def run():
sp = SpatialPooler(inputDimensions=[10, 15],
columnDimensions=[5, 10],
potentialRadius=2,
potentialPct=0.5,
synPermInactiveDec=0.1,
synPermActiveInc=0.1,
synPermConnected=0.1,
localAreaDensity=0.1,
numActiveColumnsPerInhArea=-1,
globalInhibition=True)
inputArray = numpy.zeros(sp.getNumInputs())
activeArray = numpy.zeros(sp.getNumColumns())
Patcher().patchSP(sp)
for i in range(100):
generateInput(inputArray)
sp.compute(inputArray, True, activeArray)
print "Ran iteration:\t{0}".format(i)
def run():
sp = SpatialPooler(
inputDimensions=[10, 15],
columnDimensions=[5, 10],
potentialRadius=2,
potentialPct=0.5,
synPermInactiveDec=0.1,
synPermActiveInc=0.1,
synPermConnected=0.1,
localAreaDensity=0.1,
numActiveColumnsPerInhArea=-1,
globalInhibition=True
)
inputArray = numpy.zeros(sp.getNumInputs())
activeArray = numpy.zeros(sp.getNumColumns())
Patcher().patchSP(sp)
for i in range(100):
generateInput(inputArray)
sp.compute(inputArray, True, activeArray)
print "Ran iteration:\t{0}".format(i)
spVerbosity=0)
tm = TemporalMemory(
columnDimensions=sp.getColumnDimensions(),
initialPermanence=0.4,
connectedPermanence=0.5,
minThreshold=4,
maxNewSynapseCount=4,
permanenceDecrement=0.05,
permanenceIncrement=0.05,
activationThreshold=4,
)
cla = CLAClassifierCond((1, 2))
spIn = numpy.zeros(sp.getNumInputs(), dtype=numpy.uint8)
spOut = numpy.zeros(sp.getNumColumns(), dtype=numpy.uint8)
trainingData = [['a', 'b', 'c'], ['a', 'c', 'b']]
trainingData2 = [(['b', 'c', 'a'], ['z', 'x', 'y'])]
recordNum = 0
for i in xrange(20):
# recordNum = 0
# for dataList in trainingData:
# print("----------dataList = {}----------".format(dataList))
#
# for data in dataList:
# spIn.fill(0)