def test_cpp_py_tms(self):
# Create cpp tm
tm = BacktrackingTMCPP(numberOfCols=10,
cellsPerColumn=1,
verbosity=VERBOSITY)
tm.cells4.setCellSegmentOrder(True)
# Create Python tm from same characteristics
tmPy = BacktrackingTM(numberOfCols=tm.numberOfCols,
cellsPerColumn=tm.cellsPerColumn,
initialPerm=tm.initialPerm,
connectedPerm=tm.connectedPerm,
minThreshold=tm.minThreshold,
newSynapseCount=tm.newSynapseCount,
permanenceInc=tm.permanenceInc,
permanenceDec=tm.permanenceDec,
permanenceMax=tm.permanenceMax,
globalDecay=tm.globalDecay,
activationThreshold=tm.activationThreshold,
doPooling=tm.doPooling,
segUpdateValidDuration=tm.segUpdateValidDuration,
pamLength=tm.pamLength,
maxAge=tm.maxAge,
maxSeqLength=tm.maxSeqLength,
maxSegmentsPerCell=tm.maxSegmentsPerCell,
maxSynapsesPerSegment=tm.maxSynapsesPerSegment,
seed=tm.seed,
verbosity=tm.verbosity)
# Check if both tm's are equal
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY, False))
# Build up sequences
numPatterns = 1
numRepetitions = 1
activity = 1
sequence = fdrutils.generateCoincMatrix(nCoinc=numPatterns,
length=tm.numberOfCols,
activity=activity)
# print(sequence)
sequence_row = sequence.getRow(0)
y1 = tm.learn(sequence_row)
y2 = tmPy.learn(sequence_row)
num_segments_cpp = tm.getNumSegments()
num_segments_py = tmPy.getNumSegments()
# fails since num_segments_cpp = 0 and num_segments_py = 1
self.assertTrue(num_segments_cpp=num_segments_py)
# Check if both tm's are equal
# self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY, False))
return
def _createTms(numCols):
"""Create two instances of temporal poolers (backtracking_tm.py
and backtracking_tm_cpp.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
cppTm = BacktrackingTMCPP(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 TMDiff
cppTm.retrieveLearningStates = True
pyTm = BacktrackingTM(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 cppTm, pyTm
def _createTms(numCols):
"""Create two instances of temporal poolers (backtracking_tm.py
and backtracking_tm_cpp.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
cppTm = BacktrackingTMCPP(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
cppTm.retrieveLearningStates = True
pyTm = BacktrackingTM(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 cppTm, pyTm
def _createTMs(numCols, cellsPerColumn=4, checkSynapseConsistency=True):
"""Create TM and BacktrackingTMCPP instances with identical parameters. """
# Keep these fixed for both TM'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 BacktrackingTMCPP instance"
cppTm = BacktrackingTMCPP(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 TM instance"
pyTm = BacktrackingTM(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 cppTm, pyTm
def loadNetwork(path):
""" Deserialize the network from the given path
"""
# Create the network structure
(net, layers) = createNetwork()
# Replace algorithm components with loaded (connections etc.)
for key, layer in layers.iteritems():
filename = "%s/%s.tmp" % (path, key)
print 'load', key, filename
with open(filename, "rb") as f:
# AgentStateRegion with ETM instance
if 'L5_TM' in key:
proto = AgentStateRegionProto.read_packed(f)
layers[key] = AgentStateRegion.readFromProto(proto)
# ReinforcementRegion with ETM instance
elif any(name in key for name in ['D1_TM', 'D2_TM']):
proto = ReinforcementRegionProto.read_packed(f)
layers[key] = ReinforcementRegion.readFromProto(proto)
# TemporalPoolerRegion using UnionTemporalPooler (extended Spatial Pooler)
elif 'TP' in key:
proto = MyTemporalPoolerRegionProto.read_packed(f)
layers[key] = MyTemporalPoolerRegion.readFromProto(proto)
elif 'Motor' in key:
proto = MotorRegionProto.read_packed(f)
layers[key] = MRegion.readFromProto(proto)
else:
# MySPRegion - sp cpp instance (L4,L5,D1,D2)
if 'SP' in key:
proto = SpatialPoolerProto.read_packed(f)
instance = SpatialPooler.read(proto)
# MyTMRegion - cpp backtrackingTM instance (L2,L3)
elif any(name in key for name in ['L2_TM', 'L3_TM']):
instance = BacktrackingTMCPP.readFromFile(f)
# ExtendedTemporalMemory - cpp instance (L4,L5,D1,D2)
elif 'TM' in key:
# Read from file - schema.read(file)
proto = ExtendedTemporalMemoryProto.read_packed(f)
# Generate class instance from the schema
instance = ExtendedTemporalMemory.read(proto)
elif 'sensor' in key:
continue
layer.setAlgorithmInstance(instance)
return (net, layers)
def basicTest(self):
"""Basic test (creation, pickling, basic run of learning and inference)"""
# Create TM object
tm = BacktrackingTMCPP(numberOfCols=10,
cellsPerColumn=3,
initialPerm=.2,
connectedPerm=0.8,
minThreshold=2,
newSynapseCount=5,
permanenceInc=.1,
permanenceDec=.05,
permanenceMax=1,
globalDecay=.05,
activationThreshold=4,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY)
tm.retrieveLearningStates = True
# Save and reload
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY,
checkStates=False))
# Learn
for i in xrange(5):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.learn(x)
# Save and reload after learning
tm.reset()
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY))
## Infer
patterns = numpy.zeros((4, tm.numberOfCols), dtype='uint32')
for i in xrange(4):
_RGEN.initializeUInt32Array(patterns[i], 2)
for i in xrange(10):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.infer(x)
if i > 0:
tm._checkPrediction(patterns)
def testTMs(self, short=True):
"""Call basicTest2 with multiple parameter settings and ensure the C++ and
PY versions are identical throughout."""
