def testSerialization5(self):
# This test verifies that saveToFile() and loadFromFile with JSON on Predictor are accessible from Python.
inputData = SDR( 1000 ).randomize( 0.02 )
categories = { 'A': 0, 'B': 1, 'C': 2, 'D': 3 }
c1 = Predictor( steps=[1], alpha=1.0 )
c1.learn(1, inputData, categories['B'] )
file = "Predictor_test_save.JSON"
c1.saveToFile(file, "JSON")
c2 = Predictor( steps=[1], alpha=1.0 )
c2.loadFromFile(file, "JSON")
os.remove(file)
def testMultistepSingleValue(self):
classifier = Predictor(steps=[1, 2])
inp = SDR(10)
inp.randomize(.2)
for recordNum in range(10):
classifier.learn(recordNum, inp, 0)
retval = classifier.infer(10, inp)
# Should have a probability of 100% for that bucket.
self.assertEqual(retval[1], [1.])
self.assertEqual(retval[2], [1.])
def testSingleValue0Steps(self):
"""Send same value 10 times and expect high likelihood for prediction
using 0-step ahead prediction"""
pred = Predictor(steps=[0], alpha=0.5)
# Enough times to perform Inference and learn associations
inp = SDR(10)
inp.randomize(.2)
for recordNum in range(10):
pred.learn(recordNum, inp, 2)
retval = pred.infer(10, inp)
self.assertGreater(retval[0][2], 0.9)
def testMultistepSimple(self):
classifier = Predictor(steps=[1, 2], alpha=10.0)
inp = SDR(10)
for i in range(100):
inp.sparse = [i % 10]
classifier.learn(recordNum=i, pattern=inp, classification=(i % 10))
retval = classifier.infer(99, inp)
self.assertGreater(retval[1][0], 0.99)
for i in range(1, 10):
self.assertLess(retval[1][i], 0.01)
self.assertGreater(retval[2][1], 0.99)
for i in [0] + list(range(2, 10)):
self.assertLess(retval[2][i], 0.01)
def testComputeInferOrLearnOnly(self):
c = Predictor([1], 1.0)
inp = SDR(10)
inp.randomize( .3 )
# learn only
prediction = c.infer(pattern=inp)[1]
self.assertTrue(prediction == []) # not enough training data -> []
c.learn(recordNum=0, pattern=inp, classification=4)
self.assertTrue(c.infer(pattern=inp)[1] == []) # not enough training data.
c.learn(recordNum=2, pattern=inp, classification=4)
c.learn(recordNum=3, pattern=inp, classification=4)
self.assertTrue(c.infer(pattern=inp)[1] != []) # Don't crash with enough training data.
# infer only
retval1 = c.infer(pattern=inp)
retval2 = c.infer(pattern=inp)
self.assertSequenceEqual(list(retval1[1]), list(retval2[1]))
def testExampleUsage(self):
# Make a random SDR and associate it with a category.
inputData = SDR(1000).randomize(0.02)
categories = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
clsr = Classifier()
clsr.learn(inputData, categories['B'])
assert (numpy.argmax(clsr.infer(inputData)) == categories['B'])
# Estimate a scalar value. The Classifier only accepts categories, so
# put real valued inputs into bins (AKA buckets) by subtracting the
# minimum value and dividing by a resolution.
scalar = 567.8
minimum = 500
resolution = 10
clsr.learn(inputData, int((scalar - minimum) / resolution))
assert (numpy.argmax(clsr.infer(inputData)) * resolution +
minimum == 560)
# Predict 1 and 2 time steps into the future.
# Make a sequence of 4 random SDRs, each SDR has 1000 bits and 2% sparsity.
sequence = [SDR(1000).randomize(0.02) for i in range(4)]
# Make category labels for the sequence.
labels = [4, 5, 6, 7]
# Make a Predictor and train it.
pred = Predictor([1, 2])
pred.learn(0, sequence[0], labels[0])
pred.learn(1, sequence[1], labels[1])
pred.learn(2, sequence[2], labels[2])
pred.learn(3, sequence[3], labels[3])
