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 _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 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 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