def testCellsForColumn1D(self):
tm = TemporalMemory(
columnDimensions=[2048],
cellsPerColumn=5
)
expectedCells = [5, 6, 7, 8, 9]
self.assertEqual(tm.cellsForColumn(1), expectedCells)
def __init__(
self,
bottomUpInputSize,
bottomUpOnBits,
):
self.bottomUpInputSize = bottomUpInputSize
self.bottomUpOnBits = bottomUpOnBits
self.trainingIterations = 0
self.tm = TemporalMemory(
# Must be the same dimensions as the SP
columnDimensions=(self.bottomUpInputSize, ),
# How many cells in each mini-column.
cellsPerColumn=4,
# A segment is active if it has >= activationThreshold connected synapses
# that are active due to infActiveState
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.5,
# Minimum number of active synapses for a segment to be considered during
# search for the best-matching segments.
minThreshold=1,
# The max number of synapses added to a segment during learning
maxNewSynapseCount=3,
#permanenceIncrement=0.01,
#permanenceDecrement=0.01,
predictedSegmentDecrement=0.0005,
maxSegmentsPerCell=3,
maxSynapsesPerSegment=3,
seed=42)
def testDestroySegmentsWithTooFewSynapsesToBeMatching(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=42)
prevActiveColumns = [0]
prevActiveCells = [0, 1, 2, 3]
activeColumns = [2]
expectedActiveCell = 5
matchingSegment = tm.connections.createSegment(expectedActiveCell)
tm.connections.createSynapse(matchingSegment, prevActiveCells[0], .015)
tm.connections.createSynapse(matchingSegment, prevActiveCells[1], .015)
tm.connections.createSynapse(matchingSegment, prevActiveCells[2], .015)
tm.connections.createSynapse(matchingSegment, prevActiveCells[3], .015)
tm.compute(prevActiveColumns, True)
tm.compute(activeColumns, True)
self.assertEqual(0, tm.connections.numSegments(expectedActiveCell))
def testCellsForColumn2D(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=4
)
expectedCells = [256, 257, 258, 259]
self.assertEqual(tm.cellsForColumn(64), expectedCells)
def testDestroyWeakSynapseOnActiveReinforce(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=42)
previousActiveColumns = [0]
previousActiveCells = [0, 1, 2, 3]
activeColumns = [2]
activeCell = 5
activeSegment = tm.connections.createSegment(activeCell)
tm.connections.createSynapse(activeSegment, previousActiveCells[0], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[1], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[2], .5)
# Weak inactive synapse.
tm.connections.createSynapse(activeSegment, previousActiveCells[3], .009)
tm.compute(previousActiveColumns, True)
tm.compute(activeColumns, True)
self.assertEqual(3, tm.connections.numSynapses(activeSegment))
def testMaxSynapsesPerSegmentGetter(self):
tm = TemporalMemory(
columnDimensions=[32,32],
cellsPerColumn=16,
maxSynapsesPerSegment=150
)
self.assertEqual(tm.getMaxSynapsesPerSegment(), 150)
def testMaxSegmentsPerCellGetter(self):
tm = TemporalMemory(
columnDimensions=[64,64],
cellsPerColumn=32,
maxSegmentsPerCell=200
)
self.assertEqual(tm.getMaxSegmentsPerCell(), 200)
def testReachSegmentLimitMultipleTimes(self):
""" Hit the maxSegmentsPerCell threshold multiple times. Make sure it
works more than once.
