def constructTM(self, columnCount, basalInputSize, apicalInputSize,
cellsPerColumn, initialPermanence, connectedPermanence,
minThreshold, sampleSize, permanenceIncrement,
permanenceDecrement, predictedSegmentDecrement,
activationThreshold, seed):
params = {
"columnDimensions": (columnCount,),
"basalInputDimensions": (basalInputSize,),
"apicalInputDimensions": (apicalInputSize,),
"cellsPerColumn": cellsPerColumn,
"initialPermanence": initialPermanence,
"connectedPermanence": connectedPermanence,
"minThreshold": minThreshold,
"maxNewSynapseCount": sampleSize,
"permanenceIncrement": permanenceIncrement,
"permanenceDecrement": permanenceDecrement,
"predictedSegmentDecrement": predictedSegmentDecrement,
"activationThreshold": activationThreshold,
"seed": seed,
"learnOnOneCell": False,
"formInternalBasalConnections": False,
}
self.tm = ExtendedTemporalMemory(**params)
def initialize(self):
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax else self.inputMin + 1)
numBuckets = 130.0
resolution = max(0.001, (maxVal - minVal) / numBuckets)
self.valueEncoder = RandomDistributedScalarEncoder(resolution,
w=41,
seed=42)
self.encodedValue = np.zeros(self.valueEncoder.getWidth(),
dtype=np.uint32)
self.timestampEncoder = DateEncoder(timeOfDay=(
21,
9.49,
))
self.encodedTimestamp = np.zeros(self.timestampEncoder.getWidth(),
dtype=np.uint32)
inputWidth = self.valueEncoder.getWidth()
self.sp = SpatialPooler(
**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
self.spOutput = np.zeros(2048, dtype=np.float32)
self.etm = ExtendedTemporalMemory(
**{
"activationThreshold": 13,
"cellsPerColumn": 1,
"columnDimensions": (2048, ),
"basalInputDimensions": (self.timestampEncoder.getWidth(), ),
"initialPermanence": 0.21,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 32,
"minThreshold": 10,
"maxNewSynapseCount": 20,
"permanenceDecrement": 0.1,
"permanenceIncrement": 0.1,
"seed": 1960,
"checkInputs": False,
})
learningPeriod = math.floor(self.probationaryPeriod / 2.0)
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=learningPeriod,
estimationSamples=self.probationaryPeriod - learningPeriod,
reestimationPeriod=100)
def initialize(self):
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax
else self.inputMin + 1)
numBuckets = 130.0
resolution = max(0.001, (maxVal - minVal) / numBuckets)
self.valueEncoder = RandomDistributedScalarEncoder(resolution,
w=41,
seed=42)
self.encodedValue = np.zeros(self.valueEncoder.getWidth(),
dtype=np.uint32)
self.timestampEncoder = DateEncoder(timeOfDay=(21,9.49,))
self.encodedTimestamp = np.zeros(self.timestampEncoder.getWidth(),
dtype=np.uint32)
inputWidth = self.valueEncoder.getWidth()
self.sp = SpatialPooler(**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
self.spOutput = np.zeros(2048, dtype=np.float32)
self.etm = ExtendedTemporalMemory(**{
"activationThreshold": 13,
"cellsPerColumn": 1,
"columnDimensions": (2048,),
"basalInputDimensions": (self.timestampEncoder.getWidth(),),
"initialPermanence": 0.21,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 32,
"minThreshold": 10,
"maxNewSynapseCount": 20,
"permanenceDecrement": 0.1,
"permanenceIncrement": 0.1,
"seed": 1960,
"checkInputs": False,
})
learningPeriod = math.floor(self.probationaryPeriod / 2.0)
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=learningPeriod,
estimationSamples=self.probationaryPeriod - learningPeriod,
reestimationPeriod=100
)
class ExtendedTMCPP_ApicalTiebreakSequencesTests(
ApicalTiebreakSequencesTestBase, unittest.TestCase):
"""
Run the apical tiebreak sequence tests on the C++ ExtendedTemporalMemory.
"""
def constructTM(self, columnCount, apicalInputSize, cellsPerColumn,
initialPermanence, connectedPermanence, minThreshold,
sampleSize, permanenceIncrement, permanenceDecrement,
predictedSegmentDecrement, activationThreshold, seed):
params = {
"apicalInputDimensions": (apicalInputSize, ),
"columnDimensions": (columnCount, ),
"cellsPerColumn": cellsPerColumn,
"initialPermanence": initialPermanence,
"connectedPermanence": connectedPermanence,
"minThreshold": minThreshold,
"maxNewSynapseCount": sampleSize,
"permanenceIncrement": permanenceIncrement,
"permanenceDecrement": permanenceDecrement,
"predictedSegmentDecrement": predictedSegmentDecrement,
"activationThreshold": activationThreshold,
"seed": seed,
"learnOnOneCell": False,
"formInternalBasalConnections": True,
}
self.tm = ExtendedTemporalMemory(**params)
def compute(self, activeColumns, apicalInput, learn):
activeColumns = sorted(activeColumns)
apicalInput = sorted(apicalInput)
