def train(self, training_file, num_records):
"""Create a network and training it on a CSV data source"""
dataSource = FileRecordStream(streamID=training_file)
dataSource.setAutoRewind(True)
self._network = configureNetwork(dataSource, self.network_config)
for i in xrange(num_records): # Equivalent to: network.run(num_records)
self._network.run(1)
self._network.save(self.trained_network_path)
def run():
""" Run classification network(s) on artificial sensor data """
with open("network_config_template.json", "rb") as jsonFile:
templateNetworkConfig = json.load(jsonFile)
networkConfigurations = generateSampleNetworkConfig(
templateNetworkConfig, NUM_CATEGORIES)
for networkConfig in networkConfigurations:
for noiseAmplitude in WHITE_NOISE_AMPLITUDES:
for signalMean in SIGNAL_MEANS:
for signalAmplitude in SIGNAL_AMPLITUDES:
for signalPeriod in SIGNAL_PERIODS:
sensorType = networkConfig["sensorRegionConfig"].get(
"regionType")
spEnabled = networkConfig["sensorRegionConfig"].get(
"regionEnabled")
tmEnabled = networkConfig["tmRegionConfig"].get(
"regionEnabled")
upEnabled = networkConfig["tpRegionConfig"].get(
"regionEnabled")
classifierType = networkConfig[
"classifierRegionConfig"].get("regionType")
expParams = (
"RUNNING EXPERIMENT WITH PARAMS:\n"
" * numRecords=%s\n"
" * signalAmplitude=%s\n"
" * signalMean=%s\n"
" * signalPeriod=%s\n"
" * noiseAmplitude=%s\n"
" * sensorType=%s\n"
" * spEnabled=%s\n"
" * tmEnabled=%s\n"
" * tpEnabled=%s\n"
" * classifierType=%s\n") % (
NUM_RECORDS, signalAmplitude, signalMean,
signalPeriod, noiseAmplitude,
sensorType.split(".")[1], spEnabled, tmEnabled,
upEnabled, classifierType.split(".")[1])
print expParams
inputFile = generateSensorData(
DATA_DIR, OUTFILE_NAME, signalMean, signalPeriod,
SEQUENCE_LENGTH, NUM_RECORDS, signalAmplitude,
NUM_CATEGORIES, noiseAmplitude)
dataSource = FileRecordStream(streamID=inputFile)
network = configureNetwork(dataSource, networkConfig)
partitions = generateNetworkPartitions(
networkConfig, NUM_RECORDS)
trainNetwork(network, networkConfig, partitions,
NUM_RECORDS)
def initModel(self):
"""
Initialize the network; self.networdDataPath must already be set.
"""
encoder = CioEncoder(retinaScaling=self.retinaScaling,
retina=self.retina,
apiKey=self.apiKey,
maxSparsity=self.maxSparsity,
verbosity=self.verbosity - 1)
# This encoder specifies the LanguageSensor output width.
return configureNetwork(None, self.networkConfig, encoder)
def initModel(self):
"""
Initialize the network; self.networdDataPath must already be set.
"""
recordStream = FileRecordStream(streamID=self.networkDataPath)
root = os.path.dirname(os.path.realpath(__file__))
encoder = CioEncoder(retinaScaling=self.retinaScaling,
cacheDir=os.path.join(root, "CioCache"),
retina=self.retina,
apiKey=self.apiKey)
# This encoder specifies the LanguageSensor output width.
return configureNetwork(recordStream, self.networkConfig, encoder)
def _initModel(self, cacheRoot):
"""
Initialize the network; self.networdDataPath must already be set.
