) const override {return v.dot(v); }
Vector whiten(const Vector& v) const override { return v; }
Vector unwhiten(const Vector& v) const override { return v; }
Matrix Whiten(const Matrix& H) const override { return H; }
void WhitenInPlace(Matrix& /*H*/) const override {}
void WhitenInPlace(Eigen::Block<Matrix> /*H*/) const override {}
void whitenInPlace(Vector& /*v*/) const override {}
void unwhitenInPlace(Vector& /*v*/) const override {}
void whitenInPlace(Eigen::Block<Vector>& /*v*/) const override {}
void unwhitenInPlace(Eigen::Block<Vector>& /*v*/) const override {}
private:
/** Serialization function */
@ -687,10 +702,10 @@ namespace gtsam {
: Base(noise->dim()), robust_(robust), noise_(noise) {}
/// Destructor
virtual ~Robust() {}
~Robust() {}
virtual void print(const std::string& name) const;
virtual bool equals(const Base& expected, double tol=1e-9) const;
void print(const std::string& name) const override;
bool equals(const Base& expected, double tol=1e-9) const override;
/// Return the contained robust error function
const RobustModel::shared_ptr& robust() const { return robust_; }
@ -699,28 +714,36 @@ namespace gtsam {
const NoiseModel::shared_ptr& noise() const { return noise_; }
// TODO: functions below are dummy but necessary for the noiseModel::Base
inline virtual Vector whiten(const Vector& v) const
inline Vector whiten(const Vector& v) const override
{ Vector r = v; this->WhitenSystem(r); return r; }
inline virtual Matrix Whiten(const Matrix& A) const
inline Matrix Whiten(const Matrix& A) const override
{ Vector b; Matrix B=A; this->WhitenSystem(B,b); return B; }
inline virtual Vector unwhiten(const Vector& /*v*/) const
inline Vector unwhiten(const Vector& /*v*/) const override
{ throw std::invalid_argument("unwhiten is not currently supported for robust noise models."); }
// Fold the use of the m-estimator residual(...) function into distance(...)
inline virtual double distance(const Vector& v) const
{ return robust_->residual(this->unweightedWhiten(v).norm()); }
inline virtual double distance_non_whitened(const Vector& v) const
{ return robust_->residual(v.norm()); }
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
inline double distance(const Vector& v) override {
return robust_->loss(this->unweightedWhiten(v).norm());
}
// Fold the use of the m-estimator loss(...) function into error(...)
inline double error(const Vector& v) const override
{ return robust_->loss(noise_->mahalanobisDistance(v)); }
#endif
double loss(const double squared_distance) const override {
return robust_->loss(std::sqrt(squared_distance));
}
// TODO: these are really robust iterated re-weighting support functions
virtual void WhitenSystem(Vector& b) const;
virtual void WhitenSystem(std::vector<Matrix>& A, Vector& b) const;
virtual void WhitenSystem(Matrix& A, Vector& b) const;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const;
void WhitenSystem(std::vector<Matrix>& A, Vector& b) const override;
void WhitenSystem(Matrix& A, Vector& b) const override;
void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const override;
void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const override;
virtual Vector unweightedWhiten(const Vector& v) const {
Vector unweightedWhiten(const Vector& v) const override {
return noise_->unweightedWhiten(v);
}
virtual double weight(const Vector& v) const {
double weight(const Vector& v) const override {
// Todo(mikebosse): make the robust weight function input a vector.
return robust_->weight(v.norm());
}

11
gtsam/linear/PCGSolver.cpp
View File

@ -45,6 +45,17 @@ PCGSolver::PCGSolver(const PCGSolverParameters &p) {
preconditioner_ = createPreconditioner(p.preconditioner_);
}
void PCGSolverParameters::setPreconditionerParams(const boost::shared_ptr<PreconditionerParameters> preconditioner) {
preconditioner_ = preconditioner;
}
void PCGSolverParameters::print(const std::string &s) const {
std::cout << s << std::endl;;
std::ostringstream os;
print(os);
std::cout << os.str() << std::endl;
}
/*****************************************************************************/
VectorValues PCGSolver::optimize(const GaussianFactorGraph &gfg,
const KeyInfo &keyInfo, const std::map<Key, Vector> &lambda,

5
gtsam/linear/PCGSolver.h
View File

@ -48,7 +48,12 @@ public:
return *preconditioner_;
}
// needed for python wrapper
void print(const std::string &s) const;
boost::shared_ptr<PreconditionerParameters> preconditioner_;
void setPreconditionerParams(const boost::shared_ptr<PreconditionerParameters> preconditioner);
};
/**

