def setUp(self):
print ("\n"
"======================================================\n"
"Test: {0} \n"
"{1}\n"
"======================================================\n"
).format(self.id(), self.shortDescription())
self.patternMachine = PatternMachine(1024, 20, num=100)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(
mmName="TM",
columnDimensions=[1024],
cellsPerColumn=16,
initialPermanence=0.5,
connectedPermanence=0.7,
minThreshold=20,
maxNewSynapseCount=30,
permanenceIncrement=0.1,
permanenceDecrement=0.02,
activationThreshold=20)
self.tp = MonitoredTemporalPooler(
inputDimensions=[1024, 16],
columnDimensions=[1024],
mmName="TP")
self._showInternalStatePlots(appendTitle=" (initial)")
def generateSequences(patternDimensionality, patternCardinality, sequenceLength, sequenceCount):
patternAlphabetSize = sequenceLength * sequenceCount
patternMachine = PatternMachine(patternDimensionality, patternCardinality, patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(sequenceCount, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
return generatedSequences
def generateSequences(patternDimensionality, patternCardinality,
sequenceLength, sequenceCount):
patternAlphabetSize = sequenceLength * sequenceCount
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(sequenceCount, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
return generatedSequences
class SequenceMachineTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
def testGenerateFromNumbers(self):
numbers = range(0, 10) + [None] + range(10, 19)
sequence = self.sequenceMachine.generateFromNumbers(numbers)
self.assertEqual(len(sequence), 20)
self.assertEqual(sequence[0], self.patternMachine.get(0))
self.assertEqual(sequence[10], None)
self.assertEqual(sequence[11], self.patternMachine.get(10))
def testAddSpatialNoise(self):
patternMachine = PatternMachine(10000, 1000, num=100)
sequenceMachine = SequenceMachine(patternMachine)
numbers = range(0, 100)
numbers.append(None)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.5)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertTrue(400 < overlap < 600)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.0)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertEqual(overlap, 1000)
def testGenerateNumbers(self):
numbers = self.sequenceMachine.generateNumbers(1, 100)
self.assertEqual(numbers[-1], None)
self.assertEqual(len(numbers), 101)
self.assertFalse(numbers[:-1] == range(0, 100))
self.assertEqual(sorted(numbers[:-1]), range(0, 100))
def testGenerateNumbersMultipleSequences(self):
numbers = self.sequenceMachine.generateNumbers(3, 100)
self.assertEqual(len(numbers), 303)
self.assertEqual(sorted(numbers[0:100]), range(0, 100))
self.assertEqual(sorted(numbers[101:201]), range(100, 200))
self.assertEqual(sorted(numbers[202:302]), range(200, 300))
def testGenerateNumbersWithShared(self):
numbers = self.sequenceMachine.generateNumbers(3, 100, (20, 35))
self.assertEqual(len(numbers), 303)
shared = range(300, 315)
self.assertEqual(numbers[20:35], shared)
self.assertEqual(numbers[20 + 101:35 + 101], shared)
self.assertEqual(numbers[20 + 202:35 + 202], shared)
def testWriteRead(self):
tm1 = TemporalMemory(
columnDimensions=(100,),
cellsPerColumn=4,
activationThreshold=7,
initialPermanence=0.37,
connectedPermanence=0.58,
minThreshold=4,
maxNewSynapseCount=18,
permanenceIncrement=0.23,
permanenceDecrement=0.08,
seed=91
)
# Run some data through before serializing
patternMachine = PatternMachine(100, 4)
sequenceMachine = SequenceMachine(patternMachine)
sequence = sequenceMachine.generateFromNumbers(range(5))
for _ in range(3):
for pattern in sequence:
tm1.compute(pattern)
proto1 = TemporalMemoryProto_capnp.TemporalMemoryProto.new_message()
tm1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f)
# Load the deserialized proto
tm2 = TemporalMemory.read(proto2)
# Check that the two temporal memory objects have the same attributes
self.assertEqual(tm1, tm2)
# Run a couple records through after deserializing and check results match
tm1.compute(patternMachine.get(0))
tm2.compute(patternMachine.get(0))
self.assertEqual(set(tm1.getActiveCells()), set(tm2.getActiveCells()))
self.assertEqual(set(tm1.getPredictiveCells()),
set(tm2.getPredictiveCells()))
self.assertEqual(set(tm1.getWinnerCells()), set(tm2.getWinnerCells()))
self.assertEqual(tm1.connections, tm2.connections)
tm1.compute(patternMachine.get(3))
tm2.compute(patternMachine.get(3))
self.assertEqual(set(tm1.getActiveCells()), set(tm2.getActiveCells()))
self.assertEqual(set(tm1.getPredictiveCells()),
set(tm2.getPredictiveCells()))
self.assertEqual(set(tm1.getWinnerCells()), set(tm2.getWinnerCells()))
self.assertEqual(tm1.connections, tm2.connections)
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(columnDimensions=[100],
cellsPerColumn=4,
initialPermanence=0.6,
connectedPermanence=0.5,
minThreshold=1,
maxNewSynapseCount=6,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=1)
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 testWrite(self):
tm1 = TemporalMemory(
columnDimensions=[100],
cellsPerColumn=4,
activationThreshold=7,
initialPermanence=0.37,
connectedPermanence=0.58,
minThreshold=4,
maxNewSynapseCount=18,
permanenceIncrement=0.23,
permanenceDecrement=0.08,
seed=91
)
# Run some data through before serializing
self.patternMachine = PatternMachine(100, 4)
self.sequenceMachine = SequenceMachine(self.patternMachine)
sequence = self.sequenceMachine.generateFromNumbers(range(5))
for _ in range(3):
for pattern in sequence:
tm1.compute(pattern)
proto1 = TemporalMemoryProto_capnp.TemporalMemoryProto.new_message()
tm1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f)
# Load the deserialized proto
tm2 = TemporalMemory.read(proto2)
# Check that the two temporal memory objects have the same attributes
self.assertEqual(tm1, tm2)
# Run a couple records through after deserializing and check results match
tm1.compute(self.patternMachine.get(0))
tm2.compute(self.patternMachine.get(0))
self.assertEqual(tm1.activeCells, tm2.activeCells)
self.assertEqual(tm1.predictiveCells, tm2.predictiveCells)
self.assertEqual(tm1.winnerCells, tm2.winnerCells)
self.assertEqual(tm1.connections, tm2.connections)
tm1.compute(self.patternMachine.get(3))
tm2.compute(self.patternMachine.get(3))
self.assertEqual(tm1.activeCells, tm2.activeCells)
self.assertEqual(tm1.predictiveCells, tm2.predictiveCells)
self.assertEqual(tm1.winnerCells, tm2.winnerCells)
self.assertEqual(tm1.connections, tm2.connections)
def generateSequences(n=2048, w=40, sequenceLength=5, sequenceCount=2,
sharedRange=None, seed=42):
"""
Generate high order sequences using SequenceMachine
"""
# Lots of room for noise sdrs
patternAlphabetSize = 10*(sequenceLength * sequenceCount)
patternMachine = PatternMachine(n, w, patternAlphabetSize, seed)
sequenceMachine = SequenceMachine(patternMachine, seed)
numbers = sequenceMachine.generateNumbers(sequenceCount, sequenceLength,
sharedRange=sharedRange )
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
return sequenceMachine, generatedSequences, numbers
def setUp(self):
print ("\n"
"======================================================\n"
"Test: {0} \n"
"{1}\n"
"======================================================\n"
).format(self.id(), self.shortDescription())
self.patternMachine = PatternMachine(1024, 20, num=100)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(
mmName="TM",
columnDimensions=[1024],
cellsPerColumn=16,
initialPermanence=0.5,
connectedPermanence=0.7,
minThreshold=20,
maxNewSynapseCount=30,
permanenceIncrement=0.1,
permanenceDecrement=0.02,
activationThreshold=20)
self.tp = MonitoredTemporalPooler(
inputDimensions=[1024, 16],
columnDimensions=[1024],
mmName="TP")
self._showInternalStatePlots(appendTitle=" (initial)")
def testAddSpatialNoise(self):
patternMachine = PatternMachine(10000, 1000, num=100)
sequenceMachine = SequenceMachine(patternMachine)
numbers = range(0, 100)
numbers.append(None)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.5)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertTrue(400 < overlap < 600)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.0)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertEqual(overlap, 1000)
def init(self, overrides=None):
"""
Initialize Temporal Memory, and other member variables.
:param overrides: overrides for default Temporal Memory parameters
"""
params = self._computeTMParams(overrides)
self.tm = MonitoredTemporalMemory(**params)
self.patternMachine = self.PATTERN_MACHINE
self.sequenceMachine = SequenceMachine(self.patternMachine)
def generateSequences(patternDimensionality, patternCardinality, sequenceLength,
sequenceCount):
# Generate a sequence list and an associated labeled list
# (both containing a set of sequences separated by None)
print "Generating sequences..."
patternAlphabetSize = sequenceLength * sequenceCount
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(sequenceCount, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [
str(numbers[i + i * sequenceLength: i + (i + 1) * sequenceLength])
for i in xrange(sequenceCount)]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
return generatedSequences, labeledSequences
def generateSequences(patternCardinality, patternDimensionality,
numberOfSequences, sequenceLength, consoleVerbosity):
patternAlphabetSize = sequenceLength * numberOfSequences
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(numberOfSequences, sequenceLength)
inputSequences = sequenceMachine.generateFromNumbers(numbers)
inputCategories = []
for i in xrange(numberOfSequences):
for _ in xrange(sequenceLength):
inputCategories.append(i)
inputCategories.append(None)
if consoleVerbosity > 1:
for i in xrange(len(inputSequences)):
if inputSequences[i] is None:
print
else:
print "{0} {1}".format(inputSequences[i], inputCategories[i])
return inputSequences, inputCategories
def generateSequences(patternDimensionality, patternCardinality, sequenceLength,
sequenceCount):
# Generate a sequence list and an associated labeled list
# (both containing a set of sequences separated by None)
print "Generating sequences..."
patternAlphabetSize = sequenceLength * sequenceCount
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(sequenceCount, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [
str(numbers[i + i * sequenceLength: i + (i + 1) * sequenceLength])
for i in xrange(sequenceCount)]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
return generatedSequences, labeledSequences
def generateSequences(patternCardinality, patternDimensionality,
numberOfSequences, sequenceLength, consoleVerbosity):
patternAlphabetSize = sequenceLength * numberOfSequences
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(numberOfSequences,
sequenceLength)
inputSequences = sequenceMachine.generateFromNumbers(numbers)
inputCategories = []
for i in xrange(numberOfSequences):
for _ in xrange(sequenceLength):
inputCategories.append(i)
inputCategories.append(None)
if consoleVerbosity > 1:
for i in xrange(len(inputSequences)):
if inputSequences[i] is None:
print
else:
print "{0} {1}".format(inputSequences[i], inputCategories[i])
return inputSequences, inputCategories
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(columnDimensions=[100],
cellsPerColumn=4,
initialPermanence=0.6,
connectedPermanence=0.5,
minThreshold=1,
maxNewSynapseCount=6,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=1)
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 testAddSpatialNoise(self):
patternMachine = PatternMachine(10000, 1000, num=100)
sequenceMachine = SequenceMachine(patternMachine)
numbers = range(0, 100)
numbers.append(None)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.5)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertTrue(400 < overlap < 600)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.0)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertEqual(overlap, 1000)
class SpatialTemporalPoolerTest(unittest.TestCase):
def setUp(self):
print ("\n"
"======================================================\n"
"Test: {0} \n"
"{1}\n"
"======================================================\n"
).format(self.id(), self.shortDescription())
self.patternMachine = PatternMachine(1024, 20, num=100)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(
mmName="TM",
columnDimensions=[1024],
cellsPerColumn=16,
initialPermanence=0.5,
connectedPermanence=0.7,
minThreshold=20,
maxNewSynapseCount=30,
permanenceIncrement=0.1,
permanenceDecrement=0.02,
activationThreshold=20)
self.tp = MonitoredTemporalPooler(
inputDimensions=[1024, 16],
columnDimensions=[1024],
mmName="TP")
self._showInternalStatePlots(appendTitle=" (initial)")
def testBasic(self):
"""Two basic sequences"""
sequences = [
[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15]
]
labels = ["A", "B"]
sequences = [self.sequenceMachine.generateFromNumbers(s) for s in sequences]
for _ in xrange(10):
self._feedSequences(sequences, sequenceLabels=labels)
def testOverlapping(self):
"""Overlapping sequences"""
sequences = [
[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[ 0, 16, 17, 18, 19, 20, 21, 7],
[22, 23, 24, 11, 12, 25, 26, 27]
]
labels = ["A", "B", "C", "D"]
sequences = [self.sequenceMachine.generateFromNumbers(s) for s in sequences]
for _ in xrange(10):
self._feedSequences(sequences, sequenceLabels=labels)
def testMerged(self):
"""Two distinct sequences, and merged"""
sequences = [
[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
]
labels = ["A", "B", "AB"]
sequences = [self.sequenceMachine.generateFromNumbers(s) for s in sequences]
for _ in xrange(10):
self._feedSequences(sequences, sequenceLabels=labels)
# TO DO: assert C columns contain some A and some B columns, and assert
# the split is about equal between A and B
def tearDown(self):
self._printInfo()
self._showActivityPlots()
self._showInternalStatePlots(appendTitle=" (final)")
@classmethod
def tearDownClass(cls):
if PLOT >= 1:
print
raw_input("Press any key to exit...")
def _feedSequences(self, sequences,
tmLearn=True, tpLearn=True, sequenceLabels=None):
for i in xrange(len(sequences)):
sequence = sequences[i]
label = sequenceLabels[i] if sequenceLabels is not None else None
for pattern in sequence:
self._feedPattern(pattern, tmLearn=tmLearn, tpLearn=tpLearn,
sequenceLabel=label)
self.tm.reset()
self.tp.reset()
def _printInfo(self):
if VERBOSITY >= 2:
print MonitorMixinBase.mmPrettyPrintTraces(
self.tp.mmGetDefaultTraces(verbosity=3) +
self.tm.mmGetDefaultTraces(verbosity=3),
breakOnResets=self.tm.mmGetTraceResets())
print
if VERBOSITY >= 1:
print MonitorMixinBase.mmPrettyPrintMetrics(
self.tp.mmGetDefaultMetrics() + self.tm.mmGetDefaultMetrics())
print
def _showActivityPlots(self):
if PLOT >= 1:
title = self.shortDescription()
self.tp.mmGetCellActivityPlot(showReset=True, title=title)
def _showInternalStatePlots(self, appendTitle=None):
if PLOT >= 1:
title = self.shortDescription()
if appendTitle:
title += appendTitle
self.tp.mmGetPermanencesPlot(title=title)
def _feedPattern(self, pattern,
tmLearn=True, tpLearn=True, sequenceLabel=None):
# Feed the TM
predictedCells = self.tm.predictiveCells
self.tm.compute(pattern, learn=tmLearn, sequenceLabel=sequenceLabel)
# If requested, feed the TP
if tpLearn is not None:
tpInputVector, correctlyPredictedCells = (
self._formatInputForTP(predictedCells))
activeArray = numpy.zeros(self.tp.getNumColumns())
self.tp.compute(tpInputVector,
tpLearn,
activeArray,
None, # not needed
correctlyPredictedCells,
sequenceLabel=sequenceLabel)
def _formatInputForTP(self, predictedCells):
"""
Given an instance of the TM, format the information we need to send to the
TP.