"""
encoder = CioEncoder(retinaScaling=self.retinaScaling,
retina=self.retina,
apiKey=self.apiKey,
maxSparsity=self.maxSparsity,
verbosity=self.verbosity-1,
cacheDir=cacheRoot)
# This encoder specifies the LanguageSensor output width.
return configureNetwork(None, self.networkConfig, encoder)
def _initModel(self, k):
"""
Initialize the network
"""
encoder = CioEncoder(retinaScaling=self.retinaScaling,
retina=self.retina,
fingerprintType=EncoderTypes.document,
apiKey=self.apiKey,
verbosity=self.verbosity - 1)
modelConfig["classifierRegionConfig"]["regionParams"]["k"] = k
modelConfig["classifierRegionConfig"]["regionParams"][
"maxCategoryCount"] = self.numLabels
self.networkConfig = modelConfig
self.network = configureNetwork(None, self.networkConfig, encoder)
def _initModel(self, k):
"""
Initialize the network
"""
encoder = CioEncoder(retinaScaling=self.retinaScaling,
retina=self.retina,
fingerprintType=EncoderTypes.document,
apiKey=self.apiKey,
verbosity=self.verbosity-1)
modelConfig["classifierRegionConfig"]["regionParams"]["k"] = k
modelConfig["classifierRegionConfig"]["regionParams"][
"maxCategoryCount"] = self.numLabels
self.networkConfig = modelConfig
self.network = configureNetwork(None, self.networkConfig, encoder)
def _initModel(self, k):
"""
Initialize the network
"""
root = os.path.dirname(os.path.realpath(__file__))
encoder = CioEncoder(retinaScaling=self.retinaScaling,
cacheDir=os.path.join(root, "CioCache"),
retina=self.retina,
fingerprintType=EncoderTypes.document,
apiKey=self.apiKey)
modelConfig["classifierRegionConfig"]["regionParams"]["k"] = k
modelConfig["classifierRegionConfig"]["regionParams"][
"maxCategoryCount"] = self.numLabels
self.networkConfig = modelConfig
self.network = configureNetwork(None, self.networkConfig, encoder)
def _initModel(self, k):
"""
Initialize the network
"""
root = os.path.dirname(os.path.realpath(__file__))
encoder = CioEncoder(
retinaScaling=self.retinaScaling,
cacheDir=os.path.join(root, "CioCache"),
retina=self.retina,
fingerprintType=EncoderTypes.document,
apiKey=self.apiKey,
)
modelConfig["classifierRegionConfig"]["regionParams"]["k"] = k
modelConfig["classifierRegionConfig"]["regionParams"]["maxCategoryCount"] = self.numLabels
self.networkConfig = modelConfig
self.network = configureNetwork(None, self.networkConfig, encoder)
def initModel(self):
"""
Initialize the network; self.networdDataPath must already be set.
"""
if self.networkDataPath is not None:
recordStream = FileRecordStream(streamID=self.networkDataPath)
else:
recordStream = None
root = os.path.dirname(os.path.realpath(__file__))
encoder = CioEncoder(retinaScaling=self.retinaScaling,
cacheDir=os.path.join(root, "CioCache"),
retina=self.retina,
apiKey=self.apiKey)
# This encoder specifies the LanguageSensor output width.
return configureNetwork(recordStream, self.networkConfig, encoder)
def initModel(self):
"""
Initialize the network; self.networdDataPath must already be set.
"""
if self.networkDataPath is not None:
recordStream = FileRecordStream(streamID=self.networkDataPath)
else:
recordStream = None
encoder = CioEncoder(
retinaScaling=self.retinaScaling,
cacheDir=os.path.join(self.cacheRoot, "CioCache"),
retina=self.retina,
apiKey=self.apiKey,
verbosity=self.verbosity - 1,
)
# This encoder specifies the LanguageSensor output width.
return configureNetwork(recordStream, self.networkConfig, encoder)
def initialize(self):
"""Initialize classification networks for left and right electrodes."""
data_source = FileRecordStream(streamID=self.training_set)
self.network = configureNetwork(data_source,
self.network_config)
def testClassificationAccuracy(self):
"""Test classification accuracy for sensor data."""
