def write(request):
if request.method == "POST" :
ret = "OK"
user = User()
user.userId = int(request.POST["userId"])
place = Place()
place.placeId = int(request.POST["placeId"])
if Review.objects.filter(reviewUser=user, reviewPlace=place).exists():
ret = "EXIST"
else:
review = Review()
review.reviewUser = user
review.reviewPlace = place
review.reviewPointPrice = int(request.POST["reviewPointPrice"])
review.reviewPointService = int(request.POST["reviewPointService"])
review.reviewPointLocation = int(request.POST["reviewPointLocation"])
review.reviewPointCondition = int(request.POST["reviewPointCondition"])
review.reviewPointComfort = int(request.POST["reviewPointComfort"])
review.reviewText = request.POST["reviewText"]
review.save()
return HttpResponse(ret)
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?")