115
gtsam/linear/tests/testNoiseModel.cpp
View File

@ -182,8 +182,9 @@ TEST(NoiseModel, ConstrainedMixed )
EXPECT(assert_equal(Vector3(0.5, 1.0, 0.5),d->whiten(infeasible)));
EXPECT(assert_equal(Vector3(0.5, 0.0, 0.5),d->whiten(feasible)));
DOUBLES_EQUAL(1000.0 + 0.25 + 0.25,d->distance(infeasible),1e-9);
DOUBLES_EQUAL(0.5,d->distance(feasible),1e-9);
DOUBLES_EQUAL(0.5 * (1000.0 + 0.25 + 0.25),d->loss(d->squaredMahalanobisDistance(infeasible)),1e-9);
DOUBLES_EQUAL(0.5, d->squaredMahalanobisDistance(feasible),1e-9);
DOUBLES_EQUAL(0.5 * 0.5, d->loss(0.5),1e-9);
}
/* ************************************************************************* */
@ -197,8 +198,9 @@ TEST(NoiseModel, ConstrainedAll )
EXPECT(assert_equal(Vector3(1.0, 1.0, 1.0),i->whiten(infeasible)));
EXPECT(assert_equal(Vector3(0.0, 0.0, 0.0),i->whiten(feasible)));
DOUBLES_EQUAL(1000.0 * 3.0,i->distance(infeasible),1e-9);
DOUBLES_EQUAL(0.0,i->distance(feasible),1e-9);
DOUBLES_EQUAL(0.5 * 1000.0 * 3.0,i->loss(i->squaredMahalanobisDistance(infeasible)),1e-9);
DOUBLES_EQUAL(0.0, i->squaredMahalanobisDistance(feasible), 1e-9);
DOUBLES_EQUAL(0.0, i->loss(0.0),1e-9);
}
/* ************************************************************************* */
@ -451,7 +453,7 @@ TEST(NoiseModel, WhitenInPlace)
/*
* These tests are responsible for testing the weight functions for the m-estimators in GTSAM.
* The weight function is related to the analytic derivative of the residual function. See
* The weight function is related to the analytic derivative of the loss function. See
* https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
* for details. This weight function is required when optimizing cost functions with robust
* penalties using iteratively re-weighted least squares.
@ -467,10 +469,10 @@ TEST(NoiseModel, robustFunctionFair)
DOUBLES_EQUAL(0.3333333333333333, fair->weight(error3), 1e-8);
DOUBLES_EQUAL(0.8333333333333333, fair->weight(error4), 1e-8);
DOUBLES_EQUAL(0.441961080151135, fair->residual(error1), 1e-8);
DOUBLES_EQUAL(22.534692783297260, fair->residual(error2), 1e-8);
DOUBLES_EQUAL(22.534692783297260, fair->residual(error3), 1e-8);
DOUBLES_EQUAL(0.441961080151135, fair->residual(error4), 1e-8);
DOUBLES_EQUAL(0.441961080151135, fair->loss(error1), 1e-8);
DOUBLES_EQUAL(22.534692783297260, fair->loss(error2), 1e-8);
DOUBLES_EQUAL(22.534692783297260, fair->loss(error3), 1e-8);
DOUBLES_EQUAL(0.441961080151135, fair->loss(error4), 1e-8);
}
TEST(NoiseModel, robustFunctionHuber)
@ -483,10 +485,10 @@ TEST(NoiseModel, robustFunctionHuber)
DOUBLES_EQUAL(0.5, huber->weight(error3), 1e-8);
DOUBLES_EQUAL(1.0, huber->weight(error4), 1e-8);
DOUBLES_EQUAL(0.5000, huber->residual(error1), 1e-8);
DOUBLES_EQUAL(37.5000, huber->residual(error2), 1e-8);
DOUBLES_EQUAL(37.5000, huber->residual(error3), 1e-8);
DOUBLES_EQUAL(0.5000, huber->residual(error4), 1e-8);
DOUBLES_EQUAL(0.5000, huber->loss(error1), 1e-8);
DOUBLES_EQUAL(37.5000, huber->loss(error2), 1e-8);
DOUBLES_EQUAL(37.5000, huber->loss(error3), 1e-8);
DOUBLES_EQUAL(0.5000, huber->loss(error4), 1e-8);
}
TEST(NoiseModel, robustFunctionCauchy)
@ -499,10 +501,10 @@ TEST(NoiseModel, robustFunctionCauchy)
DOUBLES_EQUAL(0.2000, cauchy->weight(error3), 1e-8);
DOUBLES_EQUAL(0.961538461538461, cauchy->weight(error4), 1e-8);
DOUBLES_EQUAL(0.490258914416017, cauchy->residual(error1), 1e-8);
DOUBLES_EQUAL(20.117973905426254, cauchy->residual(error2), 1e-8);
DOUBLES_EQUAL(20.117973905426254, cauchy->residual(error3), 1e-8);
DOUBLES_EQUAL(0.490258914416017, cauchy->residual(error4), 1e-8);
DOUBLES_EQUAL(0.490258914416017, cauchy->loss(error1), 1e-8);
DOUBLES_EQUAL(20.117973905426254, cauchy->loss(error2), 1e-8);
DOUBLES_EQUAL(20.117973905426254, cauchy->loss(error3), 1e-8);
DOUBLES_EQUAL(0.490258914416017, cauchy->loss(error4), 1e-8);
}
TEST(NoiseModel, robustFunctionGemanMcClure)
@ -514,10 +516,10 @@ TEST(NoiseModel, robustFunctionGemanMcClure)
DOUBLES_EQUAL(9.80296e-5, gmc->weight(error3), 1e-8);
DOUBLES_EQUAL(0.25 , gmc->weight(error4), 1e-8);
DOUBLES_EQUAL(0.2500, gmc->residual(error1), 1e-8);
DOUBLES_EQUAL(0.495049504950495, gmc->residual(error2), 1e-8);
DOUBLES_EQUAL(0.495049504950495, gmc->residual(error3), 1e-8);
DOUBLES_EQUAL(0.2500, gmc->residual(error4), 1e-8);
DOUBLES_EQUAL(0.2500, gmc->loss(error1), 1e-8);
DOUBLES_EQUAL(0.495049504950495, gmc->loss(error2), 1e-8);
DOUBLES_EQUAL(0.495049504950495, gmc->loss(error3), 1e-8);
DOUBLES_EQUAL(0.2500, gmc->loss(error4), 1e-8);
}
TEST(NoiseModel, robustFunctionWelsch)
@ -530,10 +532,10 @@ TEST(NoiseModel, robustFunctionWelsch)
DOUBLES_EQUAL(0.018315638888734, welsch->weight(error3), 1e-8);
DOUBLES_EQUAL(0.960789439152323, welsch->weight(error4), 1e-8);
DOUBLES_EQUAL(0.490132010595960, welsch->residual(error1), 1e-8);
DOUBLES_EQUAL(12.271054513890823, welsch->residual(error2), 1e-8);
DOUBLES_EQUAL(12.271054513890823, welsch->residual(error3), 1e-8);
DOUBLES_EQUAL(0.490132010595960, welsch->residual(error4), 1e-8);
DOUBLES_EQUAL(0.490132010595960, welsch->loss(error1), 1e-8);
DOUBLES_EQUAL(12.271054513890823, welsch->loss(error2), 1e-8);
DOUBLES_EQUAL(12.271054513890823, welsch->loss(error3), 1e-8);
DOUBLES_EQUAL(0.490132010595960, welsch->loss(error4), 1e-8);
}
TEST(NoiseModel, robustFunctionTukey)
@ -546,10 +548,10 @@ TEST(NoiseModel, robustFunctionTukey)
DOUBLES_EQUAL(0.0, tukey->weight(error3), 1e-8);
DOUBLES_EQUAL(0.9216, tukey->weight(error4), 1e-8);
DOUBLES_EQUAL(0.480266666666667, tukey->residual(error1), 1e-8);
DOUBLES_EQUAL(4.166666666666667, tukey->residual(error2), 1e-8);
DOUBLES_EQUAL(4.166666666666667, tukey->residual(error3), 1e-8);
DOUBLES_EQUAL(0.480266666666667, tukey->residual(error4), 1e-8);
DOUBLES_EQUAL(0.480266666666667, tukey->loss(error1), 1e-8);
DOUBLES_EQUAL(4.166666666666667, tukey->loss(error2), 1e-8);
DOUBLES_EQUAL(4.166666666666667, tukey->loss(error3), 1e-8);
DOUBLES_EQUAL(0.480266666666667, tukey->loss(error4), 1e-8);
}
TEST(NoiseModel, robustFunctionDCS)
@ -560,8 +562,8 @@ TEST(NoiseModel, robustFunctionDCS)
DOUBLES_EQUAL(1.0 , dcs->weight(error1), 1e-8);
DOUBLES_EQUAL(0.00039211, dcs->weight(error2), 1e-8);
DOUBLES_EQUAL(0.5 , dcs->residual(error1), 1e-8);
DOUBLES_EQUAL(0.9900990099, dcs->residual(error2), 1e-8);
DOUBLES_EQUAL(0.5 , dcs->loss(error1), 1e-8);
DOUBLES_EQUAL(0.9900990099, dcs->loss(error2), 1e-8);
}
TEST(NoiseModel, robustFunctionL2WithDeadZone)
@ -576,12 +578,12 @@ TEST(NoiseModel, robustFunctionL2WithDeadZone)
DOUBLES_EQUAL(0.00990099009, lsdz->weight(e4), 1e-8);
DOUBLES_EQUAL(0.9, lsdz->weight(e5), 1e-8);
DOUBLES_EQUAL(40.5, lsdz->residual(e0), 1e-8);
DOUBLES_EQUAL(0.00005, lsdz->residual(e1), 1e-8);
DOUBLES_EQUAL(0.0, lsdz->residual(e2), 1e-8);
DOUBLES_EQUAL(0.0, lsdz->residual(e3), 1e-8);
DOUBLES_EQUAL(0.00005, lsdz->residual(e4), 1e-8);
DOUBLES_EQUAL(40.5, lsdz->residual(e5), 1e-8);
DOUBLES_EQUAL(40.5, lsdz->loss(e0), 1e-8);
DOUBLES_EQUAL(0.00005, lsdz->loss(e1), 1e-8);
DOUBLES_EQUAL(0.0, lsdz->loss(e2), 1e-8);
DOUBLES_EQUAL(0.0, lsdz->loss(e3), 1e-8);
DOUBLES_EQUAL(0.00005, lsdz->loss(e4), 1e-8);
DOUBLES_EQUAL(40.5, lsdz->loss(e5), 1e-8);
}
/* ************************************************************************* */
@ -665,11 +667,11 @@ TEST(NoiseModel, robustNoiseL2WithDeadZone)
/*
* TODO(mike): There is currently a bug in GTSAM, where none of the mEstimator classes
* implement a residual function, and GTSAM calls the weight function to evaluate the
* total penalty, rather than calling the residual function. The weight function should be
* implement a loss function, and GTSAM calls the weight function to evaluate the
* total penalty, rather than calling the loss function. The weight function should be
* used during iteratively reweighted least squares optimization, but should not be used to
* evaluate the total penalty. The long-term solution is for all mEstimators to implement
* both a weight and a residual function, and for GTSAM to call the residual function when
* both a weight and a loss function, and for GTSAM to call the loss function when
* evaluating the total penalty. This bug causes the test below to fail, so I'm leaving it
* commented out until the underlying bug in GTSAM is fixed.
*
@ -681,13 +683,44 @@ TEST(NoiseModel, robustNoiseL2WithDeadZone)
}
TEST(NoiseModel, lossFunctionAtZero)
{
const double k = 5.0;
auto fair = mEstimator::Fair::Create(k);
DOUBLES_EQUAL(fair->loss(0), 0, 1e-8);
DOUBLES_EQUAL(fair->weight(0), 1, 1e-8);
auto huber = mEstimator::Huber::Create(k);
DOUBLES_EQUAL(huber->loss(0), 0, 1e-8);
DOUBLES_EQUAL(huber->weight(0), 1, 1e-8);
auto cauchy = mEstimator::Cauchy::Create(k);
DOUBLES_EQUAL(cauchy->loss(0), 0, 1e-8);
DOUBLES_EQUAL(cauchy->weight(0), 1, 1e-8);
auto gmc = mEstimator::GemanMcClure::Create(k);
DOUBLES_EQUAL(gmc->loss(0), 0, 1e-8);
DOUBLES_EQUAL(gmc->weight(0), 1, 1e-8);
auto welsch = mEstimator::Welsch::Create(k);
DOUBLES_EQUAL(welsch->loss(0), 0, 1e-8);
DOUBLES_EQUAL(welsch->weight(0), 1, 1e-8);
auto tukey = mEstimator::Tukey::Create(k);
DOUBLES_EQUAL(tukey->loss(0), 0, 1e-8);
DOUBLES_EQUAL(tukey->weight(0), 1, 1e-8);
auto dcs = mEstimator::DCS::Create(k);
DOUBLES_EQUAL(dcs->loss(0), 0, 1e-8);
DOUBLES_EQUAL(dcs->weight(0), 1, 1e-8);
// auto lsdz = mEstimator::L2WithDeadZone::Create(k);
// DOUBLES_EQUAL(lsdz->loss(0), 0, 1e-8);
// DOUBLES_EQUAL(lsdz->weight(0), 1, 1e-8);
}
/* ************************************************************************* */
#define TEST_GAUSSIAN(gaussian)\
EQUALITY(info, gaussian->information());\
EQUALITY(cov, gaussian->covariance());\
EXPECT(assert_equal(white, gaussian->whiten(e)));\
EXPECT(assert_equal(e, gaussian->unwhiten(white)));\
EXPECT_DOUBLES_EQUAL(251, gaussian->distance(e), 1e-9);\
EXPECT_DOUBLES_EQUAL(251.0, gaussian->squaredMahalanobisDistance(e), 1e-9);\
EXPECT_DOUBLES_EQUAL(0.5 * 251.0, gaussian->loss(251.0), 1e-9);\
Matrix A = R.inverse(); Vector b = e;\
gaussian->WhitenSystem(A, b);\
EXPECT(assert_equal(I, A));\