networkConfigurations = generateSampleNetworkConfig(
self.templateNetworkConfig, NUM_CATEGORIES)
for networkConfig in networkConfigurations:
for noiseAmplitude in WHITE_NOISE_AMPLITUDES:
for signalMean in SIGNAL_MEANS:
for signalAmplitude in SIGNAL_AMPLITUDES:
for signalPeriod in SIGNAL_PERIODS:
sensorType = networkConfig[
"sensorRegionConfig"].get("regionType")
spEnabled = networkConfig[
"sensorRegionConfig"].get("regionEnabled")
tmEnabled = networkConfig["tmRegionConfig"].get(
"regionEnabled")
upEnabled = networkConfig["tpRegionConfig"].get(
"regionEnabled")
classifierType = networkConfig[
"classifierRegionConfig"].get("regionType")
expParams = ("RUNNING EXPERIMENT WITH PARAMS:\n"
" * numRecords=%s\n"
" * signalAmplitude=%s\n"
" * signalMean=%s\n"
" * signalPeriod=%s\n"
" * noiseAmplitude=%s\n"
" * sensorType=%s\n"
" * spEnabled=%s\n"
" * tmEnabled=%s\n"
" * tpEnabled=%s\n"
" * classifierType=%s\n") % (
NUM_RECORDS, signalAmplitude,
signalMean, signalPeriod,
noiseAmplitude,
sensorType.split(".")[1],
spEnabled, tmEnabled, upEnabled,
classifierType.split(".")[1])
print expParams
inputFile = generateSensorData(
DATA_DIR, OUTFILE_NAME, signalMean,
signalPeriod, SEQUENCE_LENGTH, NUM_RECORDS,
signalAmplitude, NUM_CATEGORIES,
noiseAmplitude)
dataSource = FileRecordStream(streamID=inputFile)
network = configureNetwork(dataSource,
networkConfig)
partitions = generateNetworkPartitions(
networkConfig, NUM_RECORDS)
classificationAccuracy = trainNetwork(
network, networkConfig, partitions,
NUM_RECORDS)
if (noiseAmplitude == 0 and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == KNN_CLASSIFIER_TYPE
and spEnabled and tmEnabled
and not upEnabled):
self.assertEqual(classificationAccuracy,
100.00)
elif (noiseAmplitude == 0 and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled and tmEnabled
and not upEnabled):
self.assertEqual(classificationAccuracy,
100.00)
elif (noiseAmplitude == 0 and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled and not tmEnabled
and not upEnabled):
self.assertEqual(classificationAccuracy,
100.00)
elif (noiseAmplitude == 1.0 and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled and tmEnabled
and not upEnabled):
# using AlmostEqual until the random bug issue is fixed
self.assertAlmostEqual(classificationAccuracy,
80,
delta=5)
elif (noiseAmplitude == 1.0 and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled and not tmEnabled
and not upEnabled):
# using AlmostEqual until the random bug issue is fixed
self.assertAlmostEqual(classificationAccuracy,
81,
delta=5)
def run():
""" Run classification network(s) on artificial sensor data """
with open("network_config_template.json", "rb") as jsonFile:
templateNetworkConfig = json.load(jsonFile)
networkConfigurations = generateSampleNetworkConfig(templateNetworkConfig,
NUM_CATEGORIES)
for networkConfig in networkConfigurations:
for noiseAmplitude in WHITE_NOISE_AMPLITUDES:
for signalMean in SIGNAL_MEANS:
for signalAmplitude in SIGNAL_AMPLITUDES:
for signalPeriod in SIGNAL_PERIODS:
sensorType = networkConfig["sensorRegionConfig"].get(
"regionType")
spEnabled = networkConfig["sensorRegionConfig"].get(
"regionEnabled")
tmEnabled = networkConfig["tmRegionConfig"].get(
"regionEnabled")
upEnabled = networkConfig["tpRegionConfig"].get(
"regionEnabled")
classifierType = networkConfig["classifierRegionConfig"].get(
"regionType")
expParams = ("RUNNING EXPERIMENT WITH PARAMS:\n"
" * numRecords=%s\n"
" * signalAmplitude=%s\n"
" * signalMean=%s\n"
" * signalPeriod=%s\n"
" * noiseAmplitude=%s\n"
" * sensorType=%s\n"
" * spEnabled=%s\n"
" * tmEnabled=%s\n"
" * tpEnabled=%s\n"
" * classifierType=%s\n"
) % (NUM_RECORDS,
signalAmplitude,
signalMean,
signalPeriod,
noiseAmplitude,
sensorType.split(".")[1],
spEnabled,
tmEnabled,
upEnabled,
classifierType.split(".")[1])
print expParams
inputFile = generateSensorData(DATA_DIR,
OUTFILE_NAME,
signalMean,
signalPeriod,
SEQUENCE_LENGTH,
NUM_RECORDS,
signalAmplitude,
NUM_CATEGORIES,
noiseAmplitude)
dataSource = FileRecordStream(streamID=inputFile)
network = configureNetwork(dataSource,
networkConfig)
partitions = generateNetworkPartitions(networkConfig,
NUM_RECORDS)
trainNetwork(network, networkConfig, partitions, NUM_RECORDS)
def testClassificationAccuracy(self):
"""Test classification accuracy for sensor data."""