# Give the predictor partial information, and make predictions
# about the future.
pred.reset()
A = pred.infer(0, sequence[0])
assert (numpy.argmax(A[1]) == labels[1])
assert (numpy.argmax(A[2]) == labels[2])
B = pred.infer(1, sequence[1])
assert (numpy.argmax(B[1]) == labels[2])
assert (numpy.argmax(B[2]) == labels[3])
def testSerialization2(self):
# This test verifies that pickle works for pickle of a Predictor
SDR1 = SDR(15); SDR1.sparse = [1, 5, 9]
SDR2 = SDR(15); SDR2.sparse = [0, 6, 9, 11]
SDR3 = SDR(15); SDR3.sparse = [6, 9]
SDR4 = SDR(15); SDR4.sparse = [1, 5, 9]
c1 = Predictor( steps=[1], alpha=1.0 )
c1.learn(1, pattern=SDR1, classification=4)
c1.learn(2, pattern=SDR2, classification=5)
c1.learn(3, pattern=SDR3, classification=5)
c1.learn(4, pattern=SDR4, classification=4)
c1.learn(5, pattern=SDR4, classification=4)
serialized = pickle.dumps(c1)
c2 = pickle.loads(serialized)
result1 = c1.infer(SDR1)
result2 = c2.infer(SDR1)
#print(" testSerialization2 result: %.6f, %.6f, %.6f, %.6f, %.6f, %.6f "%( result1[1][0], result1[1][1], result1[1][2], result1[1][3], result1[1][4], result1[1][5]));
self.assertEqual(len(result1[1]), 6)
self.assertEqual(len(result1[1]), len(result2[1]))
for i in range(len(result1[1])):
self.assertAlmostEqual(result1[1][i], result2[1][i], places=5)
def testMultiStepPredictions(self):
""" Test multi-step predictions
We train the 0-step and the 1-step classifiers simultaneously on
data stream
(SDR1, bucketIdx0)
(SDR2, bucketIdx1)
(SDR1, bucketIdx0)
(SDR2, bucketIdx1)
...
We intend the 0-step classifier to learn the associations:
SDR1 => bucketIdx 0
SDR2 => bucketIdx 1
and the 1-step classifier to learn the associations
SDR1 => bucketIdx 1
SDR2 => bucketIdx 0
"""
c = Predictor([0, 1], 1.0)
SDR1 = SDR(10)
SDR1.sparse = [1, 3, 5]
SDR2 = SDR(10)
SDR2.sparse = [2, 4, 6]
recordNum = 0
for _ in range(100):
c.learn(recordNum, pattern=SDR1, classification=0)
recordNum += 1
c.learn(recordNum, pattern=SDR2, classification=1)
recordNum += 1
result1 = c.infer(recordNum, SDR1)
result2 = c.infer(recordNum, SDR2)
self.assertAlmostEqual(result1[0][0], 1.0, places=1)
self.assertAlmostEqual(result1[0][1], 0.0, places=1)
self.assertAlmostEqual(result2[0][0], 0.0, places=1)
self.assertAlmostEqual(result2[0][1], 1.0, places=1)
def building_htm(len_data):
global enc_info
global sp_info
global tm_info
global anomaly_history
global predictor
global predictor_resolution
global tm
global sp
global scalarEncoder
global encodingWidth
global dateEncoder
# Initial message
print("Building HTM for predicting trends...")