37
gtsam/navigation/CombinedImuFactor.cpp
View File

@ -17,9 +17,11 @@
* @author Vadim Indelman
* @author David Jensen
* @author Frank Dellaert
* @author Varun Agrawal
**/
#include <gtsam/navigation/CombinedImuFactor.h>
#include <boost/serialization/export.hpp>
/* External or standard includes */
#include <ostream>
@ -28,6 +30,31 @@ namespace gtsam {
using namespace std;
//------------------------------------------------------------------------------
// Inner class PreintegrationCombinedParams
//------------------------------------------------------------------------------
void PreintegrationCombinedParams::print(const string& s) const {
PreintegrationParams::print(s);
cout << "biasAccCovariance:\n[\n" << biasAccCovariance << "\n]"
<< endl;
cout << "biasOmegaCovariance:\n[\n" << biasOmegaCovariance << "\n]"
<< endl;
cout << "biasAccOmegaInt:\n[\n" << biasAccOmegaInt << "\n]"
<< endl;
}
//------------------------------------------------------------------------------
bool PreintegrationCombinedParams::equals(const PreintegratedRotationParams& other,
double tol) const {
auto e = dynamic_cast<const PreintegrationCombinedParams*>(&other);
return e != nullptr && PreintegrationParams::equals(other, tol) &&
equal_with_abs_tol(biasAccCovariance, e->biasAccCovariance,
tol) &&
equal_with_abs_tol(biasOmegaCovariance, e->biasOmegaCovariance,
tol) &&
equal_with_abs_tol(biasAccOmegaInt, e->biasAccOmegaInt, tol);
}
//------------------------------------------------------------------------------
// Inner class PreintegratedCombinedMeasurements
//------------------------------------------------------------------------------
@ -242,6 +269,13 @@ Vector CombinedImuFactor::evaluateError(const Pose3& pose_i,
return r;
}
//------------------------------------------------------------------------------
std::ostream& operator<<(std::ostream& os, const CombinedImuFactor& f) {
f._PIM_.print("combined preintegrated measurements:\n");
os << " noise model sigmas: " << f.noiseModel_->sigmas().transpose();
return os;
}
//------------------------------------------------------------------------------
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
CombinedImuFactor::CombinedImuFactor(
@ -279,3 +313,6 @@ void CombinedImuFactor::Predict(const Pose3& pose_i, const Vector3& vel_i,
}
/// namespace gtsam
/// Boost serialization export definition for derived class
BOOST_CLASS_EXPORT_IMPLEMENT(gtsam::PreintegrationCombinedParams);

