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 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 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 testAddSegmentToCellWithFewestSegments(self):
grewOnCell1 = False
grewOnCell2 = False
for seed in xrange(100):
tm = TemporalMemory(
columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=seed)
prevActiveColumns = [1, 2, 3, 4]
activeColumns = [0]
prevActiveCells = [4, 5, 6, 7]
nonMatchingCells = [0, 3]
activeCells = [0, 1, 2, 3]
segment1 = tm.connections.createSegment(nonMatchingCells[0])
tm.connections.createSynapse(segment1, prevActiveCells[0], .5)
segment2 = tm.connections.createSegment(nonMatchingCells[1])
tm.connections.createSynapse(segment2, prevActiveCells[1], .5)
tm.compute(prevActiveColumns, True)
tm.compute(activeColumns, True)
self.assertEqual(activeCells, tm.getActiveCells())
self.assertEqual(3, tm.connections.numSegments())
self.assertEqual(1, tm.connections.numSegments(0))
self.assertEqual(1, tm.connections.numSegments(3))
self.assertEqual(1, tm.connections.numSynapses(segment1))
self.assertEqual(1, tm.connections.numSynapses(segment2))
segments = list(tm.connections.segmentsForCell(1))
if len(segments) == 0:
segments2 = list(tm.connections.segmentsForCell(2))
self.assertFalse(len(segments2) == 0)
grewOnCell2 = True
segments.append(segments2[0])
else:
grewOnCell1 = True
self.assertEqual(1, len(segments))
synapses = list(tm.connections.synapsesForSegment(segments[0]))
self.assertEqual(4, len(synapses))
columnChecklist = set(prevActiveColumns)
for synapse in synapses:
synapseData = tm.connections.dataForSynapse(synapse)
self.assertAlmostEqual(.2, synapseData.permanence)
column = tm.columnForCell(synapseData.presynapticCell)
self.assertTrue(column in columnChecklist)
columnChecklist.remove(column)
self.assertTrue(len(columnChecklist) == 0)
self.assertTrue(grewOnCell1)
self.assertTrue(grewOnCell2)
tm.compute(activeColumns, learn=False)
# The following print statements prints out the active cells, predictive
# cells, active segments and winner cells.
#
# What you should notice is that the columns where active state is 1
# represent the SDR for the current input pattern and the columns where
# predicted state is 1 represent the SDR for the next expected pattern
print "\nAll the active and predicted cells:"
print("active cells " + str(tm.getActiveCells()))
print("predictive cells " + str(tm.getPredictiveCells()))
print("winner cells " + str(tm.getWinnerCells()))
print("# of active segments " + str(tm.connections.numSegments()))
activeColumnsIndeces = [tm.columnForCell(i) for i in tm.getActiveCells()]
predictedColumnIndeces = [
tm.columnForCell(i) for i in tm.getPredictiveCells()
]
# Reconstructing the active and inactive columns with 1 as active and 0 as
# inactive representation.
actColState = [
'1' if i in activeColumnsIndeces else '0'
for i in range(tm.numberOfColumns())
]
actColStr = ("".join(actColState))
predColState = [
'1' if i in predictedColumnIndeces else '0'
for i in range(tm.numberOfColumns())
def testAddSegmentToCellWithFewestSegments(self):
grewOnCell1 = False
grewOnCell2 = False
for seed in xrange(100):
tm = TemporalMemory(columnDimensions=[32],
cellsPerColumn=4,
activationThreshold=3,
initialPermanence=.2,
connectedPermanence=.50,
minThreshold=2,
maxNewSynapseCount=4,
permanenceIncrement=.10,
permanenceDecrement=.10,
predictedSegmentDecrement=0.02,
seed=seed)
prevActiveColumns = [1, 2, 3, 4]
activeColumns = [0]
prevActiveCells = [4, 5, 6, 7]
nonMatchingCells = [0, 3]
activeCells = [0, 1, 2, 3]
segment1 = tm.connections.createSegment(nonMatchingCells[0])
tm.connections.createSynapse(segment1, prevActiveCells[0], .5)
segment2 = tm.connections.createSegment(nonMatchingCells[1])
tm.connections.createSynapse(segment2, prevActiveCells[1], .5)
tm.compute(prevActiveColumns, True)
tm.compute(activeColumns, True)
self.assertEqual(activeCells, tm.getActiveCells())
self.assertEqual(3, tm.connections.numSegments())
self.assertEqual(1, len(tm.connections.segmentsForCell(0)))
self.assertEqual(1, len(tm.connections.segmentsForCell(3)))
self.assertEqual(1,
len(tm.connections.synapsesForSegment(segment1)))
self.assertEqual(1,
len(tm.connections.synapsesForSegment(segment2)))
segments = tm.connections.segmentsForCell(1)
if len(segments) == 0:
segments2 = tm.connections.segmentsForCell(2)
self.assertFalse(len(segments2) == 0)
grewOnCell2 = True
segments.append(segments2[0])
else:
grewOnCell1 = True
self.assertEqual(1, len(segments))
synapses = tm.connections.synapsesForSegment(segments[0])
self.assertEqual(4, len(synapses))
columnChecklist = set(prevActiveColumns)
for synapse in synapses:
synapseData = tm.connections.dataForSynapse(synapse)
self.assertAlmostEqual(.2, synapseData.permanence)
column = tm.columnForCell(synapseData.presynapticCell)
self.assertTrue(column in columnChecklist)
columnChecklist.remove(column)
self.assertTrue(len(columnChecklist) == 0)
self.assertTrue(grewOnCell1)
self.assertTrue(grewOnCell2)
tm.compute(activeColumns, learn = False)
