ISTOP

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) implementation in python

Please checkout this link to see more details about TOPSIS steps.

Install

You can install the latest release,

$ pip install istop

Usage

>>> import numpy as np
>>> from istop import Topsis

>>> evaluation_matrix = np.array([
...     [1, 2, 3, 4],
...     [4, 3, 2, 1],
...     [3, 3, 3, 3],
...     [4, 4, 4, 4],
...     [1, 2, 4, 4]
... ])
>>> criteria = [False, True, True, True]
>>> weights = [5, 5, 9, 0]

>>> topsis = Topsis(
...     matrix=evaluation_matrix,
...     criteria=criteria,
...     weights=weights
... )
>>> result = topsis.calculate()
>>> print(result.best_ranks)
[2, 3, 4, 1, 5]
>> print(result.worst_similarities)
[0.56842726 0.18322884 0.43760627 0.55861195 0.68474356]

>>> print(result)
best_ranks=[2, 3, 4, 1, 5]
best_similarities=[0.43157274 0.81677116 0.56239373 0.44138805 0.31525644]
worst_ranks=[2, 3, 4, 1, 5]
worst_similarities=[0.56842726 0.18322884 0.43760627 0.55861195 0.68474356]

The weights parameter is optional. If you don't send it, the default value for each attribute will be 1.

Contribution

Please check to the pylint and flake8 steps in workflow before contribution.