topsis-hamedbaziyad 0.0.2

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) CHING-LAI Hwang and Yoon introduced TOPSIS in 1981 in their Multiple Criteria Decision Making (MCDM) and Multiple Criteria Decision Analysis (MCDA) methods

TOPSIS Python

IMPORTANT: This implementation was originally developed by Intelizer. Jules was used to make it a pip-installable package.

This repository provides a Python implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a method for multiple-criteria decision analysis.

Installation

You can install this package using pip:

pip install topsis-hamedbaziyad

Usage

Here is a simple example of how to use the topsis package:

import pandas as pd
from topsis_hamedbaziyad import TOPSIS

# Create a sample decision matrix
data = {
    'Alternatives': ['A1', 'A2', 'A3', 'A4'],
    'C1': [10, 12, 15, 8],
    'C2': [8, 9, 11, 10],
    'C3': [7, 10, 8, 9]
}
decision_matrix = pd.DataFrame(data).set_index('Alternatives')

# Define the weights and attribute types
weights = [0.4, 0.3, 0.3]
attribute_types = [1, 1, 0]

# Run the TOPSIS algorithm
topsis_result = TOPSIS(decision_matrix, weights, attribute_types)
topsis_result = pd.DataFrame(topsis_result).sort_values(by=0, ascending=False)
topsis_result.columns = ["Performance Score"]

# Display the results
print(topsis_result)