This repository contains a comprehensive collection of algorithms across various domains including computational algorithms, data structures, machine learning, cryptographic methods, and more. Each algorithm is implemented with a focus on clarity, efficiency, and adherence to best practices.
To get started with this repository, clone it to your local machine using:
git clone https://github.com/thomasthaddeus/AlgorithmSelector.git
cd algorithm-repository
Install the required dependencies:
pip install -r requirements.txt
Each algorithm is encapsulated in its own class and can be used independently. Here's an example of how to use an algorithm from the repository:
from src.data_structures.binary_search_tree import BinarySearchTree
# Example usage
bst = BinarySearchTree()
bst.insert(3)
bst.insert(1)
bst.insert(4)
print(bst.search(1)) # Output: True or False
The repository is organized as follows:
src/
: Contains the source code for all algorithms.computational/
: Computational algorithms like FFT, Monte Carlo, etc.data_structures/
: Common data structures like AVL Tree, Heap, etc.ml/
: Machine learning algorithms like k-means, linear regression, etc....
(other directories following a similar structure)
tests/
: Contains unit tests for each algorithm.docs/
: Documentation related to the algorithms.scripts/
: Useful scripts like setup or build scripts.requirements.txt
: List of dependencies for the project.pyproject.toml
: Project metadata and configuration.
To run the tests, navigate to the root directory of the project and run:
python -m unittest discover -s tests
Contributions to the repository are welcome! Here's how you can contribute:
- Fork the repository and create your branch from
main
. - Write your algorithm or improvement.
- Ensure your code passes all existing tests and add new tests if necessary.
- Submit a pull request.
Please ensure your code adheres to the existing style conventions and add relevant documentation.
This project is licensed under the MIT License.