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 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 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)
inputFile="mu/right_test.csv"
classifier = HTMClassifier(networkConfig, inputFile, CATEGORIES)
minval, maxval = classifier.initialize()
points = []
classifier2 = HTMClassifier(networkConfig, inputFile, CATEGORIES)
classifier2.initialize()
net = classifier.network
sensorRegion = net.regions["sensor"]
classifierRegion = net.regions["classifier"]
with open(inputFile, 'rb') as csvfile:
reader = csv.reader(csvfile)
reader.next()
reader.next()
reader.next()
numCorrect = 0
numCorrect2 = 0
numRecords = 0
for row in reader:
timestamp = row[0]
inputData = float(row[1])
points.append(inputData)
target = int(row[2])
result = classifier.start(inputData, target,
learning_is_on=False)
sensorRegion.setParameter("useDataSource", True)
net.run(1)
result2 = CATEGORIES[int(_getClassifierInference(
classifierRegion))]
if result2 != result:
print "numRecords = %s" % numRecords
print "target = %s" % target
print "result = %s" % result
print "result2 = %s" % result2
if int(result) == int(target):
if numRecords > START_TEST:
numCorrect += 1
if int(result2) == int(target):
if numRecords > START_TEST:
numCorrect2 += 1
numRecords += 1
classificationAccuracy = round(100.0 * numCorrect / (
numRecords - START_TEST), 2)
classificationAccuracy2 = round(100.0 *
numCorrect / (
numRecords - START_TEST), 2)
print classificationAccuracy
print classificationAccuracy2
import numpy as np
bins = np.linspace(0, 1, 10)
histogram = np.histogram(np.array(points), bins)
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 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 = createAndConfigureNetwork(
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_CONFIG_TEMPLATE:
with open("config/network_config_template.json", "rb") as jsonFile:
templateNetworkConfig = simplejson.load(jsonFile)
networkConfigurations = generateSampleNetworkConfig(templateNetworkConfig,
NUM_CATEGORIES)
else:
with open('config/sdr_network_configs.json', 'rb') as fr:
networkConfigurations = simplejson.load(fr)
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:
for noiseLengths in NOISE_LENGTHS:
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,
noiseLengths)
expSetup['expId'] = len(expSetups)
expSetups.append(expSetup)
dataSource = FileRecordStream(
streamID=expSetup['inputFilePath'])
network = configureNetwork(dataSource,
networkConfig)
partitions = generateNetworkPartitions(networkConfig,
expSetup[
'numPoints'])
traces = trainNetwork(network,
networkConfig,
partitions,
expSetup['numPoints'],
VERBOSITY)
expId = "%s_sp-%s_tm-%s_tp-%s_%s" % (signalType,
spEnabled,
tmEnabled,
upEnabled,
classifierType[3:-6])
fileName = TRACES_FILE % expId
saveTraces(traces, fileName)
print '==> Results saved to %s\n' % fileName
finalAccuracy = traces['testClassificationAccuracyTrace'][
-1]
classificationResults.append({
'spEnabled': spEnabled,
'tmEnabled': tmEnabled,
'upEnabled': upEnabled,
'classifierType': classifierType.split(".")[1],
'classificationAccuracy': finalAccuracy
})
print_and_save_results(classificationResults, expSetups)
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 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)