networkConfigurations = generateSampleNetworkConfig(
self.templateNetworkConfig, NUM_CATEGORIES)
for networkConfig in networkConfigurations:
for noiseAmplitude in WHITE_NOISE_AMPLITUDES:
for signalMean in SIGNAL_MEANS:
for signalAmplitude in SIGNAL_AMPLITUDES:
for signalPeriod in SIGNAL_PERIODS:
sensorType = networkConfig["sensorRegionConfig"].get(
"regionType")
spEnabled = networkConfig["sensorRegionConfig"].get(
"regionEnabled")
tmEnabled = networkConfig["tmRegionConfig"].get(
"regionEnabled")
upEnabled = networkConfig["tpRegionConfig"].get(
"regionEnabled")
classifierType = networkConfig["classifierRegionConfig"].get(
"regionType")
expParams = ("RUNNING EXPERIMENT WITH PARAMS:\n"
" * numRecords=%s\n"
" * signalAmplitude=%s\n"
" * signalMean=%s\n"
" * signalPeriod=%s\n"
" * noiseAmplitude=%s\n"
" * sensorType=%s\n"
" * spEnabled=%s\n"
" * tmEnabled=%s\n"
" * tpEnabled=%s\n"
" * classifierType=%s\n"
) % (NUM_RECORDS,
signalAmplitude,
signalMean,
signalPeriod,
noiseAmplitude,
sensorType.split(".")[1],
spEnabled,
tmEnabled,
upEnabled,
classifierType.split(".")[1])
print expParams
inputFile = generateSensorData(DATA_DIR,
OUTFILE_NAME,
signalMean,
signalPeriod,
SEQUENCE_LENGTH,
NUM_RECORDS,
signalAmplitude,
NUM_CATEGORIES,
noiseAmplitude)
dataSource = FileRecordStream(streamID=inputFile)
network = configureNetwork(dataSource,
networkConfig)
partitions = generateNetworkPartitions(networkConfig,
NUM_RECORDS)
classificationAccuracy = trainNetwork(network, networkConfig,
partitions, NUM_RECORDS)
if (noiseAmplitude == 0
and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == KNN_CLASSIFIER_TYPE
and spEnabled
and tmEnabled
and not upEnabled):
self.assertEqual(classificationAccuracy, 100.00)
elif (noiseAmplitude == 0
and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled
and tmEnabled
and not upEnabled):
self.assertEqual(classificationAccuracy, 100.00)
elif (noiseAmplitude == 0
and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled
and not tmEnabled
and not upEnabled):
self.assertEqual(classificationAccuracy, 100.00)
elif (noiseAmplitude == 1.0
and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled
and tmEnabled
and not upEnabled):
# using AlmostEqual until the random bug issue is fixed
self.assertAlmostEqual(classificationAccuracy, 80, delta=5)
elif (noiseAmplitude == 1.0
and signalMean == 1.0
and signalAmplitude == 1.0
and signalPeriod == 20.0
and classifierType == CLA_CLASSIFIER_TYPE
and spEnabled
and not tmEnabled
and not upEnabled):
# using AlmostEqual until the random bug issue is fixed
self.assertAlmostEqual(classificationAccuracy, 81, delta=5)
def run():
""" Run classification network(s) on artificial sensor data """
if USE_JSON_CONFIG:
with open('config/network_configs.json', 'rb') as fr:
networkConfigurations = json.load(fr)
else:
with open("config/network_config_template.json", "rb") as jsonFile:
templateNetworkConfig = json.load(jsonFile)
networkConfigurations = generateSampleNetworkConfig(templateNetworkConfig,
NUM_CATEGORIES)
expSetups = []
classificationResults = []
