Metadata-Version: 2.1
Name: Topsis-Naman-101903304
Version: 1.0.0
Summary: A Python package implementing TOPSIS technique.
Home-page: UNKNOWN
Author: Naman Jain
Author-email: 1902ben10@gmail.com
License: MIT
Description: # <div align=center> TOPSIS implementation in Python
        
        
        ## What is TOPSIS
        
        **T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
        **S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
        making method. TOPSIS chooses the alternative of shortest Euclidean distance
        from the ideal solution, and greatest distance from the negative-ideal
        solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).
        
        <br>
        
        ## Installation
        Use the package manager pip to install this package.
        
        ```
        pip install Topsis-Naman-101903304
        ```
        
        ## How to use this package ?
        
        <br>
        
        
        ### In Terminal
        ```
        $ topsis data.csv "1,1,1,1,2" "+,+,-,+,+" output.csv
        ```
        <br>
        
        ### In Python IDLE:
        ```
        >>> import pandas as pd
        >>> from topsis_python.topsis import topsis
        >>> dataset = pd.read_csv('data.csv').values
        >>> d  = dataset[:,1:]
        >>> w  = [1,1,1,1]
        >>> im = ["+" , "+" , "-" , "+" ]
        >>> topsis(d,w,im)
        ```
        
        <br>
        
        ## Sample dataset
        
        Fund Name | P1   | P2   | P3  | P4   | P5
        --------- | ---  | ---- | ----| ---- | ----
        M1        | 0.92 | 0.71 | 4.5 | 43   | 12.59
        M2        | 0.71 | 0.83 | 4.4 | 41.9 | 10.11
        M3        | 0.77 | 0.62 | 3.5 | 33.2 | 13.2
        M4        | 0.92 | 0.61 | 4.4 | 50.9 | 12.55
        M5        | 0.7  | 0.88 | 6.7 | 43.7 | 16.91
        M6        | 0.64 | 0.77 | 6.9 | 64.5 | 14.91
        M7        | 0.68 | 0.44 | 4.5 | 31.1 | 13.83
        M8        | 0.6  | 0.86 | 3   | 36.4 | 10.55
        
        
        <br>
        
        ## Output
        
        Fund Name | P1   | P2   | P3  | P4   | P5    | Topsis Score        | Rank
        --------- | ---- | ---- | ----| ---- | ----- |  ---------------    |-----
        M1        | 0.92 | 0.71 | 4.5 | 43.0 | 12.59 | 0.606157764635227   | 6.0
        M2        | 0.71 | 0.83 | 4.4 | 41.9 | 10.11 | 0.630939331184659   | 3.0
        M3        | 0.77 | 0.62 | 3.5 | 33.2 | 13.23 | 0.6376673741860752  | 2.0
        M4        | 0.92 | 0.61 | 4.4 | 50.9 | 12.55 | 0.44683746237145194 | 7.0
        M5        | 0.7  | 0.88 | 6.7 | 43.7 | 16.91 | 0.6223296058794716  | 4.0
        M6        | 0.64 | 0.77 | 6.9 | 64.5 | 14.91 | 0.36651530625461226 | 8.0
        M7        | 0.68 | 0.44 | 4.5 | 31.1 | 13.83 | 0.6381151861152682  | 1.0
        M8        | 0.6  | 0.86 | 3.0 | 36.4 | 10.55 | 0.6124418308455085  | 5.0
        
        <br>
        
        The output file contains columns of input file along with two additional columns having **Topsis Score** and **Rank**
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
