def view():
# load Object data
with open('objectData.pkl', 'rb') as input:
lectureList = pickle.load(input)
personList = pickle.load(input)
# userId, userPw 받아옴
userId = request.form['userId']
userPw = request.form['userPw']
# ghost 모듈을 이용하여 crawling
a = KuKlueCrawler(id=userId, pw=userPw)
page, resource = a.openPage('http://klue.kr/myLectureEval.php')
page, resource = a.ghost.evaluate(
"document.getElementsByClassName('mainContent');")
page = unicode(page[PyQt4.QtCore.QString(u'0')][PyQt4.QtCore.QString(
u'outerHTML')]).encode('utf-8')
lec = BeautifulSoup(page)
lecContent = lec.find('div', {
'class': ['lectureEvalList']
}).findAll('div', {'class': ['content']})
# 자신에 대한 Person 객체 생성
me = Person()
me.nickName = lecContent[0].find('div', {
'class': ['wrInfo']
}).a.text.encode('utf-8')
# 자신이 평가한 항목에 대해 평균 점수를 매김
lectureCount = 0
for item in lecContent:
try:
me.difficulty += len(
item.find('div', {
'class': ['e difficulty']
}).find('div', 'center').findAll('span', 'active'))
me.total += len(
item.find('div', {
'class': ['e total']
}).find('div', 'center').findAll('span', 'active'))
me.studyTime += len(
item.find('div', {
'class': ['e studyTime']
}).find('div', 'center').findAll('span', 'active'))
me.attendance += len(
item.find('div', {
'class': ['e attendance']
}).find('div', 'center').findAll('span', 'active'))
me.grade += len(
item.find('div', {
'class': ['e grade']
}).find('div', 'center').findAll('span', 'active'))
me.achievement += len(
item.find('div', {
'class': ['e achievement']
}).find('div', 'center').findAll('span', 'active'))
lectureCount += 1
except: # except가 뜨는 부분은 신형 데이터를 읽었을 경우
me.difficulty /= lectureCount
me.total /= lectureCount
me.studyTime /= lectureCount
me.attendance /= lectureCount
me.grade /= lectureCount
me.achievement /= lectureCount
break
# 유사도 계산 알고리즘에 넣고 결과값(유사한 사람) 반환
result = topMatches(personList, me)
classList = []
# for calculating error
totalCount = 0
Etotal = 0.0
Edifficulty = 0.0
EstudyTime = 0.0
Eattendance = 0.0
Egrade = 0.0
Eachievement = 0.0
# 결과값에 대한 강의 정리
for person in result:
similarity = person[0]
for lectureID in person[1].lectureList:
for oneLecture in lectureList:
if float(oneLecture.total) < 2.5:
continue
if oneLecture.lectureID == lectureID:
##### for calculating error #####
Etotal += float(oneLecture.total)
Edifficulty += float(oneLecture.difficulty)
EstudyTime += float(oneLecture.studyTime)
Eattendance += float(oneLecture.attendance)
Egrade += float(oneLecture.grade)
Eachievement += float(oneLecture.achievement)
totalCount += 1
#################################
classList.append(
(similarity * float(oneLecture.total) / 5, oneLecture))
break
Etotal /= totalCount
Edifficulty /= totalCount
EstudyTime /= totalCount
Eattendance /= totalCount
Egrade /= totalCount
Eachievement /= totalCount
print
print "Total Eval Error : " + str(me.total - Etotal)
print "difficulty Error : " + str(me.difficulty - Edifficulty)
print "studyTime Error : " + str(me.studyTime - EstudyTime)
print "attendance Error : " + str(me.attendance - Eattendance)
print "grade Error : " + str(me.grade - Egrade)
print "achievement Error " + str(me.achievement - Eachievement)
print
classList.sort(reverse=True)
classList = [lecture[1] for lecture in classList] # 유사도*total 점수 제거
# page rendering
return render_template('ShowLectures.html', classList=classList)
def view():
# load Object data
with open('objectData.pkl', 'rb') as input:
lectureList = pickle.load(input)
personList = pickle.load(input)
# userId, userPw 받아옴
userId = request.form['userId']
userPw = request.form['userPw']