"""
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.50,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSegmentsPerCell=2)
tm.createSegment(10)
self.assertEqual(1, tm.connections.numSegments())
tm.createSegment(10)
self.assertEqual(2, tm.connections.numSegments())
tm.createSegment(10)
self.assertEqual(2, tm.connections.numSegments())
tm.createSegment(10)
self.assertEqual(2, tm.connections.numSegments())
def testWriteRead(self):
tm1 = TemporalMemory(
columnDimensions=(32,),
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=0.1,
permanenceDecrement=0.1,
predictedSegmentDecrement=0.0,
seed=42
)
self.serializationTestPrepare(tm1)
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)
self.assertEqual(tm1, tm2)
self.serializationTestVerify(tm2)
def testMaxSegmentsPerCellGetter(self):
tm = TemporalMemory(
columnDimensions=[64,64],
cellsPerColumn=32,
maxSegmentsPerCell=200
)
self.assertEqual(tm.getMaxSegmentsPerCell(), 200)
def testMaxSynapsesPerSegmentGetter(self):
tm = TemporalMemory(
columnDimensions=[32,32],
cellsPerColumn=16,
maxSynapsesPerSegment=150
)
self.assertEqual(tm.getMaxSynapsesPerSegment(), 150)
def testCellsForColumn2D(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=4
)
expectedCells = [256, 257, 258, 259]
self.assertEqual(tm.cellsForColumn(64), expectedCells)
def testCellsForColumn1D(self):
tm = TemporalMemory(
columnDimensions=[2048],
cellsPerColumn=5
)
expectedCells = [5, 6, 7, 8, 9]
self.assertEqual(tm.cellsForColumn(1), expectedCells)
def testWriteRead(self):
tm1 = TemporalMemory(
columnDimensions=(32,),
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=0.1,
permanenceDecrement=0.1,
predictedSegmentDecrement=0.0,
seed=42
)
self.serializationTestPrepare(tm1)
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)
self.assertEqual(tm1, tm2)
self.serializationTestVerify(tm2)
def testReinforceCorrectlyActiveSegments(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.08,
predictedSegmentDecrement=0.02,
seed=42)
prevActiveColumns = [0]
prevActiveCells = [0,1,2,3]
activeColumns = [1]
activeCell = 5
activeSegment = tm.connections.createSegment(activeCell)
as1 = tm.connections.createSynapse(activeSegment, prevActiveCells[0], .5)
as2 = tm.connections.createSynapse(activeSegment, prevActiveCells[1], .5)
as3 = tm.connections.createSynapse(activeSegment, prevActiveCells[2], .5)
is1 = tm.connections.createSynapse(activeSegment, 81, .5) #inactive synapse
tm.compute(prevActiveColumns, True)
tm.compute(activeColumns, True)
self.assertAlmostEqual(.6, tm.connections.dataForSynapse(as1).permanence)
self.assertAlmostEqual(.6, tm.connections.dataForSynapse(as2).permanence)
self.assertAlmostEqual(.6, tm.connections.dataForSynapse(as3).permanence)
self.assertAlmostEqual(.42, tm.connections.dataForSynapse(is1).permanence)
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 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 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 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 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 testReachSegmentLimitMultipleTimes(self):
""" Hit the maxSegmentsPerCell threshold multiple times. Make sure it
works more than once.
"""
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.50,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSegmentsPerCell=2)
tm.createSegment(10)
self.assertEqual(1, tm.connections.numSegments())
tm.createSegment(10)
self.assertEqual(2, tm.connections.numSegments())
tm.createSegment(10)
self.assertEqual(2, tm.connections.numSegments())
tm.createSegment(10)
self.assertEqual(2, tm.connections.numSegments())
def testActivateCorrectlyPredictiveCells(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.5,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0]
activeColumns = [1]
previousActiveCells = [0,1,2,3]
expectedActiveCells = [4]
activeSegment = tm.createSegment(expectedActiveCells[0])
tm.connections.createSynapse(activeSegment, previousActiveCells[0], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[1], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[2], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[3], .5)
tm.compute(previousActiveColumns, True)
self.assertEqual(expectedActiveCells, tm.getPredictiveCells())
tm.compute(activeColumns, True)
self.assertEqual(expectedActiveCells, tm.getActiveCells())
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 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 testNewSegmentAddSynapsesToSubsetOfWinnerCells(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=2,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0, 1, 2]
activeColumns = [4]
tm.compute(previousActiveColumns, True)
prevWinnerCells = tm.getWinnerCells() #[0, 8, 7]
self.assertEqual(3, len(prevWinnerCells))
tm.compute(activeColumns, True)
winnerCells = tm.getWinnerCells() #[18]
self.assertEqual(1, len(winnerCells))
segments = list(tm.connections.segmentsForCell(winnerCells[0]))
self.assertEqual(1, len(segments))
synapses = list(tm.connections.synapsesForSegment(segments[0]))
self.assertEqual(2, len(synapses))
for synapse in synapses:
synapseData = tm.connections.dataForSynapse(synapse)
self.assertAlmostEqual(.21, synapseData.permanence)
self.assertTrue(synapseData.presynapticCell in prevWinnerCells)
def testMatchingSegmentAddSynapsesToAllWinnerCells(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=1,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0, 1]
prevWinnerCells = [0, 1]
activeColumns = [4]
matchingSegment = tm.createSegment(4)
tm.connections.createSynapse(matchingSegment, 0, .5)
tm.compute(previousActiveColumns, True)
self.assertEqual(prevWinnerCells, tm.getWinnerCells())
tm.compute(activeColumns)
synapses = tm.connections.synapsesForSegment(matchingSegment)
self.assertEqual(2, len(synapses))
for synapse in synapses:
synapseData = tm.connections.dataForSynapse(synapse)
if synapseData.presynapticCell != 0:
self.assertAlmostEqual(.21, synapseData.permanence)
self.assertEqual(prevWinnerCells[1], synapseData.presynapticCell)
def testActivateCorrectlyPredictiveCells(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.5,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0]
activeColumns = [1]
previousActiveCells = [0,1,2,3]
expectedActiveCells = [4]
activeSegment = tm.createSegment(expectedActiveCells[0])
tm.connections.createSynapse(activeSegment, previousActiveCells[0], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[1], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[2], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[3], .5)
tm.compute(previousActiveColumns, True)
self.assertEqual(expectedActiveCells, tm.getPredictiveCells())
tm.compute(activeColumns, True)
self.assertEqual(expectedActiveCells, tm.getActiveCells())
def testDestroySegmentsThenReachLimit(self):
""" Destroy some segments then verify that the maxSegmentsPerCell is still
correctly applied.