101
gtsam/navigation/CombinedImuFactor.h
View File

@ -17,6 +17,7 @@
* @author Vadim Indelman
* @author David Jensen
* @author Frank Dellaert
* @author Varun Agrawal
**/
#pragma once
@ -26,6 +27,7 @@
#include <gtsam/navigation/TangentPreintegration.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/base/serialization.h>
namespace gtsam {
@ -61,10 +63,18 @@ struct GTSAM_EXPORT PreintegrationCombinedParams : PreintegrationParams {
Matrix3 biasOmegaCovariance; ///< continuous-time "Covariance" describing gyroscope bias random walk
Matrix6 biasAccOmegaInt; ///< covariance of bias used for pre-integration
/// Default constructor makes uninitialized params struct.
/// Used for serialization.
PreintegrationCombinedParams()
: biasAccCovariance(I_3x3),
biasOmegaCovariance(I_3x3),
biasAccOmegaInt(I_6x6) {}
/// See two named constructors below for good values of n_gravity in body frame
PreintegrationCombinedParams(const Vector3& n_gravity) :
PreintegrationParams(n_gravity), biasAccCovariance(I_3x3), biasOmegaCovariance(
I_3x3), biasAccOmegaInt(I_6x6) {
PreintegrationCombinedParams(const Vector3& n_gravity) :
PreintegrationParams(n_gravity), biasAccCovariance(I_3x3),
biasOmegaCovariance(I_3x3), biasAccOmegaInt(I_6x6) {
}
// Default Params for a Z-down navigation frame, such as NED: gravity points along positive Z-axis
@ -77,6 +87,9 @@ PreintegrationCombinedParams(const Vector3& n_gravity) :
return boost::shared_ptr<PreintegrationCombinedParams>(new PreintegrationCombinedParams(Vector3(0, 0, -g)));
}
void print(const std::string& s="") const;
bool equals(const PreintegratedRotationParams& other, double tol) const;
void setBiasAccCovariance(const Matrix3& cov) { biasAccCovariance=cov; }
void setBiasOmegaCovariance(const Matrix3& cov) { biasOmegaCovariance=cov; }
void setBiasAccOmegaInt(const Matrix6& cov) { biasAccOmegaInt=cov; }
@ -86,24 +99,22 @@ PreintegrationCombinedParams(const Vector3& n_gravity) :
const Matrix6& getBiasAccOmegaInt() const { return biasAccOmegaInt; }
private:
/// Default constructor makes unitialized params struct
PreintegrationCombinedParams() {}
/** Serialization function */
friend class boost::serialization::access;
template <class ARCHIVE>
void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegratedRotation::Params);
ar& BOOST_SERIALIZATION_NVP(biasAccCovariance);
ar& BOOST_SERIALIZATION_NVP(biasOmegaCovariance);
ar& BOOST_SERIALIZATION_NVP(biasAccOmegaInt);
void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegrationParams);
ar & BOOST_SERIALIZATION_NVP(biasAccCovariance);
ar & BOOST_SERIALIZATION_NVP(biasOmegaCovariance);
ar & BOOST_SERIALIZATION_NVP(biasAccOmegaInt);
}
public:
GTSAM_MAKE_ALIGNED_OPERATOR_NEW
};
/**
* PreintegratedCombinedMeasurements integrates the IMU measurements
* (rotation rates and accelerations) and the corresponding covariance matrix.
@ -128,7 +139,6 @@ public:
*/
Eigen::Matrix<double, 15, 15> preintMeasCov_;
friend class CombinedImuFactor;
public:
@ -136,11 +146,14 @@ public:
/// @{
/// Default constructor only for serialization and Cython wrapper
PreintegratedCombinedMeasurements() {}
PreintegratedCombinedMeasurements() {
preintMeasCov_.setZero();
}
/**
* Default constructor, initializes the class with no measurements
* @param bias Current estimate of acceleration and rotation rate biases
* @param p Parameters, typically fixed in a single application
* @param biasHat Current estimate of acceleration and rotation rate biases
*/
PreintegratedCombinedMeasurements(
const boost::shared_ptr<Params>& p,
@ -149,6 +162,19 @@ public:
preintMeasCov_.setZero();
}
/**
* Construct preintegrated directly from members: base class and preintMeasCov
* @param base PreintegrationType instance
* @param preintMeasCov Covariance matrix used in noise model.
*/
PreintegratedCombinedMeasurements(const PreintegrationType& base, const Eigen::Matrix<double, 15, 15>& preintMeasCov)
: PreintegrationType(base),
preintMeasCov_(preintMeasCov) {
}
/// Virtual destructor
virtual ~PreintegratedCombinedMeasurements() {}
/// @}
/// @name Basic utilities
@ -158,20 +184,25 @@ public:
void resetIntegration() override;
/// const reference to params, shadows definition in base class
Params& p() const { return *boost::static_pointer_cast<Params>(this->p_);}
Params& p() const { return *boost::static_pointer_cast<Params>(this->p_); }
/// @}
/// @name Access instance variables
/// @{
/// Return pre-integrated measurement covariance
Matrix preintMeasCov() const { return preintMeasCov_; }
/// @}
/// @name Testable
/// @{
/// print
void print(const std::string& s = "Preintegrated Measurements:") const override;
bool equals(const PreintegratedCombinedMeasurements& expected, double tol = 1e-9) const;
/// equals
bool equals(const PreintegratedCombinedMeasurements& expected,
double tol = 1e-9) const;
/// @}
/// @name Main functionality
/// @{
@ -205,6 +236,7 @@ public:
friend class boost::serialization::access;
template <class ARCHIVE>
void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegrationType);
ar& BOOST_SERIALIZATION_NVP(preintMeasCov_);
}
@ -244,9 +276,6 @@ private:
PreintegratedCombinedMeasurements _PIM_;
/** Default constructor - only use for serialization */
CombinedImuFactor() {}
public:
/** Shorthand for a smart pointer to a factor */
@ -256,6 +285,9 @@ public:
typedef boost::shared_ptr<CombinedImuFactor> shared_ptr;
#endif
/** Default constructor - only use for serialization */
CombinedImuFactor() {}
/**
* Constructor
* @param pose_i Previous pose key
@ -277,12 +309,17 @@ public:
/** implement functions needed for Testable */
/// @name Testable
/// @{
GTSAM_EXPORT friend std::ostream& operator<<(std::ostream& os,
const CombinedImuFactor&);
/// print
virtual void print(const std::string& s, const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const;
/// equals
virtual bool equals(const NonlinearFactor& expected, double tol = 1e-9) const;
/// @}
/** Access the preintegrated measurements. */
@ -321,14 +358,12 @@ public:
#endif
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
ar & boost::serialization::make_nvp("NoiseModelFactor6",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(_PIM_);
template <class ARCHIVE>
void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(NoiseModelFactor6);
ar& BOOST_SERIALIZATION_NVP(_PIM_);
}
public:
@ -336,4 +371,18 @@ public:
};
// class CombinedImuFactor
} /// namespace gtsam
template <>
struct traits<PreintegrationCombinedParams>
: public Testable<PreintegrationCombinedParams> {};
template <>
struct traits<PreintegratedCombinedMeasurements>
: public Testable<PreintegratedCombinedMeasurements> {};
template <>
struct traits<CombinedImuFactor> : public Testable<CombinedImuFactor> {};
} // namespace gtsam
/// Add Boost serialization export key (declaration) for derived class
BOOST_CLASS_EXPORT_KEY(gtsam::PreintegrationCombinedParams);

