📊 C++ Machine Learning Algorithms

C++ Machine Learning Algorithms is a collection of core machine learning algorithm implementations in C++, aimed at providing high-performance, educational reference code. This project is in its initial stages and will grow into a versatile library for classical ML techniques.

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🛠 Project Summary

• Project Title: C++ Machine Learning Algorithms
• Language: C++ (C++17)
• Status: Early development / prototype
• Purpose: Educational implementations of ML algorithms with emphasis on clarity and performance

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🚀 Planned Features

• Regression Algorithms

  • Linear Regression (Ordinary Least Squares)
  • Logistic Regression (Gradient Descent)

• Classification Algorithms

  • k-Nearest Neighbors (k-NN)
  • Support Vector Machine (SVM) - hard and soft margin

• Clustering Algorithms

  • K-Means Clustering
  • Hierarchical Agglomerative Clustering

• Dimensionality Reduction

  • Principal Component Analysis (PCA)

• Data Handling Utilities

  • CSV parser
  • Normalization and standardization functions

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📦 Project Structure

ml_cpp/
├── include/            # Public headers
│   ├── regression/     # Regression algorithms
│   ├── classification/ # Classification algorithms
│   ├── clustering/     # Clustering algorithms
│   └── utils/          # Utility functions (IO, math, preprocessing)
├── src/                # Implementation files
├── examples/           # Sample usage programs
└── tests/              # Unit tests (Google Test)

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🛠 Build & Run

Prerequisites

• C++17 compatible compiler (GCC/Clang/MSVC)
• CMake 3.10 or higher
• (Optional) Google Test for unit tests

Build with CMake

mkdir build && cd build
cmake ..
make

Run Examples

./examples/linear_regression_demo

Run Tests

disable tests by default; to enable:
cmake -DENABLE_TESTS=ON ..
make
test

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🔮 Roadmap & Future Improvements

• Algorithm Enhancements: Add regularization techniques (Ridge, Lasso) and advanced optimizers (Adam, RMSProp).
• Performance Tuning: SIMD vectorization and multi-threading support.
• Model Persistence: Serialization of trained models to disk.
• Documentation: Detailed API docs using Doxygen.
• Package Management: Publish as a CMake-compatible library or Conan package.

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📧 Contact

Your Name Email: [email protected]

Feel free to contribute via pull requests or open issues! :)