def processAll(request):
start_time = time.clock()
similarityValue = {}
response = ""
for user1 in User.objects.select_related().all():
similarityValue[user1.userId] = {}
for user2 in User.objects.select_related().exclude(userId=user1.userId):
#if Similarity.objects.extra(where=["similarityUser1_id IN (%s, %s) AND similarityUser2_id IN (%s, %s)"], params=[user1.userId,user2.userId,user1.userId,user2.userId]).exists():
if user1.userId in similarityValue:
if user2.userId in similarityValue[user1.userId]:
continue
if user2.userId in similarityValue:
if user1.userId in similarityValue[user2.userId]:
continue
sp = SimilarityProcess()
simValue = sp.process(user1, user2, 1)
response += user1.userName + " & " + user2.userName + " simValue : " + str(simValue) + "<br />"
similarityValue[user1.userId][user2.userId] = simValue
''' insert similarity value to db
sim = Similarity()
sim.similarityUser1 = user1
sim.similarityUser2 = user2
sim.similarityValue = simValue
sim.save()
#'''
response += str(time.clock() - start_time) + " seconds<br />"
return HttpResponse(response)
def processUser(request):
if request.method == "POST" :
userId = str(request.POST["userId"])
sp = SimilarityProcess()
sp.processUser(userId)
return HttpResponse("")
else :
return HttpResponse("what?")
def listReviews(request):
if request.method == "POST" :
user = User()
user.userId = int(request.POST["userId"])
place = Place()
place.placeId = int(request.POST["placeId"])
typeId = int(request.POST["typeId"])
data = []
for r in Review.objects.filter(reviewPlace_id=place).exclude(reviewUser_id=user) :
dict = {}
dict['userId'] = r.reviewUser.userId
dict['userAlias'] = r.reviewUser.userAlias
dict['reviewPointPrice'] = r.reviewPointPrice
dict['reviewPointService'] = r.reviewPointService
dict['reviewPointLocation'] = r.reviewPointLocation
dict['reviewPointCondition'] = r.reviewPointCondition
dict['reviewPointComfort'] = r.reviewPointComfort
dict['reviewText'] = r.reviewText
average = float(r.reviewPointPrice+r.reviewPointService+r.reviewPointLocation+r.reviewPointCondition+r.reviewPointComfort) / 5
dict['averagePoint'] = average
sp = SimilarityProcess()
dict['similarityValue'] = sp.process(User.objects.get(userId=user.userId), r.reviewUser, typeId)
dict['newSimilarityValue'] = float(dict['similarityValue']) * average
data.append(dict)
#''' normalizing similarity on same place
minV = min(data, key=lambda x:x['similarityValue'])
maxV = max(data, key=lambda x:x['similarityValue'])
minValue = minV['similarityValue']
maxValue = maxV['similarityValue']
for dict in data:
dict["similarityValue"] = float((dict["similarityValue"] - minValue)/(maxValue - minValue))
dict['newSimilarityValue'] = dict['similarityValue'] * dict['averagePoint']
dict['averagePoint'] = str(dict['averagePoint'])
fuzzy = Fuzzy()
dict['similarityFlag'] = str(fuzzy.process(dict['similarityValue']))
#'''
data = sorted(data, key=lambda rev: rev['newSimilarityValue'], reverse=True)
return HttpResponse(json.dumps(data))
else :
return HttpResponse("what?")