# Default parameters in HTM
default_parameters = {
# There are 2 (3) encoders: "value" (RDSE) & "time" (DateTime weekend, timeOfDay)
'enc': {
"value": {
'resolution': 0.88,
'size': 700,
'sparsity': 0.02
},
"time": {
'timeOfDay': (30, 1)
} #, 'weekend': 21}
},
'predictor': {
'sdrc_alpha': 0.1
},
'sp': {
'boostStrength': 3.0,
'columnCount': 1638,
'localAreaDensity': 0.04395604395604396,
'potentialPct': 0.85,
'synPermActiveInc': 0.04,
'synPermConnected': 0.13999999999999999,
'synPermInactiveDec': 0.006
},
'tm': {
'activationThreshold': 17,
'cellsPerColumn': 13,
'initialPerm': 0.21,
'maxSegmentsPerCell': 128,
'maxSynapsesPerSegment': 64,
'minThreshold': 10,
'newSynapseCount': 32,
'permanenceDec': 0.1,
'permanenceInc': 0.1
},
'anomaly': {
'likelihood': {
'probationaryPct': 0.1,
'reestimationPeriod': 100
}
}
}
# Make the encoder
print("- Make the encoder")
dateEncoder = DateEncoder(
timeOfDay=default_parameters["enc"]["time"]["timeOfDay"])
scalarEncoderParams = RDSE_Parameters()
scalarEncoderParams.size = default_parameters["enc"]["value"]["size"]
scalarEncoderParams.sparsity = default_parameters["enc"]["value"][
"sparsity"]
scalarEncoderParams.resolution = default_parameters["enc"]["value"][
"resolution"]
scalarEncoder = RDSE(scalarEncoderParams)
encodingWidth = (dateEncoder.size + scalarEncoder.size)
enc_info = Metrics([encodingWidth], 999999999)
# Make the SP
print("- Make the SP")
spParams = default_parameters["sp"]
sp = SpatialPooler(inputDimensions=(encodingWidth, ),
columnDimensions=(spParams["columnCount"], ),
potentialPct=spParams["potentialPct"],
potentialRadius=encodingWidth,
globalInhibition=True,
localAreaDensity=spParams["localAreaDensity"],
synPermInactiveDec=spParams["synPermInactiveDec"],
synPermActiveInc=spParams["synPermActiveInc"],
synPermConnected=spParams["synPermConnected"],
boostStrength=spParams["boostStrength"],
wrapAround=True)
sp_info = Metrics(sp.getColumnDimensions(), 999999999)
# Temporal Memory Parameters
print("- Make the TM")
tmParams = default_parameters["tm"]
tm = TemporalMemory(
columnDimensions=(spParams["columnCount"], ),
cellsPerColumn=tmParams["cellsPerColumn"],
activationThreshold=tmParams["activationThreshold"],
initialPermanence=tmParams["initialPerm"],
connectedPermanence=spParams["synPermConnected"],
minThreshold=tmParams["minThreshold"],
maxNewSynapseCount=tmParams["newSynapseCount"],
permanenceIncrement=tmParams["permanenceInc"],
permanenceDecrement=tmParams["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment=tmParams["maxSynapsesPerSegment"])
tm_info = Metrics([tm.numberOfCells()], 999999999)
# Setup Likelihood
print("- Make Anomaly Score/Likelihood")
anParams = default_parameters["anomaly"]["likelihood"]
probationaryPeriod = int(
math.floor(float(anParams["probationaryPct"]) * len_data))
learningPeriod = int(math.floor(probationaryPeriod / 2.0))
anomaly_history = AnomalyLikelihood(
learningPeriod=learningPeriod,
estimationSamples=probationaryPeriod - learningPeriod,
reestimationPeriod=anParams["reestimationPeriod"])
# Make predictor
print("- Make the predictor")
predictor = Predictor(steps=[1, 5],
alpha=default_parameters["predictor"]['sdrc_alpha'])
predictor_resolution = 1
# End message
print("- Finish the building of HTM!")
def testInitialization(self):
Classifier(.1)
Predictor([2, 3, 4], .1)
def testSerialization(self):
c = Predictor([1], 1.0)
c.compute(recordNum=0, patternNZ=[1, 5, 9], classification=4)
c.compute(recordNum=1, patternNZ=[0, 6, 9, 11], classification=5)
c.compute(recordNum=2, patternNZ=[6, 9], classification=5)
c.compute(recordNum=3, patternNZ=[1, 5, 9], classification=4)
serialized = pickle.dumps(c)
c = pickle.loads(serialized)
result = c.compute(recordNum=4, patternNZ=[1, 5, 9], classification=4)
self.assertEqual(len(result[1]), 6)
self.assertAlmostEqual(result[1][0], 0.034234, places=5)
self.assertAlmostEqual(result[1][1], 0.034234, places=5)
self.assertAlmostEqual(result[1][2], 0.034234, places=5)
self.assertAlmostEqual(result[1][3], 0.034234, places=5)
self.assertAlmostEqual(result[1][4], 0.093058, places=5)
self.assertAlmostEqual(result[1][5], 0.770004, places=5)
def testMissingRecords(self):
""" Test missing record support.
Here, we intend the classifier to learn the associations:
[1,3,5] => bucketIdx 1
[2,4,6] => bucketIdx 2
[7,8,9] => don"t care
If it doesn't pay attention to the recordNums in this test, it will learn the
wrong associations.
