def __init__(self, query_id, topic = None, user = None, condition = None, autocomplete = None, query_text = None, session = None, precision = None): DataRecord.__init__( self, str(query_id) ) HasDocuments.__init__( self ) self.topic = topic self.user = user self.condition = condition self.autocomplete = bool_(autocomplete) self.query_text = query_text self.session = session self.precision = precision self.result_list = QueryResultList(self)
def create_dataset(tfrecord_path, forgd_video_dir, backd_video_dir,
split_ratio=TRAIN_TEST_SPLIT_RATIO,
frame_size=CNN_FRAME_SIZE, width=CNN_VIDEO_WIDTH,
height=CNN_VIDEO_HEIGHT, phase="train",
fps=FRAMES_BY_SECOND,
max_samples_by_video=MAX_SAMPLES_BY_VIDEO):
"""
Create tfrecord datasets.
"""
# Create data record.
tfrecord_path = tfrecord_path.replace(".tfrecord", "_%s.tfrecord" % phase)
data_record = DataRecord(frame_size, height, width)
data_record.open(tfrecord_path, mode="w")
# Create generator.
generator = sample_generator(forgd_video_dir, backd_video_dir,
dataset=phase,
split_ratio=split_ratio,
frame_size=frame_size, width=width,
height=height, fps=fps,
max_samples_by_video=max_samples_by_video)
# Generate samples.
last_sample = False
while not last_sample:
try:
data = next(generator)
data_record.write(data)
except StopIteration:
last_sample = True
data_record.close()
def test_df(self):
data_record = DataRecord()
data_record.cache.append({
("TP9", "DELTA"): 0,
("TP9", "ALPHA"): 2,
})
data_record.cache.append({
("TP9", "DELTA"): 1,
("TP9", "ALPHA"): 3,
})
data_record.cache.append({
("TP9", "DELTA"): 9,
("TP9", "ALPHA"): 9,
})
multi_index = pd.MultiIndex.from_product(
iterables=[[c.name for c in utils.Channel],
[b.name for b in utils.Band]],
names=["channel", "band"])
# cols = list(product(
# [c.name for c in utils.Channel],
# [b.name for b in utils.Band] ))
cols = [("TP9", "DELTA"), ("TP9", "ALPHA")]
df = pd.DataFrame.from_items(items=data_record.cache,
orient="index",
columns=cols)
print(df)
def write_individual_record(self, individual):
"""
Write a data record for an individual:
- Arrival date
- Wait
- Service start date
- Service time
- Service end date
- Blocked
- Exit date
- Node
- Destination
- Previous class
- Queue size at arrival
- Queue size at departure
"""
record = DataRecord(individual.arrival_date, individual.service_time,
individual.service_start_date,
individual.exit_date, self.id_number,
individual.destination, individual.previous_class,
individual.queue_size_at_arrival,
individual.queue_size_at_departure)
if self.id_number in individual.data_records:
individual.data_records[self.id_number].append(record)
else:
individual.data_records[self.id_number] = [record]
individual.arrival_date = False
individual.service_time = False
individual.service_start_date = False
individual.service_end_date = False
individual.exit_date = False
individual.queue_size_at_arrival = False
individual.queue_size_at_departure = False
individual.destination = False
def __init__(self):
self.data_obj = DataRecord()
self.radar_para_obj = RadarParameter()
self.udp_manage_obj = UdpManage()
self.signal_processing_manage_obj = SignalManage()
self.data_trans_manage_obj = DataTransManage()
self.udp_manage_obj.get_handle(self)
self.data_trans_manage_obj.get_handle(self)
self.signal_processing_manage_obj.get_handle(self)
self.start_process()
class BatchGenerator:
"""
Batch generator.
"""
def __init__(self,
dataset,
session=None,
tfrecord_path=TFRECORD_PATH,
frame_size=CNN_FRAME_SIZE,
height=CNN_VIDEO_HEIGHT,
width=CNN_VIDEO_WIDTH,
batch_size=BATCH_SIZE):
if session is None:
self.session = tf.Session()
else:
self.session = session
if dataset not in tfrecord_path:
tfrecord_path = tfrecord_path.replace(".tfrecord",
"_%s.tfrecord" % dataset)
self.data_record = DataRecord(frame_size, height, width, dataset,
batch_size)
self.data_record.open(tfrecord_path)
self.next = self.data_record.get_next()
def get_next(self):
"""
Generate batches.
