def testCheckpointMiddleOfSequence2(self):
"""More complex test of checkpointing in the middle of a sequence."""
tp1 = TP(2048, 32, 0.21, 0.5, 11, 20, 0.1, 0.1, 1.0, 0.0, 14, False, 5,
2, False, 1960, 0, False, '', 3, 10, 5, 0, 32, 128, 32,
'normal')
tp2 = TP(2048, 32, 0.21, 0.5, 11, 20, 0.1, 0.1, 1.0, 0.0, 14, False, 5,
2, False, 1960, 0, False, '', 3, 10, 5, 0, 32, 128, 32,
'normal')
with open(resource_filename(__name__, 'data/tp_input.csv'),
'r') as fin:
reader = csv.reader(fin)
records = []
for bottomUpInStr in fin:
bottomUpIn = numpy.array(eval('[' + bottomUpInStr.strip() +
']'),
dtype='int32')
records.append(bottomUpIn)
i = 1
for r in records[:250]:
print i
i += 1
output1 = tp1.compute(r, True, True)
output2 = tp2.compute(r, True, True)
self.assertTrue(numpy.array_equal(output1, output2))
print 'Serializing and deserializing models.'
savePath1 = os.path.join(self._tmpDir, 'tp1.bin')
tp1.saveToFile(savePath1)
tp3 = pickle.loads(pickle.dumps(tp1))
tp3.loadFromFile(savePath1)
savePath2 = os.path.join(self._tmpDir, 'tp2.bin')
tp2.saveToFile(savePath2)
tp4 = pickle.loads(pickle.dumps(tp2))
tp4.loadFromFile(savePath2)
self.assertTPsEqual(tp1, tp3)
self.assertTPsEqual(tp2, tp4)
for r in records[250:]:
print i
i += 1
out1 = tp1.compute(r, True, True)
out2 = tp2.compute(r, True, True)
out3 = tp3.compute(r, True, True)
out4 = tp4.compute(r, True, True)
self.assertTrue(numpy.array_equal(out1, out2))
self.assertTrue(numpy.array_equal(out1, out3))
self.assertTrue(numpy.array_equal(out1, out4))
self.assertTPsEqual(tp1, tp2)
self.assertTPsEqual(tp1, tp3)
self.assertTPsEqual(tp2, tp4)
def testCheckpointMiddleOfSequence(self):
# Create a model and give it some inputs to learn.
tp1 = TP(numberOfCols=100, cellsPerColumn=12, verbosity=VERBOSITY)
sequences = [self.generateSequence() for _ in xrange(5)]
train = list(
itertools.chain.from_iterable(sequences[:3] + [sequences[3][:5]]))
for bottomUpInput in train:
if bottomUpInput is None:
tp1.reset()
else:
tp1.compute(bottomUpInput, True, True)
# Serialize and deserialized the TP.
checkpointPath = os.path.join(self._tmpDir, 'a')
tp1.saveToFile(checkpointPath)
tp2 = pickle.loads(pickle.dumps(tp1))
tp2.loadFromFile(checkpointPath)
# Check that the TPs are the same.
self.assertTPsEqual(tp1, tp2)
# Feed some data into the models.
test = list(
itertools.chain.from_iterable([sequences[3][5:]] + sequences[3:]))
for bottomUpInput in test:
if bottomUpInput is None:
tp1.reset()
tp2.reset()
else:
result1 = tp1.compute(bottomUpInput, True, True)
result2 = tp2.compute(bottomUpInput, True, True)
self.assertTPsEqual(tp1, tp2)
self.assertTrue(numpy.array_equal(result1, result2))
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,
checkpointDir=None):
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,
#verbosity=3, # who knows what this does...
checkSynapseConsistency=False,
pamLength=pamLength)
self.checkpointDir = checkpointDir
self.checkpointPklPath = None
self.checkpointDataPath = None
self._initCheckpoint()
def testCheckpointMiddleOfSequence2(self):
"""More complex test of checkpointing in the middle of a sequence."""
