如何建立一个网站要多少钱_扬州高端网站制作_上海市今天社会面疫情_web前端开发工程师月薪

matrix2 = []class UserBasedCF(object):''' TopN recommendation - User Based Collaborative Filtering '''def __init__(self):self.trainset = {} # 训练集self.testset = {} # 测试集self.initialset = {} # 存储要推荐的用户的信息self.n_sim_user = 30self.n_rec_movie = 10self.movie_popular = {}self.movie_count = 0 # 总电影数量print('Similar user number = %d' % self.n_sim_user, file=sys.stderr)print('recommended movie number = %d' %self.n_rec_movie, file=sys.stderr)@staticmethoddef loadfile(filename):''' load a file, return a generator. '''fp = open(filename, 'r', encoding='UTF-8')for i, line in enumerate(fp):yield line.strip('\r\n')# if i % 100000 == 0:# print ('loading %s(%s)' % (filename, i), file=sys.stderr)fp.close()print('load %s success' % filename, file=sys.stderr)def initial_dataset(self, filename1):initialset_len = 0for lines in self.loadfile(filename1):users, movies, ratings = lines.split(',')self.initialset.setdefault(users, {})self.initialset[users][movies] = (ratings)initialset_len += 1def generate_dataset(self, filename2, pivot=1.0):''' load rating data and split it to training set and test set '''trainset_len = 0testset_len = 0for line in self.loadfile(filename2):# user, movie, rating, _ = line.split('::')user, movie, rating = line.split(',')# split the data by pivotif random.random() < pivot: # pivot=0.7应该表示训练集：测试集=7：3self.trainset.setdefault(user, {})self.trainset[user][movie] = (rating) # trainset[user][movie]可以获取用户对电影的评分 都是整数trainset_len += 1else:self.testset.setdefault(user, {})self.testset[user][movie] = (rating)testset_len += 1print('split training set and test set succ', file=sys.stderr)print('train set = %s' % trainset_len, file=sys.stderr)print('test set = %s' % testset_len, file=sys.stderr)def calc_user_sim(self):movie2users = dict()for user, movies in self.trainset.items():for movie in movies:# inverse table for item-usersif movie not in movie2users:movie2users[movie] = set()movie2users[movie].add(user) # 看这个电影的用户id# print(movie) #输出的是movieId# print(movie2users[movie]) #输出的是{'userId'...}# print(movie2users) #movieId:{'userId','userId'...}# count item popularity at the same timeif movie not in self.movie_popular:self.movie_popular[movie] = 0self.movie_popular[movie] += 1# print ('build movie-users inverse table succ', file=sys.stderr)# save the total movie number, which will be used in evaluationself.movie_count = len(movie2users)print('total movie number = %d' % self.movie_count, file=sys.stderr)# count co-rated items between users 计算用户之间共同评分的物品usersim_mat = user_sim_mat# print ('building user co-rated movies matrix...', file=sys.stderr)for movie, users in movie2users.items(): # 通过.items()遍历movie2users这个字典里的所有键、值for u in users:for v in users:if u == v:continueusersim_mat.setdefault(u, {})usersim_mat[u].setdefault(v, 0)usersim_mat[u][v] += 1 / math.log(1 + len(users)) # usersim_mat二维矩阵应该存的是用户u和用户v之间共同评分的电影数目# print ('build user co-rated movies matrix succ', file=sys.stderr)# calculate similarity matrix# print ('calculating user similarity matrix...', file=sys.stderr)simfactor_count = 0PRINT_STEP = 20000for u, related_users in usersim_mat.items():for v, count in related_users.items():usersim_mat[u][v] = count / math.sqrt(len(self.trainset[u]) * len(self.trainset[v]))simfactor_count += 1def recommend(self, user):''' Find K similar users and recommend N movies. '''matrix.clear() #每次都要清空K = self.n_sim_user # 这里等于20N = self.n_rec_movie # 这里等于10rank = dict() # 用户对电影的兴趣度# print(self.initialset[user])watched_movies = self.trainset[user] # user用户已经看过的电影 只包括训练集里的# 这里之后不能是训练集# watched_movies = self.initialset[user]for similar_user, similarity_factor in sorted(user_sim_mat[user].items(),key=itemgetter(1), reverse=True)[0:K]: # itemgetter(1)表示对第2个域(相似度)排序 reverse=TRUE表示降序for imdbid in self.trainset[similar_user]: # similar_user是items里面的键，就是所有用户 similarity_factor是值，就是对应的相似度if imdbid in watched_movies:continue # 如果该电影用户已经看过，则跳过# predict the user's "interest" for each movierank.setdefault(imdbid, 0) # 没有值就为0rank[imdbid] += similarity_factor #rank[movie]就是各个电影的相似度# 这里是把和各个用户的相似度加起来，而各个用户的相似度只是基于看过的公共电影数目除以这两个用户看过的电影数量积#print(rank[movie])# return the N best movies# rank_ = dict()rank_ = sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N] #类型是list不是字典了for key,value in rank_:matrix.append(key) #matrix为存储推荐的imdbId号的数组#print(key) #得到了推荐的电影的imdbid号print(matrix)#return sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]return matrix# class UserBasedCF(object): 重庆营销型网站建设价格河南优化公司营销型网站建设公司推荐党支部品牌建设方案网站源码商城建设自己建网站做推广低价货源网站网站修改教程建筑公司网站需求网络服务者规模大的企业建站在线法律咨询免费vivo官网网站服务山东省政府办公厅马希军在线p图编辑器爱未来企业邮箱建设小程序怎么挂失开发公司的一般利润率2020建设工作室表情制作小程序查看最新疫情石家庄网页生成pdf保存到哪里了天津软件定制开发邯郸网络运营中心电话多少朋友圈推广图片网站建设与制作实现精准营销wordpress虚拟资源主题展台设计方案介绍网站模板免费下载酒店管理系统广告网站建设网58找工作求职招聘