"""
# all currently active cells in layer 4
tpInputVector = numpy.zeros(
self.tm.numberOfCells()).astype(realDType)
tpInputVector[list(self.tm.activeCells)] = 1
# correctly predicted cells in layer 4
correctlyPredictedCells = numpy.zeros(
self.tm.numberOfCells()).astype(realDType)
correctlyPredictedCells[list(predictedCells & self.tm.activeCells)] = 1
return tpInputVector, correctlyPredictedCells
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
def run(params, paramDir, outputDir, plotVerbosity=0, consoleVerbosity=0):
"""
Runs the union overlap experiment.
:param params: A dict of experiment parameters
:param paramDir: Path of parameter file
:param outputDir: Output will be written to this path
:param plotVerbosity: Plotting verbosity
:param consoleVerbosity: Console output verbosity
"""
print "Running SDR overlap experiment...\n"
print "Params dir: {0}".format(paramDir)
print "Output dir: {0}\n".format(outputDir)
# Dimensionality of sequence patterns
patternDimensionality = params["patternDimensionality"]
# Cardinality (ON / true bits) of sequence patterns
patternCardinality = params["patternCardinality"]
# TODO If this parameter is to be supported, the sequence generation code
# below must change
# Number of unique patterns from which sequences are built
# patternAlphabetSize = params["patternAlphabetSize"]
# Length of sequences shown to network
sequenceLength = params["sequenceLength"]
# Number of sequences used. Sequences may share common elements.
numberOfSequences = params["numberOfSequences"]
# Number of sequence passes for training the TM. Zero => no training.
trainingPasses = params["trainingPasses"]
tmParamOverrides = params["temporalMemoryParams"]
upParamOverrides = params["unionPoolerParams"]
# Generate a sequence list and an associated labeled list (both containing a
# set of sequences separated by None)
start = time.time()
print "\nGenerating sequences..."
patternAlphabetSize = sequenceLength * numberOfSequences
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(numberOfSequences, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [str(numbers[i + i*sequenceLength: i + (i+1)*sequenceLength])
for i in xrange(numberOfSequences)]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
# Set up the Temporal Memory and Union Pooler network
print "\nCreating network..."
experiment = UnionTemporalPoolerExperiment(tmParamOverrides, upParamOverrides)
# Train only the Temporal Memory on the generated sequences
if trainingPasses > 0:
print "\nTraining Temporal Memory..."
if consoleVerbosity > 0:
print "\nPass\tBursting Columns Mean\tStdDev\tMax"
for i in xrange(trainingPasses):
experiment.runNetworkOnSequences(generatedSequences,
labeledSequences,
tmLearn=True,
upLearn=None,
verbosity=consoleVerbosity,
progressInterval=_SHOW_PROGRESS_INTERVAL)
if consoleVerbosity > 0:
stats = experiment.getBurstingColumnsStats()
print "{0}\t{1}\t{2}\t{3}".format(i, stats[0], stats[1], stats[2])
# Reset the TM monitor mixin's records accrued during this training pass
experiment.tm.mmClearHistory()
print
print MonitorMixinBase.mmPrettyPrintMetrics(
experiment.tm.mmGetDefaultMetrics())
print
if plotVerbosity >= 2:
plotNetworkState(experiment, plotVerbosity, trainingPasses,
phase="Training")
print "\nRunning test phase..."
experiment.runNetworkOnSequences(generatedSequences,
labeledSequences,
tmLearn=False,
upLearn=False,
verbosity=consoleVerbosity,
progressInterval=_SHOW_PROGRESS_INTERVAL)
print "\nPass\tBursting Columns Mean\tStdDev\tMax"
stats = experiment.getBurstingColumnsStats()
print "{0}\t{1}\t{2}\t{3}".format(0, stats[0], stats[1], stats[2])
if trainingPasses > 0 and stats[0] > 0:
print "***WARNING! MEAN BURSTING COLUMNS IN TEST PHASE IS GREATER THAN 0***"
print
print MonitorMixinBase.mmPrettyPrintMetrics(
experiment.tm.mmGetDefaultMetrics() + experiment.up.mmGetDefaultMetrics())
print
plotNetworkState(experiment, plotVerbosity, trainingPasses, phase="Testing")
elapsed = int(time.time() - start)
print "Total time: {0:2} seconds.".format(elapsed)
# Write Union SDR trace
metricName = "activeCells"
outputFileName = "unionSdrTrace_{0}learningPasses.csv".format(trainingPasses)
writeMetricTrace(experiment, metricName, outputDir, outputFileName)
if plotVerbosity >= 1:
raw_input("Press any key to exit...")
# Length of sequences shown to network
sequenceLength = params["sequenceLength"]
# Number of sequences used. Sequences may share common elements.
numberOfSequences = params["numberOfSequences"]
# Number of sequence passes for training the TM. Zero => no training.
trainingPasses = params["trainingPasses"]
# Generate a sequence list and an associated labeled list (both containing a
# set of sequences separated by None)
print "\nGenerating sequences..."
patternAlphabetSize = sequenceLength * numberOfSequences
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(numberOfSequences, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [
str(numbers[i + i * sequenceLength:i + (i + 1) * sequenceLength])
for i in xrange(numberOfSequences)
]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
def initializeNetwork():
class TemporalMemoryMonitorMixinTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(columnDimensions=[100],
cellsPerColumn=4,
initialPermanence=0.6,
connectedPermanence=0.5,
minThreshold=1,
maxNewSynapseCount=6,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=1)
def testFeedSequence(self):
sequence = self._generateSequence()
sequenceLength = len(sequence) - 3 # without resets
# Replace last pattern (before the None) with an unpredicted one
sequence[-2] = self.patternMachine.get(4)
self._feedSequence(sequence, sequenceLabel="Test")
activeColumnsTrace = self.tm.mmGetTraceActiveColumns()
predictiveCellsTrace = self.tm.mmGetTracePredictiveCells()
sequenceLabelsTrace = self.tm.mmGetTraceSequenceLabels()
resetsTrace = self.tm.mmGetTraceResets()
predictedActiveCellsTrace = self.tm.mmGetTracePredictedActiveCells()
predictedInactiveCellsTrace = self.tm.mmGetTracePredictedInactiveCells(
)
predictedActiveColumnsTrace = self.tm.mmGetTracePredictedActiveColumns(
)
predictedInactiveColumnsTrace = self.tm.mmGetTracePredictedInactiveColumns(
)
unpredictedActiveColumnsTrace = self.tm.mmGetTraceUnpredictedActiveColumns(
)
self.assertEqual(len(activeColumnsTrace.data), sequenceLength)
self.assertEqual(len(predictiveCellsTrace.data), sequenceLength)
self.assertEqual(len(sequenceLabelsTrace.data), sequenceLength)
self.assertEqual(len(resetsTrace.data), sequenceLength)
self.assertEqual(len(predictedActiveCellsTrace.data), sequenceLength)
self.assertEqual(len(predictedInactiveCellsTrace.data), sequenceLength)
self.assertEqual(len(predictedActiveColumnsTrace.data), sequenceLength)
self.assertEqual(len(predictedInactiveColumnsTrace.data),
sequenceLength)
self.assertEqual(len(unpredictedActiveColumnsTrace.data),
sequenceLength)
self.assertEqual(activeColumnsTrace.data[-1],
self.patternMachine.get(4))
self.assertEqual(sequenceLabelsTrace.data[-1], "Test")
self.assertEqual(resetsTrace.data[0], True)
self.assertEqual(resetsTrace.data[1], False)
self.assertEqual(resetsTrace.data[10], True)
self.assertEqual(resetsTrace.data[-1], False)
self.assertEqual(len(predictedActiveCellsTrace.data[-2]), 5)
self.assertEqual(len(predictedActiveCellsTrace.data[-1]), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data[-2]), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data[-1]), 5)
self.assertEqual(len(predictedActiveColumnsTrace.data[-2]), 5)
self.assertEqual(len(predictedActiveColumnsTrace.data[-1]), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data[-2]), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data[-1]), 5)
self.assertEqual(len(unpredictedActiveColumnsTrace.data[-2]), 0)
self.assertEqual(len(unpredictedActiveColumnsTrace.data[-1]), 5)
def testClearHistory(self):
sequence = self._generateSequence()
self._feedSequence(sequence, sequenceLabel="Test")
self.tm.mmClearHistory()
activeColumnsTrace = self.tm.mmGetTraceActiveColumns()
predictiveCellsTrace = self.tm.mmGetTracePredictiveCells()
sequenceLabelsTrace = self.tm.mmGetTraceSequenceLabels()
resetsTrace = self.tm.mmGetTraceResets()
predictedActiveCellsTrace = self.tm.mmGetTracePredictedActiveCells()
predictedInactiveCellsTrace = self.tm.mmGetTracePredictedInactiveCells(
)
predictedActiveColumnsTrace = self.tm.mmGetTracePredictedActiveColumns(
)
predictedInactiveColumnsTrace = self.tm.mmGetTracePredictedInactiveColumns(
)
unpredictedActiveColumnsTrace = self.tm.mmGetTraceUnpredictedActiveColumns(
)
self.assertEqual(len(activeColumnsTrace.data), 0)
self.assertEqual(len(predictiveCellsTrace.data), 0)
self.assertEqual(len(sequenceLabelsTrace.data), 0)
self.assertEqual(len(resetsTrace.data), 0)
self.assertEqual(len(predictedActiveCellsTrace.data), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data), 0)
self.assertEqual(len(predictedActiveColumnsTrace.data), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data), 0)
self.assertEqual(len(unpredictedActiveColumnsTrace.data), 0)
def testSequencesMetrics(self):
sequence = self._generateSequence()
self._feedSequence(sequence, "Test1")
sequence.reverse()
sequence.append(sequence.pop(0)) # Move None (reset) to the end
self._feedSequence(sequence, "Test2")
sequencesPredictedActiveCellsPerColumnMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsPerColumn()
sequencesPredictedActiveCellsSharedMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsShared()
self.assertEqual(sequencesPredictedActiveCellsPerColumnMetric.mean, 1)
self.assertEqual(sequencesPredictedActiveCellsSharedMetric.mean, 1)
self._feedSequence(sequence, "Test3")
sequencesPredictedActiveCellsPerColumnMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsPerColumn()
sequencesPredictedActiveCellsSharedMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsShared()
self.assertEqual(sequencesPredictedActiveCellsPerColumnMetric.mean, 1)
self.assertTrue(sequencesPredictedActiveCellsSharedMetric.mean > 1)
# ==============================
# Helper functions
# ==============================
def _generateSequence(self):
numbers = range(0, 10)
sequence = self.sequenceMachine.generateFromNumbers(numbers)
sequence.append(None)
sequence *= 3
return sequence
def _feedSequence(self, sequence, sequenceLabel=None):
for pattern in sequence:
if pattern is None:
self.tm.reset()
else:
self.tm.compute(pattern, sequenceLabel=sequenceLabel)
class TemporalMemoryMonitorMixinTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(columnDimensions=[100],
cellsPerColumn=4,
initialPermanence=0.6,
connectedPermanence=0.5,
minThreshold=1,
maxNewSynapseCount=6,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=1)
def testFeedSequence(self):
sequence = self._generateSequence()
sequenceLength = len(sequence) - 3 # without resets
# Replace last pattern (before the None) with an unpredicted one
sequence[-2] = self.patternMachine.get(4)
self._feedSequence(sequence, sequenceLabel="Test")
activeColumnsTrace = self.tm.mmGetTraceActiveColumns()
predictiveCellsTrace = self.tm.mmGetTracePredictiveCells()
sequenceLabelsTrace = self.tm.mmGetTraceSequenceLabels()
resetsTrace = self.tm.mmGetTraceResets()
predictedActiveCellsTrace = self.tm.mmGetTracePredictedActiveCells()
predictedInactiveCellsTrace = self.tm.mmGetTracePredictedInactiveCells()
predictedActiveColumnsTrace = self.tm.mmGetTracePredictedActiveColumns()
predictedInactiveColumnsTrace = self.tm.mmGetTracePredictedInactiveColumns()
unpredictedActiveColumnsTrace = self.tm.mmGetTraceUnpredictedActiveColumns()
self.assertEqual(len(activeColumnsTrace.data), sequenceLength)
self.assertEqual(len(predictiveCellsTrace.data), sequenceLength)
self.assertEqual(len(sequenceLabelsTrace.data), sequenceLength)
self.assertEqual(len(resetsTrace.data), sequenceLength)
self.assertEqual(len(predictedActiveCellsTrace.data), sequenceLength)
self.assertEqual(len(predictedInactiveCellsTrace.data), sequenceLength)
self.assertEqual(len(predictedActiveColumnsTrace.data), sequenceLength)
self.assertEqual(len(predictedInactiveColumnsTrace.data), sequenceLength)
self.assertEqual(len(unpredictedActiveColumnsTrace.data), sequenceLength)
self.assertEqual(activeColumnsTrace.data[-1], self.patternMachine.get(4))
self.assertEqual(sequenceLabelsTrace.data[-1], "Test")
self.assertEqual(resetsTrace.data[0], True)
self.assertEqual(resetsTrace.data[1], False)
self.assertEqual(resetsTrace.data[10], True)
self.assertEqual(resetsTrace.data[-1], False)
self.assertEqual(len(predictedActiveCellsTrace.data[-2]), 5)
self.assertEqual(len(predictedActiveCellsTrace.data[-1]), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data[-2]), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data[-1]), 5)
self.assertEqual(len(predictedActiveColumnsTrace.data[-2]), 5)
self.assertEqual(len(predictedActiveColumnsTrace.data[-1]), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data[-2]), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data[-1]), 5)
