Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability. A quick intro of KANs here.

Installation

There are two ways to install pykan, through pypi or github.

Installation via github

git clone https://github.com/KindXiaoming/pykan.git
cd pykan
pip install -e .

Installation via pypi

pip install pykan

Requirements

# python==3.9.7
matplotlib==3.6.2
numpy==1.24.4
scikit_learn==1.1.3
setuptools==65.5.0
sympy==1.11.1
torch==2.2.2
tqdm==4.66.2

To install requirements:

pip install -r requirements.txt

Documentation

The documenation can be found here.

Tutorials

Quickstart

Get started with hellokan.ipynb notebook.

More demos

More Notebook tutorials can be found in tutorials.