"""
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.50,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSegmentsPerCell=2)
segment1 = tm.createSegment(11)
segment2 = tm.createSegment(11)
self.assertEqual(2, tm.connections.numSegments())
tm.connections.destroySegment(segment1)
tm.connections.destroySegment(segment2)
self.assertEqual(0, tm.connections.numSegments())
tm.createSegment(11)
self.assertEqual(1, tm.connections.numSegments())
tm.createSegment(11)
self.assertEqual(2, tm.connections.numSegments())
segment3 = tm.createSegment(11)
self.assertEqual(2, tm.connections.numSegments(11))
self.assertEqual(2, tm.connections.numSegments())
def testNoChangeToMatchingSegmentsInPredictedActiveColumn(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0]
activeColumns = [1]
previousActiveCells = [0,1,2,3]
expectedActiveCells = [4]
otherburstingCells = [5,6,7]
activeSegment = tm.createSegment(expectedActiveCells[0])
tm.connections.createSynapse(activeSegment, previousActiveCells[0], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[1], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[2], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[3], .5)
matchingSegmentOnSameCell = tm.createSegment(
expectedActiveCells[0])
s1 = tm.connections.createSynapse(matchingSegmentOnSameCell,
previousActiveCells[0], .3)
s2 = tm.connections.createSynapse(matchingSegmentOnSameCell,
previousActiveCells[1], .3)
matchingSegmentOnOtherCell = tm.createSegment(
otherburstingCells[0])
s3 = tm.connections.createSynapse(matchingSegmentOnOtherCell,
previousActiveCells[0], .3)
s4 = tm.connections.createSynapse(matchingSegmentOnOtherCell,
previousActiveCells[1], .3)
tm.compute(previousActiveColumns, True)
self.assertEqual(expectedActiveCells, tm.getPredictiveCells())
tm.compute(activeColumns, True)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s1).permanence)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s2).permanence)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s3).permanence)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s4).permanence)
class HTM(object):
"""Class implementing Traditional Temporal Memory"""
def __init__(self,
bottomUpInputSize,
bottomUpOnBits,
seed
):
self.bottomUpInputSize = bottomUpInputSize
self.bottomUpOnBits = bottomUpOnBits
self.seed = seed
self.trainingIterations = 0
self.tm = TemporalMemory(
# Must be the same dimensions as the SP
columnDimensions=(self.bottomUpInputSize,),
# How many cells in each mini-column.
cellsPerColumn=4,
# A segment is active if it has >= activationThreshold connected synapses
# that are active due to infActiveState
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.5,
# Minimum number of active synapses for a segment to be considered during
# search for the best-matching segments.
minThreshold=1,
# The max number of synapses added to a segment during learning
maxNewSynapseCount=3,
#permanenceIncrement=0.01,
#permanenceDecrement=0.01,
predictedSegmentDecrement=0.0005,
maxSegmentsPerCell=3,
maxSynapsesPerSegment=3,
seed=self.seed
)
def compute(self, bottomUpSDR, learn):
if learn:
# During learning we provide the current pose angle as bottom up input
self.train(bottomUpSDR)
self.trainingIterations += 1
else:
print >>sys.stderr, "Learn: ", learn
def train(self, bottomUp):
#print >> sys.stderr, "Bottom up: ", bottomUp
self.tm.compute(bottomUp,
learn=True)
def main():
tm0 = TM()