tp1 = TP(2048, 32, 0.21, 0.5, 11, 20, 0.1, 0.1, 1.0, 0.0, 14, False, 5,
2, False, 1960, 0, False, '', 3, 10, 5, 0, 32, 128, 32,
'normal')
tp2 = TP(2048, 32, 0.21, 0.5, 11, 20, 0.1, 0.1, 1.0, 0.0, 14, False, 5,
2, False, 1960, 0, False, '', 3, 10, 5, 0, 32, 128, 32,
'normal')
with resource_stream(__name__, 'data/tp_input.csv') as fin:
reader = csv.reader(fin)
records = []
for bottomUpInStr in fin:
bottomUpIn = numpy.array(eval('[' + bottomUpInStr.strip() +
']'),
dtype='int32')
records.append(bottomUpIn)
for r in records[:250]:
output1 = tp1.compute(r, True, True)
output2 = tp2.compute(r, True, True)
self.assertTrue(numpy.array_equal(output1, output2))
tp3 = pickle.loads(pickle.dumps(tp1))
tp4 = pickle.loads(pickle.dumps(tp2))
i = 0
for r in records[250:]:
print i
i += 1
out1 = tp1.compute(r, True, True)
out2 = tp2.compute(r, True, True)
out3 = tp3.compute(r, True, True)
out4 = tp4.compute(r, True, True)
self.assertTPsEqual(tp1, tp2)
self.assertTrue(numpy.array_equal(out1, out2))
self.assertTrue(numpy.array_equal(out1, out3))
self.assertTrue(numpy.array_equal(out1, out4))
def _createTPs(numCols, cellsPerColumn=4, checkSynapseConsistency=True):
"""Create TP and TP10X instances with identical parameters. """
# Keep these fixed for both TP's:
minThreshold = 4
activationThreshold = 4
newSynapseCount = 5
initialPerm = 0.6
connectedPerm = 0.5
permanenceInc = 0.1
permanenceDec = 0.001
globalDecay = 0.0
if VERBOSITY > 1:
print "Creating TP10X instance"
cppTp = TP10X2(numberOfCols=numCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
burnIn=1,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=checkSynapseConsistency,
pamLength=1000)
if VERBOSITY > 1:
print "Creating PY TP instance"
pyTp = TP(numberOfCols=numCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
burnIn=1,
seed=SEED,
verbosity=VERBOSITY,
pamLength=1000)
return cppTp, pyTp
def setUp(self):
self.tmPy = TemporalMemoryPy(columnDimensions=[2048],
cellsPerColumn=32,
initialPermanence=0.5,
connectedPermanence=0.8,
minThreshold=10,
maxNewSynapseCount=12,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=15)
self.tmCPP = TemporalMemoryCPP(columnDimensions=[2048],
cellsPerColumn=32,
initialPermanence=0.5,
connectedPermanence=0.8,
minThreshold=10,
maxNewSynapseCount=12,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=15)
self.tp = TP(numberOfCols=2048,
cellsPerColumn=32,
initialPerm=0.5,
connectedPerm=0.8,
minThreshold=10,
newSynapseCount=12,
permanenceInc=0.1,
permanenceDec=0.05,
activationThreshold=15,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=1)
self.tp10x2 = TP10X2(numberOfCols=2048,
cellsPerColumn=32,
initialPerm=0.5,
connectedPerm=0.8,
minThreshold=10,
newSynapseCount=12,
permanenceInc=0.1,
permanenceDec=0.05,
activationThreshold=15,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=1)
self.patternMachine = PatternMachine(2048, 40, num=100)
self.sequenceMachine = SequenceMachine(self.patternMachine)
def _createTps(numCols):
"""Create two instances of temporal poolers (TP.py and TP10X2.py) with
identical parameter settings."""
# Keep these fixed:
minThreshold = 4
activationThreshold = 5
newSynapseCount = 7
initialPerm = 0.3
connectedPerm = 0.5
permanenceInc = 0.1
permanenceDec = 0.05
globalDecay = 0
cellsPerColumn = 1
cppTp = TP10X2(numberOfCols=numCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
burnIn=1,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=True,
pamLength=1000)
# Ensure we are copying over learning states for TPDiff
cppTp.retrieveLearningStates = True
pyTp = TP(numberOfCols=numCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
burnIn=1,
seed=SEED,
verbosity=VERBOSITY,
pamLength=1000)
return cppTp, pyTp
def setUp(self):
self.tmPy = TemporalMemoryPy(columnDimensions=[2048],
cellsPerColumn=32,
initialPermanence=0.5,
connectedPermanence=0.8,
minThreshold=10,
maxNewSynapseCount=12,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=15)
self.tmCPP = TemporalMemoryCPP(columnDimensions=[2048],
cellsPerColumn=32,
initialPermanence=0.5,
connectedPermanence=0.8,
minThreshold=10,
maxNewSynapseCount=12,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=15)
self.tp = TP(numberOfCols=2048,
cellsPerColumn=32,
initialPerm=0.5,
connectedPerm=0.8,
minThreshold=10,
newSynapseCount=12,
permanenceInc=0.1,
permanenceDec=0.05,
activationThreshold=15,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=1)
self.tp10x2 = TP10X2(numberOfCols=2048,
cellsPerColumn=32,
initialPerm=0.5,
connectedPerm=0.8,
minThreshold=10,
newSynapseCount=12,
permanenceInc=0.1,
permanenceDec=0.05,
activationThreshold=15,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=1)
self.scalarEncoder = RandomDistributedScalarEncoder(0.88)
def _create_network(self, mean=128):
"""
:param mean: int, the mean of the frame pix value, will be used in BASE_ENCODE.