"""
c = Predictor(steps=[1], alpha=1.0)
recordNum = 0
inp = SDR(10)
inp.sparse = [1, 3, 5]
c.learn(recordNum=recordNum, pattern=inp, classification=0)
recordNum += 1
inp.sparse = [2, 4, 6]
c.learn(recordNum=recordNum, pattern=inp, classification=1)
recordNum += 1
inp.sparse = [1, 3, 5]
c.learn(recordNum=recordNum, pattern=inp, classification=2)
recordNum += 1
inp.sparse = [2, 4, 6]
c.learn(recordNum=recordNum, pattern=inp, classification=1)
recordNum += 1
# -----------------------------------------------------------------------
# At this point, we should have learned [1,3,5] => bucket 1
# [2,4,6] => bucket 2
inp.sparse = [1, 3, 5]
result = c.infer(recordNum=recordNum, pattern=inp)
c.learn(recordNum=recordNum, pattern=inp, classification=2)
recordNum += 1
self.assertLess(result[1][0], 0.1)
self.assertGreater(result[1][1], 0.9)
self.assertLess(result[1][2], 0.1)
inp.sparse = [2, 4, 6]
result = c.infer(recordNum=recordNum, pattern=inp)
c.learn(recordNum=recordNum, pattern=inp, classification=1)
recordNum += 1
self.assertLess(result[1][0], 0.1)
self.assertLess(result[1][1], 0.1)
self.assertGreater(result[1][2], 0.9)
# -----------------------------------------------------------------------
# Feed in records that skip and make sure they don"t mess up what we
# learned
# If we skip a record, the CLA should NOT learn that [2,4,6] from
# the previous learn associates with bucket 0
recordNum += 1
inp.sparse = [1, 3, 5]
result = c.infer(recordNum=recordNum, pattern=inp)
c.learn(recordNum=recordNum, pattern=inp, classification=0)
recordNum += 1
self.assertLess(result[1][0], 0.1)
self.assertGreater(result[1][1], 0.9)
self.assertLess(result[1][2], 0.1)
# If we skip a record, the CLA should NOT learn that [1,3,5] from
# the previous learn associates with bucket 0
recordNum += 1
inp.sparse = [2, 4, 6]
result = c.infer(recordNum=recordNum, pattern=inp)
c.learn(recordNum=recordNum, pattern=inp, classification=0)
recordNum += 1
self.assertLess(result[1][0], 0.1)
self.assertLess(result[1][1], 0.1)
self.assertGreater(result[1][2], 0.9)
# If we skip a record, the CLA should NOT learn that [2,4,6] from
# the previous learn associates with bucket 0
recordNum += 1
inp.sparse = [1, 3, 5]
result = c.infer(recordNum=recordNum, pattern=inp)
c.learn(recordNum=recordNum, pattern=inp, classification=0)
recordNum += 1
self.assertLess(result[1][0], 0.1)
self.assertGreater(result[1][1], 0.9)
self.assertLess(result[1][2], 0.1)
def testComputeComplex(self):
c = Predictor([1], 1.0)
inp = SDR(100)
inp.sparse = [1, 5, 9]
c.learn(
recordNum=0,
pattern=inp,
classification=4,
)
inp.sparse = [0, 6, 9, 11]
c.learn(
recordNum=1,
pattern=inp,
classification=5,
)
inp.sparse = [6, 9]
c.learn(
recordNum=2,
pattern=inp,
classification=5,
)
inp.sparse = [1, 5, 9]
c.learn(
recordNum=3,
pattern=inp,
classification=4,
)
inp.sparse = [1, 5, 9]
result = c.infer(recordNum=4, pattern=inp)
self.assertSetEqual(set(result.keys()), set([1]))
self.assertEqual(len(result[1]), 6)
self.assertAlmostEqual(result[1][0], 0.034234, places=5)
self.assertAlmostEqual(result[1][1], 0.034234, places=5)
self.assertAlmostEqual(result[1][2], 0.034234, places=5)
self.assertAlmostEqual(result[1][3], 0.034234, places=5)
self.assertAlmostEqual(result[1][4], 0.093058, places=5)
self.assertAlmostEqual(result[1][5], 0.770004, places=5)
def testComputeInferOrLearnOnly(self):
c = Predictor([1], 1.0)
inp = SDR(10)
inp.randomize(.3)
# learn only
c.infer(recordNum=0,
pattern=inp) # Don't crash with not enough training data.
