ML - Custom Machine Learning Library

ML is a custom Machine Learning library written primarily in C++ (75%), with additional support from Python and Fortran. This library aims to provide modular, efficient, and extensible implementations of various machine learning algorithms, featuring Python bindings for broader usability. It is designed for performance and adaptability, leveraging Eigen for high-performance numerical computations.

---

📊 Technology Stack

• Languages:

  • C++ (75%)
  • Fortran (5%)
  • CMake (5%)
  • Python (4%)
  • Others (11%)

• Libraries/Frameworks:

  • pybind11: Python bindings for C++.
  • Eigen: High-performance numerical computation.
  • CMake: Build configuration and project management.

• Testing Framework:

  • CTest integrated with CMake.
  • Pytest for Python-level validations.

---

🚀 Current Status

The library currently includes the following implementations:

Available Modules

• Regression Algorithms:

  • ElasticNetRegression
  • LinearRegression
  • RidgeRegression
  • LassoRegression
  • PolynomialRegression

• Classification Algorithms:

  • Support Vector Machines (SVM)
  • Decision Trees
  • Random Forest
  • Naive Bayes

• Clustering Algorithms:

  • KMeans
  • DBSCAN
  • Gaussian Mixture Models (GMM)

• Dimensionality Reduction:

  • PCA (Principal Component Analysis)
  • t-SNE
  • LDA (Linear Discriminant Analysis)
  • Autoencoders

• Other Modules:

  • Anomaly Detection:
    • Isolation Forest
    • Local Outlier Factor (LOF)
    • One-Class SVM
  • Bayesian Learning
  • Ensemble Learning
  • Genetic Algorithms
  • Instance-Based Learning
  • Neural Networks
  • Reinforcement Learning
  • Semi-Supervised Learning

---

🌟 Future Ideas

We are actively looking to expand the library with the following features:

• Reinforcement Learning:

  • Q-Learning
  • Deep Q-Networks (DQN)
  • Policy Gradient Methods

• Ensemble Learning:

  • Boosting
  • Bagging
  • Stacking

• Neural Networks:

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Transformers

• Bayesian Learning:

  • Bayesian Optimization
  • Gaussian Naive Bayes
  • Bayesian Networks

• Performance Enhancements:

  • Parallelization of algorithms.
  • GPU-based computation for training.

💡 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository and create your branch:

   git checkout -b feature/new_algorithm

Make your changes and add tests.

Push to your branch and create a pull request:

git push origin feature/new_algorithm

Include a detailed explanation of your contribution.

📝 License This project is licensed under the MIT License.

📞 Contact If you have any questions or suggestions, feel free to reach out:

Author: Shlok Mundhra Email: [email protected] 📂 Directory Structure

Shlok_ML/ ├── include/ # Header files for the algorithms ├── src/ # Source files for the algorithms ├── tests/ # Unit tests for each module ├── python/ # Python bindings for the library ├── build/ # Build directory (generated) ├── CMakeLists.txt # Build configuration file README.md # Project documentation