self.assertEqual(len(unpredictedActiveColumnsTrace.data[-2]), 0)
self.assertEqual(len(unpredictedActiveColumnsTrace.data[-1]), 5)
def testClearHistory(self):
sequence = self._generateSequence()
self._feedSequence(sequence, sequenceLabel="Test")
self.tm.mmClearHistory()
activeColumnsTrace = self.tm.mmGetTraceActiveColumns()
predictiveCellsTrace = self.tm.mmGetTracePredictiveCells()
sequenceLabelsTrace = self.tm.mmGetTraceSequenceLabels()
resetsTrace = self.tm.mmGetTraceResets()
predictedActiveCellsTrace = self.tm.mmGetTracePredictedActiveCells()
predictedInactiveCellsTrace = self.tm.mmGetTracePredictedInactiveCells()
predictedActiveColumnsTrace = self.tm.mmGetTracePredictedActiveColumns()
predictedInactiveColumnsTrace = self.tm.mmGetTracePredictedInactiveColumns()
unpredictedActiveColumnsTrace = self.tm.mmGetTraceUnpredictedActiveColumns()
self.assertEqual(len(activeColumnsTrace.data), 0)
self.assertEqual(len(predictiveCellsTrace.data), 0)
self.assertEqual(len(sequenceLabelsTrace.data), 0)
self.assertEqual(len(resetsTrace.data), 0)
self.assertEqual(len(predictedActiveCellsTrace.data), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data), 0)
self.assertEqual(len(predictedActiveColumnsTrace.data), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data), 0)
self.assertEqual(len(unpredictedActiveColumnsTrace.data), 0)
def testSequencesMetrics(self):
sequence = self._generateSequence()
self._feedSequence(sequence, "Test1")
sequence.reverse()
sequence.append(sequence.pop(0)) # Move None (reset) to the end
self._feedSequence(sequence, "Test2")
sequencesPredictedActiveCellsPerColumnMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsPerColumn()
sequencesPredictedActiveCellsSharedMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsShared()
self.assertEqual(sequencesPredictedActiveCellsPerColumnMetric.mean, 1)
self.assertEqual(sequencesPredictedActiveCellsSharedMetric.mean, 1)
self._feedSequence(sequence, "Test3")
sequencesPredictedActiveCellsPerColumnMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsPerColumn()
sequencesPredictedActiveCellsSharedMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsShared()
self.assertEqual(sequencesPredictedActiveCellsPerColumnMetric.mean, 1)
self.assertTrue(sequencesPredictedActiveCellsSharedMetric.mean > 1)
# ==============================
# Helper functions
# ==============================
def _generateSequence(self):
numbers = range(0, 10)
sequence = self.sequenceMachine.generateFromNumbers(numbers)
sequence.append(None)
sequence *= 3
return sequence
def _feedSequence(self, sequence, sequenceLabel=None):
for pattern in sequence:
if pattern is None:
self.tm.reset()
else:
self.tm.compute(pattern, sequenceLabel=sequenceLabel)
class ExtendedTemporalMemoryTest(unittest.TestCase):
def setUp(self):
self.tm = ExtendedTemporalMemory(learnOnOneCell=False)
def testInitInvalidParams(self):
# Invalid columnDimensions
kwargs = {"columnDimensions": [], "cellsPerColumn": 32}
self.assertRaises(ValueError, ExtendedTemporalMemory, **kwargs)
# Invalid cellsPerColumn
kwargs = {"columnDimensions": [2048], "cellsPerColumn": 0}
self.assertRaises(ValueError, ExtendedTemporalMemory, **kwargs)
kwargs = {"columnDimensions": [2048], "cellsPerColumn": -10}
self.assertRaises(ValueError, ExtendedTemporalMemory, **kwargs)
def testlearnOnOneCellParam(self):
tm = self.tm
self.assertFalse(tm.learnOnOneCell)
tm = ExtendedTemporalMemory(learnOnOneCell=True)
self.assertTrue(tm.learnOnOneCell)
def testActivateCorrectlyPredictiveCells(self):
tm = self.tm
prevPredictiveCells = set([0, 237, 1026, 26337, 26339, 55536])
activeColumns = set([32, 47, 823])
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns, predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns
)
self.assertEqual(activeCells, set([1026, 26337, 26339]))
self.assertEqual(winnerCells, set([1026, 26337, 26339]))
self.assertEqual(predictedColumns, set([32, 823]))
self.assertEqual(predictedInactiveCells, set())
def testActivateCorrectlyPredictiveCellsEmpty(self):
tm = self.tm
# No previous predictive cells, no active columns
prevPredictiveCells = set()
activeColumns = set()
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns, predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns
)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
# No previous predictive cells, with active columns
prevPredictiveCells = set()
activeColumns = set([32, 47, 823])
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns, predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns
)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
# No active columns, with previously predictive cells
prevPredictiveCells = set([0, 237, 1026, 26337, 26339, 55536])
activeColumns = set()
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns, predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns
)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
def testActivateCorrectlyPredictiveCellsOrphan(self):
tm = self.tm
tm.predictedSegmentDecrement = 0.001
prevPredictiveCells = set([])
activeColumns = set([32, 47, 823])
prevMatchingCells = set([32, 47])
(activeCells, winnerCells, predictedColumns, predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns
)
self.assertEqual(activeCells, set([]))
self.assertEqual(winnerCells, set([]))
self.assertEqual(predictedColumns, set([]))
self.assertEqual(predictedInactiveCells, set([32, 47]))
def testBurstColumns(self):
tm = ExtendedTemporalMemory(cellsPerColumn=4, connectedPermanence=0.50, minThreshold=1, seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(108)
connections.createSynapse(3, 486, 0.9)
activeColumns = set([0, 1, 26])
predictedColumns = set([26])
prevActiveCells = set([23, 37, 49, 733])
prevWinnerCells = set([23, 37, 49, 733])
prevActiveApicalCells = set()
learnOnOneCell = False
chosenCellForColumn = {}
(activeCells, winnerCells, learningSegments, apicalLearningSegments, chosenCellForColumn) = tm.burstColumns(
activeColumns,
predictedColumns,
prevActiveCells,
prevActiveApicalCells,
prevWinnerCells,
learnOnOneCell,
chosenCellForColumn,
connections,
tm.apicalConnections,
)
self.assertEqual(activeCells, set([0, 1, 2, 3, 4, 5, 6, 7]))
randomWinner = 4 # 4 should be randomly chosen cell
self.assertEqual(winnerCells, set([0, randomWinner]))
self.assertEqual(learningSegments, set([0, 4])) # 4 is new segment created
# Check that new segment was added to winner cell (6) in column 1
self.assertEqual(connections.segmentsForCell(randomWinner), set([4]))
def testBurstColumnsEmpty(self):
tm = self.tm
activeColumns = set()
predictedColumns = set()
prevActiveCells = set()
prevWinnerCells = set()
connections = tm.connections
prevActiveApicalCells = set()
learnOnOneCell = False
chosenCellForColumn = {}
(activeCells, winnerCells, learningSegments, apicalLearningSegments, chosenCellForColumn) = tm.burstColumns(
activeColumns,
predictedColumns,
prevActiveCells,
prevActiveApicalCells,
prevWinnerCells,
learnOnOneCell,
chosenCellForColumn,
connections,
tm.apicalConnections,
)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(learningSegments, set())
self.assertEqual(apicalLearningSegments, set())
def testLearnOnSegments(self):
tm = ExtendedTemporalMemory(maxNewSynapseCount=2)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(1)
connections.createSynapse(1, 733, 0.7)
connections.createSegment(8)
connections.createSynapse(2, 486, 0.9)
connections.createSegment(100)
prevActiveSegments = set([0, 2])
learningSegments = set([1, 3])
prevActiveCells = set([23, 37, 733])
winnerCells = set([0])
prevWinnerCells = set([10, 11, 12, 13, 14])
predictedInactiveCells = set()
prevMatchingSegments = set()
tm.learnOnSegments(
prevActiveSegments,
learningSegments,
prevActiveCells,
winnerCells,
prevWinnerCells,
connections,
predictedInactiveCells,
prevMatchingSegments,
)
# Check segment 0
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.7)
synapseData = connections.dataForSynapse(1)
self.assertAlmostEqual(synapseData.permanence, 0.5)
synapseData = connections.dataForSynapse(2)
self.assertAlmostEqual(synapseData.permanence, 0.8)
# Check segment 1
synapseData = connections.dataForSynapse(3)
self.assertAlmostEqual(synapseData.permanence, 0.8)
self.assertEqual(len(connections.synapsesForSegment(1)), 2)
# Check segment 2
synapseData = connections.dataForSynapse(4)
self.assertAlmostEqual(synapseData.permanence, 0.9)
self.assertEqual(len(connections.synapsesForSegment(2)), 1)
# Check segment 3
self.assertEqual(len(connections.synapsesForSegment(3)), 2)
def testComputePredictiveCells(self):
tm = ExtendedTemporalMemory(activationThreshold=2, minThreshold=2, predictedSegmentDecrement=0.004)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.5)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(1)
connections.createSynapse(1, 733, 0.7)
connections.createSynapse(1, 733, 0.4)
connections.createSegment(1)
connections.createSynapse(2, 974, 0.9)
connections.createSegment(8)
connections.createSynapse(3, 486, 0.9)
connections.createSegment(100)
activeCells = set([23, 37, 733, 974])
(activeSegments, predictiveCells, matchingSegments, matchingCells) = tm.computePredictiveCells(
activeCells, connections
)
self.assertEqual(activeSegments, set([0]))
self.assertEqual(predictiveCells, set([0]))
self.assertEqual(matchingSegments, set([0, 1]))
self.assertEqual(matchingCells, set([0, 1]))
def testBestMatchingCell(self):
tm = ExtendedTemporalMemory(connectedPermanence=0.50, minThreshold=1, seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(108)
connections.createSynapse(3, 486, 0.9)
activeCells = set([23, 37, 49, 733])
activeApicalCells = set()
self.assertEqual(
tm.bestMatchingCell(
tm.cellsForColumn(0), activeCells, activeApicalCells, connections, tm.apicalConnections
),
(0, 0, None),
)
self.assertEqual(
tm.bestMatchingCell(
tm.cellsForColumn(3), activeCells, activeApicalCells, connections, tm.apicalConnections
),
(103, None, None),
) # Random cell from column
self.assertEqual(
tm.bestMatchingCell(
tm.cellsForColumn(999), activeCells, activeApicalCells, connections, tm.apicalConnections
),
(31979, None, None),
) # Random cell from column
def testBestMatchingCellFewestSegments(self):
tm = ExtendedTemporalMemory(
columnDimensions=[2], cellsPerColumn=2, connectedPermanence=0.50, minThreshold=1, seed=42
)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 3, 0.3)
activeSynapsesForSegment = set([])
activeApicalCells = set()
for _ in range(100):
# Never pick cell 0, always pick cell 1
(cell, _, _) = tm.bestMatchingCell(
tm.cellsForColumn(0), activeSynapsesForSegment, activeApicalCells, connections, tm.apicalConnections
)
self.assertEqual(cell, 1)
def testBestMatchingSegment(self):
tm = ExtendedTemporalMemory(connectedPermanence=0.50, minThreshold=1)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(8)
connections.createSynapse(3, 486, 0.9)
activeCells = set([23, 37, 49, 733])
self.assertEqual(tm.bestMatchingSegment(0, activeCells, connections), (0, 2))
self.assertEqual(tm.bestMatchingSegment(1, activeCells, connections), (2, 1))
self.assertEqual(tm.bestMatchingSegment(8, activeCells, connections), (None, None))
self.assertEqual(tm.bestMatchingSegment(100, activeCells, connections), (None, None))
def testLeastUsedCell(self):
tm = ExtendedTemporalMemory(columnDimensions=[2], cellsPerColumn=2, seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 3, 0.3)
for _ in range(100):
# Never pick cell 0, always pick cell 1
self.assertEqual(tm.leastUsedCell(tm.cellsForColumn(0), connections), 1)
def testAdaptSegment(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
tm.adaptSegment(0, set([0, 1]), connections, tm.permanenceIncrement, tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.7)
synapseData = connections.dataForSynapse(1)
self.assertAlmostEqual(synapseData.permanence, 0.5)
synapseData = connections.dataForSynapse(2)
self.assertAlmostEqual(synapseData.permanence, 0.8)
def testAdaptSegmentToMax(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.9)
tm.adaptSegment(0, set([0]), connections, tm.permanenceIncrement, tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 1.0)
# Now permanence should be at max
tm.adaptSegment(0, set([0]), connections, tm.permanenceIncrement, tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 1.0)
def testAdaptSegmentToMin(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.1)
tm.adaptSegment(0, set(), connections, tm.permanenceIncrement, tm.permanenceDecrement)
synapses = connections.synapsesForSegment(0)
self.assertFalse(0 in synapses)
def testPickCellsToLearnOn(self):
tm = ExtendedTemporalMemory(seed=42)
connections = tm.connections
connections.createSegment(0)
winnerCells = set([4, 47, 58, 93])
self.assertEqual(tm.pickCellsToLearnOn(2, 0, winnerCells, connections), set([4, 93])) # randomly picked
self.assertEqual(tm.pickCellsToLearnOn(100, 0, winnerCells, connections), set([4, 47, 58, 93]))
