semvec/pypi-packages huggingface dataset:
jieba 0.42.1
Chinese Words Segmentation Utilities
License: MIT
Keywords: NLP


jieba
=====

“结巴”中文分词：做最好的 Python 中文分词组件

"Jieba" (Chinese for "to stutter") Chinese text segmentation: built to
be the best Python Chinese word segmentation module.

完整文档见 ``README.md``

GitHub: https://github.com/fxsjy/jieba

特点
====

-  支持三种分词模式：

   -  精确模式，试图将句子最精确地切开，适合文本分析；
   -  全模式，把句子中所有的可以成词的词语都扫描出来,
      速度非常快，但是不能解决歧义；
   -  搜索引擎模式，在精确模式的基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词。

-  支持繁体分词
-  支持自定义词典
-  MIT 授权协议

在线演示： http://jiebademo.ap01.aws.af.cm/

安装说明
========

代码对 Python 2/3 均兼容

-  全自动安装： ``easy_install jieba`` 或者 ``pip install jieba`` / ``pip3 install jieba``
-  半自动安装：先下载 https://pypi.python.org/pypi/jieba/ ，解压后运行
   python setup.py install
-  手动安装：将 jieba 目录放置于当前目录或者 site-packages 目录
-  通过 ``import jieba`` 来引用
topsis-hitesh 1.0.0
A Python package to implement Topsis analysis
License: MIT

# topsis-python
Topsis analysis of a csv file

""UCS633 Project Submission""

Name - Hitesh Gupta

Roll no. - 101703235

## About Topsis

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).

## Installation


```bash
pip install topsis-hitesh
```

## Usage

Following query on terminal will provide you the topsis analysis for input csv file.

```
topsis-hitesh -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."

```

w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize. 
Size of w and i is equal to number of features. 

Note that the first row and first column of dataset is dropped

Rank 1 signifies best decision

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)