Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C++11 and Eigen.

• rbf-interpolation: Radial basis function (RBF) interpolation
• gaussian-process-regression: Gaussian process regression (GPR)

• classical-mds: Classical multi-dimensional scaling (MDS)
• som: Self-organizing map (SOM)

• backtracking-line-search: Backtracking line search
• bayesian-optimization: Bayesian optimization
• bfgs: BFGS method
• gradient-descent: Gradient descent method
• l-bfgs: Limited-memory BFGS method
• strong-wolfe-conditions-line-search: Strong Wolfe conditions line search

• log-determinant: Log-determinant
• matrix-inversion: Matrix inversion techniques

• acquisition-functions: Acquisition functions
• constants: Constants
• data-normalization: Data normalization
• kernel-functions: Kernel functions
• probability-distributions: Probability distributions

• Eigen http://eigen.tuxfamily.org/ (brew install eigen / sudo apt install libeigen3-dev)

• pybind11 https://github.com/pybind/pybind11 (included as gitsubmodule)

• optimization-test-function https://github.com/yuki-koyama/optimization-test-functions (included as gitsubmodule)

mathtoolbox uses CMake https://cmake.org/ for building source codes. This library can be built, for example, by

git clone https://github.com/yuki-koyama/mathtoolbox.git --recursive
cd mathtoolbox
mkdir build
cd build
cmake ../
make

and optionally it can be installed to the system by

make install

When the CMake parameter MATHTOOLBOX_BUILD_EXAMPLES is set ON, the example applications are also built. (The default setting is OFF.) This is done by

cmake ../ -DMATHTOOLBOX_BUILD_EXAMPLES=ON
make

When the CMake parameter MATHTOOLBOX_PYTHON_BINDINGS is set ON, the example applications are also built. (The default setting is OFF.) This is done by

cmake ../ -DMATHTOOLBOX_PYTHON_BINDINGS=ON
make

macOS:

brew install eigen

Ubuntu:

sudo apt install libeigen3-dev

pymathtoolbox is a (sub)set of Python bindings of mathtoolbox. Tested on Python 3.6, 3.7, and 3.8.

It can be installed via PyPI:

pip install git+https://github.com/yuki-koyama/mathtoolbox

macOS

brew install cmake eigen

Ubuntu 16.04/18.04

sudo apt install cmake libeigen3-dev

See python-examples.

Bayesian optimization (bayesian-optimization) solves a one-dimensional optimization problem using only a small number of function-evaluation queries.

Classical multi-dimensional scaling (classical-mds) is applied to pixel RGB values of a target image to embed them into a two-dimensional space.

Self-organizing map (som) is also applicable to pixel RGB values of a target image to learn a two-dimensional color manifold.

• SelPh https://github.com/yuki-koyama/selph (for classical-mds)
• Sequential Line Search https://github.com/yuki-koyama/sequential-line-search (for acquisition-functions, kernel-functions, log-determinant, and probability-distributions)

Bug reports, suggestions, feature requests, and PRs are highly welcomed.

The MIT License.