self.assertEqual(tm.pickCellsToLearnOn(0, 0, winnerCells, connections), set())
def testPickCellsToLearnOnAvoidDuplicates(self):
tm = ExtendedTemporalMemory(seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
winnerCells = set([23])
# Ensure that no additional (duplicate) cells were picked
self.assertEqual(tm.pickCellsToLearnOn(2, 0, winnerCells, connections), set())
def testColumnForCell1D(self):
tm = ExtendedTemporalMemory(columnDimensions=[2048], cellsPerColumn=5)
self.assertEqual(tm.columnForCell(0), 0)
self.assertEqual(tm.columnForCell(4), 0)
self.assertEqual(tm.columnForCell(5), 1)
self.assertEqual(tm.columnForCell(10239), 2047)
def testColumnForCell2D(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
self.assertEqual(tm.columnForCell(0), 0)
self.assertEqual(tm.columnForCell(3), 0)
self.assertEqual(tm.columnForCell(4), 1)
self.assertEqual(tm.columnForCell(16383), 4095)
def testColumnForCellInvalidCell(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
try:
tm.columnForCell(16383)
except IndexError:
self.fail("IndexError raised unexpectedly")
args = [16384]
self.assertRaises(IndexError, tm.columnForCell, *args)
args = [-1]
self.assertRaises(IndexError, tm.columnForCell, *args)
def testCellsForColumn1D(self):
tm = ExtendedTemporalMemory(columnDimensions=[2048], cellsPerColumn=5)
expectedCells = set([5, 6, 7, 8, 9])
self.assertEqual(tm.cellsForColumn(1), expectedCells)
def testCellsForColumn2D(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
expectedCells = set([256, 257, 258, 259])
self.assertEqual(tm.cellsForColumn(64), expectedCells)
def testCellsForColumnInvalidColumn(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
try:
tm.cellsForColumn(4095)
except IndexError:
self.fail("IndexError raised unexpectedly")
args = [4096]
self.assertRaises(IndexError, tm.cellsForColumn, *args)
args = [-1]
self.assertRaises(IndexError, tm.cellsForColumn, *args)
def testNumberOfColumns(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64], cellsPerColumn=32)
self.assertEqual(tm.numberOfColumns(), 64 * 64)
def testNumberOfCells(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64], cellsPerColumn=32)
self.assertEqual(tm.numberOfCells(), 64 * 64 * 32)
def testMapCellsToColumns(self):
tm = ExtendedTemporalMemory(columnDimensions=[100], cellsPerColumn=4)
columnsForCells = tm.mapCellsToColumns(set([0, 1, 2, 5, 399]))
self.assertEqual(columnsForCells[0], set([0, 1, 2]))
self.assertEqual(columnsForCells[1], set([5]))
self.assertEqual(columnsForCells[99], set([399]))
def testCalculatePredictiveCells(self):
tm = ExtendedTemporalMemory(columnDimensions=[4], cellsPerColumn=5)
predictiveDistalCells = set([2, 3, 5, 8, 10, 12, 13, 14])
predictiveApicalCells = set([1, 5, 7, 11, 14, 15, 17])
self.assertEqual(tm.calculatePredictiveCells(predictiveDistalCells, predictiveApicalCells), set([2, 3, 5, 14]))
def testCompute(self):
tm = ExtendedTemporalMemory(
columnDimensions=[4],
cellsPerColumn=10,
learnOnOneCell=False,
initialPermanence=0.2,
connectedPermanence=0.7,
activationThreshold=1,
)
seg1 = tm.connections.createSegment(0)
seg2 = tm.connections.createSegment(20)
seg3 = tm.connections.createSegment(25)
try:
tm.connections.createSynapse(seg1, 15, 0.9)
tm.connections.createSynapse(seg2, 35, 0.9)
tm.connections.createSynapse(seg2, 45, 0.9) # external cell
tm.connections.createSynapse(seg3, 35, 0.9)
tm.connections.createSynapse(seg3, 50, 0.9) # external cell
except IndexError:
self.fail("IndexError raised unexpectedly for distal segments")
aSeg1 = tm.apicalConnections.createSegment(1)
aSeg2 = tm.apicalConnections.createSegment(25)
try:
tm.apicalConnections.createSynapse(aSeg1, 3, 0.9)
tm.apicalConnections.createSynapse(aSeg2, 1, 0.9)
except IndexError:
self.fail("IndexError raised unexpectedly for apical segments")
activeColumns = set([1, 3])
activeExternalCells = set([5, 10, 15])
activeApicalCells = set([1, 2, 3, 4])
tm.compute(
activeColumns, activeExternalCells=activeExternalCells, activeApicalCells=activeApicalCells, learn=False
)
activeColumns = set([0, 2])
tm.compute(activeColumns, activeExternalCells=set(), activeApicalCells=set())
self.assertEqual(tm.activeCells, set([0, 20, 25]))
def testLearning(self):
tm = ExtendedTemporalMemory(
columnDimensions=[4],
cellsPerColumn=10,
learnOnOneCell=False,
initialPermanence=0.5,
connectedPermanence=0.6,
activationThreshold=1,
minThreshold=1,
maxNewSynapseCount=2,
permanenceDecrement=0.05,
permanenceIncrement=0.2,
)
seg1 = tm.connections.createSegment(0)
seg2 = tm.connections.createSegment(10)
seg3 = tm.connections.createSegment(20)
seg4 = tm.connections.createSegment(30)
try:
tm.connections.createSynapse(seg1, 10, 0.9)
tm.connections.createSynapse(seg2, 20, 0.9)
tm.connections.createSynapse(seg3, 30, 0.9)
tm.connections.createSynapse(seg3, 41, 0.9)
tm.connections.createSynapse(seg3, 25, 0.9)
tm.connections.createSynapse(seg4, 0, 0.9)
except IndexError:
self.fail("IndexError raised unexpectedly for distal segments")
aSeg1 = tm.apicalConnections.createSegment(0)
aSeg2 = tm.apicalConnections.createSegment(20)
try:
tm.apicalConnections.createSynapse(aSeg1, 42, 0.8)
tm.apicalConnections.createSynapse(aSeg2, 43, 0.8)
except IndexError:
self.fail("IndexError raised unexpectedly for apical segments")
activeColumns = set([1, 3])
activeExternalCells = set([1]) # will be re-indexed to 41
activeApicalCells = set([2, 3]) # will be re-indexed to 42, 43
tm.compute(
activeColumns, activeExternalCells=activeExternalCells, activeApicalCells=activeApicalCells, learn=False
)
activeColumns = set([0, 2])
tm.compute(activeColumns, activeExternalCells=None, activeApicalCells=None, learn=True)
self.assertEqual(tm.activeCells, set([0, 20]))
# distal learning
synapse = list(tm.connections.synapsesForSegment(seg1))[0]
self.assertEqual(tm.connections.dataForSynapse(synapse).permanence, 1.0)
synapse = list(tm.connections.synapsesForSegment(seg2))[0]
self.assertEqual(tm.connections.dataForSynapse(synapse).permanence, 0.9)
synapse = list(tm.connections.synapsesForSegment(seg3))[0]
self.assertEqual(tm.connections.dataForSynapse(synapse).permanence, 1.0)
synapse = list(tm.connections.synapsesForSegment(seg3))[1]
self.assertEqual(tm.connections.dataForSynapse(synapse).permanence, 1.0)
synapse = list(tm.connections.synapsesForSegment(seg3))[2]
self.assertEqual(tm.connections.dataForSynapse(synapse).permanence, 0.85)
synapse = list(tm.connections.synapsesForSegment(seg4))[0]
self.assertEqual(tm.connections.dataForSynapse(synapse).permanence, 0.9)
# apical learning
synapse = list(tm.apicalConnections.synapsesForSegment(aSeg1))[0]
self.assertEqual(tm.apicalConnections.dataForSynapse(synapse).permanence, 1.0)
synapse = list(tm.apicalConnections.synapsesForSegment(aSeg2))[0]
self.assertEqual(tm.apicalConnections.dataForSynapse(synapse).permanence, 1.0)
@unittest.skipUnless(capnp is not None, "No serialization available for ETM")
def testWriteRead(self):
tm1 = ExtendedTemporalMemory(
columnDimensions=[100],
cellsPerColumn=4,
activationThreshold=7,
initialPermanence=0.37,
connectedPermanence=0.58,
minThreshold=4,
maxNewSynapseCount=18,
permanenceIncrement=0.23,
permanenceDecrement=0.08,
seed=91,
)
# Run some data through before serializing
self.patternMachine = PatternMachine(100, 4)
self.sequenceMachine = SequenceMachine(self.patternMachine)
sequence = self.sequenceMachine.generateFromNumbers(range(5))
for _ in range(3):
for pattern in sequence:
tm1.compute(pattern)
proto1 = TemporalMemoryProto_capnp.TemporalMemoryProto.new_message()
tm1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f)
# Load the deserialized proto
tm2 = ExtendedTemporalMemory.read(proto2)
# Check that the two temporal memory objects have the same attributes
self.assertEqual(tm1, tm2)
# Run a couple records through after deserializing and check results match
tm1.compute(self.patternMachine.get(0))
tm2.compute(self.patternMachine.get(0))
self.assertEqual(set(tm1.getActiveCells()), set(tm2.getActiveCells()))
self.assertEqual(set(tm1.getPredictiveCells()), set(tm2.getPredictiveCells()))
self.assertEqual(set(tm1.getWinnerCells()), set(tm2.getWinnerCells()))
self.assertEqual(tm1.connections, tm2.connections)
tm1.compute(self.patternMachine.get(3))
tm2.compute(self.patternMachine.get(3))
self.assertEqual(set(tm1.getActiveCells()), set(tm2.getActiveCells()))
self.assertEqual(set(tm1.getPredictiveCells()), set(tm2.getPredictiveCells()))
self.assertEqual(set(tm1.getWinnerCells()), set(tm2.getWinnerCells()))
self.assertEqual(tm1.connections, tm2.connections)
class TemporalMemoryPerformanceTest(unittest.TestCase):
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 testSingleSequence(self):
print "Test: Single sequence"
sequence = self.sequenceMachine.generateFromNumbers(range(50))
times = self._feedAll(sequence)
self.assertTrue(times[1] < times[0])
self.assertTrue(times[3] < times[2])
# ==============================
# Helper functions
# ==============================
def _feedAll(self, sequence, learn=True, num=1):
repeatedSequence = sequence * num
times = []
def tmComputeFn(pattern, instance):
instance.compute(pattern, learn)
def tpComputeFn(pattern, instance):
array = self._patternToNumpyArray(pattern)
instance.compute(array, enableLearn=learn, computeInfOutput=True)
elapsed = self._feedOne(repeatedSequence, self.tmPy, tmComputeFn)
times.append(elapsed)
print "TM (py):\t{0}s".format(elapsed)
elapsed = self._feedOne(repeatedSequence, self.tmCPP, tmComputeFn)
times.append(elapsed)
print "TM (C++):\t{0}s".format(elapsed)
elapsed = self._feedOne(repeatedSequence, self.tp, tpComputeFn)
times.append(elapsed)
print "TP:\t\t{0}s".format(elapsed)
elapsed = self._feedOne(repeatedSequence, self.tp10x2, tpComputeFn)
times.append(elapsed)
print "TP10X2:\t\t{0}s".format(elapsed)
return times
@staticmethod
def _feedOne(sequence, instance, computeFn):
start = time.clock()
for pattern in sequence:
if pattern == None:
instance.reset()
else:
computeFn(pattern, instance)
elapsed = time.clock() - start
return elapsed
@staticmethod
def _patternToNumpyArray(pattern):
array = numpy.zeros(2048, dtype='int32')
array[list(pattern)] = 1
return array
class TemporalMemoryPerformanceTest(unittest.TestCase):
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 testSingleSequence(self):
print "Test: Single sequence"
sequence = self.sequenceMachine.generateFromNumbers(range(50))
times = self._feedAll(sequence)
self.assertTrue(times[1] < times[0])
self.assertTrue(times[3] < times[2])
# ==============================
# Helper functions
# ==============================
def _feedAll(self, sequence, learn=True, num=1):
repeatedSequence = sequence * num
times = []
def tmComputeFn(pattern, instance):
instance.compute(pattern, learn)
def tpComputeFn(pattern, instance):
array = self._patternToNumpyArray(pattern)
instance.compute(array, enableLearn=learn, computeInfOutput=True)
elapsed = self._feedOne(repeatedSequence, self.tmPy, tmComputeFn)
times.append(elapsed)
print "TM (py):\t{0}s".format(elapsed)
elapsed = self._feedOne(repeatedSequence, self.tmCPP, tmComputeFn)
times.append(elapsed)
print "TM (C++):\t{0}s".format(elapsed)
elapsed = self._feedOne(repeatedSequence, self.tp, tpComputeFn)
times.append(elapsed)
print "TP:\t\t{0}s".format(elapsed)
elapsed = self._feedOne(repeatedSequence, self.tp10x2, tpComputeFn)
times.append(elapsed)
print "TP10X2:\t\t{0}s".format(elapsed)
return times
@staticmethod
def _feedOne(sequence, instance, computeFn):
start = time.clock()
for pattern in sequence:
if pattern == None:
instance.reset()
else:
computeFn(pattern, instance)
elapsed = time.clock() - start
return elapsed
@staticmethod
def _patternToNumpyArray(pattern):
array = numpy.zeros(2048, dtype='int32')
array[list(pattern)] = 1
return array
# Length of sequences shown to network
sequenceLength = params["sequenceLength"]
# Number of sequences used. Sequences may share common elements.
numberOfSequences = params["numberOfSequences"]
# Number of sequence passes for training the TM. Zero => no training.
trainingPasses = params["trainingPasses"]
# Generate a sequence list and an associated labeled list (both containing a
# set of sequences separated by None)
print "\nGenerating sequences..."
patternAlphabetSize = sequenceLength * numberOfSequences
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(numberOfSequences, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [str(numbers[i + i*sequenceLength: i + (i+1)*sequenceLength])
for i in xrange(numberOfSequences)]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
def initializeNetwork():
tmParamOverrides = params["temporalMemoryParams"]
upParamOverrides = params["unionPoolerParams"]
def setUp(self):
self.tm = None
self.patternMachine = self.getPatternMachine()
self.sequenceMachine = SequenceMachine(self.patternMachine)
def run(params, paramDir, outputDir, plotVerbosity=0, consoleVerbosity=0):
"""
Runs the noise robustness experiment.
