pyrkm 0.0.7

Machines learning to do machine-learning

pyrkm:

Emergent unsupervised learning with adaptive resistor networks: The Restricted Kirchhoff Machine

TODO:

• Create a proper README
• Make a logo
• Build the documentation
• fix CONTRIBUTING.md
• fix CODE_OF_CONDUCT.md
• set up pypi publishing
• track on Zenodo
• improve example.ipynb

What is a Restricted Kirchhoff Machine?

You may be familiar with Restricted Boltzmann Machines (RBMs) [1]-[2], which are a type of generative neural network that can learn a probability distribution over its input data. The Restricted Kirchhoff Machine (RKM) is a realization of a RBM using resistor networks, and Kirchhoff's laws of electrical circuits.

For more information about the capabilities of the RKM, see the original paper by Link to paper.

Overview

Repository Contents

Getting Started

To get started with the project, follow these steps:

• Prerequisites: In order to correctly install pyrkm you need python3.9 or higher. If you don't have it installed, you can download it from the official website. You will also need the header files that are required to compile Python extensions and are contained in python3-dev. On Ubuntu, you can install them with:

  apt-get install python3-dev
  

• Install the package:

  python -m pip install pyrkm
  

• Or: Clone the repository:

  git clone https://github.com/MALES-project/SpeckleCn2Profiler.git
  cd SpeckleCn2Profiler
  git submodule init
  git submodule update
  pip install .
  

Usage

To use the package, you run the commands such as:

python <mycode.py> <path_to_config.yml>

where <mycode.py> is the name of the script that trains/uses the pyrkm model and <path_to_config.yml> is the path to the configuration file.

Here you can find a typical example run and an explanation of all the main configuration parameter. In the example submodule you can find multiple examples and multiple configuration to take inspiration from.

What can we predict?

Contribution Guidelines

We welcome contributions to improve and expand the capabilities of this project. If you have ideas, bug fixes, or enhancements, please submit a pull request. Check out our Contributing Guidelines to get started with development.

Generative-AI Disclaimer

Parts of the code have been generated and/or refined using GitHub Copilot. All AI-output has been verified for correctness, accuracy and completeness, revised where needed, and approved by the author(s).

How to cite

Please consider citing this software that is published in Zenodo under the DOI 10.5281/zenodo.11447920.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.