Metadata-Version: 2.1
Name: Topsis-Joyy-102117024
Version: 0.0.8
Summary: TOPSIS Implementation
Author: Joyy Goswami
Author-email: joyy14102002@gmail.com
Keywords: topsis,decision-analysis,similarity,decision-making,multi-criteria-decision
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
Requires-Dist: pandas
Requires-Dist: numpy

# TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)


## Description

### Topsis-Joyy-102117024

For: Assignment-1(UCS654) Submitted-by: **Joyy Goswami* Roll no: *102117024* Group: *3CS-1**

Topsis-Joyy-102117024 is a Python package for dealing with Multiple Criteria Decision Making(MCDM) problems by using TOPSIS

## Installation

Use the package manager *pip* to install Topsis-Joyy-102117024

pip install Topsis-Joyy-102117024

## Usage

Enter csv filename followed by .csv extension, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma-separated signs (+,-)

bash
python sample.py sample.csv "1,1,1,1,2" "+,+,-,+,+" sample-result.csv


## 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 = [1, 1, 1, 1]
impacts vector = [+,+,-,+]

### Input:

python sample.csv "1,1,1,1" "+,+,-,+" sample-result.csv

### Output:

|   Topsis-score   | Rank |
|-------------|------|
| 0.534277    | 3    |
| 0.308368    | 5    |
| 0.691632    | 1    |
| 0.534737    | 2    |
| 0.401046    | 4    |
