# Copyright (c) 2022 Jakub Więckowski
from .aras.ifs import ifs
from .ifs.normalization import swap_normalization
from .ifs.score import wan_dong_score_1
from ..helpers import rank
from .validator import Validator
[docs]
class ifARAS():
[docs]
def __init__(self, normalization=swap_normalization, score=wan_dong_score_1):
"""
Create Intuitionistic Fuzzy ARAS method object with normalization and score functions
Parameters
----------
normalization : callable, default=swap_normalization
Function used to calculate normalized decision matrix
score : callable, default=wan_dong_score_1
Function used to calculate crisp score of IFS
"""
self.normalization = normalization
self.score = score
self.__descending = True
[docs]
def __call__(self, matrix, weights, types):
"""
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 or Intuitionistic Fuzzy 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.ifs_validation(matrix, weights, types)
self.preferences = ifs(matrix, weights, types, self.normalization, self.score).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')