8
gtsam/navigation/ImuFactor.h
View File

@ -59,7 +59,7 @@ typedef ManifoldPreintegration PreintegrationType;
*/
/**
* PreintegratedIMUMeasurements accumulates (integrates) the IMU measurements
* PreintegratedImuMeasurements accumulates (integrates) the IMU measurements
* (rotation rates and accelerations) and the corresponding covariance matrix.
* The measurements are then used to build the Preintegrated IMU factor.
* Integration is done incrementally (ideally, one integrates the measurement
@ -87,8 +87,8 @@ public:
/**
* Constructor, initializes the class with no measurements
* @param bias Current estimate of acceleration and rotation rate biases
* @param p Parameters, typically fixed in a single application
* @param p Parameters, typically fixed in a single application
* @param biasHat Current estimate of acceleration and rotation rate biases
*/
PreintegratedImuMeasurements(const boost::shared_ptr<PreintegrationParams>& p,
const imuBias::ConstantBias& biasHat = imuBias::ConstantBias()) :
@ -164,7 +164,7 @@ private:
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegrationType);
ar & bs::make_nvp("preintMeasCov_", bs::make_array(preintMeasCov_.data(), preintMeasCov_.size()));
ar & BOOST_SERIALIZATION_NVP(preintMeasCov_);
}
};

14
gtsam/navigation/ManifoldPreintegration.h
View File

@ -118,15 +118,13 @@ private:
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & BOOST_SERIALIZATION_NVP(p_);
ar & BOOST_SERIALIZATION_NVP(deltaTij_);
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegrationBase);
ar & BOOST_SERIALIZATION_NVP(deltaXij_);
ar & BOOST_SERIALIZATION_NVP(biasHat_);
ar & bs::make_nvp("delRdelBiasOmega_", bs::make_array(delRdelBiasOmega_.data(), delRdelBiasOmega_.size()));
ar & bs::make_nvp("delPdelBiasAcc_", bs::make_array(delPdelBiasAcc_.data(), delPdelBiasAcc_.size()));
ar & bs::make_nvp("delPdelBiasOmega_", bs::make_array(delPdelBiasOmega_.data(), delPdelBiasOmega_.size()));
ar & bs::make_nvp("delVdelBiasAcc_", bs::make_array(delVdelBiasAcc_.data(), delVdelBiasAcc_.size()));
ar & bs::make_nvp("delVdelBiasOmega_", bs::make_array(delVdelBiasOmega_.data(), delVdelBiasOmega_.size()));
ar & BOOST_SERIALIZATION_NVP(delRdelBiasOmega_);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasAcc_);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasOmega_);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasAcc_);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasOmega_);
}
};

6
gtsam/navigation/PreintegratedRotation.cpp
View File

@ -25,7 +25,7 @@ using namespace std;
namespace gtsam {
void PreintegratedRotation::Params::print(const string& s) const {
void PreintegratedRotationParams::print(const string& s) const {
cout << s << endl;
cout << "gyroscopeCovariance:\n[\n" << gyroscopeCovariance << "\n]" << endl;
if (omegaCoriolis)
@ -34,8 +34,8 @@ void PreintegratedRotation::Params::print(const string& s) const {
body_P_sensor->print("body_P_sensor");
}
bool PreintegratedRotation::Params::equals(
const PreintegratedRotation::Params& other, double tol) const {
bool PreintegratedRotationParams::equals(
const PreintegratedRotationParams& other, double tol) const {
if (body_P_sensor) {
if (!other.body_P_sensor
|| !assert_equal(*body_P_sensor, *other.body_P_sensor, tol))

10
gtsam/navigation/PreintegratedRotation.h
View File

@ -61,9 +61,15 @@ struct GTSAM_EXPORT PreintegratedRotationParams {
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & bs::make_nvp("gyroscopeCovariance", bs::make_array(gyroscopeCovariance.data(), gyroscopeCovariance.size()));
ar & BOOST_SERIALIZATION_NVP(omegaCoriolis);
ar & BOOST_SERIALIZATION_NVP(gyroscopeCovariance);
ar & BOOST_SERIALIZATION_NVP(body_P_sensor);
// Provide support for Eigen::Matrix in boost::optional
bool omegaCoriolisFlag = omegaCoriolis.is_initialized();
ar & boost::serialization::make_nvp("omegaCoriolisFlag", omegaCoriolisFlag);
if (omegaCoriolisFlag) {
ar & BOOST_SERIALIZATION_NVP(*omegaCoriolis);
}
}
#ifdef GTSAM_USE_QUATERNIONS

10
gtsam/navigation/PreintegrationBase.h
View File

@ -213,6 +213,16 @@ class GTSAM_EXPORT PreintegrationBase {
/// @}
#endif
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
ar & BOOST_SERIALIZATION_NVP(p_);
ar & BOOST_SERIALIZATION_NVP(biasHat_);
ar & BOOST_SERIALIZATION_NVP(deltaTij_);
}
public:
GTSAM_MAKE_ALIGNED_OPERATOR_NEW
};

6
gtsam/navigation/PreintegrationParams.cpp
View File

@ -27,7 +27,7 @@ namespace gtsam {
//------------------------------------------------------------------------------
void PreintegrationParams::print(const string& s) const {
PreintegratedRotation::Params::print(s);
PreintegratedRotationParams::print(s);
cout << "accelerometerCovariance:\n[\n" << accelerometerCovariance << "\n]"
<< endl;
cout << "integrationCovariance:\n[\n" << integrationCovariance << "\n]"
@ -39,10 +39,10 @@ void PreintegrationParams::print(const string& s) const {
}
//------------------------------------------------------------------------------
bool PreintegrationParams::equals(const PreintegratedRotation::Params& other,
bool PreintegrationParams::equals(const PreintegratedRotationParams& other,
double tol) const {
auto e = dynamic_cast<const PreintegrationParams*>(&other);
return e != nullptr && PreintegratedRotation::Params::equals(other, tol) &&
return e != nullptr && PreintegratedRotationParams::equals(other, tol) &&
use2ndOrderCoriolis == e->use2ndOrderCoriolis &&
equal_with_abs_tol(accelerometerCovariance, e->accelerometerCovariance,
tol) &&

