Topsis-Harinderjit-102017151 1.0.6

topsis package for MCDM problems

Topsis-Harinderjit-102017151

for: Assignment-1 (UCS654) submitted by: Harinderjit Singh Roll no: 102017151 Group: 3CSE7

This is a Python library for solving Multiple Criteria Decision Making(MCDM) problems by using Topsis.

Installation

Install the package using :

pip install Topsis-Harinderjit-102017151

Usage

Enter csv filename followed by .csv extension, then enter the weights vector with vector values separated by commas, then enter the impacts vector with comma separated signs (+,-), followed by filename where result will be stored.

topsis sample.csv "1,1,1,1" "+,-,+,+" output.csv

or vectors can be entered without " "

topsis sample.csv 1,1,1,1 +,-,+,+ output.csv

But the second representation does not provide for inadvertent spaces between vector values. So, if the input string contains spaces, make sure to enclose it between double quotes (" ").

Example

Consider this sample.csv file

First column of file is removed by model before processing so follow the following format.

All other columns of file should not contain any categorical values.

Model	P1	P2	P3	P4	P5
M1	0.85	0.72	4.6	41.5	11.92
M2	0.66	0.44	6.6	49.4	14.28
M3	0.9	0.81	6.7	66.5	18.73
M4	0.8	0.64	6.9	69.7	19.51
M5	0.84	0.71	4.7	36.5	10.69
M6	0.91	0.83	3.6	42.3	11.91
M7	0.65	0.42	6.9	38.1	11.52
M8	0.71	0.5	3.5	60.9	16.4

weights vector = [ 1,2,1,2,1 ]

impacts vector = [ +,-,+,+,- ]

input:

topsis sample.csv "1,2,1,2,1" "+,-,+,+,-" output.csv

output:

output.csv file will contain following data :

Model	P1	P2	P3	P4	P5	Topsis score	Rank
M1	0.85	0.72	4.6	41.5	11.92	0.3267076760116426	6
M2	0.66	0.44	6.6	49.4	14.28	0.6230956090525585	2
M3	0.9	0.81	6.7	66.5	18.73	0.5006083702087599	5
M4	0.8	0.64	6.9	69.7	19.51	0.6275096427934269	1
M5	0.84	0.71	4.7	36.5	10.69	0.3249142875298663	7
M6	0.91	0.83	3.6	42.3	11.91	0.2715902624653612	8
M7	0.65	0.42	6.9	38.1	11.52	0.5439263412940541	4
M8	0.71	0.5	3.5	60.9	16.4	0.6166791918077927	3