semvec/pypi-packages huggingface dataset:
evobandits 0.0.6
Optimization algorithm combining genetic algorithms and multi-armed bandits for stochastic problems
Requires Python: >=3.10
Keywords: optimization, evolutionary computation, genetic algorithms, multi-armed bandits, stochastic problems, machine learning, reinforcement learning

<p align="center">
  <img src="https://raw.githubusercontent.com/EvoBandits/EvoBandits/refs/heads/main/Logo.webp" alt="EvoBandits" width="200"/>
</p>

<p align="center">
<em>EvoBandits is a cutting-edge optimization algorithm that merges genetic algorithms and multi-armed bandit strategies to efficiently solve stochastic problems.</em>
</p>
<p align="center">
<a href="https://github.com/E-MAB/G-MAB/actions?query=workflow%3ARust+event%3Apush+branch%3Amain" target="_blank">
    <img src="https://github.com/E-MAB/G-MAB/actions/workflows/rust.yml/badge.svg?event=push&branch=main" alt="Build & Test">
</a>
</p>

---

EvoBandits (Evolutionary Multi-Armed Bandits) is an innovative optimization algorithm designed to tackle stochastic problems efficiently. EvoBandits offers a reinforcement learning-based approach to solving complex, large-scale optimization issues by combining genetic algorithms with multi-armed bandit mechanisms. Whether you're working in operations research, machine learning, or data science, EvoBandits provides a robust, scalable solution for optimizing your stochastic models.

## Usage
To install EvoBandits:

```bash
pip install evobandits
```

```python
from evobandits import GMAB

def test_function(number: list) -> float:
    # your function here

if __name__ == '__main__':
    bounds = [(-5, 10), (-5, 10)]
    algorithm = GMAB(test_function, bounds)
    n_trials = 10000
    result = algorithm.optimize(n_trials)
    print(result)
```

## Contributing
Pull requests are welcome. For major changes, please open a discussion first to talk about what you'd like to change.

## License
EvoBandits is licensed under the Apache-2.0 license ([LICENSE](LICENSE) or
<https://opensource.org/licenses/apache-2-0>).

## Credit
Deniz Preil wrote the initial EvoBandits prototype in C++, which Timo Kühne and Jonathan Laib rewrote. Timo Kühne ported to Rust, which is now the backend. Felix Würmseher added the Python frontend.

## Citing EvoBandits
If you use EvoBandits in your research, please cite the following paper:

```
Preil, D., & Krapp, M. (2024). Genetic Multi-Armed Bandits: A Reinforcement Learning Inspired Approach for Simulation Optimization. IEEE Transactions on Evolutionary Computation.
```
patchmind 0.1.5
AI-powered Git patch assistant with HTML report generation.
Requires Python: >=3.8

<p align="center">
  <img src="https://raw.githubusercontent.com/Darkstar420/patchmind/main/docs/patchmind.png" alt="PatchMind Banner">
</p>

# 🧠 PatchMind

[![Run PatchMind Report](https://github.com/Darkstar420/patchmind/actions/workflows/patchmind.yml/badge.svg?branch=main)](https://github.com/Darkstar420/patchmind/actions/workflows/patchmind.yml)
[![PyPI - Version](https://img.shields.io/pypi/v/patchmind?color=blue)](https://pypi.org/project/patchmind/)
[![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
[![GitHub stars](https://img.shields.io/github/stars/Darkstar420/patchmind?style=social)](https://github.com/Darkstar420/patchmind/stargazers)

---

**PatchMind** is a modular Python CLI tool for Git repositories that generates smart, visual HTML reports.  
It captures **patch-level diffs**, **tree views**, **file history timelines**, **risk scoring**, and more — all locally.

No cloud. No bloat. Just clean insight.

---

## 🚀 Key Features

- 🔍 Detects file-level and line-level changes across commits  
- 🌳 Tree-based visualization of modified paths  
- 📅 File timeline view with authorship and metadata  
- ⚠️ Risk and impact scoring per file  
- 👤 Inline blame summaries  
- 📄 Clean, standalone HTML output  
- ⚙️ Fully CLI-driven — automate in CI/CD

---

## 📸 Sample Output

![PatchMind HTML Report Sample](https://raw.githubusercontent.com/Darkstar420/patchmind/main/docs/patchmind_report_sample.png)

---

## 📦 Installation

Install from [PyPI](https://pypi.org/project/patchmind/):

```bash
pip install patchmind
````

Or install manually:

```bash
git clone https://github.com/Darkstar420/patchmind.git
cd patchmind
pip install -r requirements.txt
```

---

## 🧪 Usage

Generate an HTML report from the root of any Git repo:

```bash
python cli/main.py --report
```

The output will be saved as `patchmind_report.html` in the project root.

---

## 📂 Project Layout

```
patchmind/
├── cli/               # CLI entrypoint
├── core/              # Core analysis engine
├── tests/             # Unit tests
├── docs/              # Sample reports and images
├── config.yaml        # Config (optional)
├── requirements.txt
└── README.md
```

---

## 🧪 Run Tests

```bash
pytest -q
```

Unit tests live in `tests/` and validate key functionality, including mock Git data and report rendering.

---

## 💡 Why Use PatchMind?

✅ No setup required – run it instantly
✅ See what's changing, where, and why
✅ Stay ahead of technical debt
✅ Built for devs who want insight — not overhead

---

## 📄 License

Licensed under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0) — free to use, modify, and distribute.

```