Skip to main content

Topsis Implementation Package

Project description

Overview

This is a Python package that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision-making method that helps in ranking a set of alternatives by evaluating them based on multiple criteria.

Installation

You can install the package using pip:

pip install topsis-aayushi-102103421

USAGE

from topsis-aayushi-102103421 import topsis

#Example data (replace this with your actual data) data = { 'Alternative1': [1, 2, 3, 4], 'Alternative2': [4, 3, 2, 1], # Add more alternatives and their values }

#Criteria weights (replace this with your actual weights) weights = [0.25, 0.25, 0.25, 0.25]

#Criteria impacts ('+' or '-' for each criterion) impacts = ['+', '+', '+', '-']

#Perform TOPSIS analysis result = topsis(data, weights, impacts)

#Display the ranking print("Ranking:", result)

Parameters

data: A dictionary where keys are alternative names, and values are lists representing the performance values for each criterion.

weights: A list of weights corresponding to the importance of each criterion.

impacts: A list of impacts ('+' or '-') corresponding to the desired effect of each criterion.

License

This package is distributed under the MIT License - see the LICENSE file for details.

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

topsis-aayushi-102103421-0.1.4.tar.gz (3.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

topsis_aayushi_102103421-0.1.4-py3-none-any.whl (3.7 kB view details)

Uploaded Python 3

File details

Details for the file topsis-aayushi-102103421-0.1.4.tar.gz.

File metadata

File hashes

Hashes for topsis-aayushi-102103421-0.1.4.tar.gz
Algorithm Hash digest
SHA256 fa4282f0965a17f04d88d43e2ad99f4448f3978cea92d24cf3c3aff49b1966fd
MD5 3a107ce3ea93858c45e0e1a5e295f5d9
BLAKE2b-256 5efb25bf65d9f7c4f9d4ce35178797b18d35e7d2065a0e81b49882ca9f034379

See more details on using hashes here.

File details

Details for the file topsis_aayushi_102103421-0.1.4-py3-none-any.whl.

File metadata

File hashes

Hashes for topsis_aayushi_102103421-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 e0e7bbbe14b71a045bcb1fc154676666901f780c95760a53c7e8e050206f76b0
MD5 e701b8bc6bfbb6890ce9b09ae969d691
BLAKE2b-256 63b81620121848b9caacffdd031c2ff36567ca5b35b1f888033c626440aeaab8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page