res = [self.symbolic_aggregation_operator(j / len(res_dic.keys()), self.linguistic_set_by_id(self.best_set).size) for (i, j) in res_dic.items()]
return res
@property
def linguistic_sets(self):
return self._linguistic_sets
@property
def best_set(self):
return self._best_set
def linguistic_set_by_id(self, id):
return self.linguistic_sets.get(id, None)
def linguistic_set_by_size(self, size):
return self.linguistic_set_by_id(self.get_set_id_by_size(size))
if __name__ == '__main__':
maker = LHDecisionMaker()
maker.retrieve_sets()
print_dictionary(maker.linguistic_sets)
maker.choose_best_set()
print("best option: " + str(maker.best_set))
maker.define_alternatives()
maker.retrieve_estimates()
res,translations = maker.lh_two_tuple_decision()
print ("Best option: " + str(res))
print ("Its translations: " + str(translations))
matrix.append(row)
return matrix
def calculate_total_by_alternative(self):
sum = 0
res_dic = {}
for row in self.matrix:
for (j, val) in enumerate(row):
try:
res_dic[j] += self.symbolic_aggregation_operator_reverse(val)
except KeyError:
res_dic[j] = self.symbolic_aggregation_operator_reverse(val)
res = [self.symbolic_aggregation_operator(j / len(res_dic.keys())) for (i, j) in res_dic.items()]
return res
if __name__ == "__main__":
print("Starting to initialize the Decision Maker")
maker = DecisionMaker()
maker.define_estimate_options()
maker.define_alternatives()
estimates = maker.get_estimates_from_experts()
print("Got estimates from agents: ")
print_list(estimates)
maker.map_estimates_to_integers()
print("Mapped estimates: ")
print_dictionary(maker.get_map_estimates())
maker.two_tuples_decision()
