Metadata-Version: 2.1
Name: Topsis-Nipun-102003674
Version: 1.0.0
Summary: A Python package for handling problems of Multiple Criteria Decision Making(MCDM) for a given dataset.
Author: Nipun Garg
Author-email: ngarg4_be20@thapar.edu
License: MIT
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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas

# Topsis-Nipun-102003674
Topsis-Nipun-102003674 is a Python package for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).
Topsis is a method of compensatory aggregation that compares a set of alternatives, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.

#### Installation
```
Use the package manager pip to install Topsis-Nipun-102003674
```
#### Syntax
```
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example:
topsis inputfile.csv 1,2,1,2,1 +,+,-,+,- result.csv
```
#### Example
Sample Input Data
| Name | P1 | P2 | P3 | P4 | P5 |
| --- | --- | --- | --- | --- | --- |
| M1 | 0.71 |0.5|3.8|40.8|11.5|
| M2 | 0.94 | 0.88| 5.3|56.2 |15.83 |
| M3 |0.85 |0.72|4 |30.5 |9.02 |
| M4 |0.61  |0.37|5.4 |56.9 |15.82 |
| M5 |0.91 |0.83|3.4 |53.4 |14.64 |

Weights: 1,1,1,1,1
Impacts: +,+,+,+,+

Sample Output Data
| Name | P1 | P2 | P3 | P4 | P5 |Score|Rank|
| --- | --- | --- | --- | --- | --- |---|---|
| M1 | 0.71 |0.5|3.8|40.8|11.5|0.3015751942839768|5|
| M2 | 0.94 | 0.88| 5.3|56.2 |15.83 |0.97815026808521971|1
| M3 |0.85 |0.72|4 |30.5 |9.02 |0.4172925776259159|4
| M4 |0.61  |0.37|5.4 |56.9 |15.82 |0.5053936295885693|3
| M5 |0.91 |0.83|3.4 |53.4 |14.64 |0.6774035368116197|2

#### Note
1. Enter the path of your input csv file.
2. Enter the weights and impacts vector with each entry separated by commas.
3. Enter the name of output file in .csv format.
4. The Output file will be created in the current working directory

### License
© 2023 Nipun Garg
This repository is licensed under the MIT license.
See LICENCE for details.
