def testSerialization(self):
"""serialization using pickle"""
# instances to test
aDef = Anomaly()
aLike = Anomaly(mode=Anomaly.MODE_LIKELIHOOD)
aWeig = Anomaly(mode=Anomaly.MODE_WEIGHTED)
# test anomaly with all whistles (MovingAverage, Likelihood, ...)
aAll = Anomaly(mode=Anomaly.MODE_LIKELIHOOD, slidingWindowSize=5)
inst = [aDef, aLike, aWeig, aAll]
for a in inst:
stored = pickle.dumps(a)
restored = pickle.loads(stored)
self.assertEqual(a, restored)
def testEquals(self):
an = Anomaly()
anP = Anomaly()
self.assertEqual(an, anP, "default constructors equal")
anN = Anomaly(mode=Anomaly.MODE_LIKELIHOOD)
self.assertNotEqual(an, anN)
an = Anomaly(mode=Anomaly.MODE_LIKELIHOOD)
self.assertEqual(an, anN)
an = Anomaly(slidingWindowSize=5,
mode=Anomaly.MODE_WEIGHTED,
binaryAnomalyThreshold=0.9)
anP = Anomaly(slidingWindowSize=5,
mode=Anomaly.MODE_WEIGHTED,
binaryAnomalyThreshold=0.9)
anN = Anomaly(slidingWindowSize=4,
mode=Anomaly.MODE_WEIGHTED,
binaryAnomalyThreshold=0.9)
self.assertEqual(an, anP)
self.assertNotEqual(an, anN)
anN = Anomaly(slidingWindowSize=5,
mode=Anomaly.MODE_WEIGHTED,
binaryAnomalyThreshold=0.5)
self.assertNotEqual(an, anN)
def getAnomalyScores(activeColumnList, predictiveColumnList):
# calculates the anomaly scores from the list of active and predicted columns at each step - deprecated
anomalyScoreList = []
an = Anomaly()
for i in range(len(activeColumnList)):
score = an.computeRawAnomalyScore(activeColumnList[i],
predictiveColumnList[i])
anomalyScoreList.append(score)
return anomalyScoreList
def definir_AnomDetect(N_DATA):
"""
retorna as classes de anom_score, a classe de anom_likelihood, e os arrays que guardarão a anom_score e a anom_likelihood
"""
anom_score_txt = np.zeros((N_DATA+1,))
anom_logscore_txt = np.zeros((N_DATA+1,))
anomaly_score = Anomaly(slidingWindowSize=25)
anomaly_likelihood = AnomalyLikelihood(learningPeriod=600, historicWindowSize=313)
return anomaly_score, anomaly_likelihood, anom_score_txt, anom_logscore_txt
#On each active segment, there will be permanence increase of every active synapse, decrease on every inactive synapse and ...
#creation of new synapses to cells that were active in previous state
predictedSegmentDecrement=
0.0004, #punishment for SEGMENTS for incorrect predictions
#from nupic documentation: predictedSegmentDecrement: A good value is just a bit larger than (the column-level sparsity * permanenceIncrement)...
#So, if column-level sparsity is 2% and permanenceIncrement is 0.01, this parameter should be something like 4% * 0.01 = 0.0004).
seed=1960,
maxSegmentPerCell=255,
maxSynapsesPerSegment=255)
############## ANOMALY DETECTIONS #############
anom_score = np.zeros((N_DATA + 1, ))
anom_logscore = np.zeros((N_DATA + 1, ))
anomaly_score = Anomaly(slidingWindowSize=25)
anomaly_likelihood = AnomalyLikelihood(learningPeriod=500,
historicWindowSize=213)
dd = 0
for i, linha in enumerate(teste):
#####################################################
scalar_encoder.encodeIntoArray(linha[1], bits_scalar)
time_encoder.encodeIntoArray(linha[0], bits_time)
encoder_output = np.concatenate((bits_time, bits_scalar))