# Use depolarizeCells + activateCells rather than tm.compute so that
# getPredictiveCells returns predictions for the current timestep.
self.tm.depolarizeCells(activeCellsExternalApical=apicalInput,
learn=learn)
self.tm.activateCells(activeColumns,
reinforceCandidatesExternalApical=apicalInput,
growthCandidatesExternalApical=apicalInput,
learn=learn)
def reset(self):
self.tm.reset()
def getActiveCells(self):
return self.tm.getActiveCells()
def getPredictedCells(self):
return self.tm.getPredictiveCells()
class ExtendedTMCPP_SequenceMemoryTests(SequenceMemoryTestBase,
unittest.TestCase):
"""
Run the sequence memory tests on the C++ ExtendedTemporalMemory.
"""
def constructTM(self, columnCount, cellsPerColumn, initialPermanence,
connectedPermanence, minThreshold, sampleSize,
permanenceIncrement, permanenceDecrement,
predictedSegmentDecrement, activationThreshold, seed):
params = {
"columnDimensions": (columnCount,),
"cellsPerColumn": cellsPerColumn,
"initialPermanence": initialPermanence,
"connectedPermanence": connectedPermanence,
"minThreshold": minThreshold,
"maxNewSynapseCount": sampleSize,
"permanenceIncrement": permanenceIncrement,
"permanenceDecrement": permanenceDecrement,
"predictedSegmentDecrement": predictedSegmentDecrement,
"activationThreshold": activationThreshold,
"seed": seed,
"learnOnOneCell": False,
}
self.tm = ExtendedTemporalMemory(**params)
def compute(self, activeColumns, learn):
# Use depolarizeCells + activateCells rather than tm.compute so that
# getPredictiveCells returns predictions for the current timestep.
self.tm.depolarizeCells(learn=learn)
self.tm.activateCells(sorted(activeColumns), learn=learn)
def reset(self):
self.tm.reset()
def getActiveCells(self):
return self.tm.getActiveCells()
def getPredictedCells(self):
return self.tm.getPredictiveCells()
class DistalTimestamps1CellPerColumnDetector(AnomalyDetector):
"""The 'numenta' detector, with the following changes:
- Use pure Temporal Memory, not the classic TP that uses backtracking.
- Don't spatial pool the timestamp. Pass it in as distal input.
- 1 cell per column.
- Use w=41 in the scalar encoding, rather than w=21, to make up for the
lost timestamp input to the spatial pooler.
"""
def __init__(self, *args, **kwargs):
super(DistalTimestamps1CellPerColumnDetector, self).__init__(*args,
**kwargs)
self.valueEncoder = None
self.encodedValue = None
self.timestampEncoder = None
self.encodedTimestamp = None
self.activeExternalCells = []
self.prevActiveExternalCells = []
self.sp = None
self.spOutput = None
self.etm = None
self.anomalyLikelihood = None
def getAdditionalHeaders(self):
"""Returns a list of strings."""
return ["raw_score"]
def initialize(self):
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax
else self.inputMin + 1)
numBuckets = 130.0
resolution = max(0.001, (maxVal - minVal) / numBuckets)
self.valueEncoder = RandomDistributedScalarEncoder(resolution,
w=41,
seed=42)
self.encodedValue = np.zeros(self.valueEncoder.getWidth(),
dtype=np.uint32)
self.timestampEncoder = DateEncoder(timeOfDay=(21,9.49,))
self.encodedTimestamp = np.zeros(self.timestampEncoder.getWidth(),
dtype=np.uint32)
inputWidth = self.valueEncoder.getWidth()
self.sp = SpatialPooler(**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
self.spOutput = np.zeros(2048, dtype=np.float32)
self.etm = ExtendedTemporalMemory(**{
"activationThreshold": 13,
"cellsPerColumn": 1,
"columnDimensions": (2048,),
"basalInputDimensions": (self.timestampEncoder.getWidth(),),
"initialPermanence": 0.21,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 32,
"minThreshold": 10,
"maxNewSynapseCount": 20,
"permanenceDecrement": 0.1,
"permanenceIncrement": 0.1,
"seed": 1960,
"checkInputs": False,
})
learningPeriod = math.floor(self.probationaryPeriod / 2.0)
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=learningPeriod,
estimationSamples=self.probationaryPeriod - learningPeriod,
reestimationPeriod=100
)
def handleRecord(self, inputData):
"""Returns a tuple (anomalyScore, rawScore)."""
