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)
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")]
num_correct = 0.0
for row_id in range(_NUM_RECORDS * _NUM_CATEGORIES):
network.run(1)
actualValue = sensorRegion.getOutputData("categoryOut")[0]
inferredCategory = _getClassifierInference(classifierRegion)
if int(inferredCategory) == int(actualValue):
num_correct += 1
# headers = ["x", "y", "label"]
# readers = {category: None for category in range(_NUM_CATEGORIES)}
# for category in range(_NUM_CATEGORIES):
# csvFile = open(_INPUT_FILES[category], "rb")
# reader = csv.reader(csvFile)
# # skip 3 header rows
# reader.next()
# reader.next()
# reader.next()
# readers[category] = reader
#
