def recognize_last_activities(uuid, algorithm, feature_set, start_ts, end_ts):
start_datetime = datetime.fromtimestamp(start_ts / 1e3)
end_datetime = datetime.fromtimestamp(end_ts / 1e3)
records = DataRecord.objects.filter(user_id=uuid)\
.filter(date_time__gt=start_datetime)\
.filter(date_time__lt=end_datetime)
if records:
avg_prob = [0, 0, 0, 0, 0, 0, 0, 0]
current_activity = "none"
for j in xrange(len(records)):
prob = get_probability_for_data_record(records[j], feature_set, algorithm)
if j == 0:
current_activity = activity_table_json.get(np.argmax(prob) + 1)
for i in xrange(len(prob)):
avg_prob[i] += prob[i]/len(records)
return {"vector": avg_prob, "time": records[0].date_time, "current_activity": current_activity}
else:
raise Exception('No record found')
def recognize_last_activities(uuid, algorithm, feature_set, delta_time):
"""
Recognises the last activities within a time last delta_time seconds
:param uuid: user id of the user
:param algorithm: the requested algorithm
:param feature_set: the requested feature set
:param delta_time: the time in seconds
:return: a dict consisting of a probability vector the time and the current activity :raise Exception:
"""
datetime_point = datetime.now() - timedelta(seconds=delta_time)
records = DataRecord.objects.filter(user_id=uuid).filter(
record_date__gt=datetime_point)
if records:
avg_prob = [0, 0, 0, 0, 0, 0, 0, 0]
current_activity = "none"
for j in xrange(len(records)):
prob = get_probability_for_data_record(records[j], feature_set,
algorithm)
if j == 0:
current_activity = activity_table_json.get(np.argmax(prob) + 1)
for i in xrange(len(prob)):
avg_prob[i] += prob[i] / len(records)
return {
"vector": avg_prob,
"time": records[0].record_date,
"current_activity": current_activity
}
else:
raise Exception('No record found')
def get(self, request):
try:
if 'uuid' not in request.GET:
raise Exception("Bad request")
algorithm = request.GET['alg'] if 'alg' in request.GET else 'svm'
feature_set = request.GET['fs'] if 'fs' in request.GET else 'standard'
current_activity = request.GET["curr_act"] if 'curr_act' in request.GET else 'false'
ac = request.GET['ac'] if 'ac' in request.GET else '1'
if ac != '1':
raise Exception("Not yet implemented")
if 'tp' in request.GET:
record = recognize_last_activities(request.GET['uuid'],
algorithm,
feature_set,
int(request.GET['tp']))
else:
record = recognize_last_activity(request.GET['uuid'],
algorithm,
feature_set)
response_text = '{ "date_time":"%s", ' % record.get("time")
response_text += '"uuid" : "%s",' % request.GET['uuid']
if current_activity == "true":
response_text += '"curr_act" : "%s",' % record.get("current_activity")
elif current_activity != "false":
raise Exception("Wrong value for curr_act")
if algorithm == 'svm':
response_text += '"svm_vector" : {'
reduced_vec = reduce_activity_vector(record['vector'])
#reduced_vec = record['vector']
for i in xrange(len(reduced_vec)):
if i != len(reduced_vec) - 1:
response_text += '"%s":"%s",' % (reduced_activity_table_json.get(i+1), reduced_vec[i])
#response_text += '"%s":"%s",' % (activity_table.get(i+1), reduced_vec[i])
else:
response_text += '"%s":"%s"}' % (reduced_activity_table_json.get(i+1), reduced_vec[i])
#response_text += '"%s":"%s"}' % (activity_table.get(i+1), reduced_vec[i])
else:
index = np.argmax(record['vector']) + 1
response_text += '"dt_category" : "%s"' % activity_table_json.get(index)
response_text += "}"
response = HttpResponse(response_text)
response.status_code = 200
return response
except Exception as e:
response = HttpResponse('{"error":"%s"}' % e.message)
request.status_code = 404
return response
def get(self, request):
