Recommend to the user name and time of the movie, the movie output

# coding=utf-8
# @Time    : 2019/12/6 10:21
# @Author  : Z
# @Email   : S
# @File    : CosMoviesRecommend.py

#给用户推荐电影

import math
import sys

from texttable import Texttable   #pip install texttable安装

#   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",encoding="utf-8")
    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，索引最近用户数据，索引物品数据
#输出：与指定用户最相邻的邻居列表

def calcNearestNeighbor(userid,users_dic,item_dic):
    neighbors=[]
    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])
        neighbors_dist.append([dist,neighbor])
    neighbors_dist.sort(reverse=True)
    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:
            if movie[0] not in recommend_dic:
                recommend_dic[movie[0]]=neighbor[0]
            else:
                recommend_dic[movie[0]]+=neighbor[0]

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

    recommend_list.sort(reverse=True)
    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):
    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__':
    movies=getMoviesList("D:\\code\\python1\\RecomandPro\\SimilsrityCos\\u.item")
    #userid=179,K=80
    recommend_list,user_movie,items_movie,neighbors=recommendByUserFC("D:\\code\\python1\\RecomandPro\\SimilsrityCos\\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(["1","1","1"])
    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())