:param params: A dict containing the following experiment parameters:
patternDimensionality - Dimensionality of sequence patterns
patternCardinality - Cardinality (# ON bits) of sequence patterns
sequenceLength - Length of sequences shown to network
sequenceCount - Number of unique sequences used
trainingPasses - Number of times Temporal Memory is trained on each
sequence
testPresentations - Number of sequences presented in test phase
perturbationChance - Chance of sequence perturbations during test phase
temporalMemoryParams - A dict of Temporal Memory parameter overrides
unionPoolerParams - A dict of Union Pooler parameter overrides
:param paramDir: Path of parameter file
:param outputDir: Output will be written to this path
:param plotVerbosity: Plotting verbosity
:param consoleVerbosity: Console output verbosity
"""
print "Running Noise robustness experiment...\n"
print "Params dir: {0}".format(os.path.join(os.path.dirname(__file__),
paramDir))
print "Output dir: {0}\n".format(os.path.join(os.path.dirname(__file__),
outputDir))
patternDimensionality = params["patternDimensionality"]
patternCardinality = params["patternCardinality"]
sequenceLength = params["sequenceLength"]
sequenceCount = params["numberOfSequences"]
trainingPasses = params["trainingPasses"]
testPresentations = params["testPresentations"]
perturbationChance = params["perturbationChance"]
tmParamOverrides = params["temporalMemoryParams"]
upParamOverrides = params["unionPoolerParams"]
# TODO If this parameter is to be supported, the sequence generation
# code below must change
# Number of unique patterns from which sequences are built
# patternAlphabetSize = params["patternAlphabetSize"]
# Generate a sequence list and an associated labeled list (both containing a
# set of sequences separated by None)
start = time.time()
print "Generating sequences..."
patternAlphabetSize = sequenceLength * sequenceCount
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(sequenceCount, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [str(numbers[i + i*sequenceLength: i + (i+1)*sequenceLength])
for i in xrange(sequenceCount)]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
# Set up the Temporal Memory and Union Pooler network
print "\nCreating network..."
experiment = UnionPoolerExperiment(tmParamOverrides, upParamOverrides)
# Train only the Temporal Memory on the generated sequences
if trainingPasses > 0:
print "\nTraining Temporal Memory..."
if consoleVerbosity > 0:
print "\nPass\tMean\t\tStdDev\t\tMax\t\t(Bursting Columns)"
for i in xrange(trainingPasses):
experiment.runNetworkOnSequence(generatedSequences,
labeledSequences,
tmLearn=True,
upLearn=None,
verbosity=consoleVerbosity,
progressInterval=_SHOW_PROGRESS_INTERVAL)
if consoleVerbosity > 0:
stats = experiment.getBurstingColumnsStats()
print "{0}\t{1}\t{2}\t{3}".format(i, stats[0], stats[1], stats[2])
# Reset the TM monitor mixin's records accrued during this training pass
# experiment.tm.mmClearHistory()
print
print MonitorMixinBase.mmPrettyPrintMetrics(
experiment.tm.mmGetDefaultMetrics())
print
if plotVerbosity >= 2:
plotNetworkState(experiment, plotVerbosity, trainingPasses,
phase="Training")
print "\nRunning test phase..."
# Input sequence pattern by pattern. Sequence-to-sequence
# progression is randomly selected. At each step there is a chance of
# perturbation. Specifically the following
# perturbations may occur:
# Establish a baseline without noise
# Establish a baseline adding the following perturbations one-by-one
# 1) substitution of some other pattern for the normal expected pattern
# 2) skipping expected pattern and presenting next pattern in sequence
# 3) addition of some other pattern putting off expected pattern one time step
# Finally measure performance on more complex perturbation
# TODO 4) Jump to another sequence randomly (Random jump to start or random
# position?)
runTestPhase(experiment, generatedSequences, sequenceCount, sequenceLength,
testPresentations, perturbationChance)
print
print MonitorMixinBase.mmPrettyPrintMetrics(
experiment.tm.mmGetDefaultMetrics() + experiment.up.mmGetDefaultMetrics())
print
# plotNetworkState(experiment, plotVerbosity, trainingPasses, phase="Testing")
elapsed = int(time.time() - start)
print "Total time: {0:2} seconds.".format(elapsed)
## Write Union SDR trace
# metricName = "activeCells"
# outputFileName = "unionSdrTrace_{0}learningPasses.csv".format(trainingPasses)
# writeMetricTrace(experiment, metricName, outputDir, outputFileName)
if plotVerbosity >= 1:
raw_input("\nPress any key to exit...")
class TemporalMemoryTest(unittest.TestCase):
def setUp(self):
self.tm = TemporalMemory()
def testInitInvalidParams(self):
# Invalid columnDimensions
kwargs = {"columnDimensions": [], "cellsPerColumn": 32}
self.assertRaises(ValueError, TemporalMemory, **kwargs)
# Invalid cellsPerColumn
kwargs = {"columnDimensions": [2048], "cellsPerColumn": 0}
self.assertRaises(ValueError, TemporalMemory, **kwargs)
kwargs = {"columnDimensions": [2048], "cellsPerColumn": -10}
self.assertRaises(ValueError, TemporalMemory, **kwargs)
def testActivateCorrectlyPredictiveCells(self):
tm = self.tm
prevPredictiveCells = set([0, 237, 1026, 26337, 26339, 55536])
activeColumns = set([32, 47, 823])
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns)
self.assertEqual(activeCells, set([1026, 26337, 26339]))
self.assertEqual(winnerCells, set([1026, 26337, 26339]))
self.assertEqual(predictedColumns, set([32, 823]))
self.assertEqual(predictedInactiveCells, set())
def testActivateCorrectlyPredictiveCellsEmpty(self):
tm = self.tm
# No previous predictive cells, no active columns
prevPredictiveCells = set()
activeColumns = set()
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
# No previous predictive cells, with active columns
prevPredictiveCells = set()
activeColumns = set([32, 47, 823])
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
# No active columns, with previously predictive cells
prevPredictiveCells = set([0, 237, 1026, 26337, 26339, 55536])
activeColumns = set()
prevMatchingCells = set()
(activeCells, winnerCells, predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
def testActivateCorrectlyPredictiveCellsOrphan(self):
tm = self.tm
tm.predictedSegmentDecrement = 0.001
prevPredictiveCells = set([])
activeColumns = set([32, 47, 823])
prevMatchingCells = set([32, 47])
(activeCells, winnerCells, predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns)
self.assertEqual(activeCells, set([]))
self.assertEqual(winnerCells, set([]))
self.assertEqual(predictedColumns, set([]))
self.assertEqual(predictedInactiveCells, set([32, 47]))
def testBurstColumns(self):
tm = TemporalMemory(cellsPerColumn=4,
connectedPermanence=0.50,
minThreshold=1,
seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(108)
connections.createSynapse(3, 486, 0.9)
activeColumns = set([0, 1, 26])
predictedColumns = set([26])
prevActiveCells = set([23, 37, 49, 733])
prevWinnerCells = set([23, 37, 49, 733])
(activeCells, winnerCells,
learningSegments) = tm.burstColumns(activeColumns, predictedColumns,
prevActiveCells, prevWinnerCells,
connections)
self.assertEqual(activeCells, set([0, 1, 2, 3, 4, 5, 6, 7]))
self.assertEqual(winnerCells, set([0, 6])) # 6 is randomly chosen cell
self.assertEqual(learningSegments,
set([0, 4])) # 4 is new segment created
# Check that new segment was added to winner cell (6) in column 1
self.assertEqual(connections.segmentsForCell(6), set([4]))
def testBurstColumnsEmpty(self):
tm = self.tm
activeColumns = set()
predictedColumns = set()
prevActiveCells = set()
prevWinnerCells = set()
connections = tm.connections
(activeCells, winnerCells,
learningSegments) = tm.burstColumns(activeColumns, predictedColumns,
prevActiveCells, prevWinnerCells,
connections)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(learningSegments, set())
def testLearnOnSegments(self):
tm = TemporalMemory(maxNewSynapseCount=2)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(1)
connections.createSynapse(1, 733, 0.7)
connections.createSegment(8)
connections.createSynapse(2, 486, 0.9)
connections.createSegment(100)
prevActiveSegments = set([0, 2])
learningSegments = set([1, 3])
prevActiveCells = set([23, 37, 733])
winnerCells = set([0])
prevWinnerCells = set([10, 11, 12, 13, 14])
predictedInactiveCells = set()
prevMatchingSegments = set()
tm.learnOnSegments(prevActiveSegments, learningSegments,
prevActiveCells, winnerCells, prevWinnerCells,
connections, predictedInactiveCells,
prevMatchingSegments)
# Check segment 0
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.7)
synapseData = connections.dataForSynapse(1)
self.assertAlmostEqual(synapseData.permanence, 0.5)
synapseData = connections.dataForSynapse(2)
self.assertAlmostEqual(synapseData.permanence, 0.8)
# Check segment 1
synapseData = connections.dataForSynapse(3)
self.assertAlmostEqual(synapseData.permanence, 0.8)
self.assertEqual(len(connections.synapsesForSegment(1)), 2)
# Check segment 2
synapseData = connections.dataForSynapse(4)
self.assertAlmostEqual(synapseData.permanence, 0.9)
self.assertEqual(len(connections.synapsesForSegment(2)), 1)
# Check segment 3
self.assertEqual(len(connections.synapsesForSegment(3)), 2)
def testComputePredictiveCells(self):
tm = TemporalMemory(activationThreshold=2,
minThreshold=2,
predictedSegmentDecrement=0.004)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.5)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(1)
connections.createSynapse(1, 733, 0.7)
connections.createSynapse(1, 733, 0.4)
connections.createSegment(1)
connections.createSynapse(2, 974, 0.9)
connections.createSegment(8)
connections.createSynapse(3, 486, 0.9)
connections.createSegment(100)
activeCells = set([23, 37, 733, 974])
(activeSegments, predictiveCells, matchingSegments,
matchingCells) = tm.computePredictiveCells(activeCells, connections)
self.assertEqual(activeSegments, set([0]))
self.assertEqual(predictiveCells, set([0]))
self.assertEqual(matchingSegments, set([0, 1]))
self.assertEqual(matchingCells, set([0, 1]))
def testBestMatchingCell(self):
tm = TemporalMemory(connectedPermanence=0.50, minThreshold=1, seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(108)
connections.createSynapse(3, 486, 0.9)
activeCells = set([23, 37, 49, 733])
self.assertEqual(
tm.bestMatchingCell(tm.cellsForColumn(0), activeCells,
connections), (0, 0))
self.assertEqual(
tm.bestMatchingCell(
tm.cellsForColumn(3), # column containing cell 108
activeCells,
connections),
(96, None)) # Random cell from column
self.assertEqual(
tm.bestMatchingCell(tm.cellsForColumn(999), activeCells,
connections),
(31972, None)) # Random cell from column
def testBestMatchingCellFewestSegments(self):
tm = TemporalMemory(columnDimensions=[2],
cellsPerColumn=2,
connectedPermanence=0.50,
minThreshold=1,
seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 3, 0.3)
activeSynapsesForSegment = set([])
for _ in range(100):
# Never pick cell 0, always pick cell 1
(cell, _) = tm.bestMatchingCell(tm.cellsForColumn(0),
activeSynapsesForSegment,
connections)
self.assertEqual(cell, 1)
def testBestMatchingSegment(self):
tm = TemporalMemory(connectedPermanence=0.50, minThreshold=1)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(8)
connections.createSynapse(3, 486, 0.9)
activeCells = set([23, 37, 49, 733])
self.assertEqual(tm.bestMatchingSegment(0, activeCells, connections),
(0, 2))
self.assertEqual(tm.bestMatchingSegment(1, activeCells, connections),
(2, 1))
self.assertEqual(tm.bestMatchingSegment(8, activeCells, connections),
(None, None))
self.assertEqual(tm.bestMatchingSegment(100, activeCells, connections),
(None, None))
def testLeastUsedCell(self):
tm = TemporalMemory(columnDimensions=[2], cellsPerColumn=2, seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 3, 0.3)
for _ in range(100):
# Never pick cell 0, always pick cell 1
self.assertEqual(
tm.leastUsedCell(tm.cellsForColumn(0), connections), 1)
def testAdaptSegment(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
tm.adaptSegment(0, set([0, 1]), connections, tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.7)
synapseData = connections.dataForSynapse(1)
self.assertAlmostEqual(synapseData.permanence, 0.5)
synapseData = connections.dataForSynapse(2)
self.assertAlmostEqual(synapseData.permanence, 0.8)
def testAdaptSegmentToMax(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.9)
tm.adaptSegment(0, set([0]), connections, tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 1.0)
# Now permanence should be at max
tm.adaptSegment(0, set([0]), connections, tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 1.0)
def testAdaptSegmentToMin(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.1)
tm.adaptSegment(0, set(), connections, tm.permanenceIncrement,
tm.permanenceDecrement)
synapses = connections.synapsesForSegment(0)
self.assertFalse(0 in synapses)
def testPickCellsToLearnOn(self):
tm = TemporalMemory(seed=42)
connections = tm.connections
connections.createSegment(0)
winnerCells = set([4, 47, 58, 93])
self.assertEqual(tm.pickCellsToLearnOn(2, 0, winnerCells, connections),
set([4, 58])) # randomly picked
self.assertEqual(
tm.pickCellsToLearnOn(100, 0, winnerCells, connections),
set([4, 47, 58, 93]))
self.assertEqual(tm.pickCellsToLearnOn(0, 0, winnerCells, connections),
set())
def testPickCellsToLearnOnAvoidDuplicates(self):
tm = TemporalMemory(seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
winnerCells = set([23])
# Ensure that no additional (duplicate) cells were picked
self.assertEqual(tm.pickCellsToLearnOn(2, 0, winnerCells, connections),
set())
