基于模糊专家系统的笔记本选购推荐系统python源码+数据+项目说明.zip

from django.shortcuts import render from django.http import HttpResponse from django.shortcuts import render,redirect,HttpResponse from frontend import models import numpy as np # Create your views here. def questions(request): global weight global functions global performance global expense global other_demands if request.method == 'POST': functions = request.POST.getlist('functions[]') performance = request.POST.getlist('performance[]') weight = request.POST.get('weight') expense = request.POST.get('expense') other_demands = request.POST.getlist('other_demands[]') print(functions,performance,weight,expense,other_demands) return render(request,'mainpart/questions.html') def recommend(request): #首先根据花费设定预算阈值 scores_type = [] price_threshold = 0 if expense == "economic": price_threshold = 7000 elif expense == "average": price_threshold = 12000 if expense == "much": price_threshold = 24000 #其次根据笔记本电脑负重设定重量阈值 global weight weight_threshold = 0 if weight == "cannot": weight_threshold = 3.0 if weight == "possible": weight_threshold = 3.5 if weight == "okey": weight_threshold = 4.0 #对笔记本性能要求的计算 performance_weight = [0,0,0,0,0,0] performance_describe = ['no demand','screen','big_size','battery','disk','strong performance'] performance_describe_score = ['none','screen_score','size_score','battery_score','disk_score','processor_score'] for des in performance: idx = performance_describe.index(des) performance_weight[idx] = 1 if idx!= 0: scores_type.append(performance_describe_score[idx]) pc_score = [] pcs = models.computer.objects.filter(price__lt=price_threshold,weight__lt=weight_threshold) #pcs = models.computer.objects.filter(price__lt=price_threshold) for pc in pcs: score = [] score.append(0) score.append(pc.screen_score) score.append(pc.size_score) score.append(pc.battery_score) score.append(pc.disk_score) score.append(pc.processor_score) weight = np.array(performance_weight) score = np.array(score) performance_score = np.sum(np.multiply(weight,score)) dic = {"id":pc.id,"name":pc.name,"score":performance_score} pc_score.append(dic) #计算其他需求的得分 if "beautiful" in other_demands: scores_type.append("beauty_score") for pc in pc_score: id = pc["id"] pc_info = models.computer.objects.get(id=id) pc["score"] = pc["score"] + pc_info.beauty_score if "new types" in other_demands: scores_type.append("newtype_score") for pc in pc_score: id = pc["id"] pc_info = models.computer.objects.get(id=id) if pc_info.year == 2019: pc["score"] = pc["score"] + 0.5 if pc_info.year == 2018: pc["score"] = pc["score"] + 0.4 if pc_info.year == 2017: pc["score"] = pc["score"] + 0.3 #计算用途得分 #routine .. professional demands ... enjoyment if "routine" in functions: #如果是完成日常办公，轻薄型笔记本加分0.3, 游戏本+0.2,高端本、开发本+0.35,商务本+0.5 scores_type.append('routine_type') for pc in pc_score: id = pc["id"] pc_info = models.computer.objects.get(id = id) if pc_info.type == 'Q': pc["score"] = pc["score"] + 0.3 elif pc_info.type == 'Y': pc["score"] = pc["score"] + 0.2 elif pc_info.type == 'S': pc["score"] = pc["score"] + 0.5 else: pc["score"] = pc["score"] + 0.4 if "professional demands" in functions: #如果是专业需求，高端本和开发本加0.5,商务本+0.3，游戏本+0.55 scores_type.append('professional_demands_score') for pc in pc_score: id = pc["id"] pc_info = models.computer.objects.get(id=id) if pc_info.type == "G" or pc_info.type == 'K': pc["score"] = pc["score"] + 0.5 if pc_info.type == "S": pc["score"] = pc["score"] + 0.3 if pc_info.type == 'Y': pc["score"] = pc["score"] + 0.55 if "enjoyment" in functions: scores_type.append("enjoyment_score") for pc in pc_score: id = pc["id"] pc_info = models.computer.objects.get(id=id) if pc_info.type == 'Y': pc["score"] = pc["score"] + 0.5 # 根据经济投入再做进一步计算 if expense == "much": scores_type.append("price_score") for pc in pc_score: id = pc["id"] pc_info = models.computer.objects.get(id=id) pc["score"] = pc["score"] + pc_info.price_score sorted_score = sorted(pc_score,key = lambda pc_score:pc_score['score'],reverse = True) """ print("排名前十：") print(sorted_score[0]["name"],sorted_score[0]["score"]) print(sorted_score[1]["name"], sorted_score[1]["score"]) print(sorted_score[2]["name"], sorted_score[2]["score"]) print(sorted_score[3]["name"], sorted_score[3]["score"]) print(sorted_score[4]["name"], sorted_score[4]["score"]) print(sorted_score[5]["name"], sorted_score[5]["score"]) print(sorted_score[6]["name"], sorted_score[6]["score"]) print(sorted_score[7]["name"],sorted_score[7]["score"]) print(sorted_score[8]["name"], sorted_score[8]["score"]) print(sorted_score[9]["name"], sorted_score[9]["score"]) print(sorted_score[10]["name"], sorted_score[10]["score"]) """ #这里要进行筛选，选出5个得分最高的，并且名称不同的 id_list = [] # 5款电脑的型号 name_list = [] count = 0 overall_scores = [] top5 = [] for i in range(len(sorted_score)): if sorted_score[i]["name"] not in name_list: top5.append(sorted_score[i]) count += 1 name_list.append(sorted_score[i]["name"]) id_list.append(sorted_score[i]["id"]) overall_scores.append(sorted_score[i]["score"]) if count == 5: break #以词典方式将后端数据传入前端 id_1st,id_2nd,id_3rd,id_4th,id_5th = id_list[0], id_list[1],id_list[2],id_list[3],id_list[4] pc_set1 = models.computer.objects.filter(id=id_1st) pc_set2 = models.computer.objects.filter(id=id_2nd) pc_set3 = models.computer.objects.filter(id=id_3rd) pc_set4 = models.computer.objects.filter(id=id_4th) pc_set5 = models.computer.objects.filter(id=id_5th) pc_set = pc_set1 | pc_set2 | pc_set3 | pc_set4 | pc_set5 #照片集合 name1,name2,name3,name4,name5 = pc_set1[0].name,pc_set2[0].name,pc_set3[0].name,pc_set4[0].name,pc_set5[0].name img1 = models.computerImage.objects.filter(name=name1) img2 = models.computerImage.objects.filter(name=name2) img3 = models.computerImage.objects.filter(name=name3) img4 = models.computerImage.objects.filter(name=name4) img5 = models.computerImage.objects.filter(name=name5) img_set = img1 | img2 | img3 | img4 | img5 #computer_rawdata计算机原始数据集合 pc_s1 = models.computer_rawdata.objects.filter(id=id_1st) pc_s2 = models.computer_rawdata.objects.filter(id=id_2nd) pc_s3 = models.computer_rawdata.objects.filter(id=id_3rd) pc_s4 = models.computer_rawdata.objects.filter(id=id_4th) pc_s5 = models.computer_rawdata.objects.filter(id=id_5th) raw_data = pc_s1 | pc_s2 | pc_s3 | pc_s4 | pc_s5 #将score_butter传送给前端 - 先执行清空操作 buffer = models.score_buffer.objects.all() buffer.delete() print(scores_type) for id in id_list: ove