# Copyright (c) 2022 Jakub Więckowski
from .moora.ifs import ifs
from .ifs.score import zhang_xu_score_2
from ..helpers import rank
from .validator import Validator
[docs]
class ifMOORA():
[docs]
def __init__(self, score=zhang_xu_score_2, normalization=None):
"""
Create Intuitionistic Fuzzy MOORA method object with normalization and score functions
Parameters
----------
score: callable, default=zhang_xu_score_2
Function used to calculate crisp score of IFS
normalization: callable, default=None
Function used to normalize the decision matrix
"""
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.
Criteria types cannot be all profit or all cost.
Returns
----------
ndarray:
Preference calculated for alternatives. Greater values are placed higher in ranking
"""
# validate data
Validator.ifs_validation(matrix, weights, types, mixed_types=True)
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')