C++ ML Kit

A lightweight, header-only C++ machine learning library that provides fast implementations of common ML algorithms with minimal dependencies. Designed for high performance and ease of integration.

Features

✅ K-Nearest Neighbors (KNN) - Classification algorithm
✅ K-Means Clustering - Unsupervised clustering algorithm
🚧 More algorithms coming soon!

File Structure

c++ ML kit/
├── include/
│   └── mlkit/
│       ├── core.hpp           # Includes all algorithms
│       ├── knn.hpp            # KNN classifier implementation
│       ├── kmeans.hpp         # K-means clustering implementation
│       └── README.md          # Detailed API documentation
├── examples/
│   ├── example_knn.cpp        # KNN classification example
│   └── example_kmeans.cpp     # K-means clustering example
└── README.md

Quick Start

Include the Library

#include "mlkit/core.hpp"  // All algorithms
// OR include individually:
// #include "mlkit/knn.hpp"
// #include "mlkit/kmeans.hpp"

KNN Classification Example

mlkit::KNNClassifier<double, int> knn(5);
knn.fit(X_train, y_train);
auto predictions = knn.predict(X_test);

K-Means Clustering Example

mlkit::ClusteringAlgorithm<double> kmeans(3);
kmeans.fit(X_train);
auto labels = kmeans.get_labels();
auto centroids = kmeans.get_centroids();

Compilation

Compile with C++17 or later:

# Compile KNN example
g++ -std=c++17 examples/example_knn.cpp -Iinclude -o knn_example

# Compile K-means example  
g++ -std=c++17 examples/example_kmeans.cpp -Iinclude -o kmeans_example

# Run examples
./knn_example
./kmeans_example

Key Features

Performance

• Header-only: No separate compilation required
• Template-based: Compile-time optimizations
• STL-optimized: Efficient use of std::vector, std::algorithms
• Low memory footprint: Minimal overhead

Reliability

• Exception-safe: Proper error handling with std::invalid_argument, std::runtime_error
• Input validation: Comprehensive parameter checking
• Convergence detection: Automatic stopping for iterative algorithms

Flexibility

• Generic types: Support for float, double, and custom numeric types
• Multi-dimensional: Works with data of any dimensionality
• Configurable: Adjustable parameters for different use cases

Algorithms

1. K-Nearest Neighbors (KNN)

• Type: Supervised Classification
• Features: Multi-class support, batch prediction, configurable k
• Use cases: Pattern recognition, recommendation systems, anomaly detection

2. K-Means Clustering

• Type: Unsupervised Clustering
• Features: Configurable clusters, convergence detection, quality metrics
• Use cases: Customer segmentation, image compression, data exploration

Future Roadmap

🚧 Planned Algorithms:

• Decision Trees
• Linear Regression
• Principal Component Analysis (PCA)
• Support Vector Machines (SVM)
• Neural Networks (ANN/CNN)

Documentation

For detailed API documentation, usage examples, and implementation details, see:

• include/mlkit/README.md - Complete API reference
• examples/ - Working code examples

License

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

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines on how to contribute to this project.

Acknowledgments

• Inspired by scikit-learn's API design
• Built with modern C++ best practices
• Community-driven development

---

⭐ Star this repository if you find it useful!

Contributing

Contributions, ideas, and issues are welcome! You can