oikan 0.0.1.4

OIKAN: Optimized Interpretable Kolmogorov-Arnold Networks

OIKAN

Optimized Interpretable Kolmogorov-Arnold Networks (OIKAN)
A deep learning framework for interpretable neural networks using advanced basis functions.

Key Features

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

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
from oikan.symbolic import extract_symbolic_formula

model = OIKAN(input_dim=2, output_dim=1)
train(model, (X_train, y_train))
formula = extract_symbolic_formula(model, X_test, mode='regression')
print("Extracted formula:", formula)

Classification Example

from oikan.model import OIKAN
from oikan.trainer import train_classification
from oikan.visualize import visualize_classification
from oikan.symbolic import extract_symbolic_formula, plot_symbolic_formula, extract_latex_formula

model = OIKAN(input_dim=2, output_dim=2)
train_classification(model, (X_train, y_train))
visualize_classification(model, X_test, y_test)
formula = extract_symbolic_formula(model, X_test, mode='classification')
print("Extracted formula:", formula)
plot_symbolic_formula(model, X_test, mode='classification')
latex_formula = extract_latex_formula(model, X_test, mode='classification')
print("LaTeX:", latex_formula)

Usage

• Explore the oikan/ folder for model architectures, training routines, and symbolic extraction.
• Check the examples/ directory for complete usage examples for both regression and classification.

Contributing

Contributions are welcome! Submit a Pull Request with your improvements.

License

This project is licensed under the MIT License. See the LICENSE file for details.