self.valueEncoder.encodeIntoArray(inputData["value"],
self.encodedValue)
self.timestampEncoder.encodeIntoArray(inputData["timestamp"],
self.encodedTimestamp)
self.prevActiveExternalCells = self.activeExternalCells
self.activeExternalCells = self.encodedTimestamp.nonzero()[0]
self.sp.compute(self.encodedValue, True, self.spOutput)
activeColumns = self.spOutput.nonzero()[0]
activeColumnsSet = set(activeColumns.tolist())
prevPredictedColumns = set(self.etm.columnForCell(cell)
for cell in self.etm.getPredictiveCells())
rawScore = (len(activeColumnsSet - prevPredictedColumns) /
float(len(activeColumns)))
anomalyScore = self.anomalyLikelihood.anomalyProbability(
inputData["value"], rawScore, inputData["timestamp"])
logScore = self.anomalyLikelihood.computeLogLikelihood(anomalyScore)
self.etm.compute(activeColumns,
activeCellsExternalBasal=self.activeExternalCells,
reinforceCandidatesExternalBasal=self.prevActiveExternalCells,
growthCandidatesExternalBasal=self.prevActiveExternalCells)
return (logScore, rawScore)
class DistalTimestamps1CellPerColumnDetector(AnomalyDetector):
"""The 'numenta' detector, with the following changes:
- Use pure Temporal Memory, not the classic TP that uses backtracking.
- Don't spatial pool the timestamp. Pass it in as distal input.
- 1 cell per column.
- Use w=41 in the scalar encoding, rather than w=21, to make up for the
lost timestamp input to the spatial pooler.
"""
def __init__(self, *args, **kwargs):
super(DistalTimestamps1CellPerColumnDetector,
self).__init__(*args, **kwargs)
self.valueEncoder = None
self.encodedValue = None
self.timestampEncoder = None
self.encodedTimestamp = None
self.activeExternalCells = []
self.prevActiveExternalCells = []
self.sp = None
self.spOutput = None
self.etm = None
self.anomalyLikelihood = None
def getAdditionalHeaders(self):
"""Returns a list of strings."""
return ["raw_score"]
def initialize(self):
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax else self.inputMin + 1)
numBuckets = 130.0
resolution = max(0.001, (maxVal - minVal) / numBuckets)
self.valueEncoder = RandomDistributedScalarEncoder(resolution,
w=41,
seed=42)
self.encodedValue = np.zeros(self.valueEncoder.getWidth(),
dtype=np.uint32)
self.timestampEncoder = DateEncoder(timeOfDay=(
21,
9.49,
))
self.encodedTimestamp = np.zeros(self.timestampEncoder.getWidth(),
dtype=np.uint32)
inputWidth = self.valueEncoder.getWidth()
self.sp = SpatialPooler(
**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
self.spOutput = np.zeros(2048, dtype=np.float32)
self.etm = ExtendedTemporalMemory(
**{
"activationThreshold": 13,
"cellsPerColumn": 1,
"columnDimensions": (2048, ),
"basalInputDimensions": (self.timestampEncoder.getWidth(), ),
"initialPermanence": 0.21,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 32,
"minThreshold": 10,
"maxNewSynapseCount": 20,
"permanenceDecrement": 0.1,
"permanenceIncrement": 0.1,
"seed": 1960,
"checkInputs": False,
})
learningPeriod = math.floor(self.probationaryPeriod / 2.0)
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=learningPeriod,
estimationSamples=self.probationaryPeriod - learningPeriod,
reestimationPeriod=100)
def handleRecord(self, inputData):
"""Returns a tuple (anomalyScore, rawScore)."""
self.valueEncoder.encodeIntoArray(inputData["value"],
self.encodedValue)
self.timestampEncoder.encodeIntoArray(inputData["timestamp"],
self.encodedTimestamp)
self.prevActiveExternalCells = self.activeExternalCells
self.activeExternalCells = self.encodedTimestamp.nonzero()[0]
self.sp.compute(self.encodedValue, True, self.spOutput)
activeColumns = self.spOutput.nonzero()[0]
activeColumnsSet = set(activeColumns.tolist())
prevPredictedColumns = set(
self.etm.columnForCell(cell)
for cell in self.etm.getPredictiveCells())
rawScore = (len(activeColumnsSet - prevPredictedColumns) /
float(len(activeColumns)))
anomalyScore = self.anomalyLikelihood.anomalyProbability(
inputData["value"], rawScore, inputData["timestamp"])
logScore = self.anomalyLikelihood.computeLogLikelihood(anomalyScore)
self.etm.compute(
activeColumns,
activeCellsExternalBasal=self.activeExternalCells,
reinforceCandidatesExternalBasal=self.prevActiveExternalCells,
growthCandidatesExternalBasal=self.prevActiveExternalCells)
return (logScore, rawScore)
