import numpy as np import pandas as pd import json from datetime import datetime from myCreditCard.enterprise import enterprise_credit # 设置判断矩阵，获取各指标权重 def get_weight(): # 主准则：企业规模、招投标情况、企业信用情况 print("主准则:") main_rule = np.array([ [ 1, 1/2, 1/3], [ 2, 1, 1], [ 3, 1, 1] ]) m = AHP(main_rule) main_weight = m.cal_weight__by_eigenvalue_method() m.test_consist() ''' 1.企业规模 -->> actual_capital 实收注册资金 branch_number 分支机构总数 copy_reg_works_number 著作权数量 estiblish_time 开业时间 is_micro_ent 是否是小微企业 mortgage_info_number 动产抵押数量 patents_number 专利数量 social_staff_num 参保人数 ''' print("\n企业规模--rule1:") rule1 = np.array([ [ 1, 5, 3, 1/3, 3, 2, 2, 2], [1/5, 1, 1/2, 1/6, 1, 1/3, 1/3, 1/4], [1/3, 2, 1, 1/2, 2, 2, 1, 1/3], [ 3, 6, 2, 1, 3, 2, 2, 2], [1/3, 1, 1/2, 1/3, 1, 1/2, 1/3, 1/5], [1/2, 3, 1/2, 1/2, 2, 1, 1/2, 1/3], [1/2, 3, 1, 1/2, 3, 2, 1, 1/2], [1/2, 4, 3, 1/2, 5, 3, 2, 1] ]) r1 = AHP(rule1) r1_weight = r1.cal_weight__by_eigenvalue_method() # r1.test_consist() ''' 2、招投标情况 -->> bid_number 总的招投标数量 2.1、数量之比即为权重 [代理数量:招标数量:(投标数量+中标数量)] 2.1.1、代理情况评分 dai_li_number 代理数量 2.1.2、招标情况评分 50%:zhao_biao_number 招标数量 50%:zhao_biao_money 招标金额（平均） 2.1.3、中投标情况评分 20%:中标率 ：中标数量 / (中标数量 +tou_biao_number 投标数量) 40%:zhong_biao_number 中标数量 40%:zhong_biao_money ：中标金额（平均） ''' print("\n招投标情况--rule2:") rule2 = np.array([ [ 1, 1/5], [ 5, 1] ]) # rule2 = np.array([ # [ 1, 2, 2, 2, 1/2, 1/2], # [1/2, 1, 1, 1, 1/4, 1/3], # [1/2, 1, 1, 1, 1/4, 1/3], # [1/2, 1, 1, 1, 1/4, 1/3], # [ 2, 4, 4, 4, 1, 1/2], # [ 2, 3, 3, 3, 2, 1] # ]) r2 = AHP(rule2) r2_weight = r2.cal_weight__by_eigenvalue_method() # r2.test_consist() ''' 3.企业信用情况 -->> abnormal_number 经营异常数量 admin_penalties_number 行政处罚数量 xx bad_credit_behaviors_number 不良行为数量 xx court_announcement_number 法院公告数量 credit_black_records_number 黑名单记录数量 xx credit_china_v2_number 行政处罚数量--信用中国 (新版) 1 punishment_info_number 行政处罚数量（旧）x 2 credit_red_record_number 守信红名单数量 dishonest_number 失信人数量 good_credit_behaviors_number 良好行为数量 xx illegalinfo_number 严重违法数量 law_suit_number 法律诉讼数量 own_tax_number 欠税公告数量 zhixinginfo_number 被执行人数量 ''' print("\n企业信用情况--rule3:") rule3 = np.array([ [ 1, 1, 1/3, 1/3, 1/2, 1/5, 1, 1/2, 1/2], [ 1, 1, 1/3, 1/2, 1, 1/4, 1, 1/2, 1/2], [ 3, 3, 1, 2, 2, 1/2, 2, 2, 2], [ 3, 2, 1/2, 1, 1, 1/3, 1, 1, 1], [ 2, 1, 1/2, 1, 1, 1/4, 1, 1, 1], [ 5, 4, 2, 3, 4, 1, 3, 2, 3], [ 1, 1, 1/2, 1, 1, 1/3, 1, 2, 1], [ 2, 2, 1/2, 1, 1, 1/2, 1/2, 1, 1/2], [ 2, 2, 1/2, 1, 1, 1/3, 1, 2, 1] ]) r3 = AHP(rule3) r3_weight = r3.cal_weight__by_eigenvalue_method() # r3.test_consist() return main_weight,r1_weight,r2_weight,r3_weight # 信用评分卡 class Credit_Card(): def __init__(self): self.main_weight,self.r1_weight,self.r2_weight,self.r3_weight = get_weight() self.r1_weight = self.main_weight[0] * self.r1_weight self.r2_weight = self.main_weight[1] * self.r2_weight self.r3_weight = self.main_weight[2] * self.r3_weight self.pinf = float('inf') # 正无穷大 def set_data(self,data): # data : 传入的数据字典 data_dict = enterprise_credit().toDict() data_dict.update(data) self.data_dict = data_dict def scoring_scale(self): data_dict = self.data_dict # rule1 capital_score = self.capital_score(data_dict.get('actual_capital')) branch_score = self.branch_score(data_dict.get('branch_number')) copy_reg_works_score = self.copy_reg_works_score(data_dict.get('copy_reg_works_number')) estiblish_time_score = self.estiblish_time_score(data_dict.get('estiblish_time')) is_micro_ent_score = self.is_micro_ent_score(data_dict.get('is_micro_ent')) mortgage_score = self.mortgage_score(data_dict.get('mortgage_info_number')) patents_score = self.patents_score(data_dict.get('patents_number')) staff_num_range = self.staff_num_range(data_dict.get('social_staff_num')) rule1_score_list = [capital_score,branch_score,copy_reg_works_score,estiblish_time_score,is_micro_ent_score,mortgage_score,patents_score,staff_num_range] # rule2 bid_num_range = self.bid_num_range(data_dict.get('bid_number')) # daili_num_range = self.daili_num_range(data_dict.get('dai_li_number')) # toubiao_num_range = self.toubiao_num_range(data_dict.get('tou_biao_number')) # zhaobiao_num_range = self.zhaobiao_num_range(data_dict.get('zhao_biao_number')) # zhongbiao_num_range = self.zhongbiao_num_range(data_dict.get('zhong_biao_number')) # zhongbiao_money_range = self.zhongbiao_money_range(data_dict.get('zhong_biao_money')) zhao_tou_biao_score = self.zhao_tou_biao_range(data_dict.get("dai_li_number"),data_dict.get('zhao_biao_number'),data_dict.get('zhao_biao_money'), data_dict.get('tou_biao_number'),data_dict.get('zhong_biao_number'),data_dict.get('zhong_biao_money')) # rule2_score_list = [bid_num_range,daili_num_range,toubiao_num_range,zhaobiao_num_range,zhongbiao_num_range,zhongbiao_money_range] rule2_score_list = [bid_num_range,zhao_tou_biao_score] # rule3 abnormal_num_range = self.abnormal_num_range(data_dict.get('abnormal_number')) court_announcement_num_range = self.court_announcement_num_range(data_dict.get('court_announcement_number')) punishment_num_range = self.punishment_num_range(data_dict.get('credit_china_v2_number'),data_dict.get('punishment_info_number')) credit_red_record_num_range = self.credit_red_record_num_range(data_dict.get('credit_red_record_number')) dishonest_num_range = self.dishonest_num_range(data_dict.get('dishonest_number')) illegalinfo_num_range = self.illegalinfo_num_range(data_dict.get('illegalinfo_number')) law_suit_num_range = self.law_suit_num_range(data_dict.get('law_suit_number')) own_tax_num_range = self.own_tax_num_range(data_dict.get('own_tax_number')) zhixinginfo_num_range = self.zhixinginfo_num_range(data_dict.get('zhixinginfo_number')) rule3_score_list = [abnormal_num_range,court_announcement_num_range,punishment_num_range,credit_red_record_num_range, dishonest_num_range,illegalinfo_num_range,law_suit_num_range,own_tax_num_range,zhixinginfo_num_range] return rule1_score_list,rule2_score_list,rule3_score_list def get_score(self,get_each_score=False): rule1_score_list,rule2_score_list,rule3_score_list = self.scoring_scale() main_score = sum([np.dot(self.r1_weight,np.array(rule1_score_list)), np.dot(self.r2_weight,np.array(rule2_score_list)), np.dot(self.r3_weight,np.array(rule3_score_list))]) monthly_score = self.monthly_zhao_tou_biao_range(self.data_dict.get('monthly_dai_li_number'),self.data_dict.get('monthly_zhao_biao_number'), self.data_dict.get('monthly_tou_biao_number'),self.data_dict.get('monthly_zhong_biao_number')) credit_score = 0.9 * main_score + 0.1 * monthly_score credit_score = int(round(credit_score * 10)) if get_each_score: r1_score = np.array(rule1_score_list)*self.r1_weight r2_score = np.array(rule2_score_list)*self.r2_weight r3_score = np.array(rule3_score_list)*self.r3_weight return r1_score,r2_score,r3_score,credit_score else: return credit_score # 注册资金 def capital_score(self,value): try: