semvec/pypi-packages huggingface dataset:
OpenRadio 1.1.2
An open-source, touch-focused radio application that is written in Python. The Gui was built using Gtk3. And it is Modular AF.
Requires Python: >=3.10

# OpenRadio an Open Source Radio App written in Python.
[![pkgversion](https://img.shields.io/pypi/v/openradio)](https://pypi.org/project/openradio/)
[![repository](https://img.shields.io/badge/src-GitLab-orange)](https://gitlab.com/1337Misom/openradio)
[![versionsupport](https://img.shields.io/pypi/pyversions/openradio)](https://pypi.org/project/openradio/)
[![license](https://img.shields.io/badge/license-GPLv3-orange)](https://gitlab.com/1337Misom/OpenRadio/-/blob/main/LICENSE.md)

## Quick Start
To install from PyPi:
1. You must have at least Python 3.10 and pip 23.0.0
2. Make sure you have [pip](https://pip.pypa.io/en/stable/index.html) installed
3. Install the Dependencies
3. Install with `python3 -m pip install OpenRadio`
4. Run by typing `openradio` in your terminal
5. If you want DAB support install [simple_dab_lib](https://gitlab.com/1337Misom/simple_dab_lib)

## Dependencies
On Ubuntu install with: `sudo apt install libgtk-3-dev portaudio19-dev libpython3-dev pkg-config libboost-all-dev`
 
## Documentation
For documentation please consult the [wiki](https://gitlab.com/1337Misom/OpenRadio/-/wikis/home).

## OpenRadio in action
![Ubuntu Dark](pictures/ubuntu_dark.png)

## What it can do
- Help you with standing up in the morning
- Combine Alarm,DAB and InternetRadio into one
- Simple interface for adding new modules
- Dynamically load Modules
- It also has a Clock

## Where it can run on
- On your Unix machine
- On Raspberry Pis
- On Phones supporting Gtk and Python

## What it can't do
- Bring you Breakfast in the morning


## Social
[Mastodon](https://fosstodon.org/@openradio)
stub-bot-core 0.0.1
short package description
Requires Python: >=3.6

# stub_bot_core

نواه بوت تحديد نوع المقال اذا كان بذرة ام لا، تم فصل الكود حتي يمكن استخدمه في موقع البوت وفي نواه كود البوت الذي يعملي علي ويكيبديا وحتي في اكواد البوتات الاخري
genmrmr 0.1.0
Algorithm that uses genetic and MRMR for feature selection
Requires Python: >=3.10,<4.0


# GenMRMR

This repository proposes a new feature selection algorithm based on genetic and the MRMR algorithms. The algorithm aims to select a subset of features that maximizes classification accuracy. The algorithm works by creating a population of subsets of features, evaluating their weighted F1-score, and then hybridizing the best individuals to create new subsets. The probability of adding a feature during hybridization is estimated using the MRMR algorithm. Finally, the best subset of features is selected based on the F1-score. 

## Documentation

There are 3 methods available: fit, transform, fit_transform: 

fit method is used to fit GenMRMR to given

    Args:
        x_train, y_train, x_cv, y_cv(numpy.ndarray): train and cv data and labels that will be used for feature selection

transform method is used to transform fitted GenMRMR

    Args:
        data(numpy.ndarray): data to transform

fit_transform method is used to fit and then transform data using GenMRMR

    Args:
        data(pandas.core.frame.DataFrame): data to transform
        labels(pandas.core.series.Series): labels for data



## Authors

- Algorithm created by Maksim Tislenko(makstislenko@gmail.com)
    - Created the idea of algorithm
    - Implemented the whole algorithm
- Сomparison with existing feature selection algorithms was conducted with the participation of Rustam Paringer (rusparinger@ssau.ru) from the Department of Technical Cybernetics at Samara University.


## Acknowledgements

I would also like to express my gratitude to Arkadiy Sotnikov (cotnikoarkady@gmail.com) for assistance in preparing this readme.