oikan 0.0.1.1

OIKAN: Optimized Interpretable Kolmogorov-Arnold Networks

OIKAN Library

OIKAN (Optimized Implementation of Kolmogorov-Arnold Networks) is a PyTorch-based library for creating interpretable neural networks. It implements the KAN architecture to provide both accurate predictions and interpretable results.

Key Features

• EfficientKAN layer implementation
• Built-in visualization tools
• Support for both regression and classification tasks
• Symbolic formula extraction
• Easy-to-use training interface

Installation

git clone https://github.com/silvermete0r/OIKAN.git
cd OIKAN
pip install -e .  # Install in development mode

Quick Start

Regression Example

from oikan.model import OIKAN
from oikan.trainer import train

# Create and train model
model = OIKAN(input_dim=2, output_dim=1)
train(model, train_loader)

# Extract interpretable formula
formula = extract_symbolic_formula_regression(model, X)

Classification Example

model = OIKAN(input_dim=2, output_dim=2)
train_classification(model, train_loader)
visualize_classification(model, X, y)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.