if short == True:
print "Testing short version"
else:
print "Testing long version"
if short:
print "\nTesting with fixed resource CLA - test max segment and synapses"
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=5,
initialPerm=.5,
connectedPerm=0.5,
permanenceMax=1,
minThreshold=8,
newSynapseCount=10,
permanenceInc=0.1,
permanenceDec=0.01,
globalDecay=.0,
activationThreshold=8,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY,
maxAge=0,
maxSegmentsPerCell=2,
maxSynapsesPerSegment=10,
checkSynapseConsistency=True)
tm.cells4.setCellSegmentOrder(True)
self.basicTest2(tm, numPatterns=15, numRepetitions=1)
if not short:
print "\nTesting with fixed resource CLA - test max segment and synapses"
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=5,
initialPerm=.5,
connectedPerm=0.5,
permanenceMax=1,
minThreshold=8,
newSynapseCount=10,
permanenceInc=.1,
permanenceDec=.01,
globalDecay=.0,
activationThreshold=8,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY,
maxAge=0,
maxSegmentsPerCell=2,
maxSynapsesPerSegment=10,
checkSynapseConsistency=True)
tm.cells4.setCellSegmentOrder(1)
self.basicTest2(tm, numPatterns=30, numRepetitions=2)
print "\nTesting with permanenceInc = 0 and Dec = 0"
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=5,
initialPerm=.5,
connectedPerm=0.5,
minThreshold=3,
newSynapseCount=3,
permanenceInc=0.0,
permanenceDec=0.00,
permanenceMax=1,
globalDecay=.0,
activationThreshold=3,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=False)
tm.printParameters()
self.basicTest2(tm, numPatterns=30, numRepetitions=3)
print "Testing with permanenceInc = 0 and Dec = 0 and 1 cell per column"
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=1,
initialPerm=.5,
connectedPerm=0.5,
minThreshold=3,
newSynapseCount=3,
permanenceInc=0.0,
permanenceDec=0.0,
permanenceMax=1,
globalDecay=.0,
activationThreshold=3,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=False)
self.basicTest2(tm)
print "Testing with permanenceInc = 0.1 and Dec = .0"
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=5,
initialPerm=.5,
connectedPerm=0.5,
minThreshold=3,
newSynapseCount=3,
permanenceInc=.1,
permanenceDec=.0,
permanenceMax=1,
globalDecay=.0,
activationThreshold=3,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=False)
self.basicTest2(tm)
print(
"Testing with permanenceInc = 0.1, Dec = .01 and higher synapse "
"count")
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=2,
initialPerm=.5,
connectedPerm=0.5,
minThreshold=3,
newSynapseCount=5,
permanenceInc=.1,
permanenceDec=.01,
permanenceMax=1,
globalDecay=.0,
activationThreshold=3,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=True)
self.basicTest2(tm, numPatterns=10, numRepetitions=2)
print "Testing age based global decay"
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=5,
initialPerm=.4,
connectedPerm=0.5,
minThreshold=3,
newSynapseCount=3,
permanenceInc=0.1,
permanenceDec=0.1,
permanenceMax=1,
globalDecay=.25,
activationThreshold=3,
doPooling=False,
segUpdateValidDuration=5,
pamLength=2,
maxAge=20,
seed=SEED,
verbosity=VERBOSITY,
checkSynapseConsistency=True)
tm.cells4.setCellSegmentOrder(1)
self.basicTest2(tm)
print "\nTesting with fixed size CLA, max segments per cell"
tm = BacktrackingTMCPP(numberOfCols=30,
cellsPerColumn=5,
initialPerm=.5,
connectedPerm=0.5,
permanenceMax=1,
minThreshold=8,
newSynapseCount=10,
permanenceInc=.1,
permanenceDec=.01,
globalDecay=.0,
activationThreshold=8,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY,
maxAge=0,
maxSegmentsPerCell=2,
maxSynapsesPerSegment=100,
checkSynapseConsistency=True)
tm.cells4.setCellSegmentOrder(1)
self.basicTest2(tm, numPatterns=30, numRepetitions=2)
def createTMs(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 TM instances, placing each into a dict keyed by
name.