self.capital_range_scale except: self.capital_range_scale = [-1,0,50,100,500,1000,5000,10000,self.pinf] self.capital_range_score_scale = [30,30,40,50,60,70,75,80] score = self.compute_score(value,self.capital_range_scale,self.capital_range_score_scale) return score # 分支机构/公司 def branch_score(self,value): try: self.branch_range_scale except: self.branch_range_scale = [-1,0,1,10,50,100,self.pinf] self.branch_range_score_scale = [40,40,50,60,70,80] score = self.compute_score(value,self.branch_range_scale,self.branch_range_score_scale) return score # 著作权数量 def copy_reg_works_score(self,value): try: self.copy_reg_works_range_scale except: self.copy_reg_works_range_scale = [-1,0,1,10,50,self.pinf] self.copy_reg_works_range_score_scale = [40,40,50,60,70] score = self.compute_score(value,self.copy_reg_works_range_scale,self.copy_reg_works_range_score_scale) return score # 注册时间（多久） def estiblish_time_score(self,value): try: self.estiblish_time_range_scale except: self.nowtime = datetime.now() self.estiblish_time_range_scale = [-1,0,5,10,20,30,self.pinf] self.estiblish_time_range_score_scale = [40,40,50,60,70,80] if value == -1: years = -1 else: time = datetime.strptime(value,"%Y-%m-%d") time = self.nowtime - time years = time.days / 365 score = self.compute_score(years,self.estiblish_time_range_scale,self.estiblish_time_range_score_scale) return score # 是否是小微企业 def is_micro_ent_score(self,value): try: self.is_micro_ent_range_scale except: self.is_micro_ent_range_scale = [-1,0,1,self.pinf] self.is_micro_ent_range_score_scale = [45,40,70] score = self.compute_score(value,self.is_micro_ent_range_scale,self.is_micro_ent_range_score_scale) return score # 动产抵押数量 def mortgage_score(self,value): try: self.mortgage_range_scale except: self.mortgage_range_scale = [-1,0,1,4,10,self.pinf] self.mortgage_range_score_scale = [75,80,60,50,40] score = self.compute_score(value,self.mortgage_range_scale,self.mortgage_range_score_scale) return score # 专利数量 def patents_score(self,value): try: self.patents_range_scale except: self.patents_range_scale = [-1,0,1,10,50,100,self.pinf] self.patents_range_score_scale = [40,40,50,60,70,80] score = self.compute_score(value,self.patents_range_scale,self.patents_range_score_scale) return score # 企业人员规模 def staff_num_range(self,value): try: self.staff_num_range_scale except: self.staff_num_range_scale = [-1,0,50,100,500,1000,5000,10000,self.pinf] self.staff_num_range_score_scale = [30,30,40,50,60,70,75,80] score = self.compute_score(value,self.staff_num_range_scale,self.staff_num_range_score_scale) return score # 总招投标数量 def bid_num_range(self,value): try: self.bid_num_range_scale except: self.bid_num_range_scale = [-1,0,1,100,500,1000,2000,5000,self.pinf] self.bid_num_range_score_scale = [40,40,50,60,70,80,90,100] score = self.compute_score(value,self.bid_num_range_scale,self.bid_num_range_score_scale) return score # 招投标综合评分 def zhao_tou_biao_range(self,dai_li_num,zhao_biao_num,zhao_biao_money,tou_biao_num,zhong_biao_num,zhong_biao_money): total_tou_biao_num = tou_biao_num + zhong_biao_num if dai_li_num >= zhong_biao_num: tou_biao_num = 0 zhong_biao_num = 0 total_tou_biao_num = 0 if zhao_biao_num: zhao_biao_money = max(zhao_biao_money,1) if zhong_biao_num: zhong_biao_money = max(zhong_biao_money,1) bid_num = sum([dai_li_num,zhao_biao_num,total_tou_biao_num]) if bid_num: weight = np.array([dai_li_num,zhao_biao_num,total_tou_biao_num])/bid_num else: weight = np.array([1/3,1/3,1/3]) dai_li_score = self.daili_num_range(dai_li_num) zhao_biao_score = 0.5 * self.zhaobiao_num_range(zhao_biao_num) + 0.5 * self.zhaobiao_money_range(zhao_biao_money) tou_biao_score = 0.2 * self.zhongbiao_rate_range(tou_biao_num,zhong_biao_num) + 0.4 * self.zhongbiao_num_range(zhong_biao_num) + 0.4 * self.zhongbiao_money_range(zhong_biao_money) score = np.dot(weight,np.array([dai_li_score,zhao_biao_score,tou_biao_score])) return score # 上月招投标评分 def monthly_zhao_tou_biao_range(self,dai_li_num,zhao_biao_num,tou_biao_num,zhong_biao_num): try: self.monthly_num_range_scale except: self.monthly_num_range_scale = [-1,0,1,5,10,20,50,self.pinf] self.monthly_num_score_range_scale = [10,10,25,40,55,70,85,100] total_tou_biao_num = tou_biao_num + zhong_biao_num if dai_li_num >= zhong_biao_num: tou_biao_num = 0 zhong_biao_num = 0 total_tou_biao_num = 0 bid_num = sum([dai_li_num, zhao_biao_num, total_tou_biao_num]) if bid_num: weight = np.array([dai_li_num, zhao_biao_num, total_tou_biao_num]) / bid_num else: weight = np.array([1 / 3, 1 / 3, 1 / 3]) dai_li_score = self.compute_score(dai_li_num,self.monthly_num_range_scale,self.monthly_num_score_range_scale) zhao_biao_score = self.compute_score(zhao_biao_num,self.monthly_num_range_scale,self.monthly_num_score_range_scale) tou_biao_score = 0.2 * self.zhongbiao_rate_range(tou_biao_num, zhong_biao_num) \ + 0.8 * self.compute_score(zhong_biao_num,self.monthly_num_range_scale,self.monthly_num_score_range_scale) score = np.dot(weight, np.array([dai_li_score, zhao_biao_score, tou_biao_score])) return score # 代理数量 def daili_num_range(self,value): try: self.daili_num_range_scale except: self.daili_num_range_scale = [-1,0,1,50,200,500,1000,self.pinf] self.daili_num_range_score_scale = [40,40,55,70,85,100,115] score = self.compute_score(value,self.daili_num_range_scale,self.daili_num_range_score_scale) return score # 投标数量 def toubiao_num_range(self,value): try: self.toubiao_num_range_scale except: self.toubiao_num_range_scale = [-1,0,1,50,200,500,self.pinf] self.toubiao_num_range_score_scale = [30,30,35,40,45,50] score = self.compute_score(value,self.toubiao_num_range_scale,self.toubiao_num_range_score_scale) return score # 招标数量 def zhaobiao_num_range(self,value): try: self.zhaobiao_num_range_scale except: self.zhaobiao_num_range_scale = [-1,0,1,50,200,500,1000,self.pinf] self.zhaobiao_num_range_score_scale = [40,40,55,70,85,100,115] score = self.compute_score(value,self.zhaobiao_num_range_scale,self.zhaobiao_num_range_score_scale) return score # 中标数量 def zhongbiao_num_range(self,value): try: self.zhongbiao_num_range_scale except: self.zhongbiao_num_range_scale = [-1,0,1,50,200,500,1000,self.pinf] self.zhongbiao_num_range_score_scale = [40,40,55,70,85,100,115] score = self.compute_score(value,self.zhongbiao_num_range_scale,self.zhongbiao_num_range_score_scale) return score # 中标率 def zhongbiao_rate_range(self,toubiao_num,zhongbiao_num): try: self.zhongbiao_rate_range_scale except: self.zhongbiao_rate_range_scale = [-1,0,0.00000001,0.2,0.4,0.6,0.8,self.pinf] self.zhongbiao_rate_range_score_scale = [40,40,60,75,90,105,115] total_toubiao_num = toubiao_num + zhongbiao_num if total_toubiao_num: zhongbiao_rate = zhongbiao_num / total_toubiao_num else: zhongbiao_rate = 0 score = self.compute_score(zhongbiao_rate,self.zhongbiao_rate_range_scale,self.zhongbiao_rate_range_score_scale) return score # 平均中标金额 def zhongbiao_money_range(self,value): try: self.zhongbiao_money_range_scale except: self.zhongbiao_money_range_scale = [-1,0,1,100000,500000,1000000,5000000,10000000,50000000,100000000,self.pinf] self.zhongbiao_money_range_score_scale = [40,40,50,55,70,80,85,100,110,115] score = self.compute_score(value,self.zhongbiao_money_range_scale,self.zhongbiao_money_range_score_scale) return score # 平均招标金额 def zhaobiao_money_range(self,value): try: self.zhaobiao_money_range_scale except: self.zhaobiao_money_range_scale = [-1,0,1,100000,500000,1000000,5000000,10000000,50000000,100000000,self.pinf] self.zhaobiao_money_range_score_scale = [40,40,50,55,70,80,85,100,110,115] score = self.compute_score(value,self.zhaobiao_money_range_scale,self.zhaobiao_money_range_score_scale) return score # 经营异常数量 def abnormal_num_range(self, value): try: self.abnormal_num_range_scale except: self.abnormal_num_range_scale = [-1, 0, 1, 3, 5, self.pinf] self.abnormal_num_range_score_scale = [85, 90, 65, 55, 45] score = self.compute_score(value, self.abnormal_num_range_scale, self.abnormal_num_range_score_scale) return score # 行政处罚数量 # 取credit_china_v2_number【行政处罚数量--信用中国(新版)】和punishment_info_number 【行政处罚数量（旧）】较大值 def punishment_num_range(self, value1,value2): try: self.punishment_num_range_scale except: self.punishment_num_range_scale = [-1, 0, 1, 5, 50, 100,self.pinf] self.punishment_num_range_score_scale = [90, 100, 75, 60, 45, 30] value = max(value1,value2) score = self.compute_score(value, self.punishment_num_range_scale, self.punishment_num_range_score_scale) return score # 法院公告数量 def court_announcement_num_range(self, value): try: self.court_announcement_num_range_scale except: self.court_announcement_num_range_scale = [-1, 0, 1, 10, 50,100,500, self.pinf] self.court_announcement_num_range_score_scale = [85, 90, 70, 60, 50, 40, 30] score = self.compute_score(value, self.court_announcement_num_range_scale, self.court_announcement_num_range_score_scale) return score # 守信红名单数量 def credit_red_record_num_range(self, value): try: self.credit_red_record_num_range_scale except: self.credit_red_record_num_range_scale = [-1, 0, 1, 5, 10,self.pinf] self.credit_red_record_num_range_score_scale = [45, 45, 60, 75, 90] score = self.compute_score(value, self.credit_red_record_num_range_scale, self.credit_red_record_num_range_score_scale) return score # 失信人数量 def dishonest_num_range(self, value): try: self.dishonest_num_range_scale except: self.dishonest_num_range_scale = [-1, 0, 1, 5, 10,self.pinf] self.dishonest_num_range_score_scale = [80, 90, 65, 50, 35] score = self.compute_score(value, self.dishonest_num_range_scale, self.dishonest_num_range_score_scale) return score # 严重违法数量 def illegalinfo_num_range(self, value): try: self.illegalinfo_num_range_scale except: self.illegalinfo_num_range_scale = [-1, 0, 1, 2, 3,self.pinf] self.illegalinfo_num_range_score_scale = [100, 110, 80, 60, 40] score = self.compute_score(value, self.illegalinfo_num_range_scale, self.illegalinfo_num_range_score_scale) return score # 法律诉讼数量 def law_suit_num_range(self, value): try: self.law_suit_num_range_scale except: self.law_suit_num_range_scale = [-1, 0, 1, 20, 50, 100, 200,self.pinf] self.law_suit_num_range_score_scale = [85, 90, 70, 60, 50,40,30] score = self.compute_score(value, self.law_suit_num_range_scale, self.law_suit_num_range_score_scale) return score # 欠税公告数量 def own_tax_num_range(self, value): try: self.own_tax_num_range_scale except: self.own_tax_num_range_scale = [-1, 0, 1, 5, 20, 50, self.pinf] self.own_tax_num_range_score_scale = [85, 90, 70, 55, 40,25] score = self.compute_score(value, self.own_tax_num_range_scale, self.own_tax_num_range_score_scale) return score # 被执行人数量 def zhixinginfo_num_range(self, value): try: self.zhixinginfo_num_range_scale except: self.zhixinginfo_num_range_scale = [-1, 0, 1, 5, 20, 50, self.pinf] self.zhixinginfo_num_range_score_scale = [85, 90, 70, 55, 40,25] score = self.compute_score(value, self.zhixinginfo_num_range_scale, self.zhixinginfo_num_range_score_scale) return score # 计算得分函数 def compute_score(self,value,range_scale,score_scale): ''' :param value: 指标值 :param range_scale: 指标范围刻度 :param score_scale: 指标评分刻度 :return: score 得分 ''' assert len(range_scale) - 1 == len(score_scale) assert value >= range_scale[0] i = len(range_scale) - 2 j = len(score_scale) - 1 while i >= 0: if value >= range_scale[i]: if i == j: score = score_scale[i] elif value == range_scale[i]: score = score_scale[i] else: score_1 = score_scale[i] score_2 = score_scale[i+1] range_1 = range_scale[i] range_2 = range_scale[i+1] b_log = np.log2(1 + (value-range_1) / (range_2-range_1)) score = score_1 + (score_2 - score_1) * b_log break i -= 1 return score class AHP: def __init__(self, array): ## 记录矩阵相关信息 self.array = array ## 记录矩阵大小 self.n = array.shape[0] # 初始化RI值，用于一致性检验 self.RI_list = [0, 0, 0.52, 0.89, 1.12, 1.26, 1.36, 1.41, 1.46, 1.49, 1.52, 1.54, 1.56, 1.58, 1.59] # 矩阵的特征值和特征向量 self.eig_val, self.eig_vector = np.linalg.eig(self.array) # 矩阵的最大特征值 self.max_eig_val = np.max(self.eig_val) # 矩阵最大特征值对应的特征向量 self.max_eig_vector = self.eig_vector[:, np.argmax(self.eig_val)].real # 矩阵的一致性指标CI self.CI_val = (self.max_eig_val - self.n) / (self.n - 1) # 矩阵的一致性比例CR self.CR_val = self.CI_val / (self.RI_list[self.n - 1]) """ 一致性判断 """ def test_consist(self): # 打印矩阵的一致性指标CI和一致性比例CR print("判断矩阵的CI值为：" + str(self.CI_val)) print("判断矩阵的CR值为：" + str(self.CR_val)) # 进行一致性检验判断 if self.n == 2: # 当只有两个子因素的情况 print("仅包含两个子因素，不存在一致性问题") else: if self.CR_val < 0.1: # CR值小于0.1，可以通过一致性检验 print("判断矩阵的CR值为" + str(self.CR_val) + ",通过一致性检验") return True else: # CR值大于0.1, 一致性检验不通过 print("判断矩阵的CR值为" + str(self.CR_val) + "未通过一致性检验") return False """ 算术平均法求权重 """ def cal_weight_by_arithmetic_method(self): # 求矩阵的每列的和 col_sum = np.sum(self.array, axis=0) # 将判断矩阵按照列归一化 array_normed = self.array / col_sum # 计算权重向量 array_weight = np.sum(array_normed, axis=1) / self.n # 打印权重向量 print("算术平均法计算得到的权重向量为：\n", array_weight) # 返回权重向量的值 return array_weight """ 几何平均法求权重 """ def cal_weight__by_geometric_method(self): # 求矩阵的每列的积 col_product = np.product(self.array, axis=0) # 将得到的积向量的每个分量进行开n次方 array_power = np.power(col_product, 1 / self.n) # 将列向量归一化 array_weight = array_power / np.sum(array_power) # 打印权重向量 print("几何平均法计算得到的权重向量为：\n", array_weight) # 返回权重向量的值 return array_weight """ 特征值法求权重 """ def cal_weight__by_eigenvalue_method(self): # 将矩阵最大特征值对应的特征向量进行归一化处理就得到了权重 array_weight = self.max_eig_vector / np.sum(self.max_eig_vector) # 打印权重向量 print("特征值法计算得到的权重向量为：\n", array_weight) # 返回权重向量的值 return array_weight if __name__ == '__main__': credit_card = Credit_Card() credit_card.set_data(dict()) print("信用得分",credit_card.get_score()) pass