"""
# some rulers of creating network
# the product of the shape's two dimensions is equal to inputDimensions
# columnDimensions equal to numberOfCols
self.enc = MatrixEncoder(shape=self.shape, mean=mean)
self.sp = SpatialPooler(
inputDimensions=self.shape[0] * self.shape[1],
columnDimensions=self.column_dimensions,
potentialRadius=self.potential_radius,
numActiveColumnsPerInhArea=self.numActive_columns_perInhArea,
globalInhibition=self.global_inhibition,
synPermActiveInc=self.syn_perm_active_inc,
potentialPct=self.potential_pct,
synPermInactiveDec=self.synPermInactiveDec,
synPermConnected=self.synPermConnected,
seed=self.sp_seed,
localAreaDensity=self.localAreaDensity,
stimulusThreshold=self.stimulusThreshold,
maxBoost=self.maxBoost)
self.tp = TP(numberOfCols=self.column_dimensions,
cellsPerColumn=self.cells_per_column,
initialPerm=self.initial_perm,
connectedPerm=self.connected_perm,
minThreshold=self.min_threshold,
newSynapseCount=self.new_synapse_count,
permanenceInc=self.permanence_inc,
permanenceDec=self.permanence_dec,
activationThreshold=self.activation_threshold,
globalDecay=self.global_decay,
burnIn=self.burn_in,
pamLength=self.pam_length,
maxSynapsesPerSegment=self.maxSynapsesPerSegment,
maxSegmentsPerCell=self.maxSegmentsPerCell,
seed=self.tp_seed,
maxAge=self.maxAge)
def basicTest2(self,
tp,
numPatterns=100,
numRepetitions=3,
activity=15,
testTrimming=False,
testRebuild=False):
"""Basic test (basic run of learning and inference)"""
# Create PY TP object that mirrors the one sent in.
tpPy = TP(numberOfCols=tp.numberOfCols,
cellsPerColumn=tp.cellsPerColumn,
initialPerm=tp.initialPerm,
connectedPerm=tp.connectedPerm,
minThreshold=tp.minThreshold,
newSynapseCount=tp.newSynapseCount,
permanenceInc=tp.permanenceInc,
permanenceDec=tp.permanenceDec,
permanenceMax=tp.permanenceMax,
globalDecay=tp.globalDecay,
activationThreshold=tp.activationThreshold,
doPooling=tp.doPooling,
segUpdateValidDuration=tp.segUpdateValidDuration,
pamLength=tp.pamLength,
maxAge=tp.maxAge,
maxSeqLength=tp.maxSeqLength,
maxSegmentsPerCell=tp.maxSegmentsPerCell,
maxSynapsesPerSegment=tp.maxSynapsesPerSegment,
seed=tp.seed,
verbosity=tp.verbosity)
# Ensure we are copying over learning states for TPDiff
tp.retrieveLearningStates = True
verbosity = VERBOSITY
# Learn
# Build up sequences
sequence = fdrutils.generateCoincMatrix(nCoinc=numPatterns,
length=tp.numberOfCols,
activity=activity)
for r in xrange(numRepetitions):
for i in xrange(sequence.nRows()):
#if i > 11:
# setVerbosity(6, tp, tpPy)
if i % 10 == 0:
tp.reset()
tpPy.reset()
if verbosity >= 2:
print "\n\n ===================================\nPattern:",
print i, "Round:", r, "input:", sequence.getRow(i)
y1 = tp.learn(sequence.getRow(i))
y2 = tpPy.learn(sequence.getRow(i))
# Ensure everything continues to work well even if we continuously
# rebuild outSynapses structure
if testRebuild:
tp.cells4.rebuildOutSynapses()
if testTrimming:
tp.trimSegments()
tpPy.trimSegments()
if verbosity > 2:
print "\n ------ CPP states ------ ",
tp.printStates()
print "\n ------ PY states ------ ",
tpPy.printStates()
if verbosity > 6:
print "C++ cells: "
tp.printCells()
print "PY cells: "
tpPy.printCells()
if verbosity >= 3:
print "Num segments in PY and C++", tpPy.getNumSegments(), \
tp.getNumSegments()
