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Copy pathCollaborative filtering base user.py
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Collaborative filtering base user.py
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# coding:utf-8
"""
author:Lindow
date:2018/1/27
"""
from numpy import *
# 生成矩阵
def load_data(path):
f = open(path).readlines()
data = []
for i in f:
data.append([float(j) for j in i.strip().split(' ')])
return data
# 生成用户间关联度
def create_matrix(dataset):
data = mat(dataset)
m,n = shape(data)
p = zeros([m,m])
for i in xrange(m):
for j in xrange(m):
value1 = 0
value2 = 0
value3 = 0
for k in xrange(n):
#print data[i][k]
value1 += (data[i, k] * data[j, k])
value2 += data[i, k] ** 2
value3 += data[j, k] ** 2
p[i, j] = value1 / (sqrt(value2*value3))
return p
# 获取指定用户被推荐的图书
def high_score_user(dataset, marrix, topnum, user):
tmp = {}
for key, value in enumerate(marrix[user-1]):
if len(tmp) <topnum:
if value == 1.0:
continue
else:
tmp[key] = value
else:
for i in range(topnum):
tmp1 = sorted(tmp.items(), key=lambda item: item[1])
if value == 1:
break
elif value > tmp1[i][1]:
del tmp[tmp1[i][0]]
tmp[key] = value
break
final = []
for i in range(len(dataset[finaluser-1])):
if dataset[finaluser-1][i] == 0.0:
total_a = 0
total_b = 0
for j in tmp.keys():
if dataset[j][i] !=0.0:
total_a += tmp[j]*dataset[j][i]
total_b += tmp[j]
if total_b != 0:
dic = {}
dic[i] = total_a/total_b
final.append(dic)
return final
if __name__ == "__main__":
# x轴为图书评分,y轴位用户
dataset = load_data('./data1')
marrix = create_matrix(dataset)
# 被推荐图书的用户
finaluser = 2
# 推荐图书的前几位
topuser = 3
user = high_score_user(dataset, marrix, topuser, finaluser)
print user