Command line implementation of TOPSIS method
Project description
TOPSIS Python Package
This project implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method as a command line tool.
TOPSIS is a multi-criteria decision making technique used to rank alternatives based on their distance from an ideal best and ideal worst solution.
Installation
pip install Topsis-Prigya-102313061
Input File Format
The input must be a CSV file with:
- First column: Names of alternatives
- Remaining columns: Numeric criteria values
Example Input CSV
| Laptop Model | Performance | Battery Life | Weight (kg) | Price ($) |
|---|---|---|---|---|
| Model A | 90 | 8 | 1.8 | 1200 |
| Model B | 85 | 10 | 1.5 | 1500 |
| Model C | 88 | 7 | 2.0 | 1000 |
| Model D | 92 | 9 | 1.6 | 1800 |
Meaning of Criteria
- Performance : Higher is better (+)
- Battery Life : Higher is better (+)
- Weight : Lower is better (-)
- Price : Lower is better (-)
Parameters Used
- Weights vector =
[1, 1, 1, 1] - Impacts vector =
[+, +, -, -]
Command Line Usage
topsis data.csv "1,1,1,1" "+,+,-,-" output.csv
Output
The output CSV file will contain two additional columns:
- Topsis Score
- Rank
| Laptop Model | Topsis Score | Rank |
|---|---|---|
| Model A | 0.534277 | 3 |
| Model B | 0.308368 | 4 |
| Model C | 0.691632 | 1 |
| Model D | 0.534737 | 2 |
Notes
- The first column must contain the names of alternatives.
- All remaining columns must contain numeric values only.
- Number of weights and impacts must match the number of criteria columns.
Requirements
- Python 3.6+
- pandas
- numpy
Author
Prigya Goyal
License
This project is released under the MIT License for educational purposes. See the LICENSE file for details.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file topsis_prigya_102313061-1.0.3.tar.gz.
File metadata
- Download URL: topsis_prigya_102313061-1.0.3.tar.gz
- Upload date:
- Size: 3.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
36ae363eb1a8eaced7ff2cc14ca95952edbe45445edba10fcc016f8a21fa0e84
|
|
| MD5 |
dfb00d371e594b80c5c322aa71223a01
|
|
| BLAKE2b-256 |
423fb52b9e75020b4a2116ba2ac8e91b7e972d915e9dcc6ca3c6718ea2d99b90
|
File details
Details for the file topsis_prigya_102313061-1.0.3-py3-none-any.whl.
File metadata
- Download URL: topsis_prigya_102313061-1.0.3-py3-none-any.whl
- Upload date:
- Size: 4.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c68f659068b7eef5e85228548b1ad7838d5a0026effa6a6d75d6f1766c68e7bf
|
|
| MD5 |
4f7f9456d3c32c213c60a651149d97d8
|
|
| BLAKE2b-256 |
6a2802c441cf27ca0a689059d64b60daca16010fbefb98b77c1dbb77e79c6194
|