semvec/pypi-packages huggingface dataset:
axit 0.0.5
Axit is a package for ax things.
Requires Python: >=3.6

# axit
Axit means Ax it!  Aims for speedup develop efficient.


# features
- ins()  inspect a variable






# releases

## v0.0.5
- CUR_TIME
- dict2csvfile
- dict2jsonfile
- save_obj and load_obj

## v0.0.3
- integrate vcs

## v0.0.2
- test new version

## v0.0.1
- born at 2021-01-25
exgrex-py 0.3a2
Exgrex implementation for the Python programming language.
Requires Python: >=3.6
Keywords: mooc,grader,python,python3,education,exgrex,coursera

exgrex-py
=========

Пакет exgrex-py предоставляет унифицированный инструмент, облегчающий
создание и поддержку грейдеров (систем автоматической проверки решений
учащихся) в курсах по программированию на языке Python.

|image0| |image1| |image2| |GitHub tag (latest by date)|

Documentation
-------------

-  `История создания
   пакета <https://github.com/vshagur/exgrex-py/blob/docs/docs/history.md>`__
-  `Основные возможности и
   особенности <https://github.com/vshagur/exgrex-py/blob/docs/docs/features.md>`__
-  `Установка <https://github.com/vshagur/exgrex-py/blob/docs/docs/Installation.md>`__
-  `Использование <https://github.com/vshagur/exgrex-py/blob/docs/docs/usage.md>`__

Authors
-------

-  **Valeriy Shagur** - `vshagur <https://github.com/vshagur>`__, email:
   vshagur@gmail.com

License
-------

This project is licensed under the MIT License - see the
`LICENSE <https://github.com/vshagur/exgrex-py/blob/docs/LICENSE>`__
file for details

.. |image0| image:: https://img.shields.io/static/v1?label=Python&message=3.6&color=blue
.. |image1| image:: https://img.shields.io/static/v1?label=OS&message=linux&color=blue
.. |image2| image:: https://img.shields.io/github/license/vshagur/exgrex
.. |GitHub tag (latest by date)| image:: https://img.shields.io/github/v/tag/vshagur/exgrex-py
mi-hj3415 0.1.6
Get finance data from https://finance.naver.com
Requires Python: >=3.6

mi-hj3415
==========

mi-hj3415 is a scrapy project for gathering naver financial data.


Quick start
------------

1. This program use mongodb for saving data. so you should install mongodb on your local server.

2. For use mi
   > from mi_hj3415 import scraper, db
   >
   > \# Use scraper module in code (It may need to execute from main process in windows OS)
   > 
   > scraper.mi()
   >
   > scraper.mihistory(year:int)
   > 
   > \# For use scraped data in mongoDB
   > 
   > mydb = db.MI(page)
   
If you meet the error during installation, please install *Twisted-20.3.0-cp39-cp39-win_amd64.whl* file first.
APL-Brochu 0.0.1
APL-Brochu - The Implementation of the paper Active Preference Learning with Discrete Choice Data by Brochu et al. (2011)

# Active Preference Learning with Discrete Choice Data
This is an unofficial implementation of the [Active Preference Learning with Discrete Choice Data](https://papers.nips.cc/paper/2007/hash/b6a1085a27ab7bff7550f8a3bd017df8-Abstract.html) by Brochu et al. as published in NIPS 2007.

Why would this package be useful for you?
> Imagine a scenario where you are trying to find a place to have lunch today. 
> There are tons of places to eat around. 
> An app presents you two restaurants to compare at a time and can help you reach a good enough restaurant in as few queries as possible.
> Each time you pick a restaurant, the model gradually learns what you want and hopefully, its suggestions get gradually better.
> This will save you a lot of time and when designed well, can be a much more fun way to search as opposed to going through a boring list view.

![](readme_photo.jpg)

In general, if the following conditions are present, active preference can be useful for you:
- User is searching for an item in a very large set of items that's impossible go through one by one.
- User is okay with a good enough solution if it is going to be found shortly.
- Items can be embedded in a vectors space where proximity in that space implies similarity in preference between items.

# Installation
There are currently two modes of installation: bare bones, extras, development.

Whichever mode, first clone the repository.

`git clone git@github.com:dorukhansergin/APL-Brochu.git`

## Bare Bones
The package requires:
```
numpy~=1.20.1 
scikit-learn~=0.24.1 
scipy
```

Change into the folder of the cloned repository and use `pip` to install.

`pip install .`

## Extras
In addition to bare bones, the following packages will be installed:
```
streamlit
matplotlib
```

`pip install .\[extras\]`

## Development
In addition to bare bones, the following packages will be installed:
```
pytest
pylint 
black 
rope
```

Change into the folder of the cloned repository and use `pip` to install, with the dev mode. The `-e` flag listens to change in code so it's recommended.

`pip install -e .\[dev\]`

You can run the tests using pytest with the simple `pytest` command.

# Play with the Demo
See above for the installation of extras.
The extras has a demo for you to get a feeling for the algorithm.
Use the streamlit command to run it.

`streamlit run extras/brochu2d.py`