def testCheckpointLearned(self):
# Create a model and give it some inputs to learn.
tm1 = BacktrackingTM(numberOfCols=100,
cellsPerColumn=12,
verbosity=VERBOSITY)
sequences = [self.generateSequence() for _ in range(5)]
train = list(itertools.chain.from_iterable(sequences[:3]))
for bottomUpInput in train:
if bottomUpInput is None:
tm1.reset()
else:
tm1.compute(bottomUpInput, True, True)
# Serialize and deserialized the TM.
checkpointPath = os.path.join(self._tmpDir, 'a')
tm1.saveToFile(checkpointPath)
tm2 = pickle.loads(pickle.dumps(tm1))
tm2.loadFromFile(checkpointPath)
# Check that the TMs are the same.
self.assertTMsEqual(tm1, tm2)
# Feed some data into the models.
test = list(itertools.chain.from_iterable(sequences[3:]))
for bottomUpInput in test:
if bottomUpInput is None:
tm1.reset()
tm2.reset()
else:
result1 = tm1.compute(bottomUpInput, True, True)
result2 = tm2.compute(bottomUpInput, True, True)
self.assertTMsEqual(tm1, tm2)
self.assertTrue(numpy.array_equal(result1, result2))
def testCheckpointMiddleOfSequence(self):
# Create a model and give it some inputs to learn.
tm1 = BacktrackingTM(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:
tm1.reset()
else:
tm1.compute(bottomUpInput, True, True)
# Serialize and deserialized the TM.
checkpointPath = os.path.join(self._tmpDir, 'a')
tm1.saveToFile(checkpointPath)
tm2 = pickle.loads(pickle.dumps(tm1))
tm2.loadFromFile(checkpointPath)
# Check that the TMs are the same.
self.assertTMsEqual(tm1, tm2)
# 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:
tm1.reset()
tm2.reset()
else:
result1 = tm1.compute(bottomUpInput, True, True)
result2 = tm2.compute(bottomUpInput, True, True)
self.assertTMsEqual(tm1, tm2)
self.assertTrue(numpy.array_equal(result1, result2))
def reset(self):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.reset`.
"""
if self.verbosity >= 3:
print "TM Reset"
self._setStatePointers()
self.cells4.reset()
BacktrackingTM.reset(self)
def reset(self):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.reset`.
"""
if self.verbosity >= 3:
print "TM Reset"
self._setStatePointers()
self.cells4.reset()
BacktrackingTM.reset(self)
def reset(self):
""" Reset the state of all cells.
This is normally used between sequences while training. All internal states
are reset to 0.
"""
if self.verbosity >= 3:
print "TM Reset"
self._setStatePointers()
self.cells4.reset()
BacktrackingTM.reset(self)
def reset(self):
""" Reset the state of all cells.
This is normally used between sequences while training. All internal states
are reset to 0.
"""
if self.verbosity >= 3:
print "TM Reset"
self._setStatePointers()
self.cells4.reset()
BacktrackingTM.reset(self)
def testSerializationMiddleOfSequence(self):
# Create a model and give it some inputs to learn.
tm1 = BacktrackingTM(numberOfCols=100,
cellsPerColumn=12,
verbosity=VERBOSITY)
sequences = [self.generateSequence() for _ in range(5)]
train = list(
itertools.chain.from_iterable(sequences[:3] + [sequences[3][:5]]))
for bottomUpInput in train:
if bottomUpInput is None:
tm1.reset()
else:
tm1.compute(bottomUpInput, True, True)
# Serialize and deserialized the TM.
tmProto = BacktrackingTM.getSchema().new_message()
tm1.write(tmProto)
checkpointPath = os.path.join(self._tmpDir, 'a')
with open(checkpointPath, "wb") as f:
tmProto.write(f)
with open(checkpointPath, "rb") as f:
tmProto = BacktrackingTM.getSchema().read(f)
tm2 = BacktrackingTM.read(tmProto)
# Check that the TMs are the same.
self.assertTMsEqual(tm1, tm2)
# 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:
tm1.reset()
tm2.reset()
else:
result1 = tm1.compute(bottomUpInput, True, True)
result2 = tm2.compute(bottomUpInput, True, True)
self.assertTMsEqual(tm1, tm2)
self.assertTrue(numpy.array_equal(result1, result2))
def testSerializationMiddleOfSequence(self):
# Create a model and give it some inputs to learn.
tm1 = BacktrackingTM(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:
tm1.reset()
else:
tm1.compute(bottomUpInput, True, True)
# Serialize and deserialized the TM.
tmProto = BacktrackingTM.getSchema().new_message()
tm1.write(tmProto)
checkpointPath = os.path.join(self._tmpDir, 'a')
with open(checkpointPath, "wb") as f:
tmProto.write(f)
with open(checkpointPath, "rb") as f:
tmProto = BacktrackingTM.getSchema().read(f)
tm2 = BacktrackingTM.read(tmProto)
# Check that the TMs are the same.
self.assertTMsEqual(tm1, tm2)
# 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:
tm1.reset()
tm2.reset()
else:
result1 = tm1.compute(bottomUpInput, True, True)
result2 = tm2.compute(bottomUpInput, True, True)
self.assertTMsEqual(tm1, tm2)
self.assertTrue(numpy.array_equal(result1, result2))
def basicTest2(self,
tm,
numPatterns=100,
numRepetitions=3,
activity=15,
testTrimming=False,
testRebuild=False):
"""Basic test (basic run of learning and inference)"""
