Skip to main content

Topsis-Package

What is TOPSIS

TOPSIS is an acronym that stands for ‘Technique of Order Preference Similarity to the Ideal Solution’ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those. It was presented in Hwang and Yoon (Multiple attribute decision making: methods and applications. Springer, Berlin, 1981) and Chen and Hwang (Fuzzy multiple attribute decision making methods. Springer, Berlin, 1992), and can be considered as one of the classical MCDA methods that has received a lot of attention from scholars and researchers. It has been successfully applied in various instances.

How to ise this Package:

TOPSIS-Nikhal-101816034 can be run by typing the following code snippet:

In Command Prompt

python topsis.py "data.csv" "1,1,1,2" "+,+,-,+" "Result.csv"

Sample Dataset

The decison-matrix should be constructed with each row representing a model alternative and each column representing criterian such as accuracy,R-Sqaure,Root Mean Squared error,Correlation etc.

Model Corr Rseq RMSE Accuracy
M1 0.79 0.62 1.25 60.89
M2 0.66 0.44 2.89 63.07
M3 0.56 0.31 1.57 62.87
M4 0.82 0.67 2.68 70.19
M5 0.75 0.56 1.3 80.39

Output

Model Score Rank
1 0.476957713840877 2
2 0.476572577796742 3
3 0.477495853743771 1
4 0.475616911138615 5
5 0.475948812224928 4

The Ranking are displayed in the result csv file, with 1st rank offering the best decision and last rank offering the worst decision according to the TOPSIS method.

Release files for TOPSIS-Nikhal-101816034 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for TOPSIS-Nikhal-101816034 0.0.1
File Size Uploaded
TOPSIS-Nikhal-101816034-0.0.1.tar.gz 2.9 kB Details

Release files / TOPSIS-Nikhal-101816034-0.0.1.tar.gz

Download URL TOPSIS-Nikhal-101816034-0.0.1.tar.gz
Size 2.9 kB
Tags Source
SHA-256 checksum
How to use checksums
443dd716af1885f6e8d7fc48b5e46a9cc09505428b464565d9c86e85acf1f36f
BLAKE2b-256 checksum
How to use checksums
b14d3b04c1df72bccd58e72686777814cfaa22890e6f2e8b3e3a5e0de697f96d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.2.0 pkginfo/1.6.1 requests/2.22.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.7.3

Release history Release notifications | RSS feed

This release

0.0.1 This release

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page