This project provides Machine Learning algorithms and models implemented from scratch. These implementation aren't meant to be performatic, but instead to expose the logic of the components/blocks that make the Machine Learning models possible. For this reason the routines employed by the models are also provided and tested separately.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
To make use of this project you need both python3 and pip3. Both are readily available in packages:
sudo apt update
sudo apt install python3
sudo apt install python3-pip
To run the testing environments we have provided you'll also need to install tox
sudo apt update
sudo apt install tox
Optionally: venv
Clone and enter the directory using cd
git clone https://github.com/Benardi/touvlo
cd touvlo
Use venv to keep dependencies tidy, but you may opt not to use it. Create a new directory inside the project directory where will keep the dependencies as 'venv'.
python3 -m venv ./venv
Source the venv to activate it.
source venv/bin/activate
Use pip to install the requirements
pip3 install -r requirements.txt
To execute all testing environments simply run
tox
To execute only the unit tests, run
tox -e py35
To execute only the coding style tests, run
tox -e pep8
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
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Check pull_request_template.md for the expected format of a pull request
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Check issue templates for the suggested issue formats
We use SemVer for versioning. For the versions available, see the tags on this repository.
- Benardi Nunes - Initial work - Benardi
- Héricles Emanuel - Logo design - hericlesme
See also the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE file for details
- johnthagen - python-blueprint example repo