tm = TemporalMemory(
# Must be the same dimensions as the SP
columnDimensions=(32768,),
# How many cells in each mini-column.
cellsPerColumn=4,
# A segment is active if it has >= activationThreshold connected synapses
# that are active due to infActiveState
activationThreshold=1,#1,4(melhor),
#initialPermanence=0.4,
connectedPermanence=0,
# Minimum number of active synapses for a segment to be considered during
# search for the best-matching segments.
minThreshold=1, #1
# The max number of synapses added to a segment during learning
maxNewSynapseCount=1, #6
#permanenceIncrement=0.1,
#permanenceDecrement=0.1,
predictedSegmentDecrement=0.0005,#0.0001,#0.0005,
maxSegmentsPerCell=1, #8 16(colou)
maxSynapsesPerSegment=1, #8 16(colou)
seed=42
)
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 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 __init__(self,
numberOfCols=16384,
cellsPerColumn=8,
activationThreshold=60,
minThreshold=60,
verbosity=0
):
self.tm = TM(
columnDimensions=(numberOfCols,),
cellsPerColumn=cellsPerColumn,
activationThreshold=activationThreshold,
minThreshold=minThreshold,
maxNewSynapseCount=164,
initialPermanence=0.21,
connectedPermanence=0.3,
permanenceIncrement=0.1,
permanenceDecrement=0.0,
predictedSegmentDecrement=0.0,
)
if verbosity > 0:
print "TM Params:"
print "columnDimensions: {}".format(self.tm.getColumnDimensions())
print "cellsPerColumn: {}".format(self.tm.getCellsPerColumn())
print "activationThreshold: {}".format(self.tm.getActivationThreshold())
print "minThreshold: {}".format(self.tm.getMinThreshold())
print "maxNewSynapseCount {}".format(self.tm.getMaxNewSynapseCount())
print "initialPermanence {}".format(self.tm.getInitialPermanence())
print "connectedPermanence {}".format(self.tm.getConnectedPermanence())
print "permanenceIncrement {}".format(self.tm.getPermanenceIncrement())
print "permanenceDecrement {}".format(self.tm.getPermanenceDecrement())
print "predictedSegmentDecrement {}".format(self.tm.getPredictedSegmentDecrement())
def testPunishMatchingSegmentsInInactiveColumns(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=42)
previousActiveColumns = [0]
previousActiveCells = [0, 1, 2, 3]
activeColumns = [1]
previousInactiveCell = 81
activeSegment = tm.connections.createSegment(42)
as1 = tm.connections.createSynapse(activeSegment,
previousActiveCells[0], .5)
as2 = tm.connections.createSynapse(activeSegment,
previousActiveCells[1], .5)
as3 = tm.connections.createSynapse(activeSegment,
previousActiveCells[2], .5)
is1 = tm.connections.createSynapse(activeSegment,
previousInactiveCell, .5)
matchingSegment = tm.connections.createSegment(43)
as4 = tm.connections.createSynapse(matchingSegment,
previousActiveCells[0], .5)
as5 = tm.connections.createSynapse(matchingSegment,
previousActiveCells[1], .5)
is2 = tm.connections.createSynapse(matchingSegment,
previousInactiveCell, .5)
tm.compute(previousActiveColumns, True)
tm.compute(activeColumns, True)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as1).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as2).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as3).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as4).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as5).permanence)
self.assertAlmostEqual(.50, tm.connections.dataForSynapse(is1).permanence)
self.assertAlmostEqual(.50, tm.connections.dataForSynapse(is2).permanence)
def testReadTestFile(self):
with open("TemporalMemorySerializationWrite.tmp", "r") as f:
proto = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f)
# Load the deserialized proto
tm = TemporalMemory.read(proto)
self.serializationTestVerify(tm)
def testReadTestFile(self):
with open("TemporalMemorySerializationWrite.tmp", "r") as f:
proto = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f)
# Load the deserialized proto
tm = TemporalMemory.read(proto)
self.serializationTestVerify(tm)
def testDestroySegmentsWithTooFewSynapsesToBeMatching(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=42)
prevActiveColumns = [0]
prevActiveCells = [0, 1, 2, 3]
activeColumns = [2]
expectedActiveCell = 5
matchingSegment = tm.createSegment(expectedActiveCell)
tm.connections.createSynapse(matchingSegment, prevActiveCells[0], .015)
tm.connections.createSynapse(matchingSegment, prevActiveCells[1], .015)
tm.connections.createSynapse(matchingSegment, prevActiveCells[2], .015)
tm.connections.createSynapse(matchingSegment, prevActiveCells[3], .015)
tm.compute(prevActiveColumns, True)
tm.compute(activeColumns, True)
self.assertEqual(0, tm.connections.numSegments(expectedActiveCell))
def testDestroyWeakSynapseOnActiveReinforce(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=42)
previousActiveColumns = [0]
previousActiveCells = [0, 1, 2, 3]
activeColumns = [2]
activeCell = 5
activeSegment = tm.createSegment(activeCell)
tm.connections.createSynapse(activeSegment, previousActiveCells[0], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[1], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[2], .5)
