#coding utf-8
import os
import sys
import re

f1=open("/home/alber/data_base/bigdata/movielens_train_result.txt",'r')  #讀取train文件，已經(jīng)處理成每一行代表一位用戶的item，項(xiàng)之間用空格。
f2=open("/home/alber/data_base/bigdata/movielens_train_result3.txt",'a')
txt=f1.readlines()
contxt=[]
f1.close()
userdic={}
for line in txt:
  line_clean=" ".join(line.split())
  position=line_clean.index(",")
  ID=line_clean[0:position]
  item=line_clean[position+1:]
  userdic.setdefault(ID,item)
  if len(item)>=5:           #對(duì)觀影量少于5的用戶不計(jì)入相似性計(jì)算的范圍
    contxt.append(item)
for key in userdic.keys():        #計(jì)算每位用戶的4個(gè)最相似用戶
  ID_num=key
  value=userdic[key]
   user_item=value.split(' ')
   Sim_user=[]
   for lines in contxt:
     lines_clean=lines.split(' ')
     intersection=list(set(lines_clean).intersection(set(user_item)))
     lenth_intersection=len(intersection)
     difference=list(set(lines_clean).difference(set(user_item)))
     lenth_difference=len(difference)
     if lenth_difference!=0:                     
       Similarity=float(lenth_intersection)/lenth_difference          #交集除以差集作為相似性的判斷條件
       Sim_user.append(Similarity)
     else:
       Sim_user.append("0")
   Sim_user_copy=Sim_user[:]
   Sim_user_copy.sort()
   Sim_best=Sim_user_copy[-4:]
   position1=Sim_user.index(Sim_best[3])
   position2=Sim_user.index(Sim_best[2])
   position3=Sim_user.index(Sim_best[1])
   position4=Sim_user.index(Sim_best[0])
   if position1!=0 and position2!=0 and position3!=0 and position4!=0:
     recommender=userdic[str(position1)]+" "+userdic[str(position2)]+" "+userdic[str(position3)]+" "+userdic[str(position4)] #將4位用戶的看過的電影作為推薦 
  else:
    recommender="none"   
  reco_list=recommender.split(' ')
  recomm=[]
  for good in reco_list:
    if good not in user_item:
      recomm.append(good)
    else:
      pass
  f2.write((" ".join(recomm)+"\n"))
f2.close()