# Check if the two TP's are identical or not. This check is slow so
# we do it every other iteration. Make it every iteration for debugging
# as needed.
self.assertTrue(fdrutils.tpDiff2(tp, tpPy, verbosity, False))
# Check that outputs are identical
self.assertLess(abs((y1 - y2).sum()), 3)
print "Learning completed"
self.assertTrue(fdrutils.tpDiff2(tp, tpPy, verbosity))
# TODO: Need to check - currently failing this
#checkCell0(tpPy)
# Remove unconnected synapses and check TP's again
# Test rebuild out synapses
print "Rebuilding outSynapses"
tp.cells4.rebuildOutSynapses()
self.assertTrue(fdrutils.tpDiff2(tp, tpPy, VERBOSITY))
print "Trimming segments"
tp.trimSegments()
tpPy.trimSegments()
self.assertTrue(fdrutils.tpDiff2(tp, tpPy, VERBOSITY))
# Save and reload after learning
print "Pickling and unpickling"
tp.makeCells4Ephemeral = False
pickle.dump(tp, open("test_tp10x.pkl", "wb"))
tp2 = pickle.load(open("test_tp10x.pkl"))
self.assertTrue(fdrutils.tpDiff2(tp, tp2, VERBOSITY,
checkStates=False))
# Infer
print "Testing inference"
# Setup for inference
tp.reset()
tpPy.reset()
setVerbosity(INFERENCE_VERBOSITY, tp, tpPy)
patterns = numpy.zeros((40, tp.numberOfCols), dtype='uint32')
for i in xrange(4):
_RGEN.initializeUInt32Array(patterns[i], 2)
for i, x in enumerate(patterns):
x = numpy.zeros(tp.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
y = tp.infer(x)
yPy = tpPy.infer(x)
self.assertTrue(
fdrutils.tpDiff2(tp, tpPy, VERBOSITY, checkLearn=False))
if abs((y - yPy).sum()) > 0:
print "C++ output", y
print "Py output", yPy
assert False
if i > 0:
tp.checkPrediction2(patterns)
tpPy.checkPrediction2(patterns)
print "Inference completed"
print "===================================="
return tp, tpPy
def createTPs(includeCPP=True,
includePy=True,
numCols=100,
cellsPerCol=4,
activationThreshold=3,
minThreshold=3,
newSynapseCount=3,
initialPerm=0.6,
permanenceInc=0.1,
permanenceDec=0.0,
globalDecay=0.0,
pamLength=0,
checkSynapseConsistency=True,
maxInfBacktrack=0,
maxLrnBacktrack=0,
**kwargs):
"""Create one or more TP instances, placing each into a dict keyed by
name.
Parameters:
------------------------------------------------------------------
retval: tps - dict of TP instances
"""
# Keep these fixed:
connectedPerm = 0.5
tps = dict()
if includeCPP:
if VERBOSITY >= 2:
print "Creating TP10X2 instance"
cpp_tp = TP10X2(
numberOfCols=numCols,
cellsPerColumn=cellsPerCol,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
burnIn=1,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=checkSynapseConsistency,
collectStats=True,
pamLength=pamLength,
maxInfBacktrack=maxInfBacktrack,
maxLrnBacktrack=maxLrnBacktrack,
)
# Ensure we are copying over learning states for TPDiff
cpp_tp.retrieveLearningStates = True
tps['CPP'] = cpp_tp
if includePy:
if VERBOSITY >= 2:
print "Creating PY TP instance"
py_tp = TP(
numberOfCols=numCols,
cellsPerColumn=cellsPerCol,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
burnIn=1,
seed=SEED,
verbosity=VERBOSITY,
collectStats=True,
pamLength=pamLength,
maxInfBacktrack=maxInfBacktrack,
maxLrnBacktrack=maxLrnBacktrack,
)
tps['PY '] = py_tp
return tps
def _createTPs(self,
numCols,
fixedResources=False,
checkSynapseConsistency=True):
"""Create an instance of the appropriate temporal pooler. We isolate
all parameters as constants specified here."""
