Metadata-Version: 2.1
Name: Topsis_Aniket_102003643
Version: 0.1
Summary: It is a topsis package.
Home-page: https://github.com/Aniket-Sharma27/Topsis_Aniket_102003643
Author: Aniket Sharma
Author-email: anikets1023@gmail.com
License: MIT
Download-URL: https://github.com/Aniket-Sharma27/Topsis_Aniket_102003643/archive/refs/tags/0.1.tar.gz
Description: # Topsis_Aniket_102003643
        
        _for: **Topsis Project**_
        _submitted by: **Aniket Sharma**_
        _Roll no: **102003643**_
        _Group: **3COE25**_
        
        
        Topsis_Aniket_102003643 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis_Aniket_102003643.
        
        ```bash
        pip install Topsis_Aniket_102003643
        ```
        
        ## Usage
        
        Enter csv filename followed by _.csv_ extentsion, then enter the _weights_ vector with vector values separated by commas, followed by the _impacts_ vector with comma separated signs _(+,-)_
        ```bash
        topsis sample.csv "1,1,1,1" "+,-,+,+"
        ```
        or vectors can be entered without " "
        ```bash
        topsis sample.csv 1,1,1,1 +,-,+,+
        ```
        But the second representation does not provide for inadvertent spaces between vector values. So, if the input string contains spaces, make sure to enclose it between double quotes _(" ")_.
        
        To view usage __help__, use
        ```
        topsis /h
        ```
        ## Example
        
        #### sample.csv
        
        A csv file showing data for different mobile handsets having varying features.
        
        | Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) |
        | :----: |:--------------------:|:------------:|:------------:|:---------------:|
        | M1 | 16 | 12 | 250 | 5 |
        | M2 | 16 | 8  | 200 | 3 |
        | M3 | 32 | 16 | 300 | 4 |
        | M4 | 32 | 8  | 275 | 4 |
        | M5 | 16 | 16 | 225 | 2 |
        
        weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]
        
        impacts vector = [ + , + , - , + ]
        
        ### input:
        
        ```python
        topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"
        ```
        
        ### output:
        ```
              TOPSIS RESULTS
        -----------------------------
        
            P-Score  Rank
        1  0.534277     3
        2  0.308368     5
        3  0.691632     1
        4  0.534737     2
        5  0.401046     4
        
        ``` 
        
        ## Other notes
        
        * The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.
        * Make sure the csv does not contain categorical values
        
Keywords: MCDM,102003643,TOPSIS
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
