semvec/pypi-packages huggingface dataset:
UsamaAliceWhite 1.0.1
A versatile utility library designed to streamline common development tasks.
License: MIT
Requires Python: >=3.13

## ------ ロギング機能 ------
### 説明
「アプリケーション全体でログを一元管理する。簡単にログを出力できる。」  
これらをコンセプトとしている。  

本機能はアプリ全体で一つの設定を共有する。  
その為、最初にGetLoggerを呼び出した際の設定がアプリ終了時まで全てのロガーに適応される。  

### 関数
GetLogger()  
- ログハンドラの設定とロガーの取得を行う。
- 初期値は「毎日00時00分にログファイルをローテーションする。過去のログファイルは99ファイルまで保管される。」

### サンプルコード
```
from UsamaAliceWhite.Logging import GetLogger  
logger = GetLogger(logger_name= __name__)
logger.info("usama alice white")
```

## ------ ディレクトリ構造 ------
```
UsamaAliceWhite_Ver1.0.0/
├── UsamaAliceWhite/
│   ├── Logging/
│   │   ├── Core/
│   │   │   ├── __init__.py
│   │   │   ├── Handler.py
│   │   │   └── Logger.py
│   │   ├── Shared/
│   │   │   ├── __init__.py
│   │   │   └── Singleton.py
│   │   └── __init__.py
│   ├── __init__.py
│   └── py.typed
├── pyproject.toml
└── README.md
```
neuralRaven 1.1.0
Library of neural network with a evolutive algorithm of autolearned
License: MIT
Requires Python: >=3.8

# NeuralRaven — Evolutionary Neural Network Library

NeuralRaven is a Python library that implements a fully customizable neural network architecture capable of training itself using an evolutionary algorithm (genetic optimization).  
Unlike traditional machine-learning libraries, neuralRaven **does not use gradient descent, backpropagation, or optimizers**.  
Instead, it evolves network weights over generations.

This makes RAVEN suitable for:
- Environments where gradient-based training performs poorly.
- Reinforcement learning tasks.
- Black-box optimization problems.
- Scenarios with non-differentiable fitness functions.

---

## ✨ Features

- Fully implemented neural network written in Python.
- Genetic/Evolutionary algorithm for self-training.
- Support for mutation, crossover, elitism, and population-based evolution.
- Save/Load functionality for evolved models.
- Small footprint and easy to integrate.
- Cross-platform wheel support (Windows, Linux, macOS).
- Compatible with Python 3.8–3.14.

---

## 📦 Installation

Once the package is published on PyPI:

```bash
pip install neuralRaven
```
If installing from source:

```bash
pip install .
```

## 🚀 Quick Example

```bash
# main.py

import neuralRaven

def main():
    print("=== Executing in neuralRaven.IO.fromFiles() ===")

    # Name files
    config_file = "configurationfile"
    data_file = "datatest"

    try:
        neuralRaven.IO.fromFiles(config_file, data_file)
        print("\n=== Execution complete without errors ===")
    except Exception as e:
        print("\n*** Error in the execution ***")
        print(e)

if __name__ == "__main__":
    main()
```
## 📁 Project Structure

```bash
raven/
    __init__.py
    neural_network.py
    evolutionary.py
test/
    test_basic.py
data/
    example_input.json
    example_output.json
```
## 🛠 Building Wheels (for PyPI)

This project uses cibuildwheel to build platform-specific wheels.

GitHub Actions workflows:

.github/workflows/build_wheels.yml — builds wheels for Windows/Linux/macOS.

.github/workflows/publish.yml — publishes wheels to PyPI.

## 📄 License

This project is released under the MIT License — see the LICENSE file for details.

## 👥 Authors

**Carlos Gerardo Euresty Uribe**  
Lead author and primary developer of the neural network architecture and evolutionary algorithm.  
Email: gerardo.eeuresty@itcelaya.edu.mx

**Jesús Miguel Cerda González**  
Contributor responsible for packaging the project as a Python library, optimizing modules, and improving usability.  
Email: cerda.gonzalez.jesus@gmail.com