Parameters:
------------------------------------------------------------------
retval: tms - dict of TM instances
"""
# Keep these fixed:
connectedPerm = 0.5
tms = dict()
if includeCPP:
if VERBOSITY >= 2:
print "Creating BacktrackingTMCPP instance"
cpp_tm = BacktrackingTMCPP(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 TMDiff
cpp_tm.retrieveLearningStates = True
tms['CPP'] = cpp_tm
if includePy:
if VERBOSITY >= 2:
print "Creating PY TM instance"
py_tm = BacktrackingTM(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,
)
tms['PY '] = py_tm
return tms
def _createTMs(self, numCols, fixedResources=False,
checkSynapseConsistency = True):
"""Create an instance of the appropriate temporal memory. 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_testCPPTM:
if g_options.verbosity > 1:
print "Creating BacktrackingTMCPP instance"
cppTM = BacktrackingTMCPP(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 TMDiff
cppTM.retrieveLearningStates = True
else:
cppTM = None
if g_options.verbosity > 1:
print "Creating PY TM instance"
pyTM = BacktrackingTM(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 cppTM, pyTM
def createTMs(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 TM instances, placing each into a dict keyed by
name.
Parameters:
------------------------------------------------------------------
retval: tms - dict of TM instances
"""
# Keep these fixed:
connectedPerm = 0.5
tms = dict()
if includeCPP:
if VERBOSITY >= 2:
print "Creating BacktrackingTMCPP instance"
cpp_tm = BacktrackingTMCPP(
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 TMDiff
cpp_tm.retrieveLearningStates = True
tms['CPP'] = cpp_tm
if includePy:
if VERBOSITY >= 2:
print "Creating PY TM instance"
py_tm = BacktrackingTM(
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,
)
tms['PY '] = py_tm
return tms
def _createTMs(self,
numCols,
fixedResources=False,
checkSynapseConsistency=True):
"""Create an instance of the appropriate temporal memory. 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_testCPPTM:
if g_options.verbosity > 1:
print("Creating BacktrackingTMCPP instance")
cppTM = BacktrackingTMCPP(
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 TMDiff
cppTM.retrieveLearningStates = True
else:
cppTM = None
if g_options.verbosity > 1:
print("Creating PY TM instance")
pyTM = BacktrackingTM(
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 cppTM, pyTM
def basicTest(self):
"""Basic test (creation, pickling, basic run of learning and inference)"""
# Create TM object
tm = BacktrackingTMCPP(numberOfCols=10, cellsPerColumn=3,
initialPerm=.2, connectedPerm= 0.8,
minThreshold=2, newSynapseCount=5,
permanenceInc=.1, permanenceDec= .05,
permanenceMax=1, globalDecay=.05,
activationThreshold=4, doPooling=False,
segUpdateValidDuration=5, seed=SEED,
verbosity=VERBOSITY)
tm.retrieveLearningStates = True
# Save and reload
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY, checkStates=False))
# Learn
for i in xrange(5):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.learn(x)
# Save and reload after learning
tm.reset()
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY))
## Infer
patterns = numpy.zeros((4, tm.numberOfCols), dtype='uint32')
for i in xrange(4):
_RGEN.initializeUInt32Array(patterns[i], 2)
for i in xrange(10):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.infer(x)
if i > 0:
tm.checkPrediction2(patterns)
def testTMs(self, short=True):
"""Call basicTest2 with multiple parameter settings and ensure the C++ and
PY versions are identical throughout."""