def testColumnForCell1D(self):
tm = TemporalMemory(columnDimensions=[2048], cellsPerColumn=5)
self.assertEqual(tm.columnForCell(0), 0)
self.assertEqual(tm.columnForCell(4), 0)
self.assertEqual(tm.columnForCell(5), 1)
self.assertEqual(tm.columnForCell(10239), 2047)
def testColumnForCell2D(self):
tm = TemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
self.assertEqual(tm.columnForCell(0), 0)
self.assertEqual(tm.columnForCell(3), 0)
self.assertEqual(tm.columnForCell(4), 1)
self.assertEqual(tm.columnForCell(16383), 4095)
def testColumnForCellInvalidCell(self):
tm = TemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
try:
tm.columnForCell(16383)
except IndexError:
self.fail("IndexError raised unexpectedly")
args = [16384]
self.assertRaises(IndexError, tm.columnForCell, *args)
args = [-1]
self.assertRaises(IndexError, tm.columnForCell, *args)
def testCellsForColumn1D(self):
tm = TemporalMemory(columnDimensions=[2048], cellsPerColumn=5)
expectedCells = set([5, 6, 7, 8, 9])
self.assertEqual(tm.cellsForColumn(1), expectedCells)
def testCellsForColumn2D(self):
tm = TemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
expectedCells = set([256, 257, 258, 259])
self.assertEqual(tm.cellsForColumn(64), expectedCells)
def testCellsForColumnInvalidColumn(self):
tm = TemporalMemory(columnDimensions=[64, 64], cellsPerColumn=4)
try:
tm.cellsForColumn(4095)
except IndexError:
self.fail("IndexError raised unexpectedly")
args = [4096]
self.assertRaises(IndexError, tm.cellsForColumn, *args)
args = [-1]
self.assertRaises(IndexError, tm.cellsForColumn, *args)
def testNumberOfColumns(self):
tm = TemporalMemory(columnDimensions=[64, 64], cellsPerColumn=32)
self.assertEqual(tm.numberOfColumns(), 64 * 64)
def testNumberOfCells(self):
tm = TemporalMemory(columnDimensions=[64, 64], cellsPerColumn=32)
self.assertEqual(tm.numberOfCells(), 64 * 64 * 32)
def testMapCellsToColumns(self):
tm = TemporalMemory(columnDimensions=[100], cellsPerColumn=4)
columnsForCells = tm.mapCellsToColumns(set([0, 1, 2, 5, 399]))
self.assertEqual(columnsForCells[0], set([0, 1, 2]))
self.assertEqual(columnsForCells[1], set([5]))
self.assertEqual(columnsForCells[99], set([399]))
def testWrite(self):
tm1 = TemporalMemory(columnDimensions=[100],
cellsPerColumn=4,
activationThreshold=7,
initialPermanence=0.37,
connectedPermanence=0.58,
minThreshold=4,
maxNewSynapseCount=18,
permanenceIncrement=0.23,
permanenceDecrement=0.08,
seed=91)
# Run some data through before serializing
self.patternMachine = PatternMachine(100, 4)
self.sequenceMachine = SequenceMachine(self.patternMachine)
sequence = self.sequenceMachine.generateFromNumbers(range(5))
for _ in range(3):
for pattern in sequence:
tm1.compute(pattern)
proto1 = TemporalMemoryProto_capnp.TemporalMemoryProto.new_message()
tm1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f)
# Load the deserialized proto
tm2 = TemporalMemory.read(proto2)
# Check that the two temporal memory objects have the same attributes
self.assertEqual(tm1, tm2)
# Run a couple records through after deserializing and check results match
tm1.compute(self.patternMachine.get(0))
tm2.compute(self.patternMachine.get(0))
self.assertEqual(tm1.activeCells, tm2.activeCells)
self.assertEqual(tm1.predictiveCells, tm2.predictiveCells)
self.assertEqual(tm1.winnerCells, tm2.winnerCells)
self.assertEqual(tm1.connections, tm2.connections)
tm1.compute(self.patternMachine.get(3))
tm2.compute(self.patternMachine.get(3))
self.assertEqual(tm1.activeCells, tm2.activeCells)
self.assertEqual(tm1.predictiveCells, tm2.predictiveCells)
self.assertEqual(tm1.winnerCells, tm2.winnerCells)
self.assertEqual(tm1.connections, tm2.connections)
def experiment1():
paramDir = 'params/1024_baseline/5_trainingPasses.yaml'
outputDir = 'results/'
params = yaml.safe_load(open(paramDir, 'r'))
options = {'plotVerbosity': 2, 'consoleVerbosity': 2}
plotVerbosity = 2
consoleVerbosity = 1
print "Running SDR overlap experiment...\n"
print "Params dir: {0}".format(paramDir)
print "Output dir: {0}\n".format(outputDir)
# Dimensionality of sequence patterns
patternDimensionality = params["patternDimensionality"]
# Cardinality (ON / true bits) of sequence patterns
patternCardinality = params["patternCardinality"]
# TODO If this parameter is to be supported, the sequence generation code
# below must change
# Number of unique patterns from which sequences are built
# patternAlphabetSize = params["patternAlphabetSize"]
# Length of sequences shown to network
sequenceLength = params["sequenceLength"]
# Number of sequences used. Sequences may share common elements.
numberOfSequences = params["numberOfSequences"]
# Number of sequence passes for training the TM. Zero => no training.
trainingPasses = params["trainingPasses"]
tmParamOverrides = params["temporalMemoryParams"]
upParamOverrides = params["unionPoolerParams"]
# Generate a sequence list and an associated labeled list (both containing a
# set of sequences separated by None)
start = time.time()
print "\nGenerating sequences..."
patternAlphabetSize = sequenceLength * numberOfSequences
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(numberOfSequences, sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [str(numbers[i + i*sequenceLength: i + (i+1)*sequenceLength])
for i in xrange(numberOfSequences)]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
# Set up the Temporal Memory and Union Pooler network
print "\nCreating network..."
experiment = UnionTemporalPoolerExperiment(tmParamOverrides, upParamOverrides)
# Train only the Temporal Memory on the generated sequences
if trainingPasses > 0:
print "\nTraining Temporal Memory..."
if consoleVerbosity > 0:
print "\nPass\tBursting Columns Mean\tStdDev\tMax"
for i in xrange(trainingPasses):
experiment.runNetworkOnSequences(generatedSequences,
labeledSequences,
tmLearn=True,
upLearn=None,
verbosity=consoleVerbosity,
progressInterval=_SHOW_PROGRESS_INTERVAL)
if consoleVerbosity > 0:
stats = experiment.getBurstingColumnsStats()
print "{0}\t{1}\t{2}\t{3}".format(i, stats[0], stats[1], stats[2])
# Reset the TM monitor mixin's records accrued during this training pass
# experiment.tm.mmClearHistory()
print
print MonitorMixinBase.mmPrettyPrintMetrics(
experiment.tm.mmGetDefaultMetrics())
print
experiment.tm.mmClearHistory()
experiment.up.mmClearHistory()
print "\nRunning test phase..."
inputSequences = generatedSequences
inputCategories = labeledSequences
tmLearn = True
upLearn = False
classifierLearn = False
currentTime = time.time()
experiment.tm.reset()
experiment.up.reset()
poolingActivationTrace = numpy.zeros((experiment.up._numColumns, 1))
activeCellsTrace = numpy.zeros((experiment.up._numColumns, 1))
activeSPTrace = numpy.zeros((experiment.up._numColumns, 1))
for _ in xrange(trainingPasses):
experiment.tm.reset()
for i in xrange(len(inputSequences)):
sensorPattern = inputSequences[i]
inputCategory = inputCategories[i]
if sensorPattern is None:
pass
else:
experiment.tm.compute(sensorPattern,
learn=tmLearn,
sequenceLabel=inputCategory)
if upLearn is not None:
activeCells, predActiveCells, burstingCols, = experiment.getUnionTemporalPoolerInput()
experiment.up.compute(activeCells,
predActiveCells,
learn=upLearn,
sequenceLabel=inputCategory)
currentPoolingActivation = experiment.up._poolingActivation
currentPoolingActivation = experiment.up._poolingActivation.reshape((experiment.up._numColumns, 1))
poolingActivationTrace = numpy.concatenate((poolingActivationTrace, currentPoolingActivation), 1)
currentUnionSDR = numpy.zeros((experiment.up._numColumns, 1))
currentUnionSDR[experiment.up._unionSDR] = 1
activeCellsTrace = numpy.concatenate((activeCellsTrace, currentUnionSDR), 1)
currentSPSDR = numpy.zeros((experiment.up._numColumns, 1))
currentSPSDR[experiment.up._activeCells] = 1
activeSPTrace = numpy.concatenate((activeSPTrace, currentSPSDR), 1)
print "\nPass\tBursting Columns Mean\tStdDev\tMax"
stats = experiment.getBurstingColumnsStats()
print "{0}\t{1}\t{2}\t{3}".format(0, stats[0], stats[1], stats[2])
print
print MonitorMixinBase.mmPrettyPrintMetrics(\
experiment.tm.mmGetDefaultMetrics() + experiment.up.mmGetDefaultMetrics())
print
experiment.tm.mmClearHistory()
# estimate fraction of shared bits across adjacent time point
unionSDRshared = experiment.up._mmComputeUnionSDRdiff()
bitLifeList = experiment.up._mmComputeBitLifeStats()
bitLife = numpy.array(bitLifeList)
# Plot SP outputs, UP persistence and UP outputs in testing phase
def showSequenceStartLine(ax, trainingPasses, sequenceLength):
for i in xrange(trainingPasses):
ax.vlines(i*sequenceLength, 0, 100, linestyles='--')
plt.figure()
ncolShow = 100
f, (ax1, ax2, ax3) = plt.subplots(nrows=1,ncols=3)
ax1.imshow(activeSPTrace[1:ncolShow,:], cmap=cm.Greys,interpolation="nearest",aspect='auto')
showSequenceStartLine(ax1, trainingPasses, sequenceLength)
ax1.set_title('SP SDR')
ax1.set_ylabel('Columns')
ax2.imshow(poolingActivationTrace[1:100,:], cmap=cm.Greys, interpolation="nearest",aspect='auto')
showSequenceStartLine(ax2, trainingPasses, sequenceLength)
ax2.set_title('Persistence')
ax3.imshow(activeCellsTrace[1:ncolShow,:], cmap=cm.Greys, interpolation="nearest",aspect='auto')
showSequenceStartLine(ax3, trainingPasses, sequenceLength)
plt.title('Union SDR')
ax2.set_xlabel('Time (steps)')
pp = PdfPages('results/UnionPoolingOnLearnedTM_Experiment1.pdf')
pp.savefig()
pp.close()
f, (ax1, ax2, ax3) = plt.subplots(nrows=3,ncols=1)
ax1.plot((sum(activeCellsTrace))/experiment.up._numColumns*100)
ax1.set_ylabel('Union SDR size (%)')
ax1.set_xlabel('Time (steps)')
ax1.set_ylim(0,25)
ax2.plot(unionSDRshared)
ax2.set_ylabel('Shared Bits')
ax2.set_xlabel('Time (steps)')
ax3.hist(bitLife)
ax3.set_xlabel('Life duration for each bit')
pp = PdfPages('results/UnionSDRproperty_Experiment1.pdf')
pp.savefig()
pp.close()
class SequenceMachineTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
def testGenerateFromNumbers(self):
numbers = range(0, 10) + [None] + range(10, 19)
sequence = self.sequenceMachine.generateFromNumbers(numbers)
self.assertEqual(len(sequence), 20)
self.assertEqual(sequence[0], self.patternMachine.get(0))
self.assertEqual(sequence[10], None)
self.assertEqual(sequence[11], self.patternMachine.get(10))
def testAddSpatialNoise(self):
patternMachine = PatternMachine(10000, 1000, num=100)
sequenceMachine = SequenceMachine(patternMachine)
numbers = range(0, 100)
numbers.append(None)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.5)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertTrue(400 < overlap < 600)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.0)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertEqual(overlap, 1000)
def testGenerateNumbers(self):
numbers = self.sequenceMachine.generateNumbers(1, 100)
self.assertEqual(numbers[-1], None)
self.assertEqual(len(numbers), 101)
self.assertFalse(numbers[:-1] == range(0, 100))
self.assertEqual(sorted(numbers[:-1]), range(0, 100))
def testGenerateNumbersMultipleSequences(self):
numbers = self.sequenceMachine.generateNumbers(3, 100)
self.assertEqual(len(numbers), 303)
self.assertEqual(sorted(numbers[0:100]), range(0, 100))
self.assertEqual(sorted(numbers[101:201]), range(100, 200))
self.assertEqual(sorted(numbers[202:302]), range(200, 300))
def testGenerateNumbersWithShared(self):
numbers = self.sequenceMachine.generateNumbers(3, 100, (20, 35))
self.assertEqual(len(numbers), 303)
shared = range(300, 315)
self.assertEqual(numbers[20:35], shared)
self.assertEqual(numbers[20+101:35+101], shared)
self.assertEqual(numbers[20+202:35+202], shared)
class SpatialTemporalPoolerTest(unittest.TestCase):
def setUp(self):
print ("\n"
"======================================================\n"
"Test: {0} \n"
"{1}\n"
"======================================================\n"
).format(self.id(), self.shortDescription())
self.patternMachine = PatternMachine(1024, 20, num=100)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(
mmName="TM",
columnDimensions=[1024],
cellsPerColumn=16,
initialPermanence=0.5,
connectedPermanence=0.7,
minThreshold=20,
maxNewSynapseCount=30,
permanenceIncrement=0.1,
permanenceDecrement=0.02,
activationThreshold=20)
self.tp = MonitoredTemporalPooler(
inputDimensions=[1024, 16],
columnDimensions=[1024],
mmName="TP")
self._showInternalStatePlots(appendTitle=" (initial)")
def testBasic(self):
"""Two basic sequences"""
sequences = [
[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15]
]
labels = ["A", "B"]
sequences = [self.sequenceMachine.generateFromNumbers(s) for s in sequences]
for _ in xrange(10):
self._feedSequences(sequences, sequenceLabels=labels)
def testOverlapping(self):
"""Overlapping sequences"""
sequences = [
[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[ 0, 16, 17, 18, 19, 20, 21, 7],
[22, 23, 24, 11, 12, 25, 26, 27]
]
labels = ["A", "B", "C", "D"]
sequences = [self.sequenceMachine.generateFromNumbers(s) for s in sequences]
for _ in xrange(10):
self._feedSequences(sequences, sequenceLabels=labels)
def testMerged(self):
"""Two distinct sequences, and merged"""
sequences = [
[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
]
labels = ["A", "B", "AB"]
sequences = [self.sequenceMachine.generateFromNumbers(s) for s in sequences]
for _ in xrange(10):
self._feedSequences(sequences, sequenceLabels=labels)
# TO DO: assert C columns contain some A and some B columns, and assert
# the split is about equal between A and B
def tearDown(self):
self._printInfo()
self._showActivityPlots()
self._showInternalStatePlots(appendTitle=" (final)")
@classmethod
def tearDownClass(cls):
if PLOT >= 1:
print
raw_input("Press any key to exit...")