17
gtsam/navigation/PreintegrationParams.h
View File

@ -31,7 +31,8 @@ struct GTSAM_EXPORT PreintegrationParams: PreintegratedRotationParams {
/// Default constructor for serialization only
PreintegrationParams()
: accelerometerCovariance(I_3x3),
: PreintegratedRotationParams(),
accelerometerCovariance(I_3x3),
integrationCovariance(I_3x3),
use2ndOrderCoriolis(false),
n_gravity(0, 0, -1) {}
@ -39,7 +40,8 @@ struct GTSAM_EXPORT PreintegrationParams: PreintegratedRotationParams {
/// The Params constructor insists on getting the navigation frame gravity vector
/// For convenience, two commonly used conventions are provided by named constructors below
PreintegrationParams(const Vector3& n_gravity)
: accelerometerCovariance(I_3x3),
: PreintegratedRotationParams(),
accelerometerCovariance(I_3x3),
integrationCovariance(I_3x3),
use2ndOrderCoriolis(false),
n_gravity(n_gravity) {}
@ -54,8 +56,8 @@ struct GTSAM_EXPORT PreintegrationParams: PreintegratedRotationParams {
return boost::shared_ptr<PreintegrationParams>(new PreintegrationParams(Vector3(0, 0, -g)));
}
void print(const std::string& s) const;
bool equals(const PreintegratedRotation::Params& other, double tol) const;
void print(const std::string& s="") const;
bool equals(const PreintegratedRotationParams& other, double tol) const;
void setAccelerometerCovariance(const Matrix3& cov) { accelerometerCovariance = cov; }
void setIntegrationCovariance(const Matrix3& cov) { integrationCovariance = cov; }
@ -73,10 +75,9 @@ protected:
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & boost::serialization::make_nvp("PreintegratedRotation_Params",
boost::serialization::base_object<PreintegratedRotationParams>(*this));
ar & bs::make_nvp("accelerometerCovariance", bs::make_array(accelerometerCovariance.data(), accelerometerCovariance.size()));
ar & bs::make_nvp("integrationCovariance", bs::make_array(integrationCovariance.data(), integrationCovariance.size()));
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegratedRotationParams);
ar & BOOST_SERIALIZATION_NVP(accelerometerCovariance);
ar & BOOST_SERIALIZATION_NVP(integrationCovariance);
ar & BOOST_SERIALIZATION_NVP(use2ndOrderCoriolis);
ar & BOOST_SERIALIZATION_NVP(n_gravity);
}

4
gtsam/navigation/TangentPreintegration.cpp
View File

@ -68,7 +68,7 @@ Vector9 TangentPreintegration::UpdatePreintegrated(const Vector3& a_body,
Matrix3 w_tangent_H_theta, invH;
const Vector3 w_tangent = // angular velocity mapped back to tangent space
local.applyInvDexp(w_body, A ? &w_tangent_H_theta : 0, C ? &invH : 0);
const Rot3 R(local.expmap());
const Rot3 R(local.expmap()); // nRb: rotation of body in nav frame
const Vector3 a_nav = R * a_body;
const double dt22 = 0.5 * dt * dt;
@ -110,7 +110,7 @@ void TangentPreintegration::update(const Vector3& measuredAcc,
Vector3 acc = biasHat_.correctAccelerometer(measuredAcc);
Vector3 omega = biasHat_.correctGyroscope(measuredOmega);
// Possibly correct for sensor pose
// Possibly correct for sensor pose by converting to body frame
Matrix3 D_correctedAcc_acc, D_correctedAcc_omega, D_correctedOmega_omega;
if (p().body_P_sensor)
boost::tie(acc, omega) = correctMeasurementsBySensorPose(acc, omega,

10
gtsam/navigation/TangentPreintegration.h
View File

@ -132,12 +132,10 @@ private:
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & BOOST_SERIALIZATION_NVP(p_);
ar & BOOST_SERIALIZATION_NVP(biasHat_);
ar & BOOST_SERIALIZATION_NVP(deltaTij_);
ar & bs::make_nvp("preintegrated_", bs::make_array(preintegrated_.data(), preintegrated_.size()));
ar & bs::make_nvp("preintegrated_H_biasAcc_", bs::make_array(preintegrated_H_biasAcc_.data(), preintegrated_H_biasAcc_.size()));
ar & bs::make_nvp("preintegrated_H_biasOmega_", bs::make_array(preintegrated_H_biasOmega_.data(), preintegrated_H_biasOmega_.size()));
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegrationBase);
ar & BOOST_SERIALIZATION_NVP(preintegrated_);
ar & BOOST_SERIALIZATION_NVP(preintegrated_H_biasAcc_);
ar & BOOST_SERIALIZATION_NVP(preintegrated_H_biasOmega_);
}
public:

60
gtsam/navigation/tests/testImuFactorSerialization.cpp
View File

@ -16,15 +16,19 @@
* @author Frank Dellaert
* @author Richard Roberts
* @author Stephen Williams
* @author Varun Agrawal
*/
#include <gtsam/navigation/ImuFactor.h>
#include <gtsam/base/serializationTestHelpers.h>
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/serializationTestHelpers.h>
#include <gtsam/navigation/CombinedImuFactor.h>
#include <gtsam/navigation/ImuFactor.h>
#include <fstream>
using namespace std;
using namespace gtsam;
using namespace gtsam::serializationTestHelpers;
BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Constrained,
"gtsam_noiseModel_Constrained");
@ -38,23 +42,23 @@ BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Isotropic,
BOOST_CLASS_EXPORT_GUID(gtsam::SharedNoiseModel, "gtsam_SharedNoiseModel");
BOOST_CLASS_EXPORT_GUID(gtsam::SharedDiagonal, "gtsam_SharedDiagonal");
TEST(ImuFactor, serialization) {
using namespace gtsam::serializationTestHelpers;
template <typename P>
P getPreintegratedMeasurements() {
// Create default parameters with Z-down and above noise paramaters
auto p = PreintegrationParams::MakeSharedD(9.81);
p->body_P_sensor = Pose3(Rot3::Ypr(0, 0, M_PI), Point3(0,0,0));
auto p = P::Params::MakeSharedD(9.81);
p->body_P_sensor = Pose3(Rot3::Ypr(0, 0, M_PI), Point3(0, 0, 0));
p->accelerometerCovariance = 1e-7 * I_3x3;
p->gyroscopeCovariance = 1e-8 * I_3x3;
p->integrationCovariance = 1e-9 * I_3x3;
const double deltaT = 0.005;
const imuBias::ConstantBias priorBias(
Vector3(0, 0, 0), Vector3(0, 0.01, 0)); // Biases (acc, rot)
PreintegratedImuMeasurements pim(p, priorBias);
// Biases (acc, rot)
const imuBias::ConstantBias priorBias(Vector3(0, 0, 0), Vector3(0, 0.01, 0));
// measurements are needed for non-inf noise model, otherwise will throw err
P pim(p, priorBias);
// measurements are needed for non-inf noise model, otherwise will throw error
// when deserialize
const Vector3 measuredOmega(0, 0.01, 0);
const Vector3 measuredAcc(0, 0, -9.81);
@ -62,6 +66,16 @@ TEST(ImuFactor, serialization) {
for (int j = 0; j < 200; ++j)
pim.integrateMeasurement(measuredAcc, measuredOmega, deltaT);
return pim;
}
TEST(ImuFactor, serialization) {
auto pim = getPreintegratedMeasurements<PreintegratedImuMeasurements>();
EXPECT(equalsObj<PreintegratedImuMeasurements>(pim));
EXPECT(equalsXML<PreintegratedImuMeasurements>(pim));
EXPECT(equalsBinary<PreintegratedImuMeasurements>(pim));
ImuFactor factor(1, 2, 3, 4, 5, pim);
EXPECT(equalsObj<ImuFactor>(factor));
@ -69,6 +83,30 @@ TEST(ImuFactor, serialization) {
EXPECT(equalsBinary<ImuFactor>(factor));
}
TEST(ImuFactor2, serialization) {
auto pim = getPreintegratedMeasurements<PreintegratedImuMeasurements>();
ImuFactor2 factor(1, 2, 3, pim);
EXPECT(equalsObj<ImuFactor2>(factor));
EXPECT(equalsXML<ImuFactor2>(factor));
EXPECT(equalsBinary<ImuFactor2>(factor));
}
TEST(CombinedImuFactor, Serialization) {
auto pim = getPreintegratedMeasurements<PreintegratedCombinedMeasurements>();
EXPECT(equalsObj<PreintegratedCombinedMeasurements>(pim));
EXPECT(equalsXML<PreintegratedCombinedMeasurements>(pim));
EXPECT(equalsBinary<PreintegratedCombinedMeasurements>(pim));
const CombinedImuFactor factor(1, 2, 3, 4, 5, 6, pim);
EXPECT(equalsObj<CombinedImuFactor>(factor));
EXPECT(equalsXML<CombinedImuFactor>(factor));
EXPECT(equalsBinary<CombinedImuFactor>(factor));
}
/* ************************************************************************* */
int main() {
TestResult tr;

