semvec/pypi-packages huggingface dataset:
apigram 0.0.5
Python library to make bot with Telegram Bot API
Requires Python: >=3.4

**Apigram** â€“ Python Telegram Bot API wrapper

* [Telegram Bot API Manual](https://core.telegram.org/bots/api)
* [My telegram channel](https://core.telegram.org/bots/api) [RU]

```python
import apigram

bot = apigram.session('123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11')
api = apigram.get_api(bot)

print(api.getMe())
```
VerdictFormat 1.6
To format verdict
Requires Python: >=3.6

# 判決書格式轉換
## 使用方法
安裝套件
`
    pip install VerdictFormat
`

把正式格式轉成測試格式
```python
from VerdictFormat import Formal_to_Test
Formal_to_Test(Formal_format_path,output_path)
```

把測試格式轉成正式格式
```python
from VerdictFormat import Test_to_Formal
Test_to_Formal(Test_format_path,output_path)
```

把標記好的格式轉成測試格式
```python
from VerdictFormat import Labeled_to_Test
Labeled_to_Test(Labeled_data)
```

正規化多個法條
```python
from VerdictFormat import Multilaws_to_Normalize
Multilaws_to_Normalize(CJ_text,Match_laws_list,Multilaws_dict_list)
CJ_text : 判決書全文
Match_laws_list : 執掌法條清單
Multilaws_dict_list : 要正規化的法條
```

Formal Format
```python
[
{
    "name": "姓名",
    "statuses": [
        {
          "status":"公務員", 
          "locations": 
          [
            {
              "field":"JFull",
              "start": 28, 
              "end":40
            }
          ]
        }
      ], 
    "positions": [
        {
          "work unit": "勞動部職業安全衛生署南部職業安全衛生中心",
          "title": "檢查員",
          "locations": 
          [
            {
              "field":"JFull",
              "start": 28, 
              "end":40
            }
          ]
        },
        {
          "work unit": "勞動部職業安全衛生署",
          "title": "職員",
          "locations": []
        }
      ],
    "laws": [
        {
          "act": "貪污治罪條例",
          "article": 4, 
          "paragraph":  1,
          "subparagraph": 2, 
          "locations": 
          [
            {
              "field":"JLaw",
              "start": 28, 
              "end":40
            }, 
            {
              "field":"JLaw",
              "start": 156, 
              "end":168
            }
          ]
        } 
      ]  
  }
]

  ```

Test Format
```python
[
{
		"content_id" : "1" ,
		"name" : "柯森" ,
		"job_location" : [] ,
		"job_title" : [] ,
		"laws" : ["中華民國刑法第276條第1項","中華民國刑法第140條"]
    },{
		"content_id" : "2" ,
		"name" : "吳柯森" ,
		"job_location" : ["停車場"] ,
		"job_title" : ["管理員"] ,
		"laws" : ["中華民國刑法第276條第1項","中華民國刑法第140條"]
    }
]
```

Multilaws Normalize
```python

Multilaws_dict_list=[
        {"start": 2933, "content": "毒品危害防制條例第11條"},
        {"start": 2946, "content": "第13條"},
        {"start": 2951, "content": "第15\r\n條"},
        {"start": 3315, "content": "貪污治罪條例第11條"},
        {"start": 3326, "content": "第133333條第8項\r\n第6款"},
    ]
Normalized_laws_list=['毒品危害防制條例第11條', '毒品危害防制條例第13條', '毒品危害防制條例第15條', '貪污治罪條例第11條', '貪污治罪條例第133333條第8項第6款']


```
quickest 1.1.1
Tools for studying POMDP quickest change problems
Requires Python: >=3.5, <4
Keywords: sample,setuptools,development

# Install

```
$ pip install quickest
```
# Quickstart

Get a config file:

```
$ quickest getconf
```

Run a simulation and save the result locally:

```
$ quickest simulate --conf [CONFIG].json
```

View a result from a folder:

```
$ quickest view --loc [FOLDER]
```

See more options:

```
$ quickest -h
```
IDEAL-NPU 0.4.3
A Python module for machine learning
Requires Python: >=3.6

# IDEAL_NPU ![Version][version-badge]

## A Python module for machine learning

### install
```
$ pip install IDEAL_NPU
```

## FCDMF: Fast Clustering with co-clustering via Discrete non-negative Matrix Factorization,
A Python implementation of ["Fast Clustering With Co-Clustering Via Discrete Non-Negative Matrix Factorization for Image Identification".](https://ieeexplore.ieee.org/abstract/document/9053820)

### usage: see demo/demo_FCDMF please

## PCN: A Portable clustering algorithm based on Compact Neighbors
A Python implementation of "A Portable Clustering Algorithm Based on Compact Neighbors
for Face Tagging".
### usage: see demo/demo_PCN please

## EDG: An Efficient Density-based clustering incorporated with Graph partitioning
A Python implementation of "An Efficient Density-based Clustering Algorithm for Face Identification".
### usage: see demo/demo_EDG please

### Contact
If you have any inquiries, please email me directly (shenfeipei@gmail.com).

### License
[MIT](https://github.com/ShenfeiPei/IDEAL/blob/master/LICENSE)


[version-badge]: https://img.shields.io/badge/release-0.4.3-brightgreen.svg
fancyDES 0.1.0
DES Custom with Fancy Algorithm
Requires Python: >=3.6

# fancy-DES-algorithm (BeRez Algorithm)

[![Build Status](https://travis-ci.org/berv-uni-project/fancy-DES-algorithm.svg?branch=master)](https://travis-ci.org/berv-uni-project/fancy-DES-algorithm)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/8720d423a7f34034aac1b1b571e51157)](https://app.codacy.com/app/berviantoleo/fancy-DES-algorithm?utm_source=github.com&utm_medium=referral&utm_content=berv-uni-project/fancy-DES-algorithm&utm_campaign=Badge_Grade_Settings)

Block Cipher with Feistel Network Looks Like DES. Using fancy transposition and f function.

## LICENSE

MIT