semvec/pypi-packages huggingface dataset:
RailgunS 0.55
Only My Railgun
Requires Python: >=3.10

# RailgunS

[![Downloads](https://pepy.tech/badge/railguns)](https://pepy.tech/project/railguns)
[![PyPI](https://img.shields.io/pypi/v/railguns)](https://pypi.org/project/RailgunS/)
[![Python](https://img.shields.io/pypi/pyversions/railguns)](https://www.python.org)
[![Django](https://img.shields.io/pypi/djversions/railguns)](https://www.djangoproject.com)
[![License](https://img.shields.io/pypi/l/railguns)](https://opensource.org/licenses/MIT)

Only My Railgun

---

## Installation

```sh
pip install railguns
```

## Vendor

框架用到的库

- [Django](https://www.djangoproject.com)
- [Django REST framework](https://www.django-rest-framework.org)
- [Simple JWT](https://github.com/davesque/django-rest-framework-simplejwt)

部署

- [Gunicorn](https://www.gunicorn.org)
- [uvicorn](https://www.uvicorn.org)

常用库

- [Django CKEditor](https://github.com/django-ckeditor/django-ckeditor)
- [Django Filter](https://django-filter.readthedocs.io)
- [django-htmlmin](https://github.com/cobrateam/django-htmlmin)
- [hiredis](https://github.com/redis/hiredis)
- [mysqlclient](https://github.com/PyMySQL/mysqlclient-python)
- [redis-py](https://github.com/redis/redis-py)

前端库

- [axios](https://github.com/axios/axios)
- [jQuery](https://jquery.com)
- [jQuery Lazy](http://jquery.eisbehr.de/lazy/)
- [Material Components for the Web](https://material.io/components/web/)
- [Vue.js](https://vuejs.org)
- [WeUI](https://github.com/Tencent/weui)
- [weui.js](https://github.com/Tencent/weui.js)

## License

RailgunS is released under the MIT license. [See LICENSE](https://github.com/nyssance/railguns/blob/main/LICENSE) for details.
contester-cm 0.14
создание задач для Contester 2.4
Requires Python: >=3.5

## Структура
```
tests/        - каталог с тестами для задачи
checker.cpp   - чекер
info.ini      - информация о задаче
statement.md  - описание условия задачи
```

## Создание задачи
В произвольном каталоге выполнить команду:
```
contester-cm create problem
```
где __problem__ - название новой задачи. Если в текущей директории уже есть каталог с именем __problem__, то задача создана не будет.

В файле _statement.md_ указывается описание задачи с тестовыми примерами. В файле _info.ini_ указывается мета-информация информация о задаче, название, тема, раздел и т.п.

## Тесты
По умолчанию добавлена одна пара тестовых файлов (_1_ и _1.a_) в качестве примера.

Могут быть ситуации, когда нет необходимости добавлять файлы _*.a_, поскольку чекер может сам проверять правильность решения. В таком случае эти файлы можно удалить.

#### Добавление (удаление) тестов
Добавить _n_ тестов в задачу __problem__:
```
contester-cm addtests problem n
```

Удалить все тесты из задачи __problem__:
```
contester-cm deltests problem
```

## Сборка
Для системы Contester 2.4 требуются zip-файлы задач. Чтобы собрать zip-файл для задачи __problem__ необходимо выполнить команду в директории с каталогом задачи:
```
contester-cm build problem
```

Чтобы собрать все задачи в данной директории, нужно выполнить:
```
contester-cm build *
```
informative-feature-selection 2.0.0
Package which provides an algorithm feature selection which considers class separability and an implementation of Informative Normalized Difference Index (INDI)

# Install

`pip install IFE`

# Features

* Several implementations of feature selection algorithms based on discriminant analysis
* Binary implementation of Informative Normalized Difference Index (INDI)
* Multiclass implementation of INDI 

INDI may be extremely usefully in hyperspectral imaging analysis.

Implemented algorithms were proposed in the following papers:
1. [Paringer RA, Mukhin AV, Kupriyanov AV. Formation of an informative index for recognizing specified 
objects in hyperspectral data. Computer Optics 2021; 45(6): 873-878. DOI: 10.18287/2412-6179-CO-930.](http://www.computeroptics.ru/KO/PDF/KO45-6/450611.pdf)

2. [Mukhin, A., Paringer, R. and Ilyasova, N., 2021, September. Feature selection algorithm with feature space
separability estimation using discriminant analysis. In 2021 International Conference on Information Technology
and Nanotechnology (ITNT) (pp. 1-4). IEEE.](https://ieeexplore.ieee.org/document/9649144)

# Usage example

See jupyter notebook file in `examples` folder. 

# License

[MIT License](LICENSE)
FeatureExtractor 2.0.0
Package which provides an algorithm feature selection which considers class separability and an implementation of Informative Normalized Difference Index (INDI)

# Install

`pip install IFE`

# Features

* Several implementations of feature selection algorithms based on discriminant analysis
* Binary implementation of Informative Normalized Difference Index (INDI)
* Multiclass implementation of INDI 

INDI may be extremely usefully in hyperspectral imaging analysis.

Implemented algorithms were proposed in the following papers:
1. [Paringer RA, Mukhin AV, Kupriyanov AV. Formation of an informative index for recognizing specified 
objects in hyperspectral data. Computer Optics 2021; 45(6): 873-878. DOI: 10.18287/2412-6179-CO-930.](http://www.computeroptics.ru/KO/PDF/KO45-6/450611.pdf)

2. [Mukhin, A., Paringer, R. and Ilyasova, N., 2021, September. Feature selection algorithm with feature space
separability estimation using discriminant analysis. In 2021 International Conference on Information Technology
and Nanotechnology (ITNT) (pp. 1-4). IEEE.](https://ieeexplore.ieee.org/document/9649144)

# Usage example

See jupyter notebook file in `examples` folder. 

# License

[MIT License](LICENSE)