def _feedSequences(self, sequences,
tmLearn=True, tpLearn=True, sequenceLabels=None):
for i in xrange(len(sequences)):
sequence = sequences[i]
label = sequenceLabels[i] if sequenceLabels is not None else None
for pattern in sequence:
self._feedPattern(pattern, tmLearn=tmLearn, tpLearn=tpLearn,
sequenceLabel=label)
self.tm.reset()
self.tp.reset()
def _printInfo(self):
if VERBOSITY >= 2:
print MonitorMixinBase.mmPrettyPrintTraces(
self.tp.mmGetDefaultTraces(verbosity=3) +
self.tm.mmGetDefaultTraces(verbosity=3),
breakOnResets=self.tm.mmGetTraceResets())
print
if VERBOSITY >= 1:
print MonitorMixinBase.mmPrettyPrintMetrics(
self.tp.mmGetDefaultMetrics() + self.tm.mmGetDefaultMetrics())
print
def _showActivityPlots(self):
if PLOT >= 1:
title = self.shortDescription()
self.tp.mmGetCellActivityPlot(showReset=True, title=title)
def _showInternalStatePlots(self, appendTitle=None):
if PLOT >= 1:
title = self.shortDescription()
if appendTitle:
title += appendTitle
self.tp.mmGetPermanencesPlot(title=title)
def _feedPattern(self, pattern,
tmLearn=True, tpLearn=True, sequenceLabel=None):
# Feed the TM
predictedCells = self.tm.predictiveCells
self.tm.compute(pattern, learn=tmLearn, sequenceLabel=sequenceLabel)
# If requested, feed the TP
if tpLearn is not None:
tpInputVector, correctlyPredictedCells = (
self._formatInputForTP(predictedCells))
activeArray = numpy.zeros(self.tp.getNumColumns())
self.tp.compute(tpInputVector,
tpLearn,
activeArray,
None, # not needed
correctlyPredictedCells,
sequenceLabel=sequenceLabel)
def _formatInputForTP(self, predictedCells):
"""
Given an instance of the TM, format the information we need to send to the
TP.
"""
# all currently active cells in layer 4
tpInputVector = numpy.zeros(
self.tm.numberOfCells()).astype(realDType)
tpInputVector[list(self.tm.activeCells)] = 1
# correctly predicted cells in layer 4
correctlyPredictedCells = numpy.zeros(
self.tm.numberOfCells()).astype(realDType)
correctlyPredictedCells[list(predictedCells & self.tm.activeCells)] = 1
return tpInputVector, correctlyPredictedCells
def setUp(self): self.patternMachine = ConsecutivePatternMachine(100, 5) self.sequenceMachine = SequenceMachine(self.patternMachine)
class TemporalMemoryTest(unittest.TestCase):
def setUp(self):
self.tm = TemporalMemory()
def testInitInvalidParams(self):
# Invalid columnDimensions
kwargs = {"columnDimensions": [], "cellsPerColumn": 32}
self.assertRaises(ValueError, TemporalMemory, **kwargs)
# Invalid cellsPerColumn
kwargs = {"columnDimensions": [2048], "cellsPerColumn": 0}
self.assertRaises(ValueError, TemporalMemory, **kwargs)
kwargs = {"columnDimensions": [2048], "cellsPerColumn": -10}
self.assertRaises(ValueError, TemporalMemory, **kwargs)
def testActivateCorrectlyPredictiveCells(self):
tm = self.tm
prevPredictiveCells = set([0, 237, 1026, 26337, 26339, 55536])
activeColumns = set([32, 47, 823])
prevMatchingCells = set()
(activeCells,
winnerCells,
predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(prevPredictiveCells,
prevMatchingCells,
activeColumns)
self.assertEqual(activeCells, set([1026, 26337, 26339]))
self.assertEqual(winnerCells, set([1026, 26337, 26339]))
self.assertEqual(predictedColumns, set([32, 823]))
self.assertEqual(predictedInactiveCells, set())
def testActivateCorrectlyPredictiveCellsEmpty(self):
tm = self.tm
# No previous predictive cells, no active columns
prevPredictiveCells = set()
activeColumns = set()
prevMatchingCells = set()
(activeCells,
winnerCells,
predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(prevPredictiveCells,
prevMatchingCells,
activeColumns)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
# No previous predictive cells, with active columns
prevPredictiveCells = set()
activeColumns = set([32, 47, 823])
prevMatchingCells = set()
(activeCells,
winnerCells,
predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(prevPredictiveCells,
prevMatchingCells,
activeColumns)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
# No active columns, with previously predictive cells
prevPredictiveCells = set([0, 237, 1026, 26337, 26339, 55536])
activeColumns = set()
prevMatchingCells = set()
(activeCells,
winnerCells,
predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(prevPredictiveCells,
prevMatchingCells,
activeColumns)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(predictedColumns, set())
self.assertEqual(predictedInactiveCells, set())
def testActivateCorrectlyPredictiveCellsOrphan(self):
tm = self.tm
prevPredictiveCells = set([])
activeColumns = set([32, 47, 823])
prevMatchingCells = set([32, 47])
(activeCells,
winnerCells,
predictedColumns,
predictedInactiveCells) = tm.activateCorrectlyPredictiveCells(prevPredictiveCells,
prevMatchingCells,
activeColumns)
self.assertEqual(activeCells, set([]))
self.assertEqual(winnerCells, set([]))
self.assertEqual(predictedColumns, set([]))
self.assertEqual(predictedInactiveCells, set([32,47]))
def testBurstColumns(self):
tm = TemporalMemory(
cellsPerColumn=4,
connectedPermanence=0.50,
minThreshold=1,
seed=42
)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(108)
connections.createSynapse(3, 486, 0.9)
activeColumns = set([0, 1, 26])
predictedColumns = set([26])
prevActiveCells = set([23, 37, 49, 733])
prevWinnerCells = set([23, 37, 49, 733])
(activeCells,
winnerCells,
learningSegments) = tm.burstColumns(activeColumns,
predictedColumns,
prevActiveCells,
prevWinnerCells,
connections)
self.assertEqual(activeCells, set([0, 1, 2, 3, 4, 5, 6, 7]))
self.assertEqual(winnerCells, set([0, 6])) # 6 is randomly chosen cell
self.assertEqual(learningSegments, set([0, 4])) # 4 is new segment created
# Check that new segment was added to winner cell (6) in column 1
self.assertEqual(connections.segmentsForCell(6), set([4]))
def testBurstColumnsEmpty(self):
tm = self.tm
activeColumns = set()
predictedColumns = set()
prevActiveCells = set()
prevWinnerCells = set()
connections = tm.connections
(activeCells,
winnerCells,
learningSegments) = tm.burstColumns(activeColumns,
predictedColumns,
prevActiveCells,
prevWinnerCells,
connections)
self.assertEqual(activeCells, set())
self.assertEqual(winnerCells, set())
self.assertEqual(learningSegments, set())
def testLearnOnSegments(self):
tm = TemporalMemory(maxNewSynapseCount=2)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(1)
connections.createSynapse(1, 733, 0.7)
connections.createSegment(8)
connections.createSynapse(2, 486, 0.9)
connections.createSegment(100)
prevActiveSegments = set([0, 2])
learningSegments = set([1, 3])
prevActiveCells = set([23, 37, 733])
winnerCells = set([0])
prevWinnerCells = set([10, 11, 12, 13, 14])
predictedInactiveCells = set()
prevMatchingSegments = set()
tm.learnOnSegments(prevActiveSegments,
learningSegments,
prevActiveCells,
winnerCells,
prevWinnerCells,
connections,
predictedInactiveCells,
prevMatchingSegments)
# Check segment 0
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.7)
synapseData = connections.dataForSynapse(1)
self.assertAlmostEqual(synapseData.permanence, 0.5)
synapseData = connections.dataForSynapse(2)
self.assertAlmostEqual(synapseData.permanence, 0.8)
# Check segment 1
synapseData = connections.dataForSynapse(3)
self.assertAlmostEqual(synapseData.permanence, 0.8)
self.assertEqual(len(connections.synapsesForSegment(1)), 2)
# Check segment 2
synapseData = connections.dataForSynapse(4)
self.assertAlmostEqual(synapseData.permanence, 0.9)
self.assertEqual(len(connections.synapsesForSegment(2)), 1)
# Check segment 3
self.assertEqual(len(connections.synapsesForSegment(3)), 2)
def testComputePredictiveCells(self):
tm = TemporalMemory(activationThreshold=2, minThreshold=2)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.5)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(1)
connections.createSynapse(1, 733, 0.7)
connections.createSynapse(1, 733, 0.4)
connections.createSegment(1)
connections.createSynapse(2, 974, 0.9)
connections.createSegment(8)
connections.createSynapse(3, 486, 0.9)
connections.createSegment(100)
activeCells = set([23, 37, 733, 974])
(activeSegments,
predictiveCells,
matchingSegments,
matchingCells) = tm.computePredictiveCells(activeCells, connections)
self.assertEqual(activeSegments, set([0]))
self.assertEqual(predictiveCells, set([0]))
self.assertEqual(matchingSegments, set([0,1]))
self.assertEqual(matchingCells, set([0,1]))
def testBestMatchingCell(self):
tm = TemporalMemory(
connectedPermanence=0.50,
minThreshold=1,
seed=42
)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(108)
connections.createSynapse(3, 486, 0.9)
activeCells = set([23, 37, 49, 733])
self.assertEqual(tm.bestMatchingCell(tm.cellsForColumn(0),
activeCells,
connections),
(0, 0))
self.assertEqual(tm.bestMatchingCell(tm.cellsForColumn(3), # column containing cell 108
activeCells,
connections),
(96, None)) # Random cell from column
self.assertEqual(tm.bestMatchingCell(tm.cellsForColumn(999),
activeCells,
connections),
(31972, None)) # Random cell from column
def testBestMatchingCellFewestSegments(self):
tm = TemporalMemory(
columnDimensions=[2],
cellsPerColumn=2,
connectedPermanence=0.50,
minThreshold=1,
seed=42
)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 3, 0.3)
activeSynapsesForSegment = set([])
for _ in range(100):
# Never pick cell 0, always pick cell 1
(cell, _) = tm.bestMatchingCell(tm.cellsForColumn(0),
activeSynapsesForSegment,
connections)
self.assertEqual(cell, 1)
def testBestMatchingSegment(self):
tm = TemporalMemory(
connectedPermanence=0.50,
minThreshold=1
)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
connections.createSegment(0)
connections.createSynapse(1, 49, 0.9)
connections.createSynapse(1, 3, 0.8)
connections.createSegment(1)
connections.createSynapse(2, 733, 0.7)
connections.createSegment(8)
connections.createSynapse(3, 486, 0.9)
activeCells = set([23, 37, 49, 733])
self.assertEqual(tm.bestMatchingSegment(0,
activeCells,
connections),
(0, 2))
self.assertEqual(tm.bestMatchingSegment(1,
activeCells,
connections),
(2, 1))
self.assertEqual(tm.bestMatchingSegment(8,
activeCells,
connections),
(None, None))
self.assertEqual(tm.bestMatchingSegment(100,
activeCells,
connections),
(None, None))
def testLeastUsedCell(self):
tm = TemporalMemory(
columnDimensions=[2],
cellsPerColumn=2,
seed=42
)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 3, 0.3)
for _ in range(100):
# Never pick cell 0, always pick cell 1
self.assertEqual(tm.leastUsedCell(tm.cellsForColumn(0),
connections),
1)
def testAdaptSegment(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
connections.createSynapse(0, 37, 0.4)
connections.createSynapse(0, 477, 0.9)
tm.adaptSegment(0, set([0, 1]), connections,
tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.7)
synapseData = connections.dataForSynapse(1)
self.assertAlmostEqual(synapseData.permanence, 0.5)
synapseData = connections.dataForSynapse(2)
self.assertAlmostEqual(synapseData.permanence, 0.8)
def testAdaptSegmentToMax(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.9)
tm.adaptSegment(0, set([0]), connections,
tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 1.0)
# Now permanence should be at max
tm.adaptSegment(0, set([0]), connections,
tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 1.0)