# Weak inactive synapse.
tm.connections.createSynapse(activeSegment, previousActiveCells[3], .009)
tm.compute(previousActiveColumns, True)
tm.compute(activeColumns, True)
self.assertEqual(3, tm.connections.numSynapses(activeSegment))
def testMatchingSegmentAddSynapsesToAllWinnerCells(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=1,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0, 1]
prevWinnerCells = [0, 1]
activeColumns = [4]
matchingSegment = tm.createSegment(4)
tm.connections.createSynapse(matchingSegment, 0, .5)
tm.compute(previousActiveColumns, True)
self.assertEqual(prevWinnerCells, tm.getWinnerCells())
tm.compute(activeColumns)
synapses = tm.connections.synapsesForSegment(matchingSegment)
self.assertEqual(2, len(synapses))
for synapse in synapses:
synapseData = tm.connections.dataForSynapse(synapse)
if synapseData.presynapticCell != 0:
self.assertAlmostEqual(.21, synapseData.permanence)
self.assertEqual(prevWinnerCells[1], synapseData.presynapticCell)
def testNewSegmentAddSynapsesToSubsetOfWinnerCells(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=2,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0, 1, 2]
activeColumns = [4]
tm.compute(previousActiveColumns, True)
prevWinnerCells = tm.getWinnerCells() #[0, 8, 7]
self.assertEqual(3, len(prevWinnerCells))
tm.compute(activeColumns, True)
winnerCells = tm.getWinnerCells() #[18]
self.assertEqual(1, len(winnerCells))
segments = list(tm.connections.segmentsForCell(winnerCells[0]))
self.assertEqual(1, len(segments))
synapses = list(tm.connections.synapsesForSegment(segments[0]))
self.assertEqual(2, len(synapses))
for synapse in synapses:
synapseData = tm.connections.dataForSynapse(synapse)
self.assertAlmostEqual(.21, synapseData.permanence)
self.assertTrue(synapseData.presynapticCell in prevWinnerCells)
def testReinforceCorrectlyActiveSegments(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.08,
predictedSegmentDecrement=0.02,
seed=42)
prevActiveColumns = [0]
prevActiveCells = [0,1,2,3]
activeColumns = [1]
activeCell = 5
activeSegment = tm.createSegment(activeCell)
as1 = tm.connections.createSynapse(activeSegment, prevActiveCells[0], .5)
as2 = tm.connections.createSynapse(activeSegment, prevActiveCells[1], .5)
as3 = tm.connections.createSynapse(activeSegment, prevActiveCells[2], .5)
is1 = tm.connections.createSynapse(activeSegment, 81, .5) #inactive synapse
tm.compute(prevActiveColumns, True)
tm.compute(activeColumns, True)
self.assertAlmostEqual(.6, tm.connections.dataForSynapse(as1).permanence)
self.assertAlmostEqual(.6, tm.connections.dataForSynapse(as2).permanence)
self.assertAlmostEqual(.6, tm.connections.dataForSynapse(as3).permanence)
self.assertAlmostEqual(.42, tm.connections.dataForSynapse(is1).permanence)
def testRecycleWeakestSynapseToMakeRoomForNewSynapse(self):
tm = TemporalMemory(columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=1,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSynapsesPerSegment=3)
prevActiveColumns = [0, 1, 2]
prevWinnerCells = [0, 1, 2]
activeColumns = [4]
matchingSegment = tm.connections.createSegment(4)
tm.connections.createSynapse(matchingSegment, 81, .6)
weakestSynapse = tm.connections.createSynapse(matchingSegment, 0, .11)
tm.compute(prevActiveColumns)
self.assertEqual(prevWinnerCells, tm.getWinnerCells())
tm.compute(activeColumns)
synapses = tm.connections.synapsesForSegment(matchingSegment)
self.assertEqual(3, len(synapses))
presynapticCells = set(synapse.presynapticCell for synapse in synapses)
self.assertFalse(0 in presynapticCells)
def testDestroySegmentsThenReachLimit(self):
""" Destroy some segments then verify that the maxSegmentsPerCell is still
correctly applied.
"""
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.50,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSegmentsPerCell=2)
segment1 = tm.createSegment(11)
segment2 = tm.createSegment(11)
self.assertEqual(2, tm.connections.numSegments())
tm.connections.destroySegment(segment1)
tm.connections.destroySegment(segment2)
self.assertEqual(0, tm.connections.numSegments())
tm.createSegment(11)
self.assertEqual(1, tm.connections.numSegments())
tm.createSegment(11)
self.assertEqual(2, tm.connections.numSegments())
segment3 = tm.createSegment(11)
self.assertEqual(2, tm.connections.numSegments(11))
self.assertEqual(2, tm.connections.numSegments())
def testWriteTestFile(self):
tm = TemporalMemory(columnDimensions=(32, ),
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=0.1,
permanenceDecrement=0.1,
predictedSegmentDecrement=0.0,
seed=42)
self.serializationTestPrepare(tm)
proto = TemporalMemoryProto_capnp.TemporalMemoryProto.new_message()
tm.write(proto)
with open("TemporalMemorySerializationWrite.tmp", "w") as f:
proto.write(f)
