semvec/pypi-packages huggingface dataset:
exhaustive-cal 0.1.6.213809b0
Exhaust every operator and calculate the expressions for objects.

# exhaustive-cal

## Usage

This package is using for exhausting every operator and calculate the expressions for objects.

## How to use it

import it by: `from exhaucal import *`. No any additional modules or packages will be installed. If you cannot install it by pip, please download the source and extract it into sys.path.

To see examples, please type `ValueExhaustive.__doc__` after importing.

Or you can make some fun things!
```python3
>>> from exhaucal import *
>>> ValueExhaustive((1, 1, 4, 5, 1, 4), "+-*/%&|^", "+-*/%&|^", ("+", "-", "*", "/", "%", "&", "|", "^", "**"), "+-*/%&|^", "+-*/%&|^").listexpr()
('1+1+4+5+1+4', '1+1+4+5+1-4', [[36860 omitted]], '1^1^4**5^1|4', '1^1^4**5^1^4')
```
apachecn-ailearning 2022.1.3.0
访问 http://localhost:{port} 查看文档
Requires Python: >=3.6

<p align="center">
    <a href="https://www.apachecn.org">
        <img width="200" src="docs/img/logo.jpg">
    </a>
    <br >
    <a href="https://www.apachecn.org/"><img src="https://img.shields.io/badge/%3E-HOME-green.svg"></a>
    <a href="http://home.apachecn.org/about/"><img src="https://img.shields.io/badge/%3E-ABOUT-green.svg"></a>
    <a href="mailto:apache@163.com"><img src="https://img.shields.io/badge/%3E-Email-green.svg"></a>
</p>

<h1 align="center"><a href="https://github.com/apachecn/AiLearning">AI learning</a></h1>

> 协议：[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh)
> 
> 一种新技术一旦开始流行，你要么坐上压路机，要么成为铺路石。——Stewart Brand

* [在线阅读](https://ailearning.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/ailearning)
* [ApacheCN 面试求职交流群 724187166](https://jq.qq.com/?_wv=1027&k=54ujcL3)
* [ApacheCN 学习资源](http://www.apachecn.org/)

## 公告

⚠⚠⚠此项目已正式封板⚠⚠⚠

ApacheCN 的 AI 相关项目告一段落，请大家关注我们的其他计划，例如：

+   [QuantLearning](https://github.com/apachecn/quant-learning)
+   [Quant 译文集](https://github.com/apachecn/apachecn-quant-zh)
+   [Quant WIKI](https://github.com/apachecn/quant-wiki)

## 下载

### Docker

```
docker pull apachecn0/ailearning
docker run -tid -p <port>:80 apachecn0/ailearning
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install apachecn-ailearning
apachecn-ailearning <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ailearning
ailearning <port>
# 访问 http://localhost:{port} 查看文档
```

## 组织介绍

> 我们是布客（iBooker），一个大型开源社区、自媒体和网赚社区，旗下 QQ 群共一万余人，订阅用户至少一万人。Github Star 数量超过 60k 个，在所有 Github 组织中[排名前 100][top100]。旗下所有网站日 uip 超过 4k，[Alexa 排名的峰值为 20k][top20k]。我们的核心成员拥有[ CSDN 博客专家][csdn-pro]和[简书程序员优秀作者][js-pro]认证。我们建立了公益性文档和教程翻译项目 [ApacheCN][apachecn]。
> 
> 与商业组织不同，我们并不会追逐热点，或者唯利是图。作为公益组织，我们将完成项目放在首要位置，并有足够时间把项目打磨到极致。我们希望做出广大 AI 爱好者和个人开发者真正需要的东西，打造真正有价值的长尾作品。

* **We are ApacheCN Open Source Organization, not ASF! We are fans of AI, and have no relationship with ASF!**
* 合作 or 侵权，请联系 <apachecn@163.com> | 请抄送一份到 <wizard.z@foxmail.com>

