class Client(object):
def __init__(self,
numberOfCols=1024,
cellsPerColumn=8,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=164,
newSynapseCount=164,
permanenceInc=0.1,
permanenceDec=0.0,
activationThreshold=20,
pamLength=10):
self.tp = TP(
numberOfCols=numberOfCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
# 1/2 of the on bits = (1024 * .02) / 2
activationThreshold=activationThreshold,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=pamLength)
def feed(self, sdr):
tp = self.tp
narr = numpy.array(sdr, dtype="uint32")
tp.compute(narr, enableLearn=True, computeInfOutput=True)
predicted_cells = tp.getPredictedState()
# print predicted_cells.tolist()
predicted_columns = predicted_cells.max(axis=1)
# print predicted_columns.tolist()
# import pdb; pdb.set_trace()
return predicted_columns.nonzero()[0].tolist()
def printParameters(self):
"""
Print CLA parameters
"""
self.tp.printParameters()
def reset(self):
self.tp.reset()
class Client(object):
def __init__(self,
numberOfCols=16384, cellsPerColumn=8,
initialPerm=0.5, connectedPerm=0.5,
minThreshold=164, newSynapseCount=164,
permanenceInc=0.1, permanenceDec=0.0,
activationThreshold=164,
pamLength=10):
self.tp = TP(numberOfCols=numberOfCols, cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm, connectedPerm=connectedPerm,
minThreshold=minThreshold, newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc, permanenceDec=permanenceDec,
# 1/2 of the on bits = (16384 * .02) / 2
activationThreshold=activationThreshold,
globalDecay=0, burnIn=1,
checkSynapseConsistency=False,
pamLength=pamLength)
def feed(self, sdr):
tp = self.tp
narr = numpy.array(sdr, dtype="uint32")
tp.compute(narr, enableLearn = True, computeInfOutput = True)
predicted_cells = tp.getPredictedState()
# print predicted_cells.tolist()
predicted_columns = predicted_cells.max(axis=1)
# print predicted_columns.tolist()
# import pdb; pdb.set_trace()
return predicted_columns.nonzero()[0].tolist()
def printParameters(self):
"""
Print CLA parameters
"""
self.tp.printParameters()
def reset(self):
self.tp.reset()