"""
while True:
try:
return self.session.run(self.next)
except Exception as e:
print(str(e))
self.data_record.reset(self.session)
def close(self):
"""
Close session.
"""
self.session.close()
def __init__(self,
dataset,
session=None,
tfrecord_path=TFRECORD_PATH,
frame_size=CNN_FRAME_SIZE,
height=CNN_VIDEO_HEIGHT,
width=CNN_VIDEO_WIDTH,
batch_size=BATCH_SIZE):
if session is None:
self.session = tf.Session()
else:
self.session = session
if dataset not in tfrecord_path:
tfrecord_path = tfrecord_path.replace(".tfrecord",
"_%s.tfrecord" % dataset)
self.data_record = DataRecord(frame_size, height, width, dataset,
batch_size)
self.data_record.open(tfrecord_path)
self.next = self.data_record.get_next()
def __init__(self,
timestamp,
condition,
session,
action_type,
query,
action_parameters,
serp_page_num=None):
DataRecord.__init__(self, session.record_id + '-' + str(timestamp))
self.timestamp = timestamp
self.session = session
self.topic = self.session.topic
self.condition = condition
self.action_type = action_type
self.query = query
self.bare_action_parameters = action_parameters
self.serp_page_num = serp_page_num
self.__parse_action_parameters()
self.__update_session()
self.__update_global_stats()
self.__index()
def write_individual_record(self, individual):
"""
Write a data record for an individual:
- Arrival date
- Wait
- Service start date
- Service time
- Service end date
- Blocked
- Exit date
An example showing the data records written; can only write records once an exit date has been determined.
>>> from simulation import Simulation
>>> from individual import Individual
>>> seed(7)
>>> Q = Simulation('datafortesting/logs_test_for_simulation/')
>>> N = Q.transitive_nodes[0]
>>> ind = Individual(6)
>>> N.accept(ind, 3)
>>> ind.service_start_date = 3.5
>>> ind.exit_date = 9
>>> N.write_individual_record(ind)
>>> ind.data_records[1][0].arrival_date
3
>>> ind.data_records[1][0].wait
0.5
>>> ind.data_records[1][0].service_start_date
3.5
>>> round(ind.data_records[1][0].service_time, 5)
0.0559
>>> round(ind.data_records[1][0].service_end_date, 5)
3.5559
>>> round(ind.data_records[1][0].blocked, 5)
5.4441
>>> ind.data_records[1][0].exit_date
9
"""
record = DataRecord(individual.arrival_date, individual.service_time,
individual.service_start_date,
individual.exit_date, self.id_number)
if self.id_number in individual.data_records:
individual.data_records[self.id_number].append(record)
else:
individual.data_records[self.id_number] = [record]
individual.arrival_date = False
individual.service_time = False
individual.service_start_date = False
individual.service_end_date = False
individual.exit_date = False
def __init__(self, topic, document, relevance_level): DataRecord.__init__(self, topic.record_id + '-' + document.record_id) self.topic = topic self.document = document self.relevance_level = uint8(relevance_level) self.document.add_relevance( self )
def __init__(self, condition_id): DataRecord.__init__( self, uint8(condition_id) )
def __init__(self, user_id):
DataRecord.__init__(self, user_id)
def __init__(self, condition_id):
DataRecord.__init__(self, uint8(condition_id))
def __init__(self, docid):
DataRecord.__init__( self, docid )
self.relevances = {}
def __init__(self, user_id): DataRecord.__init__( self, user_id )
def __init__(self, topic_identifier):
DataRecord.__init__(self, uint16(topic_identifier))
self.relevances = []
def __init__(self, topic_identifier): DataRecord.__init__( self, uint16(topic_identifier) ) self.relevances = []
def __init__(self, topic, document, relevance_level):
DataRecord.__init__(self, topic.record_id + '-' + document.record_id)
self.topic = topic
self.document = document
self.relevance_level = uint8(relevance_level)
self.document.add_relevance(self)
def __init__(self, session_id, user, topic, condition): DataRecord.__init__(self, session_id) ActsAsSession.__init__(self, user, topic, condition)