[top100]: https://gitstar-ranking.com/apachecn
[top20k]: http://home.apachecn.org/img/about/alexa_201906.png
[csdn-pro]: https://blog.csdn.net/wizardforcel
[js-pro]: https://www.jianshu.com/u/b508a6aa98eb
[apachecn]: https://github.com/apachecn

## 参考

* [比赛收集平台](https://github.com/iphysresearch/DataSciComp)
* [pbharrin/machinelearninginaction](https://github.com/pbharrin/machinelearninginaction)
* [ML Mastery](https://machinelearningmastery.com/datasets-natural-language-processing)

## 致谢

最近无意收到群友推送的链接，发现得到大佬高度的认可，并在热心的推广。在此感谢:

* [量子位](https://www.zhihu.com/question/20472776/answer/691646493)
* [人工智能前沿讲习](https://mp.weixin.qq.com/s/f2dqulxOPkt7k5hqPsydyQ)

## 赞助我们

<img src="http://data.apachecn.org/img/about/donate.jpg" alt="微信&支付宝" />
parseUALF 0.0.1
Python class that allows for a python requests response received in the universal ascii lightning format to be parsed to a pandas dataframe object.
Requires Python: >=3.6

# Data Parser for Universal ascii lightning format âš¡
<br>
Python class that allows for a python requests response received in the universal ascii lightning format to be parsed to a pandas dataframe object.
<br><br>

Publicly available lightning data though MET Norway and Swedish SMHI are returned in the universal ascii lightning format. <br>The purpose of this Python project is to easily parse that data into a pandas dataframe (and numpy array).
<br>

PyPI link: https://pypi.org/project/UALF-dataframe-parser/0.0.1/

<br>
A visualisation of the format can be found on MET Norway's Frost API resource pages:

![ualf](https://frost.met.no/images/concepts/UALF_format.png)
geeksforgeeks-sys-zh 2022.1.3.0
GeeksForGeeks 计算机系统中文系列教程
Requires Python: >=3.6

# GeeksForGeeks 计算机系统中文系列教程

> 原文：[GeeksForGeeks](https://geeksforgeeks.org/)
> 
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
> 
> 阶段：机翻（1）
> 
> 团结、勇气和绝不动摇的忠诚是人类最大的优点。——《动物园规则怪谈》

* [在线阅读](https://g4g-sys.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/doc-template/)
* [ApacheCN 学习资源](http://docs.apachecn.org/)

## 贡献指南

本项目需要校对，欢迎大家提交 Pull Request。

> 请您勇敢地去翻译和改进翻译。虽然我们追求卓越，但我们并不要求您做到十全十美，因此请不要担心因为翻译上犯错——在大部分情况下，我们的服务器已经记录所有的翻译，因此您不必担心会因为您的失误遭到无法挽回的破坏。（改编自维基百科）

## 联系方式

### 负责人

* [飞龙](https://github.com/wizardforcel): 562826179

### 其他

*   在我们的 [apachecn/geeksforgeeks-sys-zh](https://github.com/apachecn/geeksforgeeks-sys-zh) github 上提 issue.
*   发邮件到 Email: `apachecn@163.com`.
*   在我们的 [组织学习交流群](https://www.apachecn.org/#/docs/join) 中联系群主/管理员即可.

## 下载

### Docker

```
docker pull apachecn0/geeksforgeeks-sys-zh
docker run -tid -p <port>:80 apachecn0/geeksforgeeks-sys-zh
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install geeksforgeeks-sys-zh
geeksforgeeks-sys-zh <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g geeksforgeeks-sys-zh
geeksforgeeks-sys-zh <port>
# 访问 http://localhost:{port} 查看文档
```

## 赞助我们

![](http://data.apachecn.org/img/about/donate.jpg)
spoken-macedonian-annotation 1.0.0
Library and CLI to for simple morphological annotation of spoken Macedonian
Requires Python: >=3.8

# Annotation of Spoken Macedonian

## A simple morphological tagger for annotation of Macedonian written and spoken language data

This is a package for simple annotation of macedonian spoken (also dialectal) texts. 
The package uses the token-dictionary created during the development  
of the [Macedonian Spoken Corpus](https://escher.pythonanywhere.com/) 
and will annotate the tokens of your text if they are present in that dictionary.
See the list of the tags  [here](https://escher.pythonanywhere.com/tagset) .
The  dictionary is constantly being expanded in new releases of the tagger. 