# Create PY TM object that mirrors the one sent in.
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)
# Ensure we are copying over learning states for TMDiff
tm.retrieveLearningStates = True
verbosity = VERBOSITY
# Learn
# Build up sequences
sequence = fdrutils.generateCoincMatrix(nCoinc=numPatterns,
length=tm.numberOfCols,
activity=activity)
for r in xrange(numRepetitions):
for i in xrange(sequence.nRows()):
#if i > 11:
# setVerbosity(6, tm, tmPy)
if i % 10 == 0:
tm.reset()
tmPy.reset()
if verbosity >= 2:
print "\n\n ===================================\nPattern:",
print i, "Round:", r, "input:", sequence.getRow(i)
y1 = tm.learn(sequence.getRow(i))
y2 = tmPy.learn(sequence.getRow(i))
# Ensure everything continues to work well even if we continuously
# rebuild outSynapses structure
if testRebuild:
tm.cells4.rebuildOutSynapses()
if testTrimming:
tm.trimSegments()
tmPy.trimSegments()
if verbosity > 2:
print "\n ------ CPP states ------ ",
tm.printStates()
print "\n ------ PY states ------ ",
tmPy.printStates()
if verbosity > 6:
print "C++ cells: "
tm.printCells()
print "PY cells: "
tmPy.printCells()
if verbosity >= 3:
print "Num segments in PY and C++", tmPy.getNumSegments(), \
tm.getNumSegments()
# Check if the two TM'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.tmDiff2(tm, tmPy, verbosity, False))
# Check that outputs are identical
self.assertLess(abs((y1 - y2).sum()), 3)
print "Learning completed"
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, verbosity))
# TODO: Need to check - currently failing this
#checkCell0(tmPy)
# Remove unconnected synapses and check TM's again
# Test rebuild out synapses
print "Rebuilding outSynapses"
tm.cells4.rebuildOutSynapses()
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY))
print "Trimming segments"
tm.trimSegments()
tmPy.trimSegments()
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY))
# Save and reload after learning
print "Pickling and unpickling"
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))
# Infer
print "Testing inference"
# Setup for inference
tm.reset()
tmPy.reset()
setVerbosity(INFERENCE_VERBOSITY, tm, tmPy)
patterns = numpy.zeros((40, tm.numberOfCols), dtype='uint32')
for i in xrange(4):
_RGEN.initializeUInt32Array(patterns[i], 2)
for i, x in enumerate(patterns):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
y = tm.infer(x)
yPy = tmPy.infer(x)
self.assertTrue(
fdrutils.tmDiff2(tm, tmPy, VERBOSITY, checkLearn=False))
if abs((y - yPy).sum()) > 0:
print "C++ output", y
print "Py output", yPy
assert False
if i > 0:
tm._checkPrediction(patterns)
tmPy._checkPrediction(patterns)
print "Inference completed"
print "===================================="
return tm, tmPy
def basicTest2(self, tm, numPatterns=100, numRepetitions=3, activity=15,
testTrimming=False, testRebuild=False):
"""Basic test (basic run of learning and inference)"""
# Create PY TM object that mirrors the one sent in.
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)
# Ensure we are copying over learning states for TMDiff
tm.retrieveLearningStates = True
verbosity = VERBOSITY
# Learn
# Build up sequences
sequence = fdrutils.generateCoincMatrix(nCoinc=numPatterns,
length=tm.numberOfCols,
activity=activity)
for r in xrange(numRepetitions):
for i in xrange(sequence.nRows()):
#if i > 11:
# setVerbosity(6, tm, tmPy)
if i % 10 == 0:
tm.reset()
tmPy.reset()
if verbosity >= 2:
print "\n\n ===================================\nPattern:",
print i, "Round:", r, "input:", sequence.getRow(i)
y1 = tm.learn(sequence.getRow(i))
y2 = tmPy.learn(sequence.getRow(i))
# Ensure everything continues to work well even if we continuously
# rebuild outSynapses structure
if testRebuild:
tm.cells4.rebuildOutSynapses()
if testTrimming:
tm.trimSegments()
tmPy.trimSegments()
if verbosity > 2:
print "\n ------ CPP states ------ ",
tm.printStates()
print "\n ------ PY states ------ ",
tmPy.printStates()
if verbosity > 6:
print "C++ cells: "
tm.printCells()
print "PY cells: "
tmPy.printCells()
if verbosity >= 3:
print "Num segments in PY and C++", tmPy.getNumSegments(), \
tm.getNumSegments()
# Check if the two TM'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.tmDiff2(tm, tmPy, verbosity, False))
# Check that outputs are identical
self.assertLess(abs((y1 - y2).sum()), 3)
print "Learning completed"
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, verbosity))
# TODO: Need to check - currently failing this
#checkCell0(tmPy)
# Remove unconnected synapses and check TM's again
# Test rebuild out synapses
print "Rebuilding outSynapses"
tm.cells4.rebuildOutSynapses()
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY))
print "Trimming segments"
tm.trimSegments()
tmPy.trimSegments()
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY))
# Save and reload after learning
print "Pickling and unpickling"
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))
# Infer
print "Testing inference"
# Setup for inference
tm.reset()
tmPy.reset()
setVerbosity(INFERENCE_VERBOSITY, tm, tmPy)
patterns = numpy.zeros((40, tm.numberOfCols), dtype='uint32')
for i in xrange(4):
_RGEN.initializeUInt32Array(patterns[i], 2)
for i, x in enumerate(patterns):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
y = tm.infer(x)
yPy = tmPy.infer(x)
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY, checkLearn=False))
if abs((y - yPy).sum()) > 0:
print "C++ output", y
print "Py output", yPy
assert False
if i > 0:
tm.checkPrediction2(patterns)
tmPy.checkPrediction2(patterns)
print "Inference completed"
print "===================================="
return tm, tmPy