Topsis-Akshat-102017147 1.0.4

Calculates Topsis Score and Rank them accordingly

Topsis_Akshat

TOPSIS

Submitted By: Akshat Girdhar - 102017147.

Title: TOPSIS method for Multiple Criteria Decision Making (MCDM).

Version: 1.0.4.

Author: Akshat Girdhar.

Maintainer: [e-mail].

Description: Evaluation of alternatives based on multiple criteria using TOPSIS method..

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What is TOPSIS?

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is 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.
Weight vector represent the importance we want to give to a particular feature.
Impacts tells whether we want to maximize or minimize that feature. '+'->For maximizing,'-'->For minimizing

How to install this package:

pip install Topsis-Akshat-102017147

Usage

topsis <InputDataFile.csv> <Weights> <Impacts> <Result.csv>

weights and impacts can be given in string format each separated by comma(',') like-:

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

or can also be given without double quotes("") like-:

topsis data.csv 1,1,1,1 +,-,+,- result.csv

But,each argument should be separated by a space.

Example

Let's understand how to use the package with the help of an example.

Input file (data.csv)

Model	Price (in $)	Storage Space (in GB)	Camera (in MP)	Looks
M1	250	16	12	5
M2	200	16	8	3
M3	300	32	16	4
M4	275	32	8	4
M5	225	16	16	2

weights =[1,1,1,1]
impacts=["-,+,+,+"]

Input

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

Output file(result.csv)

Model	Price (in $)	Storage Space (in GB)	Camera (in MP)	Looks	Topsis Score	Rank
M1	250	16	12	5	0.5343	3
M2	200	16	8	3	0.3085	5
M3	300	32	16	4	0.6916	1
M4	275	32	8	4	0.5348	2
M5	225	16	16	2	0.4010	4

The output file contains columns of input file along with two additional columns having Topsis Score and Rank