Topsis-Rakshita-102303498 1.0.0

TOPSIS implementation for MCDM problems

topsis-3283

Project Description

topsis-3283 is a Python library developed for solving Multiple Criteria Decision Making (MCDM) problems using the
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.

This project is developed as part of academic coursework and provides a command-line interface to rank alternatives based on multiple criteria, weights, and impacts.

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What is TOPSIS?

TOPSIS is a decision-making method based on the concept that the chosen alternative should have the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution.

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Installation

Use the Python package manager pip to install the package from PyPI:

pip install topsis-3283

Usage
Run the package from the command line using the following format:
topsis <input_file.csv> <weights> <impacts>
Example Commands
topsis sample.csv "1,1,1,1" "+,-,+,+"
OR (without quotes):
topsis sample.csv 1,1,1,1 +,-,+,+
⚠️ Note:
If the input vectors contain spaces, they must be enclosed in double quotes " ".
To view help:
topsis /h
Example
Sample Input File (sample.csv)
A CSV file containing data for different mobile handsets:
Model	Storage space (GB)	Camera (MP)	Price ($)	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
[+, +, -, +]
Command Used
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"
Output
        TOPSIS RESULTS
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    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 automatically to eliminate indices and headers.
Ensure the CSV file:
Contains only numerical values (except the first column)
Does not contain categorical data
Follows the same structure as sample.csv