pykan is a library for implementing Kolmogorov-Arnold Networks, replacing fixed node activation functions with learnable spline functions located on the network edges. It serves as an interpretable AI framework and symbolic regression tool designed to derive transparent mathematical rules from complex data.
The project focuses on converting learned numerical functions into human-readable symbolic expressions through library matching and formula conversion. It utilizes additive-compositional topologies and learnable piecewise polynomial segments to approximate non-linear mappings.
The framework includes capabilities for model optimization through regularization-driven sparsification and the pruning of redundant neurons and connections. It also provides utilities for visualizing network topology and inspecting activation functions throughout the training process.