def run():
""" Run classification network(s) on artificial sensor data """
with open("network_config.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["upRegionConfig"].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"
" * upEnabled=%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)
runNetwork(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["upRegionConfig"].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"
" * upEnabled=%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)
(numCorrect, numTestRecords, predictionAccuracy) = runNetwork(
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(predictionAccuracy, 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(predictionAccuracy, 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(predictionAccuracy, 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(predictionAccuracy, 80, delta=1)
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(predictionAccuracy, 81, delta=1)