A lightweight numerical computing and machine learning library with core components implemented in C++. Designed for learners and small-scale projects, Hahaha provides efficient tensor operations, automatic differentiation foundation, and essential ML components.

• Tensor Module: Comprehensive tensor data structure with shape management (TensorShape), stride control (TensorStride), and nested data handling (NestedData) for efficient numerical operations
• Automatic Differentiation Ready: Foundation for computational graphs through ComputeGraph.h
• High-Performance Computing: Optimized for modern C++23 standards with CUDA 13.0 acceleration support
• Complete Development Environment: Docker-based development container with .devcontainer configuration for consistent setup across platforms
• Professional Tooling: Integrated code formatting (clang-format), static analysis (clang-tidy), and unit testing (Google Test)
• Example Applications: Complete MNIST handwritten digit recognition example demonstrating network layers, activation functions, optimizers, and training workflow

• Docker (for containerized development)
• Git
• CMake ≥ 3.10
• NVIDIA GPU with CUDA 13.0 support (optional, for GPU acceleration)

# Clone the repository
git clone https://github.com/Napbad/Hahaha.git && cd Hahaha

# Build the development image
docker build -t hahaha-dev .

# Run the development container
docker run --gpus all -it --rm -v $(pwd):/workspace hahaha-dev

# Compile the project
mkdir build && cd build
cmake .. && make

The project includes full .devcontainer support:

1. Open this folder in VS Code
2. Click "Reopen in Container" when prompted
3. The development environment will be automatically configured

#include "math/ds/TensorData.h"

// Create tensors with initializer lists
hahaha::math::TensorData<int> tensor1({{1, 2}, {3, 4}});
hahaha::math::TensorData<double> tensor2({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}});

// Single value tensor
hahaha::math::TensorData<float> scalar(42.0f);

Contributions are welcome! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a pull request

Please ensure your code follows the project's coding standards by running format.sh before submitting.

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.