# Copyright (c) 2022 Jakub Więckowski
import numpy as np
[docs]
def fuzzy(matrix, weights, types, normalization):
"""
Calculates the alternatives preferences based on Triangular Fuzzy Number extension
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 form
types : ndarray
Types of criteria, 1 profit, -1 cost
normalization: callable
Function used to normalize the decision matrix
Returns
-------
ndarray:
Crisp preferences of alternatives
"""
# normalized decision matrix
nmatrix = normalization(matrix, types)
if weights.ndim == 1:
weights = np.repeat(weights, 3).reshape((len(weights), 3))
# weighted normalized decision matrix
wmatrix = nmatrix * weights
# profit and cost overall ratings
Sp = np.sum(wmatrix[:, types == 1], axis=1)
Sm = np.sum(wmatrix[:, types == -1], axis=1)
# preference value
S = np.array([np.sqrt(1/3 * ((sp[0]-sm[0])**2 + (sp[1]-sm[1])
** 2 + (sp[2]-sm[2])**2)) for sp, sm in zip(Sp, Sm)])
return S