Metadata-Version: 2.1
Name: Topsis_Arjun_102017005
Version: 1.0.1
Summary: A Python package for Multiple Criteria Decision Analysis (MCDA) using TOPSIS Method made by Arjun Khanchandani.
Author: Arjun Khanchandani
Author-email: <akhanchandani_be20@thapar.edu>
Keywords: python,topsis,mcda
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
License-File: LICENSE.txt

# topsis_nitanshjain_102017025

## What is TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in 1981 as a multi-criteria decision analysis method.<br> 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

`pip install Topsis-Arjun-102017005`

## How to use it?

Command Prompt

> Topsis-Arjun-102017005 input.csv "1,1,1,1,1" "+,-,+,+,+" output.csv

Command Prompt<br>
```
Topsis-Arjun-102017005 <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>
```
<br>

## Input csv format
Input file contain three or more columns<br>
First column is the variable/quantity name <br>
From 2nd to last columns contain numeric (int/float) values only

Example:<br>
```
topsis topsis.py inputfile.csv “1,1,1,1,2” “+,+,+,+,-” result.csv
```
<br><br>
<i>Note: The weights and impacts should be ',' seperated, input file should be in pwd.</i> 

## Sample input data
| Model       | P1 | P2 | P3 | P4 | P5 |
| ------------- |:-------------:| -----:|-----:|-----:|-----:|
| M1    | 0.62 | 0.38 | 3.8 | 33.8 | 9.65  | 
 | M2    | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 | 
 | M3    | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 | 
 | M4    | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 | 
 | M5    | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 | 
 | M6    | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 | 
 | M7    | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 | 
 | M8    | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 | 

## Sample output data
| Model       | P1 | P2 | P3 | P4 | P5 | Performance Score | Topsis Rank |
| ------------- |:-------------:| -----:|-----:|-----:|-----:| ---: | ---: |
| M1    | 0.62 | 0.38 | 3.8 | 33.8 | 9.65  |  0.317272185       | 8           | 
| M2    | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 |  0.452068871       | 4           | 
| M3    | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 |  0.689037307       | 1           | 
| M4    | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 |  0.340383903       | 7           | 
| M5    | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 |  0.367206376       | 6           |
| M6    | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 |  0.481350901       | 3           | 
| M7    | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 |  0.372999972       | 5           | 
| M8    | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 |  0.51226635        | 2           | 

 
 




 