for signalType in SIGNAL_TYPES:
for networkConfig in networkConfigurations:
for noiseAmplitude in WHITE_NOISE_AMPLITUDES:
for signalMean in SIGNAL_MEANS:
for signalAmplitude in SIGNAL_AMPLITUDES:
for numCategories in NUM_CATEGORIES:
for numReps in NUM_REPS:
for numPhases in NUM_PHASES:
spEnabled = networkConfig["sensorRegionConfig"].get(
"regionEnabled")
tmEnabled = networkConfig["tmRegionConfig"].get(
"regionEnabled")
upEnabled = networkConfig["tpRegionConfig"].get(
"regionEnabled")
classifierType = networkConfig["classifierRegionConfig"].get(
"regionType")
expSetup = generateSensorData(signalType,
DATA_DIR,
numPhases,
numReps,
signalMean,
signalAmplitude,
numCategories,
noiseAmplitude)
expSetup['expId'] = len(expSetups)
expSetups.append(expSetup)
dataSource = FileRecordStream(streamID=expSetup['filePath'])
network = configureNetwork(dataSource,
networkConfig)
partitions = generateNetworkPartitions(networkConfig,
expSetup['numPoints'])
classificationAccuracy = trainNetwork(network,
networkConfig,
partitions,
expSetup['numPoints'],
VERBOSITY)
classificationResults.append(
{
'spEnabled': spEnabled,
'tmEnabled': tmEnabled,
'upEnabled': upEnabled,
'classifierType':
classifierType.split(".")[1],
'classificationAccuracy':
classificationAccuracy
})
print_and_save_results(classificationResults, expSetups)
def run():
""" Run classification network(s) on artificial sensor data """
if USE_JSON_CONFIG:
with open('config/network_configs.json', 'rb') as fr:
networkConfigurations = json.load(fr)
else:
with open("config/network_config_template.json", "rb") as jsonFile:
templateNetworkConfig = json.load(jsonFile)
networkConfigurations = generateSampleNetworkConfig(
templateNetworkConfig, NUM_CATEGORIES)
headers = [
'numRecords', 'seqLength', 'numClasses', 'signalAmplitude',
'signalMean', 'signalPeriod', 'noiseAmplitude', 'spEnabled',
'tmEnabled', 'tpEnabled', 'classifierType', 'classificationAccuracy'
]
with open(RESULTS_FILE, 'wb') as fw:
writer = csv.writer(fw)
writer.writerow(headers)
t = PrettyTable(headers)
for networkConfig in networkConfigurations:
for noiseAmplitude in WHITE_NOISE_AMPLITUDES:
for signalMean in SIGNAL_MEANS:
for signalAmplitude in SIGNAL_AMPLITUDES:
for signalPeriod in SIGNAL_PERIODS:
spEnabled = networkConfig[
"sensorRegionConfig"].get("regionEnabled")
tmEnabled = networkConfig["tmRegionConfig"].get(
"regionEnabled")
upEnabled = networkConfig["tpRegionConfig"].get(
"regionEnabled")
classifierType = networkConfig[
"classifierRegionConfig"].get("regionType")
inputFile = generateSensorData(
DATA_DIR, OUTFILE_NAME, signalMean,
signalPeriod, SEQUENCE_LENGTH, NUM_RECORDS,
signalAmplitude, NUM_CATEGORIES,
noiseAmplitude)
dataSource = FileRecordStream(streamID=inputFile)
network = configureNetwork(dataSource,
networkConfig)
partitions = generateNetworkPartitions(
networkConfig, NUM_RECORDS)
classificationAccuracy = trainNetwork(
network, networkConfig, partitions,
NUM_RECORDS, VERBOSITY)
results = [
NUM_RECORDS, SEQUENCE_LENGTH, NUM_CATEGORIES,
signalAmplitude, signalMean, signalPeriod,
