Topsis package for MCDM problems
Project description
Topsis-Gurpreet-102003070
For : Assignment(UCS654)
Submitted by: Gurpreet Singh
Roll no:102003070
Group:3COE3
Description
This is a python package used to implement TOPSIS(Technique of Order Preference Similarity to the Ideal Solution) for MCDA(Multiple criteria decision analysis)
How to use this package:
Installation
pip install Topsis-Gurpreet-102003070
Example:
Sample dataset
Fund Name | P1 | P2 | P3 | P4 | P5 |
---|---|---|---|---|---|
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 |
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 |
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 |
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 |
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 |
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 |
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 |
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 |
Input
In Command Prompt
Enter filename followed by .csv extension, then enter values of weights separated by commas like "1,1,1,2,2",then enter values of impacts separated by commas like "+,+,-,-,+" without giving space in between comma value, then enter name of file where you want to save output followed by .csv extension
topsis data.csv "1,1,1,2,2" "+,+,-,-,+" out.csv
Output
This will be in our Output csv file
Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |
---|---|---|---|---|---|---|---|
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.5303740545041122 | 4 |
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5372510220778413 | 3 |
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.4715707210914604 | 8 |
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.5099483054760279 | 6 |
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.57723478293325 | 1 |
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.49447887833737925 | 7 |
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.5244107252631429 | 5 |
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.5576533672285703 | 2 |
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
Hashes for Topsis-Gurpreet-102003070-1.0.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 05d2885c16c2d938fa9bab2a8c6a682a073dfe3708f1b0c3746614ca7f732c3d |
|
MD5 | eed4d2a773fab048413b287b7ed393be |
|
BLAKE2b-256 | 5c56c30e843f8f352a951083a353b2f06bcb9d056e17876639048e8639f28c7d |
Hashes for Topsis_Gurpreet_102003070-1.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2dc69a277a3ff626ecda6aacd1c6cd7719e5e63283dcf6f5a96b776bccf967c0 |
|
MD5 | 01933e4dfc6bc2b272fc5da31c76d701 |
|
BLAKE2b-256 | dd14be0931809ed2e9bc53e8bc56eb44e8023efe4105c21eabb095c0c5ceec8c |