Topsis-Varchasva-102017190 1.0.0

Topsis package for MCDM problems

Topsis-Varchava-102017190

for: Assignment-1 (UCS654) submitted by: Varchasva Narain Singh Roll no: 102017190 Group: 3CSE8

Topsis-Varchava-102017190 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).

Installation

pip install Topsis-Varchava-102017190

Usage

Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-)

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

or vectors can be entered without " "

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

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 (" ").

To view usage help, use

topsis /h

Example

sample.csv

A csv file showing data for different mobile handsets having varying features.

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

weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]

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

input:

topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"

output:

      TOPSIS RESULTS
-----------------------------

    P-Score  Rank
1  0.534277     3
2  0.308368     5
3  0.691632     1
4  0.534737     2
5  0.401046     4

Other notes

• The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.
• Make sure the csv does not contain categorical values

License

MIT