def testGetMethods(self):
"""check if the get-Methodes return correct values"""
# first create instance of Tm with some parameters
tm = TM(
columnDimensions=(parameters1["sp"]["columnCount"],),
cellsPerColumn=parameters1["tm"]["cellsPerColumn"],
activationThreshold=parameters1["tm"]["activationThreshold"],
initialPermanence=parameters1["tm"]["initialPerm"],
connectedPermanence=parameters1["sp"]["synPermConnected"],
minThreshold=parameters1["tm"]["minThreshold"],
maxNewSynapseCount=parameters1["tm"]["newSynapseCount"],
permanenceIncrement=parameters1["tm"]["permanenceInc"],
permanenceDecrement=parameters1["tm"]["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=parameters1["tm"]["maxSegmentsPerCell"],
maxSynapsesPerSegment=parameters1["tm"]["maxSynapsesPerSegment"],
checkInputs = True
)
# second call each function to get the values
columnDimension = tm.getColumnDimensions()
cellsPerColumn = tm.getCellsPerColumn()
activationThreshold = tm.getActivationThreshold()
initialPermanence = tm.getInitialPermanence()
connectedPermanence = tm.getConnectedPermanence()
minThreshold = tm.getMinThreshold()
maxNewSynapseCount = tm.getMaxNewSynapseCount()
permanenceIncrement = tm.getPermanenceIncrement()
permanenceDecrement = tm.getPermanenceDecrement()
predictedSegmentDecrement = tm.getPredictedSegmentDecrement()
maxSegmentsPerCell = tm.getMaxSegmentsPerCell()
maxSynapsesPerSegment = tm.getMaxSynapsesPerSegment()
checkInputs = tm.getCheckInputs()
# third and final, compare the input parameters with the parameters from the get-Methods
# floating point numbers maybe not 100 % equal...
self.assertEqual([parameters1["sp"]["columnCount"]], columnDimension, "using method (getColumnDimension) failed")
self.assertEqual(parameters1["tm"]["cellsPerColumn"], cellsPerColumn, "using method (getCellsPerColumn) failed")
self.assertEqual(parameters1["tm"]["activationThreshold"], activationThreshold, "using (getActivationThreshold) failed")
self.assertAlmostEqual(parameters1["sp"]["synPermConnected"], connectedPermanence, msg="using method (getConnectedPermanence) failed")
self.assertEqual(parameters1["tm"]["minThreshold"], minThreshold, "using method (getMinThreshold) failed")
self.assertEqual(parameters1["tm"]["newSynapseCount"], maxNewSynapseCount, "using method (getMaxNewSynapseCount) failed")
self.assertAlmostEqual(parameters1["tm"]["permanenceInc"], permanenceIncrement, msg="using method (getPermanenceIncrement) failed")
self.assertAlmostEqual(parameters1["tm"]["permanenceDec"], permanenceDecrement, msg="using method (getPermanenceDecrement) failed")
self.assertAlmostEqual(0.0, predictedSegmentDecrement, msg="using Methode (getPredictedSegmentDecrement) failed")
self.assertEqual(parameters1["tm"]["maxSegmentsPerCell"], maxSegmentsPerCell, "using Method (getMaxSegmentsPerCell) failed")
self.assertEqual(parameters1["tm"]["maxSynapsesPerSegment"], maxSynapsesPerSegment, "using Method (getMaxSynapsesPerSegment) failed")
self.assertEqual(True, checkInputs, "using Method (getCheckInputs) failed")
for epoch in range(10):
# if run without the reset, eventually get the following error
# RuntimeError: Exception: SDRClassifier.cpp(228)
# message: CHECK FAILED: "recordNum >= lastRecordNum"
# The record number must increase monotonically.
predict.reset()
# need this for predictions later
pred_actCells = []
# anamoly_forall = []
for count, i in enumerate(range(len(train_set))):
# encode the current integer
rdseSDR = rdseEncoder.encode(train_set[i])
# create an SDR for SP output
activeColumns = SDR( dimensions = tm.getColumnDimensions()[0] )
# convert the SDR to SP
# this is optional if the output from the encoder is
# already a sparse binary representation
# otherwise this step may be skipped as seen in
# tutorials online
sp.compute(rdseSDR, True, activeColumns)
tm.compute(activeColumns, learn=True)
tm.activateDendrites(True)
tm_actCells = tm.getActiveCells()
pred_actCells.append(tm_actCells)
# anamoly_forall.append(tm.anomaly)
label = int(train_set[i])
# this is a neural network being trained to