Topsis-Harjot-101803217 1.1.9

Implementation of Topsis

TOPSIS

Code by: Harjot Singh

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Introduction

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) developed by Hwang & Yoon,is a technique to evaluate the performance of alternatives through the similarity with the ideal solution. According to this technique, the best alternative would be one that is closest to the positive-ideal solution and farthest from the negative-ideal solution. The positive-ideal solution is one that maximizes the benefit criteria and minimizes the cost criteria. The negative-ideal solution maximizes the cost criteria and minimizes the benefit criteria. In summary, the positive-ideal solution is composed of all best values attainable of criteria, and the negative-ideal solution consists of all the worst values attainable of criteria.

How to run

Before running, make sure you have pandas installed on your system

Open Terminal and input the following commands

pip install Topsis-Harjot-101803217

python

from topsis.code import topsis topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")

Sample Input

This input was used to test the module

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	Corr	Rseq	RMSE	Accuracy	Topsis Score	Rank
M1	0.79	0.62	1.25	60.89	0.639133	2.0
M2	0.66	0.44	2.89	63.07	0.212592	5.0
M3	0.56	0.31	1.57	62.87	0.407846	4.0
M4	0.82	0.67	2.68	70.19	0.519153	3.0
M5	0.75	0.56	1.3	80.39	0.828267	1.0

License

© 2020 Harjot Singh

This repository is licensed under the MIT license. See LICENSE for details.