查看: 5298|回复: 3

求协同过滤算法程序

字体大小: 正常放大

1 主题	10 听众	312 积分

升级 4%

TA的每日心情

	衰 2014-12-6 00:46

签到天数: 86 天

[LV.6]常住居民II

自我介绍: 数学建模的爱好者

群组: 第三届数模基础实训

群组: 2014年地区赛数学建模

群组: 数学建模培训课堂1

电梯直达

1^#

发表于 2014-7-18 23:21 |只看该作者 |倒序浏览

|招呼Ta 关注Ta

本人在学校参加暑假培训，需要这个算法做题！求大神赐教，感激不尽

zan

转播0 淘帖0 分享0 收藏0 支持0 反对0 微信

使用道具举报

百年孤独

3503 主题	538 听众	5990 积分

TA的每日心情

	开心 2017-2-7 15:12

签到天数: 691 天

[LV.9]以坛为家II

群组: 2013年国赛赛前培训

群组: 2014年地区赛数学建模

群组: 数学中国第二期SAS培训

群组: 物联网工程师考试

群组: 2013年美赛优秀论文解

2^#

发表于 2014-7-19 09:22 |只看该作者 |招呼Ta 关注Ta

# -*- coding=utf-8 -*-

import math
import sys
from texttable import Texttable

#
# 使用 |A&B|/sqrt(|A || B |)计算余弦距离
#
#
#
def calcCosDistSpe(user1,user2):
avg_x=0.0
avg_y=0.0
for key in user1:
      avg_x+=key[1]
avg_x=avg_x/len(user1)

for key in user2:
      avg_y+=key[1]
avg_y=avg_y/len(user2)

u1_u2=0.0
for key1 in user1:
      for key2 in user2:
         if key1[1] > avg_x and key2[1]>avg_y and key1[0]==key2[0]:
            u1_u2+=1
u1u2=len(user1)*len(user2)*1.0
sx_sy=u1_u2/math.sqrt(u1u2)
return sx_sy

#
# 计算余弦距离
#
#
def calcCosDist(user1,user2):
sum_x=0.0
sum_y=0.0
sum_xy=0.0
for key1 in user1:
      for key2 in user2:
         if key1[0]==key2[0] :
            sum_xy+=key1[1]*key2[1]
            sum_y+=key2[1]*key2[1]
            sum_x+=key1[1]*key1[1]

if sum_xy == 0.0 :
      return 0
sx_sy=math.sqrt(sum_x*sum_y)
return sum_xy/sx_sy

#
#
# 相似余弦距离
#
#
#
def calcSimlaryCosDist(user1,user2):
sum_x=0.0
sum_y=0.0
sum_xy=0.0
avg_x=0.0
avg_y=0.0
for key in user1:
      avg_x+=key[1]
avg_x=avg_x/len(user1)

for key in user2:
      avg_y+=key[1]
avg_y=avg_y/len(user2)

for key1 in user1:
      for key2 in user2:
         if key1[0]==key2[0] :
            sum_xy+=(key1[1]-avg_x)*(key2[1]-avg_y)
            sum_y+=(key2[1]-avg_y)*(key2[1]-avg_y)
      sum_x+=(key1[1]-avg_x)*(key1[1]-avg_x)

if sum_xy == 0.0 :
      return 0
sx_sy=math.sqrt(sum_x*sum_y)
return sum_xy/sx_sy

#
# 读取文件
#
#
def readFile(file_name):
contents_lines=[]
f=open(file_name,"r")
contents_lines=f.readlines()
f.close()
return contents_lines

#
# 解压rating信息，格式：用户id\t硬盘id\t用户rating\t时间
# 输入：数据集合
# 输出:已经解压的排名信息
#
def getRatingInformation(ratings):
rates=[]
for line in ratings:
      rate=line.split("\t")
      rates.append([int(rate[0]),int(rate[1]),int(rate[2])])
return rates