if short == True:
print "Testing short version"
else:
print "Testing long version"
if short:
print "\nTesting with fixed resource CLA - test max segment and synapses"
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=5,
initialPerm=.5, connectedPerm= 0.5,
permanenceMax=1,
minThreshold=8, newSynapseCount=10,
permanenceInc=0.1, permanenceDec=0.01,
globalDecay=.0, activationThreshold=8,
doPooling=False, segUpdateValidDuration=5,
seed=SEED, verbosity=VERBOSITY,
maxAge=0,
maxSegmentsPerCell=2, maxSynapsesPerSegment=10,
checkSynapseConsistency=True)
tm.cells4.setCellSegmentOrder(True)
self.basicTest2(tm, numPatterns=15, numRepetitions=1)
if not short:
print "\nTesting with fixed resource CLA - test max segment and synapses"
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=5,
initialPerm = .5, connectedPerm= 0.5,
permanenceMax = 1,
minThreshold = 8, newSynapseCount = 10,
permanenceInc = .1, permanenceDec= .01,
globalDecay = .0, activationThreshold = 8,
doPooling = False, segUpdateValidDuration = 5,
seed=SEED, verbosity = VERBOSITY,
maxAge = 0,
maxSegmentsPerCell = 2, maxSynapsesPerSegment = 10,
checkSynapseConsistency = True)
tm.cells4.setCellSegmentOrder(1)
self.basicTest2(tm, numPatterns=30, numRepetitions=2)
print "\nTesting with permanenceInc = 0 and Dec = 0"
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=5,
initialPerm = .5, connectedPerm= 0.5,
minThreshold = 3, newSynapseCount = 3,
permanenceInc = 0.0, permanenceDec= 0.00,
permanenceMax = 1,
globalDecay = .0, activationThreshold = 3,
doPooling = False, segUpdateValidDuration = 5,
seed=SEED, verbosity = VERBOSITY,
checkSynapseConsistency = False)
tm.printParameters()
self.basicTest2(tm, numPatterns = 30, numRepetitions = 3)
print "Testing with permanenceInc = 0 and Dec = 0 and 1 cell per column"
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=1,
initialPerm = .5, connectedPerm= 0.5,
minThreshold = 3, newSynapseCount = 3,
permanenceInc = 0.0, permanenceDec= 0.0,
permanenceMax = 1,
globalDecay = .0, activationThreshold = 3,
doPooling = False, segUpdateValidDuration = 5,
seed=SEED, verbosity = VERBOSITY,
checkSynapseConsistency = False)
self.basicTest2(tm)
print "Testing with permanenceInc = 0.1 and Dec = .0"
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=5,
initialPerm = .5, connectedPerm= 0.5,
minThreshold = 3, newSynapseCount = 3,
permanenceInc = .1, permanenceDec= .0,
permanenceMax = 1,
globalDecay = .0, activationThreshold = 3,
doPooling = False, segUpdateValidDuration = 5,
seed=SEED, verbosity = VERBOSITY,
checkSynapseConsistency = False)
self.basicTest2(tm)
print ("Testing with permanenceInc = 0.1, Dec = .01 and higher synapse "
"count")
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=2,
initialPerm = .5, connectedPerm= 0.5,
minThreshold = 3, newSynapseCount = 5,
permanenceInc = .1, permanenceDec= .01,
permanenceMax = 1,
globalDecay = .0, activationThreshold = 3,
doPooling = False, segUpdateValidDuration = 5,
seed=SEED, verbosity = VERBOSITY,
checkSynapseConsistency = True)
self.basicTest2(tm, numPatterns=10, numRepetitions=2)
print "Testing age based global decay"
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=5,
initialPerm = .4, connectedPerm= 0.5,
minThreshold = 3, newSynapseCount = 3,
permanenceInc = 0.1, permanenceDec= 0.1,
permanenceMax = 1,
globalDecay = .25, activationThreshold = 3,
doPooling = False, segUpdateValidDuration = 5,
pamLength = 2, maxAge = 20,
seed=SEED, verbosity = VERBOSITY,
checkSynapseConsistency = True)
tm.cells4.setCellSegmentOrder(1)
self.basicTest2(tm)
print "\nTesting with fixed size CLA, max segments per cell"
tm = BacktrackingTMCPP(numberOfCols=30, cellsPerColumn=5,
initialPerm = .5, connectedPerm= 0.5, permanenceMax = 1,
minThreshold = 8, newSynapseCount = 10,
permanenceInc = .1, permanenceDec= .01,
globalDecay = .0, activationThreshold = 8,
doPooling = False, segUpdateValidDuration = 5,
seed=SEED, verbosity = VERBOSITY,
maxAge = 0,
maxSegmentsPerCell = 2, maxSynapsesPerSegment = 100,
checkSynapseConsistency = True)
tm.cells4.setCellSegmentOrder(1)
self.basicTest2(tm, numPatterns=30, numRepetitions=2)
