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 train(self, training_set_size, partitions):
"""
Train the HTM network
@param training_set_size: size of the training set.
@param partitions: (list of namedtuple) list of partitions to train the
network. E.g:
[Partition(partName=SP, index=0), ..., Partition(partName=test, index=0)]
@return classification_accuracy: (float) classification accuracy, 0 to 100.
"""
return trainNetwork(self.network,
self.network_config,
partitions,
training_set_size)
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
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)