try:
if 'uuid' not in request.GET:
raise Exception("Bad request")
algorithm = request.GET['alg'] if 'alg' in request.GET else 'svm'
feature_set = request.GET['fs'] if 'fs' in request.GET else 'standard'
current_activity = request.GET["curr_act"] if 'curr_act' in request.GET else 'false'
ac = request.GET['ac'] if 'ac' in request.GET else '1'
if ac != '1':
raise Exception("Not yet implemented")
if 'start_ts' in request.GET and 'end_ts':
record = recognize_last_activities(request.GET['uuid'],
algorithm,
feature_set,
int(request.GET['start_ts']),
int(request.GET['end_ts']))
else:
record = recognize_last_activity(request.GET['uuid'],
algorithm,
feature_set)
response_text = '{ "date_time":"%s", ' % record.get("time")
response_text += '"uuid" : "%s",' % request.GET['uuid']
if current_activity == "true":
response_text += '"curr_act" : "%s",' % record.get("current_activity")
elif current_activity != "false":
raise Exception("Wrong value for curr_act")
if algorithm == 'svm':
response_text += '"svm_vector" : {'
reduced_vec = reduce_activity_vector(record['vector'])
for i in xrange(len(reduced_vec)):
if i != len(reduced_vec) - 1:
response_text += '"%s":"%s",' % (reduced_activity_table_json.get(i+1), reduced_vec[i])
else:
response_text += '"%s":"%s"}' % (reduced_activity_table_json.get(i+1), reduced_vec[i])
else:
index = np.argmax(record['vector']) + 1
response_text += '"dt_category" : "%s"' % activity_table_json.get(index)
response_text += "}"
response = HttpResponse(response_text)
response.status_code = 200
return response
except Exception as e:
response = HttpResponse('{"error":"%s"}' % e.message)
request.status_code = 404
return response
def recognize_last_activity(uuid, algorithm, feature_set):
"""
Recognises the last activity for the given user.
:param uuid: user id of the user
:param algorithm: the requested algorithm
:param feature_set: the requested feature set
:return: a dict consisting of a probability vector the time and the current activity
"""
record = DataRecord.objects.filter(user_id=uuid).latest('record_date')
prob = get_probability_for_data_record(record, feature_set, algorithm)
current_activity = activity_table_json.get(np.argmax(prob) + 1)
return {
"vector": prob,
"time": record.record_date,
"current_activity": current_activity
}
def recognize_last_activity(uuid, algorithm, feature_set):
record = DataRecord.objects.raw_query({'user_id': {'$eq': uuid}}).reverse()[:1]
if algorithm == 'svm' and feature_set == 'standard':
prob = record[0].activity.svm
elif algorithm == 'svm' and feature_set == 'enhanced':
prob = record[0].activity.svm_ech
elif algorithm == 'dt' and feature_set == 'standard':
prob = record[0].activity.dt
elif algorithm == 'dt' and feature_set == 'enhanced':
prob = record[0].activity.dt_ech
else:
raise Exception("Bad request")
current_activity = activity_table_json.get(np.argmax(prob) + 1)
return {"vector": prob, "time": record[0].date_time, "current_activity": current_activity}
def recognize_last_activities(uuid, algorithm, feature_set, delta_time):
datetime_point = datetime.now() - timedelta(seconds=delta_time)
records = DataRecord.objects.filter(user_id=uuid).filter(date_time__gt=datetime_point)
if records:
avg_prob = [0, 0, 0, 0, 0, 0, 0, 0]
current_activity = "none"
for j in xrange(len(records)):
prob = get_probability_for_data_record(records[j], feature_set, algorithm)
if j == 0:
current_activity = activity_table_json.get(np.argmax(prob) + 1)
for i in xrange(len(prob)):
avg_prob[i] += prob[i]/len(records)
return {"vector": avg_prob, "time": records[0].date_time, "current_activity": current_activity}
else:
raise Exception('No record found')
