def resolve_statstotal(self, info, **kwargs):
initial_timestamp = kwargs.get('initial_timestamp')
final_timestamp = kwargs.get('final_timestamp')
if initial_timestamp is None:
initial_timestamp = datetime.min()
if final_timestamp is None:
final_timestamp = datetime.max()
if final_timestamp and initial_timestamp is not None:
s = Visit.objects.select_related(
visit__timestamp_range=(initial_timestamp, final_timestamp))
return s.stats.count()
def aware(request: HttpRequest, memory_id: int) -> HttpResponse:
memory = get_object_or_404(Memory, pk=memory_id) # type: Memory
# 첫 테스트에 바로 맞췄다면
if memory.group_level <= 0:
# 통계 업데이트
statistics = __get_statistics(memory)
statistics.aware_cnt += 1
statistics.save()
# step을 증가시키고, 그에 맞게 unlock_dt 시각을 변경한다
if memory.step == 0:
memory.unlock_dt = timezone.now() + timedelta(days=1)
elif memory.step == 1:
memory.unlock_dt = timezone.now() + timedelta(days=7)
elif memory.step == 2:
memory.unlock_dt = timezone.now() + timedelta(days=28)
elif memory.step == 3:
memory.unlock_dt = timezone.now() + timedelta(days=28*3)
elif memory.step == 4:
memory.unlock_dt = datetime.max()
memory.unlock_dt = memory.unlock_dt - timedelta(hours=8)
memory.step += 1
# 한번에 맞췄다면 이 단어는 안다고 볼 수 있다
memory.status = MemoryStatus.Aware
# 한번에 맞췄다는 숫자 표시
memory.aware_cnt += 1
# 두번째 이후에 맞췄다면
else:
# 내일 다시 테스트한다
memory.unlock_dt = timezone.now() + timedelta(hours=16)
# 한번이라도 틀리면 스텝0부터 다시 시작한다
memory.step = 0
memory.group_level = 0
memory.save()
return redirect('exam', book_id=memory.book.id, exam_type=memory.type)
def calc_similarity(self, session_items, sessions, dwelling_times,
timestamp):
'''
Calculates the configured similarity for the items in session_items and each session in sessions.
Parameters
--------
session_items: set of item ids
sessions: list of session ids
Returns
--------
out : list of tuple (session_id,similarity)
'''
pos_map = {}
length = len(session_items)
count = 1
for item in session_items:
if self.weighting is not None:
pos_map[item] = getattr(self, self.weighting)(count, length)
count += 1
else:
pos_map[item] = 1
if self.dwelling_time:
dt = dwelling_times.copy()
dt.append(0)
dt = pd.Series(dt, index=session_items)
dt = dt / dt.max()
# dt[session_items[-1]] = dt.mean() if len(session_items) > 1 else 1
dt[session_items[-1]] = 1
# print(dt)
for i in range(len(dt)):
pos_map[session_items[i]] *= dt.iloc[i]
# print(pos_map)
if self.idf_weighting_session:
max = -1
for item in session_items:
pos_map[item] = self.idf[item] if item in self.idf else 0
# if pos_map[item] > max:
# max = pos_map[item]
# for item in session_items:
# pos_map[item] = pos_map[item] / max
# print 'nb of sessions to test ', len(sessionsToTest), ' metric: ', self.metric
items = set(session_items)
neighbors = []
cnt = 0
for session in sessions:
cnt = cnt + 1
# get items of the session, look up the cache first
n_items = self.items_for_session(session)
sts = self.session_time[session]
# dot product
similarity = self.vec(items, n_items, pos_map)
if similarity > 0:
if self.weighting_time:
diff = timestamp - sts
days = round(diff / 60 / 60 / 24)
decay = pow(7 / 8, days)
similarity *= decay
# print("days:",days," => ",decay)
neighbors.append((session, similarity))
return neighbors