# The following print statements prints out the active cells, predictive
# cells, active segments and winner cells.
#
# What you should notice is that the columns where active state is 1
# represent the SDR for the current input pattern and the columns where
# predicted state is 1 represent the SDR for the next expected pattern
print "\nAll the active and predicted cells:"
print("active cells " + str(tm.getActiveCells()))
print("predictive cells " + str(tm.getPredictiveCells()))
print("winner cells " + str(tm.getWinnerCells()))
print("# of active segments " + str(tm.connections.numSegments()))
activeColumnsIndeces = [tm.columnForCell(i) for i in tm.getActiveCells()]
predictedColumnIndeces = [tm.columnForCell(i) for i in tm.getPredictiveCells()]
# Reconstructing the active and inactive columns with 1 as active and 0 as
# inactive representation.
actColState = ['1' if i in activeColumnsIndeces else '0' for i in range(tm.numberOfColumns())]
actColStr = ("".join(actColState))
predColState = ['1' if i in predictedColumnIndeces else '0' for i in range(tm.numberOfColumns())]
predColStr = ("".join(predColState))
# For convenience the cells are grouped
# 10 at a time. When there are multiple cells per column the printout
# is arranged so the cells in a column are stacked together
print "Active columns: " + formatRow(actColStr)
# The following print statements prints out the active cells, predictive
# cells, active segments and winner cells.
#
# What you should notice is that the columns where active state is 1
# represent the SDR for the current input pattern and the columns where
# predicted state is 1 represent the SDR for the next expected pattern
print "\nAll the active and predicted cells:"
print("active cells " + str(tm.activeCells))
print("predictive cells " + str(tm.predictiveCells))
print("active segments " + str(tm.activeSegments))
print("winnercells" + str(tm.winnerCells))
activeColumnsIndeces = [tm.columnForCell(i) for i in tm.activeCells]
predictedColumnIndeces = [tm.columnForCell(i) for i in tm.predictiveCells]
# Reconstructing the active and inactive columns with 1 as active and 0 as
# inactive representation.
actColState = [
'1' if i in activeColumnsIndeces else '0'
for i in range(tm.numberOfColumns())
]
actColStr = ("".join(actColState))
predColState = [
'1' if i in predictedColumnIndeces else '0'
for i in range(tm.numberOfColumns())
]
predColStr = ("".join(predColState))
# The following print statements prints out the active cells, predictive
# cells, active segments and winner cells.
#
# What you should notice is that the columns where active state is 1
# represent the SDR for the current input pattern and the columns where
# predicted state is 1 represent the SDR for the next expected pattern
print "\nAll the active and predicted cells:"
print("active cells " + str(tm.activeCells))
print("predictive cells "+ str(tm.predictiveCells))
print("active segments "+ str(tm.activeSegments))
print("winnercells" + str(tm.winnerCells))
activeColumnsIndeces = [tm.columnForCell(i) for i in tm.activeCells]
predictedColumnIndeces = [tm.columnForCell(i) for i in tm.predictiveCells]
# Reconstructing the active and inactive columns with 1 as active and 0 as
# inactive representation.
actColState = ['1' if i in activeColumnsIndeces else '0' for i in range(tm.numberOfColumns())]
actColStr = ("".join(actColState))
predColState = ['1' if i in predictedColumnIndeces else '0' for i in range(tm.numberOfColumns())]
predColStr = ("".join(predColState))
# For convenience the cells are grouped
# 10 at a time. When there are multiple cells per column the printout
# is arranged so the cells in a column are stacked together
print "Active columns: " + formatRow(actColStr)