2
gtsam/nonlinear/AdaptAutoDiff.h
View File

@ -57,7 +57,7 @@ class AdaptAutoDiff {
if (H1 || H2) {
// Get derivatives with AutoDiff
const double* parameters[] = {v1.data(), v2.data()};
double rowMajor1[M * N1], rowMajor2[M * N2]; // on the stack
double rowMajor1[M * N1] = {}, rowMajor2[M * N2] = {}; // on the stack
double* jacobians[] = {rowMajor1, rowMajor2};
success = AutoDiff<FUNCTOR, double, N1, N2>::Differentiate(
f, parameters, M, result.data(), jacobians);

148
gtsam/nonlinear/FunctorizedFactor.h Normal file
View File

@ -0,0 +1,148 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file FunctorizedFactor.h
* @date May 31, 2020
* @author Varun Agrawal
**/
#pragma once
#include <gtsam/base/Testable.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <cmath>
namespace gtsam {
/**
* Factor which evaluates provided unary functor and uses the result to compute
* error with respect to the provided measurement.
*
* Template parameters are
* @param R: The return type of the functor after evaluation.
* @param T: The argument type for the functor.
*
* Example:
* Key key = Symbol('X', 0);
* auto model = noiseModel::Isotropic::Sigma(9, 1);
*
* /// Functor that takes a matrix and multiplies every element by m
* class MultiplyFunctor {
* double m_; ///< simple multiplier
* public:
* MultiplyFunctor(double m) : m_(m) {}
* Matrix operator()(const Matrix &X,
* OptionalJacobian<-1, -1> H = boost::none) const {
* if (H)
* *H = m_ * Matrix::Identity(X.rows()*X.cols(), X.rows()*X.cols());
* return m_ * X;
* }
* };
*
* Matrix measurement = Matrix::Identity(3, 3);
* double multiplier = 2.0;
*
* FunctorizedFactor<Matrix, Matrix> factor(keyX, measurement, model,
* MultiplyFunctor(multiplier));
*/
template <typename R, typename T>
class GTSAM_EXPORT FunctorizedFactor : public NoiseModelFactor1<T> {
private:
using Base = NoiseModelFactor1<T>;
R measured_; ///< value that is compared with functor return value
SharedNoiseModel noiseModel_; ///< noise model
std::function<R(T, boost::optional<Matrix &>)> func_; ///< functor instance
public:
/** default constructor - only use for serialization */
FunctorizedFactor() {}
/** Construct with given x and the parameters of the basis
*
* @param key: Factor key
* @param z: Measurement object of same type as that returned by functor
* @param model: Noise model
* @param func: The instance of the functor object
*/
FunctorizedFactor(Key key, const R &z, const SharedNoiseModel &model,
const std::function<R(T, boost::optional<Matrix &>)> func)
: Base(model, key), measured_(z), noiseModel_(model), func_(func) {}
virtual ~FunctorizedFactor() {}
/// @return a deep copy of this factor
virtual NonlinearFactor::shared_ptr clone() const {
return boost::static_pointer_cast<NonlinearFactor>(
NonlinearFactor::shared_ptr(new FunctorizedFactor<R, T>(*this)));
}
Vector evaluateError(const T &params,
boost::optional<Matrix &> H = boost::none) const {
R x = func_(params, H);
Vector error = traits<R>::Local(measured_, x);
return error;
}
/// @name Testable
/// @{
void print(const std::string &s = "",
const KeyFormatter &keyFormatter = DefaultKeyFormatter) const {
Base::print(s, keyFormatter);
std::cout << s << (s != "" ? " " : "") << "FunctorizedFactor("
<< keyFormatter(this->key()) << ")" << std::endl;
traits<R>::Print(measured_, " measurement: ");
std::cout << " noise model sigmas: " << noiseModel_->sigmas().transpose()
<< std::endl;
}
virtual bool equals(const NonlinearFactor &other, double tol = 1e-9) const {
const FunctorizedFactor<R, T> *e =
dynamic_cast<const FunctorizedFactor<R, T> *>(&other);
const bool base = Base::equals(*e, tol);
return e && Base::equals(other, tol) &&
traits<R>::Equals(this->measured_, e->measured_, tol);
}
/// @}
private:
/** Serialization function */
friend class boost::serialization::access;
template <class ARCHIVE>
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
ar &boost::serialization::make_nvp(
"NoiseModelFactor1", boost::serialization::base_object<Base>(*this));
ar &BOOST_SERIALIZATION_NVP(measured_);
ar &BOOST_SERIALIZATION_NVP(func_);
}
};
/// traits
template <typename R, typename T>
struct traits<FunctorizedFactor<R, T>>
: public Testable<FunctorizedFactor<R, T>> {};
/**
* Helper function to create a functorized factor.
*
* Uses function template deduction to identify return type and functor type, so
* template list only needs the functor argument type.
*/
template <typename T, typename R, typename FUNC>
FunctorizedFactor<R, T> MakeFunctorizedFactor(Key key, const R &z,
const SharedNoiseModel &model,
const FUNC func) {
return FunctorizedFactor<R, T>(key, z, model, func);
}
} // namespace gtsam

2
gtsam/nonlinear/NonlinearFactor.cpp
View File

@ -121,7 +121,7 @@ double NoiseModelFactor::error(const Values& c) const {
const Vector b = unwhitenedError(c);
check(noiseModel_, b.size());
if (noiseModel_)
return 0.5 * noiseModel_->distance(b);
return noiseModel_->loss(noiseModel_->squaredMahalanobisDistance(b));
else
return 0.5 * b.squaredNorm();
} else {

