Skip to main content

A Python package implementing TOPSIS technique.

Project description

TOPSIS-Python

Project 1 : UCS654

Submitted By: HIMANSHU NAGPAL 102017110

What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution. More details at wikipedia.


How to use this package:

TOPSIS-HIMANSHU-102017110 can be run as in the following example:

In Command Prompt

>> topsis 102017110-data.csv "1,1,1,1,1" "+,+,-,+,+" 102017110-result.csv

Sample dataset

The decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

Model Correlation R2 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

Weights (w) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in I.


Output

Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Topsis-Himanshu-102017110-1.0.1.tar.gz (4.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

Topsis_Himanshu_102017110-1.0.1-py2-none-any.whl (4.9 kB view details)

Uploaded Python 2

File details

Details for the file Topsis-Himanshu-102017110-1.0.1.tar.gz.

File metadata

File hashes

Hashes for Topsis-Himanshu-102017110-1.0.1.tar.gz
Algorithm Hash digest
SHA256 6ff312d1eee9953441542121689ee3f535407e038baccc304266630fa84dc9ae
MD5 e844bbc9ec04e3d73ad2dd4aff0ec556
BLAKE2b-256 2e97880f85d3d92c21d55bd75a038eea6de02353fab0b71dc34767ab5612ecf2

See more details on using hashes here.

File details

Details for the file Topsis_Himanshu_102017110-1.0.1-py2-none-any.whl.

File metadata

File hashes

Hashes for Topsis_Himanshu_102017110-1.0.1-py2-none-any.whl
Algorithm Hash digest
SHA256 8628012419b26130704c333fd2fb9b6f5f78636ed5f36b30b4ead250bdabe7f0
MD5 11a59718e18646e33e0501921fa0a104
BLAKE2b-256 c2a76aefd2803b51ff20c7dd2ec5506d131d8e1e18983ac24b6008d0bb75573c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page