hanbuck30 / Pytorch_ESN

Pytorch version of ESN. Learning algorithms include FORCE, inverse matrix, and Gradient Descent. Unlike traditional ESNs, large storage nodes can be used using memory much more efficiently.

Pytorch_ESN

ESN version of pytorch

Echo State Network (ESN) Implementation

This repository contains an implementation of the Echo State Network (ESN) algorithm along with an example usage notebook and utility functions.

Repository Structure

• ESN_example.ipynb: Jupyter notebook providing a usage example of the ESN implementation.
• ESN_py/: Core package directory containing the implementation of the ESN.
  • __init__.py: Initialization file for the ESN_py package.
  • config.py: Configuration file for the ESN parameters.
  • ESN.py: Core implementation of the Echo State Network.
  • learning_algorithm/: Directory containing different learning algorithms for the ESN.
    • Gradient_descent.py: Implementation of gradient descent learning algorithm.
    • Inverse_matrix.py: Implementation of inverse matrix learning algorithm.
    • Online_learning.py: Implementation of online learning algorithm.
  • utils/: Directory containing utility functions and settings for the ESN.
    • func.py: Utility functions for the ESN.
    • setting.py: Settings and configurations for the ESN utilities.
    • sparseLIB.py: Sparse matrix operations library.
• MackeyGlass_t17.txt: Dataset used in the example notebook.

Getting Started

Prerequisites

Ensure you have Python 3.8 or higher installed. Install the necessary dependencies using the following command:

pip install -r requirements.txt

Installation

Clone the repository and navigate to the project directory:

git clone https://github.com/hanbuck30/Pytorch_ESN.git