8
gtsam/nonlinear/tests/testAdaptAutoDiff.cpp
View File

@ -168,7 +168,11 @@ Camera camera(Pose3(Rot3().retract(Vector3(0.1, 0.2, 0.3)), Point3(0, 5, 0)),
Point3 point(10, 0, -5); // negative Z-axis convention of Snavely!
Vector9 P = Camera().localCoordinates(camera);
Vector3 X = point;
#ifdef GTSAM_POSE3_EXPMAP
Vector2 expectedMeasurement(1.3124675, 1.2057287);
#else
Vector2 expectedMeasurement(1.2431567, 1.2525694);
#endif
Matrix E1 = numericalDerivative21<Vector2, Vector9, Vector3>(adapted, P, X);
Matrix E2 = numericalDerivative22<Vector2, Vector9, Vector3>(adapted, P, X);
}
@ -177,7 +181,11 @@ Matrix E2 = numericalDerivative22<Vector2, Vector9, Vector3>(adapted, P, X);
// Check that Local worked as expected
TEST(AdaptAutoDiff, Local) {
using namespace example;
#ifdef GTSAM_POSE3_EXPMAP
Vector9 expectedP = (Vector9() << 0.1, 0.2, 0.3, 0.7583528428, 4.9582357859, -0.224941471539, 1, 0, 0).finished();
#else
Vector9 expectedP = (Vector9() << 0.1, 0.2, 0.3, 0, 5, 0, 1, 0, 0).finished();
#endif
EXPECT(equal_with_abs_tol(expectedP, P));
Vector3 expectedX(10, 0, -5); // negative Z-axis convention of Snavely!
EXPECT(equal_with_abs_tol(expectedX, X));

185
gtsam/nonlinear/tests/testFunctorizedFactor.cpp Normal file
View File

@ -0,0 +1,185 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------1-------------------------------------------
*/
/**
* @file testFunctorizedFactor.cpp
* @date May 31, 2020
* @author Varun Agrawal
* @brief unit tests for FunctorizedFactor class
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/nonlinear/FunctorizedFactor.h>
#include <gtsam/nonlinear/factorTesting.h>
using namespace std;
using namespace gtsam;
Key key = Symbol('X', 0);
auto model = noiseModel::Isotropic::Sigma(9, 1);
/// Functor that takes a matrix and multiplies every element by m
class MultiplyFunctor {
double m_; ///< simple multiplier
public:
MultiplyFunctor(double m) : m_(m) {}
Matrix operator()(const Matrix &X,
OptionalJacobian<-1, -1> H = boost::none) const {
if (H) *H = m_ * Matrix::Identity(X.rows() * X.cols(), X.rows() * X.cols());
return m_ * X;
}
};
/* ************************************************************************* */
// Test identity operation for FunctorizedFactor.
TEST(FunctorizedFactor, Identity) {
Matrix X = Matrix::Identity(3, 3), measurement = Matrix::Identity(3, 3);
double multiplier = 1.0;
auto functor = MultiplyFunctor(multiplier);
auto factor = MakeFunctorizedFactor<Matrix>(key, measurement, model, functor);
Vector error = factor.evaluateError(X);
EXPECT(assert_equal(Vector::Zero(9), error, 1e-9));
}
/* ************************************************************************* */
// Test FunctorizedFactor with multiplier value of 2.
TEST(FunctorizedFactor, Multiply2) {
double multiplier = 2.0;
Matrix X = Matrix::Identity(3, 3);
Matrix measurement = multiplier * Matrix::Identity(3, 3);
auto factor = MakeFunctorizedFactor<Matrix>(key, measurement, model,
MultiplyFunctor(multiplier));
Vector error = factor.evaluateError(X);
EXPECT(assert_equal(Vector::Zero(9), error, 1e-9));
}
/* ************************************************************************* */
// Test equality function for FunctorizedFactor.
TEST(FunctorizedFactor, Equality) {
Matrix measurement = Matrix::Identity(2, 2);
double multiplier = 2.0;
auto factor1 = MakeFunctorizedFactor<Matrix>(key, measurement, model,
MultiplyFunctor(multiplier));
auto factor2 = MakeFunctorizedFactor<Matrix>(key, measurement, model,
MultiplyFunctor(multiplier));
EXPECT(factor1.equals(factor2));
}
/* *************************************************************************** */
// Test Jacobians of FunctorizedFactor.
TEST(FunctorizedFactor, Jacobians) {
Matrix X = Matrix::Identity(3, 3);
Matrix actualH;
double multiplier = 2.0;
auto factor =
MakeFunctorizedFactor<Matrix>(key, X, model, MultiplyFunctor(multiplier));
Values values;
values.insert<Matrix>(key, X);
// Check Jacobians
EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-7, 1e-5);
}
/* ************************************************************************* */
// Test print result of FunctorizedFactor.
TEST(FunctorizedFactor, Print) {
Matrix X = Matrix::Identity(2, 2);
double multiplier = 2.0;
auto factor =
MakeFunctorizedFactor<Matrix>(key, X, model, MultiplyFunctor(multiplier));
// redirect output to buffer so we can compare
stringstream buffer;
streambuf *old = cout.rdbuf(buffer.rdbuf());
factor.print();
// get output string and reset stdout
string actual = buffer.str();
cout.rdbuf(old);
string expected =
" keys = { X0 }\n"
" noise model: unit (9) \n"
"FunctorizedFactor(X0)\n"
" measurement: [\n"
" 1, 0;\n"
" 0, 1\n"
"]\n"
" noise model sigmas: 1 1 1 1 1 1 1 1 1\n";
CHECK_EQUAL(expected, actual);
}
/* ************************************************************************* */
// Test FunctorizedFactor using a std::function type.
TEST(FunctorizedFactor, Functional) {
double multiplier = 2.0;
Matrix X = Matrix::Identity(3, 3);
Matrix measurement = multiplier * Matrix::Identity(3, 3);
std::function<Matrix(Matrix, boost::optional<Matrix &>)> functional =
MultiplyFunctor(multiplier);
auto factor =
MakeFunctorizedFactor<Matrix>(key, measurement, model, functional);
Vector error = factor.evaluateError(X);
EXPECT(assert_equal(Vector::Zero(9), error, 1e-9));
}
/* ************************************************************************* */
// Test FunctorizedFactor with a lambda function.
TEST(FunctorizedFactor, Lambda) {
double multiplier = 2.0;
Matrix X = Matrix::Identity(3, 3);
Matrix measurement = multiplier * Matrix::Identity(3, 3);
auto lambda = [multiplier](const Matrix &X,
OptionalJacobian<-1, -1> H = boost::none) {
if (H)
*H = multiplier *
Matrix::Identity(X.rows() * X.cols(), X.rows() * X.cols());
return multiplier * X;
};
// FunctorizedFactor<Matrix> factor(key, measurement, model, lambda);
auto factor = MakeFunctorizedFactor<Matrix>(key, measurement, model, lambda);
Vector error = factor.evaluateError(X);
EXPECT(assert_equal(Vector::Zero(9), error, 1e-9));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

7
tests/testNonlinearOptimizer.cpp
View File

@ -459,11 +459,12 @@ TEST(NonlinearOptimizer, RobustMeanCalculation) {
init.insert(0, 100.0);
expected.insert(0, 3.33333333);
LevenbergMarquardtParams params;
DoglegParams params_dl;
params_dl.setRelativeErrorTol(1e-10);
auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize();
auto dl_result = DoglegOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init).optimize();
auto dl_result = DoglegOptimizer(fg, init, params_dl).optimize();
EXPECT(assert_equal(expected, gn_result, tol));
EXPECT(assert_equal(expected, lm_result, tol));