def testNoChangeToMatchingSegmentsInPredictedActiveColumn(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0]
activeColumns = [1]
previousActiveCells = [0,1,2,3]
expectedActiveCells = [4]
otherburstingCells = [5,6,7]
activeSegment = tm.createSegment(expectedActiveCells[0])
tm.connections.createSynapse(activeSegment, previousActiveCells[0], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[1], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[2], .5)
tm.connections.createSynapse(activeSegment, previousActiveCells[3], .5)
matchingSegmentOnSameCell = tm.createSegment(
expectedActiveCells[0])
s1 = tm.connections.createSynapse(matchingSegmentOnSameCell,
previousActiveCells[0], .3)
s2 = tm.connections.createSynapse(matchingSegmentOnSameCell,
previousActiveCells[1], .3)
matchingSegmentOnOtherCell = tm.createSegment(
otherburstingCells[0])
s3 = tm.connections.createSynapse(matchingSegmentOnOtherCell,
previousActiveCells[0], .3)
s4 = tm.connections.createSynapse(matchingSegmentOnOtherCell,
previousActiveCells[1], .3)
tm.compute(previousActiveColumns, True)
self.assertEqual(expectedActiveCells, tm.getPredictiveCells())
tm.compute(activeColumns, True)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s1).permanence)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s2).permanence)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s3).permanence)
self.assertAlmostEqual(.3, tm.connections.dataForSynapse(s4).permanence)
def testBurstUnpredictedColumns(self):
tm = TemporalMemory(columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.5,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
activeColumns = [0]
burstingCells = [0, 1, 2, 3]
tm.compute(activeColumns, True)
self.assertEqual(burstingCells, tm.getActiveCells())
def testReinforceSelectedMatchingSegmentInBurstingColumn(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.08,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0]
previousActiveCells = [0,1,2,3]
activeColumns = [1]
burstingCells = [4,5,6,7]
selectedMatchingSegment = tm.connections.createSegment(burstingCells[0])
as1 = tm.connections.createSynapse(selectedMatchingSegment,
previousActiveCells[0], .3)
as2 = tm.connections.createSynapse(selectedMatchingSegment,
previousActiveCells[1], .3)
as3 = tm.connections.createSynapse(selectedMatchingSegment,
previousActiveCells[2], .3)
is1 = tm.connections.createSynapse(selectedMatchingSegment, 81, .3)
otherMatchingSegment = tm.connections.createSegment(burstingCells[1])
tm.connections.createSynapse(otherMatchingSegment,
previousActiveCells[0], .3)
tm.connections.createSynapse(otherMatchingSegment,
previousActiveCells[1], .3)
tm.connections.createSynapse(otherMatchingSegment, 81, .3)
tm.compute(previousActiveColumns, True)
tm.compute(activeColumns, True)
self.assertAlmostEqual(.4, tm.connections.dataForSynapse(as1).permanence)
self.assertAlmostEqual(.4, tm.connections.dataForSynapse(as2).permanence)
self.assertAlmostEqual(.4, tm.connections.dataForSynapse(as3).permanence)
self.assertAlmostEqual(.22, tm.connections.dataForSynapse(is1).permanence)
def testBurstUnpredictedColumns(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.5,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
activeColumns = [0]
burstingCells = [0, 1, 2, 3]
tm.compute(activeColumns, True)
self.assertEqual(burstingCells, tm.getActiveCells())
def testWriteTestFile(self):
tm = TemporalMemory(
columnDimensions=(32,),
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=0.1,
permanenceDecrement=0.1,
predictedSegmentDecrement=0.0,
seed=42
)
self.serializationTestPrepare(tm)
proto = TemporalMemoryProto_capnp.TemporalMemoryProto.new_message()
tm.write(proto)
with open("TemporalMemorySerializationWrite.tmp", "w") as f:
proto.write(f)
def testNoNewSegmentIfNotEnoughWinnerCells(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
zeroColumns = []
activeColumns = [0]
tm.compute(zeroColumns, True)
tm.compute(activeColumns, True)
self.assertEqual(0, tm.connections.numSegments())
def testRecycleWeakestSynapseToMakeRoomForNewSynapse(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=1,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSynapsesPerSegment=3)
prevActiveColumns = [0, 1, 2]
prevWinnerCells = [0, 1, 2]
activeColumns = [4]
matchingSegment = tm.connections.createSegment(4)
tm.connections.createSynapse(matchingSegment, 81, .6)
weakestSynapse = tm.connections.createSynapse(matchingSegment, 0, .11)
tm.compute(prevActiveColumns)
self.assertEqual(prevWinnerCells, tm.getWinnerCells())
tm.compute(activeColumns)
synapses = tm.connections.synapsesForSegment(matchingSegment)
self.assertEqual(3, len(synapses))
presynapticCells = set(synapse.presynapticCell for synapse in synapses)
self.assertFalse(0 in presynapticCells)
