A library for random feature maps and linear models with random feature maps in Python.

1. Download the source codes by

   git clone https://github.com/neonnnnn/pyrfm.git

or download as a ZIP from GitHub.

2. Install the dependencies:

   cd pyrfm

   pip install -r requirements.txt

3. Finally, build and install pyrfm by

   python setup.py install

For running example codes (pyrfm/benchmarks), jupyter and matplotlib are required.

• Python >= 3.6 with NumPy >= 1.18, SciPy >= 1.4, Cython >= 0.29 and scikit-learn >= 0.23

https://neonnnnn.github.io/pyrfm/

Using random feature maps is a promising way for large-scale kernel methods. They are maps from an original feature space to a randomized feature space approximating a kernel-induced feature space. The idea is to run linear models on such randomized feature space for classification, regression, clustering, etc. When the dimension of the random feature map D is not so high and the number of training example N is large, this approach is very efficient compared to canonical kernel methods.

• Kyohei Atarashi, 2018-present