#
# 生成用户评分的数据结构
#
# 输入:所以数据 [[2,1,5],[2,4,2]...]
# 输出:1.用户打分字典 2.电影字典
# 使用字典，key是用户id，value是用户对电影的评价，
# rate_dic[2]=[(1,5),(4,2)].... 表示用户2对电影1的评分是5，对电影4的评分是2
#
def createUserRankDic(rates):
user_rate_dic={}
item_to_user={}
for i in rates:
      user_rank=(i[1],i[2])
      if i[0] in user_rate_dic:
         user_rate_dic[i[0]].append(user_rank)
      else:
         user_rate_dic[i[0]]=[user_rank]

      if i[1] in item_to_user:
         item_to_user[i[1]].append(i[0])
      else:
         item_to_user[i[1]]=[i[0]]

return user_rate_dic,item_to_user

#
# 计算与指定用户最相近的邻居
# 输入:指定用户ID，所以用户数据，所以物品数据
# 输出:与指定用户最相邻的邻居列表
#
def calcNearestNeighbor(userid,users_dic,item_dic):
neighbors=[]
#neighbors.append(userid)
for item in users_dic[userid]:
      for neighbor in item_dic[item[0]]:
         if neighbor != userid and neighbor not in neighbors:
            neighbors.append(neighbor)

neighbors_dist=[]
for neighbor in neighbors:
      dist=calcSimlaryCosDist(users_dic[userid],users_dic[neighbor])  #calcSimlaryCosDist  calcCosDist calcCosDistSpe
      neighbors_dist.append([dist,neighbor])
neighbors_dist.sort(reverse=True)
#print neighbors_dist
return  neighbors_dist

#
# 使用UserFC进行推荐
# 输入：文件名,用户ID,邻居数量
# 输出：推荐的电影ID,输入用户的电影列表,电影对应用户的反序表，邻居列表
#
def recommendByUserFC(file_name,userid,k=5):

#读取文件数据
test_contents=readFile(file_name)

#文件数据格式化成二维数组 List[[用户id,电影id,电影评分]...]
test_rates=getRatingInformation(test_contents)

#格式化成字典数据
# 1.用户字典：dic[用户id]=[(电影id,电影评分)...]
# 2.电影字典：dic[电影id]=[用户id1,用户id2...]
test_dic,test_item_to_user=createUserRankDic(test_rates)

#寻找邻居
neighbors=calcNearestNeighbor(userid,test_dic,test_item_to_user)[:k]

recommend_dic={}
for neighbor in neighbors:
      neighbor_user_id=neighbor[1]
      movies=test_dic[neighbor_user_id]
      for movie in movies:
         #print movie
         if movie[0] not in recommend_dic:
            recommend_dic[movie[0]]=neighbor[0]
         else:
            recommend_dic[movie[0]]+=neighbor[0]
#print len(recommend_dic)

#建立推荐列表
recommend_list=[]
for key in recommend_dic:
      #print key
      recommend_list.append([recommend_dic[key],key])

recommend_list.sort(reverse=True)
#print recommend_list
user_movies = [ i[0] for i in test_dic[userid]]

return [i[1] for i in recommend_list],user_movies,test_item_to_user,neighbors

#
#
# 获取电影的列表
#
#
#
def getMoviesList(file_name):
#print sys.getdefaultencoding()
movies_contents=readFile(file_name)
movies_info={}
for movie in movies_contents:
      movie_info=movie.split("|")
      movies_info[int(movie_info[0])]=movie_info[1:]
return movies_info

#主程序
#输入：测试数据集合
if __name__ == '__main__':
reload(sys)
sys.setdefaultencoding('utf-8')
movies=getMoviesList("/Users/wuyinghao/Downloads/ml-100k/u.item")
recommend_list,user_movie,items_movie,neighbors=recommendByUserFC("/Users/wuyinghao/Downloads/ml-100k/u.data",179,80)
neighbors_id=[ i[1] for i in neighbors]
table = Texttable()
table.set_deco(Texttable.HEADER)
table.set_cols_dtype(['t',  # text
                        't',  # float (decimal)
                        't']) # automatic
table.set_cols_align(["l", "l", "l"])
rows=[]
rows.append([u"movie name",u"release", u"from userid"])
for movie_id in recommend_list[:20]:
      from_user=[]
      for user_id in items_movie[movie_id]:
         if user_id in neighbors_id:
            from_user.append(user_id)
      rows.append([movies[movie_id][0],movies[movie_id][1],""])
table.add_rows(rows)
print table.draw()