def testPunishMatchingSegmentsInInactiveColumns(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=42)
previousActiveColumns = [0]
previousActiveCells = [0, 1, 2, 3]
activeColumns = [1]
previousInactiveCell = 81
activeSegment = tm.createSegment(42)
as1 = tm.connections.createSynapse(activeSegment,
previousActiveCells[0], .5)
as2 = tm.connections.createSynapse(activeSegment,
previousActiveCells[1], .5)
as3 = tm.connections.createSynapse(activeSegment,
previousActiveCells[2], .5)
is1 = tm.connections.createSynapse(activeSegment,
previousInactiveCell, .5)
matchingSegment = tm.createSegment(43)
as4 = tm.connections.createSynapse(matchingSegment,
previousActiveCells[0], .5)
as5 = tm.connections.createSynapse(matchingSegment,
previousActiveCells[1], .5)
is2 = tm.connections.createSynapse(matchingSegment,
previousInactiveCell, .5)
tm.compute(previousActiveColumns, True)
tm.compute(activeColumns, True)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as1).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as2).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as3).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as4).permanence)
self.assertAlmostEqual(.48, tm.connections.dataForSynapse(as5).permanence)
self.assertAlmostEqual(.50, tm.connections.dataForSynapse(is1).permanence)
self.assertAlmostEqual(.50, tm.connections.dataForSynapse(is2).permanence)
def testConnectionsNeverChangeWhenLearningDisabled(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=42)
prevActiveColumns = [0]
prevActiveCells = [0, 1, 2, 3]
activeColumns = [1, 2] #1 is predicted, 2 is bursting
prevInactiveCell = 81
expectedActiveCells = [4]
correctActiveSegment = tm.connections.createSegment(expectedActiveCells[0])
tm.connections.createSynapse(correctActiveSegment, prevActiveCells[0], .5)
tm.connections.createSynapse(correctActiveSegment, prevActiveCells[1], .5)
tm.connections.createSynapse(correctActiveSegment, prevActiveCells[2], .5)
wrongMatchingSegment = tm.connections.createSegment(43)
tm.connections.createSynapse(wrongMatchingSegment, prevActiveCells[0], .5)
tm.connections.createSynapse(wrongMatchingSegment, prevActiveCells[1], .5)
tm.connections.createSynapse(wrongMatchingSegment, prevInactiveCell, .5)
before = copy.deepcopy(tm.connections)
tm.compute(prevActiveColumns, False)
tm.compute(activeColumns, False)
self.assertEqual(before, tm.connections)
def definir_TM(N_COLUMNS):
"""
retorna a clase da TM
"""
tm= TemporalMemory(
columnDimensions= (N_COLUMNS,), # number of columns - it must be the same number of SP's columns
cellsPerColumn= 32, # num of cells per colums - this parameter dicatates how many different cotexts TM ...
# could learn, so it's really importante to not select really lower numbers as 2 or 1, and higher numbers ...
#than 40 can be useless, because the number of cells gets REALLY HIGH (40^2048 in this case)
activationThreshold= 13, # Segment: usually each cell has only one segment, but some cells could eventually have more...
#. Segments can be interpreted clusters of distal synapses of one cell in respect to others. Those segments are created ...
# during the learning phase, while a column burst and a "winner cell" search for new segments to create context. ...
# Those segmets are said to be active if the activationThreshold > 13.
initialPermanence= 0.21, # synapses are "connection" between cells within the layer. The permanence is the "strength" of this...
# conection. Though, even if 2 cells have a "permance" value for their synapses, it doesn't mean that they will be connected.
connectedPermanence= 0.5, # two cells will only be connected if the synapse permanence is higher than 0.5
minThreshold= 10, # A segment will only be active/connected if there are more than 10 sucessefull connected synapses within..
#it.
maxNewSynapseCount= 20, # How many synapses can be added to a segment during learning
#incremante and decrement of permanences only occur within a cell activation - same as in SPATIAL POOLER - but here we're talking...
#about cells
permanenceIncrement= 0.1, #amount that will be added o a synapse during learning if the synapse is active or potential active
permanenceDecrement= 0.1, #amount that will be decreased if the synapse is in the dendritic distal segment and if the cell ...
#wasn't active on previous state
#On each active segment, there will be permanence increase of every active synapse, decrease on every inactive synapse and ...
#creation of new synapses to cells that were active in previous state
predictedSegmentDecrement= 0.0005, #punishment for SEGMENTS for incorrect predictions
#from nupic documentation: predictedSegmentDecrement: A good value is just a bit larger than (the column-level sparsity * permanenceIncrement)...