# Keep these fixed:
minThreshold = 4
activationThreshold = 8
newSynapseCount = 15
initialPerm = 0.3
connectedPerm = 0.5
permanenceInc = 0.1
permanenceDec = 0.05
if fixedResources:
permanenceDec = 0.1
maxSegmentsPerCell = 5
maxSynapsesPerSegment = 15
globalDecay = 0
maxAge = 0
else:
permanenceDec = 0.05
maxSegmentsPerCell = -1
maxSynapsesPerSegment = -1
globalDecay = 0.0001
maxAge = 1
if g_testCPPTP:
if g_options.verbosity > 1:
print "Creating TP10X2 instance"
cppTP = TP10X2(
numberOfCols=numCols,
cellsPerColumn=4,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
maxAge=maxAge,
burnIn=1,
seed=g_options.seed,
verbosity=g_options.verbosity,
checkSynapseConsistency=checkSynapseConsistency,
pamLength=1000,
maxSegmentsPerCell=maxSegmentsPerCell,
maxSynapsesPerSegment=maxSynapsesPerSegment,
)
# Ensure we are copying over learning states for TPDiff
cppTP.retrieveLearningStates = True
else:
cppTP = None
if g_options.verbosity > 1:
print "Creating PY TP instance"
pyTP = TP(
numberOfCols=numCols,
cellsPerColumn=4,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
activationThreshold=activationThreshold,
globalDecay=globalDecay,
maxAge=maxAge,
burnIn=1,
seed=g_options.seed,
verbosity=g_options.verbosity,
pamLength=1000,
maxSegmentsPerCell=maxSegmentsPerCell,
maxSynapsesPerSegment=maxSynapsesPerSegment,
)
return cppTP, pyTP
def testInitDefaultTP(self):
self.assertTrue(isinstance(TP(), TP))
from nupic.research.TP import TP
import numpy as np
tp = TP(numberOfCols=20,
cellsPerColumn=3,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=10,
newSynapseCount=10,
permanenceInc=0.1,
permanenceDec=0.0,
activationThreshold=6,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=10)
list = np.array([[1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1]])
list1 = np.array([])
for i in range(30):
for j in range(len(list)):
tp.compute(list[j], enableLearn=True, computeInfOutput=False)
tp.reset()
def setUp(cls):
tmPy = TemporalMemoryPy(columnDimensions=[2048],
cellsPerColumn=32,
initialPermanence=0.5,
connectedPermanence=0.8,
minThreshold=10,
maxNewSynapseCount=12,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=15)
tmCPP = TemporalMemoryCPP(columnDimensions=[2048],
cellsPerColumn=32,
initialPermanence=0.5,
connectedPermanence=0.8,
minThreshold=10,
maxNewSynapseCount=12,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=15)
tp = TP(numberOfCols=2048,
cellsPerColumn=32,
initialPerm=0.5,
connectedPerm=0.8,
minThreshold=10,
newSynapseCount=12,
permanenceInc=0.1,
permanenceDec=0.05,
activationThreshold=15,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=1)
tp10x2 = TP10X2(numberOfCols=2048,
cellsPerColumn=32,
initialPerm=0.5,
connectedPerm=0.8,
minThreshold=10,
newSynapseCount=12,
permanenceInc=0.1,
permanenceDec=0.05,
activationThreshold=15,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=1)
def tmComputeFn(pattern, instance):
instance.compute(pattern, True)
def tpComputeFn(pattern, instance):
array = cls._patternToNumpyArray(pattern)
instance.compute(array, enableLearn=True, computeInfOutput=True)
return (
("TM (py)", tmPy, tmComputeFn),
("TM (C++)", tmCPP, tmComputeFn),
("TP", tp, tpComputeFn),
("TP10X2", tp10x2, tpComputeFn),
)
def create_network():
enc = MatrixEncoder((64, 64))
sp = SpatialPooler(inputDimensions=4096, columnDimensions=1024)
tp = TP(numberOfCols=1024)
return enc, sp, tp