The tagger is designed for smaller projects since it contains several 
limitations which would require a manual after-processing from the user. 
First, this concerns the processing homonyms.

## Dealing with homonyms
The tagger can not distinguish homonyms

* The word 'se' is always marked as a participle Q (tag for participle).
  You need to manually correct the cases where it stands for the third person plural for on the verb "to be" in presence
* The word 'si' is always marked as a participle P (tag for pronoun).
  You need to manually correct the cases where it stands for the second person singular for on the verb "to be" in presence
  
The annotation for homonyms can be either marked with the string "HOMONYM" or left empty in the output. 

## Dealing with unknown words

The annotation for unknown words can be either marked with the string "UNKNOWN" or left empty in the output. 

## Usage

###Installation

```shell
pip install spoken_macedonian_annotation
```
 In a code editor, you can annotate texts by passing the string to the ```annotate``` method of a ```MacAnnotator``` object:

```python
from spoken_macedonian_annotation.annotate import MacAnnotator

text = 'Ова е мојата куќа.'

annotator = MacAnnotator(print_to_txt_file=True, mark_homonyms=False, mark_unknown_tokens=False)
result = annotator.annotate(text)
print(result)
```
In command line, you can pass a file with a plain text  to the command line script ```annotateMac```: 

```shell
annotateMac -i your_text_to_annotate.txt --print_to_txt
```

The argument ``` --print_to_txt``` creates an output file in the working directory and writes the result in it. 

You can also use optional arguments for marking homonyms and/or unknown words:

```shell
annotateMac -i your_text_to_annotate.txt --print_to_txt --mark_homonyms --mark_unknown
```
tf-alloc 0.0.3
Simplication of GPU allocation for Tensorflow2

# tf_alloc
> Simpliying GPU allocation for Tensorflow

## ⭐️ Why tf_alloc? Problems?
* Compare to pytorch, tensorflow allocate all GPU memory to single training.
* However, it is too much waste because, some training does not use whole GPU memory.
* To solve this problem, TF engineers use two methods.

1. Limit to use only single GPU
2. Limit the use of only a certain percentage of GPUs.

* However, these methods require complex code and memory management.

## ⭐️ Why tf_alloc? How to solve?
#### tf_alloc simplfy and automate GPU allocation using two methods.

## ⭐️ How to allocate?
* Before using tf_alloc, you have to install tensorflow fits for your environment.
* This library does not install specific tensorflow version.

```python
# On the top of the code
from tf_alloc import allocate as talloc
talloc(gpu=1, percentage=0.5)

import tensorflow as tf
""" your code"""
```
#### It is only code for allocating GPU in certain percentage.

### Parameters:
* gpu = which gpu you want to use (if you have two gpu than [0, 1] is possible)
* percentage = the percentage of memory usage on single gpu. 1.0 for maximum use.


## ⭐️ Additional Function.
### GET GPU Objects
    gpu_objs = get_gpu_objects()

* To use this code, you can get gpu objects that contains gpu information.
* You can set GPU backend by using this function.

### GET CURRENT STATE
##### Defualt
    current(
        gpu_id = False, 
        total_memory=False, 
        used = False, 
        free = False, 
        percentage_of_use = False,
        percentage_of_free = False,
    )

* You can use this functions to see current GPU state and possible maximum allocation percentage.
* Without any parameters, than it only visualize possible maximum allocation percentage.
* It is cmd line visualizer. It doesn't return values.

#### Parameters
* gpu_id = visualize the gpu id number
* total_memory = visualize the total memory of GPU
* used = visualize the used memory of GPU
* free = visualize the free memory of GPU
* percentage_of_used = visualize the percentage of used memory of GPU
* percentage_of_free = visualize the percentage of free memory of GPU