# Copyright (c) 2023 Jakub Więckowski
import numpy as np
[docs]
def ifs(matrix, weights, types, normalization, score):
"""
Calculates the alternatives preferences based on Intuitionistic Fuzzy Sets
Parameters
----------
matrix : ndarray
Decision matrix / alternatives data.
Alternatives are in rows and Criteria are in columns.
weights : ndarray
Vector of criteria weights in a crisp or Intuitionistic Fuzzy form
types : ndarray
Types of criteria, 1 profit, -1 cost
normalization: callable
Function used to normalize the decision matrix
distance: callable
Function used to calculate distance between two IFS
Returns
-------
ndarray
Crisp preferences of alternatives
"""
# normalized matrix
if normalization is not None:
nmatrix = normalization(matrix, types)
else:
nmatrix = matrix.copy()
# crisp weights
if weights.ndim == 1:
weights = np.repeat(weights, 2).reshape((len(weights), 2))
# weighted decision matrix
wmatrix = matrix.copy()
wmatrix[:, :, 0] = nmatrix[:, :, 0] ** weights[:, 0]
wmatrix[:, :, 1] = 1 - (1 - nmatrix[:, :, 1])** weights[:, 1]
# product
Q = np.prod(wmatrix, axis=1)
# assessment score
return np.array([score(q) for q in Q])