# Copyright (c) 2022 Jakub Więckowski
from .mabac.fuzzy import fuzzy
from .utils.defuzzifications import mean_defuzzification
from .utils.normalizations import minmax_normalization
from ..helpers import rank
from .validator import Validator
[docs]
class fMABAC():
def __init__(self, normalization=minmax_normalization, defuzzify=mean_defuzzification):
"""
Create fuzzy MAIRCA method object with minmax normalization and mean defuzzification functions
Parameters
----------
normalization: callable
Function used to normalize the decision matrix
defuzzify: callable
Function used to defuzzify the TFN into crisp value
"""
self.normalization = normalization
self.defuzzify = defuzzify
self.__descending = True
[docs]
def __call__(self, matrix, weights, types, *args, **kwargs):
"""
Calculates the alternatives preferences
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
Returns
----------
ndarray:
Preference calculated for alternatives. Greater values are placed higher in ranking
"""
# validate data
Validator.fuzzy_validation(matrix, weights)
self.preferences = fuzzy(matrix, weights, types, self.normalization, self.defuzzify).astype(float)
return self.preferences
[docs]
def rank(self):
"""
Calculates the alternatives ranking based on the obtained preferences
Returns
----------
ndarray:
Ranking of alternatives
"""
try:
return rank(self.preferences, self.__descending)
except AttributeError:
raise AttributeError('Cannot calculate ranking before assessment')
except:
raise ValueError('Error occurred in ranking calculation')