#So, if column-level sparsity is 2% and permanenceIncrement is 0.01, this parameter should be something like 4% * 0.01 = 0.0004).
seed = 1960,
maxSegmentPerCell= 128,
maxSynapsesPerSegment= 32
)
return tm
def testReinforceSelectedMatchingSegmentInBurstingColumn(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=3,
permanenceIncrement=.10,
permanenceDecrement=.08,
predictedSegmentDecrement=0.0,
seed=42)
previousActiveColumns = [0]
previousActiveCells = [0,1,2,3]
activeColumns = [1]
burstingCells = [4,5,6,7]
selectedMatchingSegment = tm.createSegment(burstingCells[0])
as1 = tm.connections.createSynapse(selectedMatchingSegment,
previousActiveCells[0], .3)
as2 = tm.connections.createSynapse(selectedMatchingSegment,
previousActiveCells[1], .3)
as3 = tm.connections.createSynapse(selectedMatchingSegment,
previousActiveCells[2], .3)
is1 = tm.connections.createSynapse(selectedMatchingSegment, 81, .3)
otherMatchingSegment = tm.createSegment(burstingCells[1])
tm.connections.createSynapse(otherMatchingSegment,
previousActiveCells[0], .3)
tm.connections.createSynapse(otherMatchingSegment,
previousActiveCells[1], .3)
tm.connections.createSynapse(otherMatchingSegment, 81, .3)
tm.compute(previousActiveColumns, True)
tm.compute(activeColumns, True)
self.assertAlmostEqual(.4, tm.connections.dataForSynapse(as1).permanence)
self.assertAlmostEqual(.4, tm.connections.dataForSynapse(as2).permanence)
self.assertAlmostEqual(.4, tm.connections.dataForSynapse(as3).permanence)
self.assertAlmostEqual(.22, tm.connections.dataForSynapse(is1).permanence)
def testRecycleWeakestSynapseToMakeRoomForNewSynapse(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=1,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSynapsesPerSegment=4)
prevActiveColumns = [1, 2, 3]
prevWinnerCells = [1, 2, 3]
activeColumns = [4]
matchingSegment = tm.createSegment(4)
tm.connections.createSynapse(matchingSegment, 81, .6)
# Create a weak synapse. Make sure it's not so weak that permanenceIncrement
# destroys it.
tm.connections.createSynapse(matchingSegment, 0, .11)
# Create a synapse that will match.
tm.connections.createSynapse(matchingSegment, 1, .20)
# Create a synapse with a high permanence
tm.connections.createSynapse(matchingSegment, 31, .60)
tm.compute(prevActiveColumns)
self.assertEqual(prevWinnerCells, tm.getWinnerCells())
tm.compute(activeColumns)
synapses = tm.connections.synapsesForSegment(matchingSegment)
self.assertEqual(4, len(synapses))
presynapticCells = set(synapse.presynapticCell for synapse in synapses)
self.assertEqual(set([1, 2, 3, 31]), presynapticCells)
def testRecycleWeakestSynapseToMakeRoomForNewSynapse(self):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=3,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=1,
maxNewSynapseCount=3,
permanenceIncrement=.02,
permanenceDecrement=.02,
predictedSegmentDecrement=0.0,
seed=42,
maxSynapsesPerSegment=4)
prevActiveColumns = [1, 2, 3]
prevWinnerCells = [1, 2, 3]
activeColumns = [4]
matchingSegment = tm.createSegment(4)
tm.connections.createSynapse(matchingSegment, 81, .6)
# Create a weak synapse. Make sure it's not so weak that permanenceIncrement
# destroys it.
tm.connections.createSynapse(matchingSegment, 0, .11)
# Create a synapse that will match.
tm.connections.createSynapse(matchingSegment, 1, .20)
# Create a synapse with a high permanence
tm.connections.createSynapse(matchingSegment, 31, .60)
tm.compute(prevActiveColumns)
self.assertEqual(prevWinnerCells, tm.getWinnerCells())
tm.compute(activeColumns)
synapses = tm.connections.synapsesForSegment(matchingSegment)
self.assertEqual(4, len(synapses))
presynapticCells = set(synapse.presynapticCell for synapse in synapses)
self.assertEqual(set([1, 2, 3, 31]), presynapticCells)
def testActiveSegmentGrowSynapsesAccordingToPotentialOverlap(self):
"""
When a segment becomes active, grow synapses to previous winner cells.
The number of grown synapses is calculated from the "matching segment"
overlap, not the "active segment" overlap.
"""
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=1,
activationThreshold=2,
initialPermanence=.21,
connectedPermanence=.50,
minThreshold=1,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.0,
seed=42)
# Use 1 cell per column so that we have easy control over the winner cells.
previousActiveColumns = [0, 1, 2, 3, 4]
prevWinnerCells = [0, 1, 2, 3, 4]
activeColumns = [5]
activeSegment = tm.createSegment(5)
tm.connections.createSynapse(activeSegment, 0, .5)
tm.connections.createSynapse(activeSegment, 1, .5)
tm.connections.createSynapse(activeSegment, 2, .2)
tm.compute(previousActiveColumns, True)
self.assertEqual(prevWinnerCells, tm.getWinnerCells())
tm.compute(activeColumns, True)
presynapticCells = set(synapse.presynapticCell for synapse in
tm.connections.synapsesForSegment(activeSegment))
self.assertTrue(presynapticCells == set([0, 1, 2, 3]) or
presynapticCells == set([0, 1, 2, 4]))