# ghost 모듈을 이용하여 crawling
a = KuKlueCrawler(id=userId,pw=userPw)
page, resource = a.openPage('http://klue.kr/myLectureEval.php')
page, resource = a.ghost.evaluate("document.getElementsByClassName('mainContent');")
page = unicode(page[PyQt4.QtCore.QString(u'0')][PyQt4.QtCore.QString(u'outerHTML')]).encode('utf-8')
lec = BeautifulSoup(page)
lecContent = lec.find('div',{'class':['lectureEvalList']}).findAll('div',{'class':['content']})
# 자신에 대한 Person 객체 생성
me = Person()
me.nickName = lecContent[0].find('div',{'class':['wrInfo']}).a.text.encode('utf-8')
# 자신이 평가한 항목에 대해 평균 점수를 매김
lectureCount = 0
for item in lecContent:
try:
me.difficulty += len(item.find('div',{'class':['e difficulty']}).find('div','center').findAll('span','active'))
me.total += len(item.find('div',{'class':['e total']}).find('div','center').findAll('span','active'))
me.studyTime += len(item.find('div',{'class':['e studyTime']}).find('div','center').findAll('span','active'))
me.attendance += len(item.find('div',{'class':['e attendance']}).find('div','center').findAll('span','active'))
me.grade += len(item.find('div',{'class':['e grade']}).find('div','center').findAll('span','active'))
me.achievement += len(item.find('div',{'class':['e achievement']}).find('div','center').findAll('span','active'))
lectureCount+=1
except: # except가 뜨는 부분은 신형 데이터를 읽었을 경우
me.difficulty /= lectureCount
me.total /= lectureCount
me.studyTime /= lectureCount
me.attendance /= lectureCount
me.grade /= lectureCount
me.achievement /= lectureCount
break
# 유사도 계산 알고리즘에 넣고 결과값(유사한 사람) 반환
result = topMatches(personList,me)
classList = []
# for calculating error
totalCount = 0
Etotal = 0.0
Edifficulty = 0.0
EstudyTime = 0.0
Eattendance = 0.0
Egrade = 0.0
Eachievement = 0.0
# 결과값에 대한 강의 정리
for person in result:
similarity = person[0]
for lectureID in person[1].lectureList:
for oneLecture in lectureList:
if float(oneLecture.total)<2.5:
continue
if oneLecture.lectureID == lectureID:
##### for calculating error #####
Etotal += float(oneLecture.total)
Edifficulty += float(oneLecture.difficulty)
EstudyTime += float(oneLecture.studyTime)
Eattendance += float(oneLecture.attendance)
Egrade += float(oneLecture.grade)
Eachievement += float(oneLecture.achievement)
totalCount += 1
#################################
classList.append((similarity*float(oneLecture.total)/5,oneLecture))
break
Etotal /= totalCount
Edifficulty /= totalCount
EstudyTime /= totalCount
Eattendance /= totalCount
Egrade /= totalCount
Eachievement /= totalCount
print
print "Total Eval Error : " + str(me.total-Etotal)
print "difficulty Error : " + str(me.difficulty - Edifficulty)
print "studyTime Error : " + str(me.studyTime - EstudyTime)
print "attendance Error : " + str(me.attendance - Eattendance)
print "grade Error : " + str(me.grade - Egrade)
print "achievement Error " + str(me.achievement - Eachievement)
print
classList.sort(reverse=True)
classList = [lecture[1] for lecture in classList] # 유사도*total 점수 제거
# page rendering
return render_template('ShowLectures.html',classList=classList)
from recommendation import critics
import recommendation
import pydelicious
from deliciousrec import *
a = recommendation.sim_distance(recommendation.critics,
'Lisa Rose',
'Gene Seymour')
print(a)
a = recommendation.topMatches(recommendation.critics,
'Toby', n = 3)
print(a)
#test function setdefault..
b = {}
b.setdefault('ha', 2)
b['xi'] = 3
for i in b:
print(i)
b['ha'] += 1
b.setdefault('ha', 2)
print(b['ha'])
#test for in in dictionary..
set = {'a':0,'b':1,'c':2}
res = [(val/0.5,key) for key,val in set.items()]
res.sort()
print(res)
haha = {}
haha[(1,2)] = 2
print(haha)
