A python module to implement Multiple Criteria Decision Making using TOPSIS ranking.
Project description
TOPSIS-suryansh-101983044
This python module aims to implement Multiple Criteria Decision Making using TOPSIS ranking. The module is available at https://pypi.org/project/TOPSIS-suryansh-101983044/0.1/.
Description
This module basically is a function which requires a CSV file as input which contains the model on which you wish to implement TOPSIS along with impacts,weights and a name for the result file.The model basically builts a result csv file which has two additional columns, TOPSIS score and rank respectively.
Installation
Use package manager pip to install the module.
pip install TOPSIS-suryansh-101983044
Usage
After intalling the package use the following code:
module =__import__('TOPSIS-suryansh-101983044')
module.topsis(file_name,weights,impacts,resultant_filename)
Usage Contsraints
The first attribute is a string containg the name of the input csv file.It should contain the input model and should be present in the working directory.
* It should have more than 3 columns.
* First column is the object/variable name.
* From 2nd to last columns contain numeric values only.
The second attribute is a string containing comma seperated weights. Example: "1,1,1,1"
* Weights should be numerical and should be separated by commas.
The third attribute is a string containing comma seperated impacts. Example: "+,-,-,+"
* Impacts could either be '+' or '-'. Impacts should be comma separated.
* Number of WEIGHTS and IMPACTS must be equal to the number of COLUMNS(excluding the first object column) in the input file
Authors
Suryansh Bhadwaj
License
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
Hashes for TOPSIS-suryansh-101983044-999.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | aa5824cf2177dff6c40e8e9b95df7e0d1a12d86615ff0180f9ec3221e75bb280 |
|
MD5 | 37b4236d344b4db5361e160a2bd71992 |
|
BLAKE2b-256 | 842a8f0e0f3ef31a0766a6b8a05a3218a5ad4c3b48210ac60c5dde89451b23bf |