def testAdaptSegmentToMin(self):
tm = self.tm
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.1)
tm.adaptSegment(0, set(), connections,
tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.0)
# Now permanence should be at min
tm.adaptSegment(0, set(), connections,
tm.permanenceIncrement,
tm.permanenceDecrement)
synapseData = connections.dataForSynapse(0)
self.assertAlmostEqual(synapseData.permanence, 0.0)
def testPickCellsToLearnOn(self):
tm = TemporalMemory(seed=42)
connections = tm.connections
connections.createSegment(0)
winnerCells = set([4, 47, 58, 93])
self.assertEqual(tm.pickCellsToLearnOn(2, 0, winnerCells, connections),
set([4, 58])) # randomly picked
self.assertEqual(tm.pickCellsToLearnOn(100, 0, winnerCells, connections),
set([4, 47, 58, 93]))
self.assertEqual(tm.pickCellsToLearnOn(0, 0, winnerCells, connections),
set())
def testPickCellsToLearnOnAvoidDuplicates(self):
tm = TemporalMemory(seed=42)
connections = tm.connections
connections.createSegment(0)
connections.createSynapse(0, 23, 0.6)
winnerCells = set([23])
# Ensure that no additional (duplicate) cells were picked
self.assertEqual(tm.pickCellsToLearnOn(2, 0, winnerCells, connections),
set())
def testColumnForCell1D(self):
tm = TemporalMemory(
columnDimensions=[2048],
cellsPerColumn=5
)
self.assertEqual(tm.columnForCell(0), 0)
self.assertEqual(tm.columnForCell(4), 0)
self.assertEqual(tm.columnForCell(5), 1)
self.assertEqual(tm.columnForCell(10239), 2047)
def testColumnForCell2D(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=4
)
self.assertEqual(tm.columnForCell(0), 0)
self.assertEqual(tm.columnForCell(3), 0)
self.assertEqual(tm.columnForCell(4), 1)
self.assertEqual(tm.columnForCell(16383), 4095)
def testColumnForCellInvalidCell(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=4
)
try:
tm.columnForCell(16383)
except IndexError:
self.fail("IndexError raised unexpectedly")
args = [16384]
self.assertRaises(IndexError, tm.columnForCell, *args)
args = [-1]
self.assertRaises(IndexError, tm.columnForCell, *args)
def testCellsForColumn1D(self):
tm = TemporalMemory(
columnDimensions=[2048],
cellsPerColumn=5
)
expectedCells = set([5, 6, 7, 8, 9])
self.assertEqual(tm.cellsForColumn(1), expectedCells)
def testCellsForColumn2D(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=4
)
expectedCells = set([256, 257, 258, 259])
self.assertEqual(tm.cellsForColumn(64), expectedCells)
def testCellsForColumnInvalidColumn(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=4
)
try:
tm.cellsForColumn(4095)
except IndexError:
self.fail("IndexError raised unexpectedly")
args = [4096]
self.assertRaises(IndexError, tm.cellsForColumn, *args)
args = [-1]
self.assertRaises(IndexError, tm.cellsForColumn, *args)
def testNumberOfColumns(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=32
)
self.assertEqual(tm.numberOfColumns(), 64 * 64)
def testNumberOfCells(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=32
)
self.assertEqual(tm.numberOfCells(), 64 * 64 * 32)
def testMapCellsToColumns(self):
tm = TemporalMemory(
columnDimensions=[100],
cellsPerColumn=4
)
columnsForCells = tm.mapCellsToColumns(set([0, 1, 2, 5, 399]))
self.assertEqual(columnsForCells[0], set([0, 1, 2]))
self.assertEqual(columnsForCells[1], set([5]))
self.assertEqual(columnsForCells[99], set([399]))
def testWrite(self):
tm1 = TemporalMemory(
columnDimensions=[100],
cellsPerColumn=4,
activationThreshold=7,
initialPermanence=0.37,
connectedPermanence=0.58,
minThreshold=4,
maxNewSynapseCount=18,
permanenceIncrement=0.23,
permanenceDecrement=0.08,
seed=91
)
# Run some data through before serializing
self.patternMachine = PatternMachine(100, 4)
self.sequenceMachine = SequenceMachine(self.patternMachine)
sequence = self.sequenceMachine.generateFromNumbers(range(5))
for _ in range(3):
for pattern in sequence:
tm1.compute(pattern)
proto1 = TemporalMemoryProto_capnp.TemporalMemoryProto.new_message()
tm1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f)
# Load the deserialized proto
tm2 = TemporalMemory.read(proto2)
# Check that the two temporal memory objects have the same attributes
self.assertEqual(tm1, tm2)
# Run a couple records through after deserializing and check results match
tm1.compute(self.patternMachine.get(0))
tm2.compute(self.patternMachine.get(0))
self.assertEqual(tm1.activeCells, tm2.activeCells)
self.assertEqual(tm1.predictiveCells, tm2.predictiveCells)
self.assertEqual(tm1.winnerCells, tm2.winnerCells)
self.assertEqual(tm1.connections, tm2.connections)
tm1.compute(self.patternMachine.get(3))
tm2.compute(self.patternMachine.get(3))
self.assertEqual(tm1.activeCells, tm2.activeCells)
self.assertEqual(tm1.predictiveCells, tm2.predictiveCells)
self.assertEqual(tm1.winnerCells, tm2.winnerCells)
self.assertEqual(tm1.connections, tm2.connections)
def experiment1():
paramDir = 'params/1024_baseline/5_trainingPasses.yaml'
outputDir = 'results/'
params = yaml.safe_load(open(paramDir, 'r'))
options = {'plotVerbosity': 2, 'consoleVerbosity': 2}
plotVerbosity = 2
consoleVerbosity = 1
print "Running SDR overlap experiment...\n"
print "Params dir: {0}".format(paramDir)
print "Output dir: {0}\n".format(outputDir)
# Dimensionality of sequence patterns
patternDimensionality = params["patternDimensionality"]
# Cardinality (ON / true bits) of sequence patterns
patternCardinality = params["patternCardinality"]
# TODO If this parameter is to be supported, the sequence generation code
# below must change
# Number of unique patterns from which sequences are built
# patternAlphabetSize = params["patternAlphabetSize"]
# Length of sequences shown to network
sequenceLength = params["sequenceLength"]
# Number of sequences used. Sequences may share common elements.
numberOfSequences = params["numberOfSequences"]
# Number of sequence passes for training the TM. Zero => no training.
trainingPasses = params["trainingPasses"]
tmParamOverrides = params["temporalMemoryParams"]
upParamOverrides = params["unionPoolerParams"]
# Generate a sequence list and an associated labeled list (both containing a
# set of sequences separated by None)
start = time.time()
print "\nGenerating sequences..."
patternAlphabetSize = sequenceLength * numberOfSequences
patternMachine = PatternMachine(patternDimensionality, patternCardinality,
patternAlphabetSize)
sequenceMachine = SequenceMachine(patternMachine)
numbers = sequenceMachine.generateNumbers(numberOfSequences,
sequenceLength)
generatedSequences = sequenceMachine.generateFromNumbers(numbers)
sequenceLabels = [
str(numbers[i + i * sequenceLength:i + (i + 1) * sequenceLength])
for i in xrange(numberOfSequences)
]
labeledSequences = []
for label in sequenceLabels:
for _ in xrange(sequenceLength):
labeledSequences.append(label)
labeledSequences.append(None)
# Set up the Temporal Memory and Union Pooler network
print "\nCreating network..."
experiment = UnionTemporalPoolerExperiment(tmParamOverrides,
upParamOverrides)
# Train only the Temporal Memory on the generated sequences
if trainingPasses > 0:
print "\nTraining Temporal Memory..."
if consoleVerbosity > 0:
print "\nPass\tBursting Columns Mean\tStdDev\tMax"
for i in xrange(trainingPasses):
experiment.runNetworkOnSequences(
generatedSequences,
labeledSequences,
tmLearn=True,
upLearn=None,
verbosity=consoleVerbosity,
progressInterval=_SHOW_PROGRESS_INTERVAL)
if consoleVerbosity > 0:
stats = experiment.getBurstingColumnsStats()
print "{0}\t{1}\t{2}\t{3}".format(i, stats[0], stats[1],
stats[2])
# Reset the TM monitor mixin's records accrued during this training pass
# experiment.tm.mmClearHistory()
print
print MonitorMixinBase.mmPrettyPrintMetrics(
experiment.tm.mmGetDefaultMetrics())
print
experiment.tm.mmClearHistory()
experiment.up.mmClearHistory()
print "\nRunning test phase..."
inputSequences = generatedSequences
inputCategories = labeledSequences
tmLearn = True
upLearn = False
classifierLearn = False
currentTime = time.time()
experiment.tm.reset()
experiment.up.reset()
poolingActivationTrace = numpy.zeros((experiment.up._numColumns, 1))
activeCellsTrace = numpy.zeros((experiment.up._numColumns, 1))
activeSPTrace = numpy.zeros((experiment.up._numColumns, 1))
for _ in xrange(trainingPasses):
experiment.tm.reset()
for i in xrange(len(inputSequences)):
sensorPattern = inputSequences[i]
inputCategory = inputCategories[i]
if sensorPattern is None:
pass
else:
experiment.tm.compute(sensorPattern,
learn=tmLearn,
sequenceLabel=inputCategory)
if upLearn is not None:
activeCells, predActiveCells, burstingCols, = experiment.getUnionTemporalPoolerInput(
)
experiment.up.compute(activeCells,
predActiveCells,
learn=upLearn,
sequenceLabel=inputCategory)
currentPoolingActivation = experiment.up._poolingActivation
currentPoolingActivation = experiment.up._poolingActivation.reshape(
(experiment.up._numColumns, 1))
poolingActivationTrace = numpy.concatenate(
(poolingActivationTrace, currentPoolingActivation), 1)
currentUnionSDR = numpy.zeros(
(experiment.up._numColumns, 1))
currentUnionSDR[experiment.up._unionSDR] = 1
activeCellsTrace = numpy.concatenate(
(activeCellsTrace, currentUnionSDR), 1)
currentSPSDR = numpy.zeros((experiment.up._numColumns, 1))
currentSPSDR[experiment.up._activeCells] = 1
activeSPTrace = numpy.concatenate(
(activeSPTrace, currentSPSDR), 1)
print "\nPass\tBursting Columns Mean\tStdDev\tMax"
stats = experiment.getBurstingColumnsStats()
print "{0}\t{1}\t{2}\t{3}".format(0, stats[0], stats[1], stats[2])
print
print MonitorMixinBase.mmPrettyPrintMetrics(\
experiment.tm.mmGetDefaultMetrics() + experiment.up.mmGetDefaultMetrics())
print
experiment.tm.mmClearHistory()
# estimate fraction of shared bits across adjacent time point
unionSDRshared = experiment.up._mmComputeUnionSDRdiff()
bitLifeList = experiment.up._mmComputeBitLifeStats()
bitLife = numpy.array(bitLifeList)
# Plot SP outputs, UP persistence and UP outputs in testing phase
def showSequenceStartLine(ax, trainingPasses, sequenceLength):
for i in xrange(trainingPasses):
ax.vlines(i * sequenceLength, 0, 100, linestyles='--')
plt.figure()
ncolShow = 100
f, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3)
ax1.imshow(activeSPTrace[1:ncolShow, :],
cmap=cm.Greys,
interpolation="nearest",
aspect='auto')
showSequenceStartLine(ax1, trainingPasses, sequenceLength)
ax1.set_title('SP SDR')
ax1.set_ylabel('Columns')
ax2.imshow(poolingActivationTrace[1:100, :],
cmap=cm.Greys,
interpolation="nearest",
aspect='auto')
showSequenceStartLine(ax2, trainingPasses, sequenceLength)
ax2.set_title('Persistence')
ax3.imshow(activeCellsTrace[1:ncolShow, :],
cmap=cm.Greys,
interpolation="nearest",
aspect='auto')
showSequenceStartLine(ax3, trainingPasses, sequenceLength)
plt.title('Union SDR')
ax2.set_xlabel('Time (steps)')
pp = PdfPages('results/UnionPoolingOnLearnedTM_Experiment1.pdf')
pp.savefig()
pp.close()
f, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1)
ax1.plot((sum(activeCellsTrace)) / experiment.up._numColumns * 100)
ax1.set_ylabel('Union SDR size (%)')
ax1.set_xlabel('Time (steps)')
ax1.set_ylim(0, 25)
ax2.plot(unionSDRshared)
ax2.set_ylabel('Shared Bits')
ax2.set_xlabel('Time (steps)')
ax3.hist(bitLife)
ax3.set_xlabel('Life duration for each bit')
pp = PdfPages('results/UnionSDRproperty_Experiment1.pdf')
pp.savefig()
pp.close()