noiseAmplitude, spEnabled, tmEnabled,
upEnabled,
classifierType.split(".")[1],
classificationAccuracy
]
writer.writerow(results)
t.add_row(results)
print '%s\n' % t
print '==> Results saved to %s\n' % RESULTS_FILE
def run():
""" Run classification network(s) on artificial sensor data """
if USE_JSON_CONFIG:
with open('config/network_configs.json', 'rb') as fr:
networkConfigurations = json.load(fr)
else:
with open("config/network_config_template.json", "rb") as jsonFile:
templateNetworkConfig = json.load(jsonFile)
networkConfigurations = generateSampleNetworkConfig(templateNetworkConfig,
NUM_CATEGORIES)
headers = ['numRecords', 'seqLength', 'numClasses', 'signalAmplitude',
'signalMean', 'signalPeriod', 'noiseAmplitude', 'spEnabled',
'tmEnabled', 'tpEnabled', 'classifierType',
'classificationAccuracy']
with open(RESULTS_FILE, 'wb') as fw:
writer = csv.writer(fw)
writer.writerow(headers)
t = PrettyTable(headers)
for networkConfig in networkConfigurations:
for noiseAmplitude in WHITE_NOISE_AMPLITUDES:
for signalMean in SIGNAL_MEANS:
for signalAmplitude in SIGNAL_AMPLITUDES:
for signalPeriod in SIGNAL_PERIODS:
spEnabled = networkConfig["sensorRegionConfig"].get(
"regionEnabled")
tmEnabled = networkConfig["tmRegionConfig"].get(
"regionEnabled")
upEnabled = networkConfig["tpRegionConfig"].get(
"regionEnabled")
classifierType = networkConfig["classifierRegionConfig"].get(
"regionType")
inputFile = generateSensorData(DATA_DIR,
OUTFILE_NAME,
signalMean,
signalPeriod,
SEQUENCE_LENGTH,
NUM_RECORDS,
signalAmplitude,
NUM_CATEGORIES,
noiseAmplitude)
dataSource = FileRecordStream(streamID=inputFile)
network = configureNetwork(dataSource,
networkConfig)
partitions = generateNetworkPartitions(networkConfig,
NUM_RECORDS)
classificationAccuracy = trainNetwork(network, networkConfig,
partitions, NUM_RECORDS,
VERBOSITY)
results = [NUM_RECORDS,
SEQUENCE_LENGTH,
NUM_CATEGORIES,
signalAmplitude,
signalMean,
signalPeriod,
noiseAmplitude,
spEnabled,
tmEnabled,
upEnabled,
classifierType.split(".")[1],
classificationAccuracy]
writer.writerow(results)
t.add_row(results)
print '%s\n' % t
print '==> Results saved to %s\n' % RESULTS_FILE
_CONFIG = simplejson.load(open(_CONFIG_JSON, "rb"))
_REGION_CONFIG_KEYS = ("spRegionConfig", "tmRegionConfig",
"tpRegionConfig",
"classifierRegionConfig")
_REGION_NAMES = []
for region in _REGION_CONFIG_KEYS:
if _CONFIG[region].get("regionEnabled"):
_REGION_NAMES.append(_CONFIG[region]["regionName"])
if __name__ == "__main__":
dataSource = FileRecordStream(streamID="backup/training-data-attention.csv")
network = configureNetwork(dataSource, _CONFIG)
setNetworkLearningMode(network, _REGION_NAMES, True)
sensorRegion = network.regions[
_CONFIG["sensorRegionConfig"].get("regionName")]
classifierRegion = network.regions[
_CONFIG["classifierRegionConfig"].get("regionName")]
headers = ["x", "y", "label"]
num_correct = 0
for category in range(_NUM_CATEGORIES):
csvFile = open(_INPUT_FILES[category], "rb")
reader = csv.reader(csvFile)
# skip 3 header rows
reader.next()
