def __init__(self, numberOfCols =500, burnIn =2, # Used for evaluating the prediction score collectStats =False, # If true, collect training and inference stats seed =42, verbosity =VERBOSITY, predictionMethod = 'random', # "random" or "zeroth" **kwargs ): # Init the base class TP.__init__(self, numberOfCols = numberOfCols, cellsPerColumn = 1, burnIn = burnIn, collectStats = collectStats, seed = seed, verbosity = verbosity) self.predictionMethod = predictionMethod #--------------------------------------------------------------------------------- # Create basic data structures for keeping track of column statistics # Number of times each column has been active during learning self.columnCount = numpy.zeros(numberOfCols, dtype="int32") # Running average of input density self.averageDensity = 0.05
def __init__( self, numberOfCols=500, burnIn=2, # Used for evaluating the prediction score collectStats=False, # If true, collect training and inference stats seed=42, verbosity=VERBOSITY, predictionMethod='random', # "random" or "zeroth" **kwargs): # Init the base class TP.__init__(self, numberOfCols=numberOfCols, cellsPerColumn=1, burnIn=burnIn, collectStats=collectStats, seed=seed, verbosity=verbosity) self.predictionMethod = predictionMethod #--------------------------------------------------------------------------------- # Create basic data structures for keeping track of column statistics # Number of times each column has been active during learning self.columnCount = numpy.zeros(numberOfCols, dtype="int32") # Running average of input density self.averageDensity = 0.05
def __init__( self, numberOfCols=500, cellsPerColumn=10, initialPerm=0.11, # TODO: check perm numbers with Ron connectedPerm=0.50, minThreshold=8, newSynapseCount=15, permanenceInc=0.10, permanenceDec=0.10, permanenceMax=1.0, # never exceed this value globalDecay=0.10, activationThreshold=12, # 3/4 of newSynapseCount TODO make fraction doPooling=False, # allows to turn off pooling segUpdateValidDuration=5, burnIn=2, # Used for evaluating the prediction score collectStats=False, # If true, collect training and inference stats seed=42, verbosity=VERBOSITY, checkSynapseConsistency=False, # List (as string) of trivial predictions to compute alongside # the full TP. See TrivialPredictor.py for a list of allowed methods trivialPredictionMethods='', pamLength=1, maxInfBacktrack=10, maxLrnBacktrack=5, maxAge=100000, maxSeqLength=32, # Fixed size mode params maxSegmentsPerCell=-1, maxSynapsesPerSegment=-1, # Output control outputType='normal', ): #--------------------------------------------------------------------------------- # Save our __init__ args for debugging self._initArgsDict = _extractCallingMethodArgs() #--------------------------------------------------------------------------------- # These two variables are for testing # If set to True, Cells4 will perform (time consuming) invariance checks self.checkSynapseConsistency = checkSynapseConsistency # If set to False, Cells4 will *not* be treated as an ephemeral member # and full TP10X pickling is possible. This is useful for testing # pickle/unpickle without saving Cells4 to an external file self.makeCells4Ephemeral = True #--------------------------------------------------------------------------------- # Init the base class TP.__init__( self, numberOfCols=numberOfCols, cellsPerColumn=cellsPerColumn, initialPerm=initialPerm, connectedPerm=connectedPerm, minThreshold=minThreshold, newSynapseCount=newSynapseCount, permanenceInc=permanenceInc, permanenceDec=permanenceDec, permanenceMax=permanenceMax, # never exceed this value globalDecay=globalDecay, activationThreshold=activationThreshold, doPooling=doPooling, segUpdateValidDuration=segUpdateValidDuration, burnIn=burnIn, collectStats=collectStats, seed=seed, verbosity=verbosity, trivialPredictionMethods=trivialPredictionMethods, pamLength=pamLength, maxInfBacktrack=maxInfBacktrack, maxLrnBacktrack=maxLrnBacktrack, maxAge=maxAge, maxSeqLength=maxSeqLength, maxSegmentsPerCell=maxSegmentsPerCell, maxSynapsesPerSegment=maxSynapsesPerSegment, outputType=outputType, )
def __init__(self, numberOfCols = 500, cellsPerColumn = 10, initialPerm = 0.11, # TODO: check perm numbers with Ron connectedPerm = 0.50, minThreshold = 8, newSynapseCount = 15, permanenceInc = 0.10, permanenceDec = 0.10, permanenceMax = 1.0, # never exceed this value globalDecay = 0.10, activationThreshold = 12, # 3/4 of newSynapseCount TODO make fraction doPooling = False, # allows to turn off pooling segUpdateValidDuration = 5, burnIn = 2, # Used for evaluating the prediction score collectStats = False, # If true, collect training and inference stats seed = 42, verbosity = VERBOSITY, checkSynapseConsistency = False, # List (as string) of trivial predictions to compute alongside # the full TP. See TrivialPredictor.py for a list of allowed methods trivialPredictionMethods = '', pamLength = 1, maxInfBacktrack = 10, maxLrnBacktrack = 5, maxAge = 100000, maxSeqLength = 32, # Fixed size mode params maxSegmentsPerCell = -1, maxSynapsesPerSegment = -1, # Output control outputType = 'normal', ): #--------------------------------------------------------------------------------- # Save our __init__ args for debugging self._initArgsDict = _extractCallingMethodArgs() #--------------------------------------------------------------------------------- # These two variables are for testing # If set to True, Cells4 will perform (time consuming) invariance checks self.checkSynapseConsistency = checkSynapseConsistency # If set to False, Cells4 will *not* be treated as an ephemeral member # and full TP10X pickling is possible. This is useful for testing # pickle/unpickle without saving Cells4 to an external file self.makeCells4Ephemeral = True #--------------------------------------------------------------------------------- # Init the base class TP.__init__(self, numberOfCols = numberOfCols, cellsPerColumn = cellsPerColumn, initialPerm = initialPerm, connectedPerm = connectedPerm, minThreshold = minThreshold, newSynapseCount = newSynapseCount, permanenceInc = permanenceInc, permanenceDec = permanenceDec, permanenceMax = permanenceMax, # never exceed this value globalDecay = globalDecay, activationThreshold = activationThreshold, doPooling = doPooling, segUpdateValidDuration = segUpdateValidDuration, burnIn = burnIn, collectStats = collectStats, seed = seed, verbosity = verbosity, trivialPredictionMethods = trivialPredictionMethods, pamLength = pamLength, maxInfBacktrack = maxInfBacktrack, maxLrnBacktrack = maxLrnBacktrack, maxAge = maxAge, maxSeqLength = maxSeqLength, maxSegmentsPerCell = maxSegmentsPerCell, maxSynapsesPerSegment = maxSynapsesPerSegment, outputType = outputType, )