c.learn(recordNum=0, pattern=inp, classification=4)
c.infer(recordNum=1,
pattern=inp) # Don't crash with not enough training data.
c.learn(recordNum=2, pattern=inp, classification=4)
c.learn(recordNum=3, pattern=inp, classification=4)
# infer only
retval1 = c.infer(recordNum=5, pattern=inp)
retval2 = c.infer(recordNum=6, pattern=inp)
self.assertSequenceEqual(list(retval1[1]), list(retval2[1]))
def main(parameters=default_parameters, argv=None, verbose=True):
if verbose:
import pprint
print("Parameters:")
pprint.pprint(parameters, indent=4)
print("")
# Read the input file.
records = []
with open(_INPUT_FILE_PATH, "r") as fin:
reader = csv.reader(fin)
headers = next(reader)
next(reader)
next(reader)
for record in reader:
records.append(record)
# Make the Encoders. These will convert input data into binary representations.
dateEncoder = DateEncoder(timeOfDay= parameters["enc"]["time"]["timeOfDay"],
weekend = parameters["enc"]["time"]["weekend"])
scalarEncoderParams = RDSE_Parameters()
scalarEncoderParams.size = parameters["enc"]["value"]["size"]
scalarEncoderParams.sparsity = parameters["enc"]["value"]["sparsity"]
scalarEncoderParams.resolution = parameters["enc"]["value"]["resolution"]
scalarEncoder = RDSE( scalarEncoderParams )
encodingWidth = (dateEncoder.size + scalarEncoder.size)
enc_info = Metrics( [encodingWidth], 999999999 )
# Make the HTM. SpatialPooler & TemporalMemory & associated tools.
spParams = parameters["sp"]
sp = SpatialPooler(
inputDimensions = (encodingWidth,),
columnDimensions = (spParams["columnCount"],),
potentialPct = spParams["potentialPct"],
potentialRadius = encodingWidth,
globalInhibition = True,
localAreaDensity = spParams["localAreaDensity"],
synPermInactiveDec = spParams["synPermInactiveDec"],
synPermActiveInc = spParams["synPermActiveInc"],
synPermConnected = spParams["synPermConnected"],
boostStrength = spParams["boostStrength"],
wrapAround = True
)
sp_info = Metrics( sp.getColumnDimensions(), 999999999 )
tmParams = parameters["tm"]
tm = TemporalMemory(
columnDimensions = (spParams["columnCount"],),
cellsPerColumn = tmParams["cellsPerColumn"],
activationThreshold = tmParams["activationThreshold"],
initialPermanence = tmParams["initialPerm"],
connectedPermanence = spParams["synPermConnected"],
minThreshold = tmParams["minThreshold"],
maxNewSynapseCount = tmParams["newSynapseCount"],
permanenceIncrement = tmParams["permanenceInc"],
permanenceDecrement = tmParams["permanenceDec"],
predictedSegmentDecrement = 0.0,
maxSegmentsPerCell = tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment = tmParams["maxSynapsesPerSegment"]
)
tm_info = Metrics( [tm.numberOfCells()], 999999999 )
# setup likelihood, these settings are used in NAB
anParams = parameters["anomaly"]["likelihood"]
probationaryPeriod = int(math.floor(float(anParams["probationaryPct"])*len(records)))
learningPeriod = int(math.floor(probationaryPeriod / 2.0))
anomaly_history = AnomalyLikelihood(learningPeriod= learningPeriod,
estimationSamples= probationaryPeriod - learningPeriod,
reestimationPeriod= anParams["reestimationPeriod"])
predictor = Predictor( steps=[1, 5], alpha=parameters["predictor"]['sdrc_alpha'] )
predictor_resolution = 1
# Iterate through every datum in the dataset, record the inputs & outputs.
inputs = []
anomaly = []
anomalyProb = []
predictions = {1: [], 5: []}
for count, record in enumerate(records):
# Convert date string into Python date object.
dateString = datetime.datetime.strptime(record[0], "%m/%d/%y %H:%M")
# Convert data value string into float.
consumption = float(record[1])
inputs.append( consumption )
# Call the encoders to create bit representations for each value. These are SDR objects.
dateBits = dateEncoder.encode(dateString)
consumptionBits = scalarEncoder.encode(consumption)
