def recommend1():
j=0
csvfile = file('csv_result19.csv', 'wb')
writer=csv.writer(csvfile)
user_item,user_item_time,ignore_user=csv_to_list()
user_latest_time=map_time()
item_issue_time=map_item_issue_time()
predict=list()
print ignore_user
print len(ignore_user)
for user in user_item:
if user not in ignore_user:
recos=pearsSim(user,user_item,user_item_time)#求解相似用户
item_fre,local_user_item=create_feature(user,recos,user_item)#编号处理
item_score=interest_distribution(user,local_user_item,user_item_time,item_fre,user_latest_time,item_issue_time,h=0.4)#计算核密度
number=len(user_item[user])
if number>30:
number=20
final_result=sorted(item_score, key=lambda jj:jj[1], reverse=True)[:(number/10)+1]#结果排序
print final_result
for line in final_result:
if line[1]>0.25:
writer.writerow((user,line[0]))
j=j+1
print j
csvfile.close()
return predict
def recommend1():
j = 0
csvfile = file('csv_result19.csv', 'wb')
writer = csv.writer(csvfile)
user_item, user_item_time, ignore_user = csv_to_list()
user_latest_time = map_time()
item_issue_time = map_item_issue_time()
predict = list()
print ignore_user
print len(ignore_user)
for user in user_item:
if user not in ignore_user:
recos = pearsSim(user, user_item, user_item_time) #求解相似用户
item_fre, local_user_item = create_feature(user, recos,
user_item) #编号处理
item_score = interest_distribution(user,
local_user_item,
user_item_time,
item_fre,
user_latest_time,
item_issue_time,
h=0.4) #计算核密度
number = len(user_item[user])
if number > 30:
number = 20
final_result = sorted(item_score,
key=lambda jj: jj[1],
reverse=True)[:(number / 10) + 1] #结果排序
print final_result
for line in final_result:
if line[1] > 0.25:
writer.writerow((user, line[0]))
j = j + 1
print j
csvfile.close()
return predict
day_late[day] = time
early = day_early.get(day)
if early is not None:
if cmp_time(early,time) > 0:
day_early[day] = time
else:
day_early[day] = time
return day_news, day_late, day_early
if __name__=='__main__':
latest_time,news_time=map_time()
day_news, day_late, day_early = draw_time(news_time)
print 'every day\'s news:'
print day_news
print
print 'every day\'s earliest issue time:'
print day_early
print
print 'every day\'s latest issue time:'
print day_late
# print day_news.keys()
# print day_late.keys()
# print day_early.keys()