# Concatenate all these encodings into one large encoding for Spatial Pooling.
encoding = SDR( encodingWidth ).concatenate([consumptionBits, dateBits])
enc_info.addData( encoding )
# Create an SDR to represent active columns, This will be populated by the
# compute method below. It must have the same dimensions as the Spatial Pooler.
activeColumns = SDR( sp.getColumnDimensions() )
# Execute Spatial Pooling algorithm over input space.
sp.compute(encoding, True, activeColumns)
sp_info.addData( activeColumns )
# Execute Temporal Memory algorithm over active mini-columns.
tm.compute(activeColumns, learn=True)
tm_info.addData( tm.getActiveCells().flatten() )
# Predict what will happen, and then train the predictor based on what just happened.
pdf = predictor.infer( count, tm.getActiveCells() )
for n in (1, 5):
if pdf[n]:
predictions[n].append( np.argmax( pdf[n] ) * predictor_resolution )
else:
predictions[n].append(float('nan'))
predictor.learn( count, tm.getActiveCells(), int(consumption / predictor_resolution))
anomalyLikelihood = anomaly_history.anomalyProbability( consumption, tm.anomaly )
anomaly.append( tm.anomaly )
anomalyProb.append( anomalyLikelihood )
# Print information & statistics about the state of the HTM.
print("Encoded Input", enc_info)
print("")
print("Spatial Pooler Mini-Columns", sp_info)
print(str(sp))
print("")
print("Temporal Memory Cells", tm_info)
print(str(tm))
print("")
# Shift the predictions so that they are aligned with the input they predict.
for n_steps, pred_list in predictions.items():
for x in range(n_steps):
pred_list.insert(0, float('nan'))
pred_list.pop()
# Calculate the predictive accuracy, Root-Mean-Squared
accuracy = {1: 0, 5: 0}
accuracy_samples = {1: 0, 5: 0}
for idx, inp in enumerate(inputs):
for n in predictions: # For each [N]umber of time steps ahead which was predicted.
val = predictions[n][ idx ]
if not math.isnan(val):
accuracy[n] += (inp - val) ** 2
accuracy_samples[n] += 1
for n in sorted(predictions):
accuracy[n] = (accuracy[n] / accuracy_samples[n]) ** .5
print("Predictive Error (RMS)", n, "steps ahead:", accuracy[n])
# Show info about the anomaly (mean & std)
print("Anomaly Mean", np.mean(anomaly))
print("Anomaly Std ", np.std(anomaly))
# Plot the Predictions and Anomalies.
if verbose:
try:
import matplotlib.pyplot as plt
except:
print("WARNING: failed to import matplotlib, plots cannot be shown.")
return -accuracy[5]
plt.subplot(2,1,1)
plt.title("Predictions")
plt.xlabel("Time")
plt.ylabel("Power Consumption")
plt.plot(np.arange(len(inputs)), inputs, 'red',
np.arange(len(inputs)), predictions[1], 'blue',
np.arange(len(inputs)), predictions[5], 'green',)
plt.legend(labels=('Input', '1 Step Prediction, Shifted 1 step', '5 Step Prediction, Shifted 5 steps'))
plt.subplot(2,1,2)
plt.title("Anomaly Score")
plt.xlabel("Time")
plt.ylabel("Power Consumption")
inputs = np.array(inputs) / max(inputs)
plt.plot(np.arange(len(inputs)), inputs, 'red',
np.arange(len(inputs)), anomaly, 'blue',)
plt.legend(labels=('Input', 'Anomaly Score'))
plt.show()
return -accuracy[5]
permanenceIncrement=0.1,
permanenceDecrement=0.1,
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=128,
maxSynapsesPerSegment=64)
records = 1200
probationaryPeriod = int(math.floor(float(0.1) * records))
learningPeriod = int(math.floor(probationaryPeriod / 2.0))
anomaly_history = AnomalyLikelihood(learningPeriod=learningPeriod,
estimationSamples=probationaryPeriod -
learningPeriod,
reestimationPeriod=100)
predictor = Predictor(steps=[1, 5], alpha=0.1)
predictor_resolution = 1
inputs = []
anomaly = []
anomalyProb = []
predictions = {1: [], 5: []}
plot = plt.figure(figsize=(25, 15), dpi=60)
warnings.simplefilter('ignore')
for count in range(records):
dateObject = datetime.datetime.now()
cp = subprocess.run(['vcgencmd', 'measure_temp'],
encoding='utf-8',