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 constructTM(self, columnCount, basalInputSize, apicalInputSize,
cellsPerColumn, initialPermanence, connectedPermanence,
minThreshold, sampleSize, permanenceIncrement,
permanenceDecrement, predictedSegmentDecrement,
activationThreshold, seed):
params = {
"columnDimensions": (columnCount,),
"basalInputDimensions": (basalInputSize,),
"apicalInputDimensions": (apicalInputSize,),
"cellsPerColumn": cellsPerColumn,
"initialPermanence": initialPermanence,
"connectedPermanence": connectedPermanence,
"minThreshold": minThreshold,
"maxNewSynapseCount": sampleSize,
"permanenceIncrement": permanenceIncrement,
"permanenceDecrement": permanenceDecrement,
"predictedSegmentDecrement": predictedSegmentDecrement,
"activationThreshold": activationThreshold,
"seed": seed,
"learnOnOneCell": False,
"formInternalBasalConnections": False,
}
self.tm = ExtendedTemporalMemory(**params)
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 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 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 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 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 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 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)
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 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 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 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 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 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 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 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)
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 testNumberOfCells(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64],
cellsPerColumn=32)
self.assertEqual(tm.numberOfCells(), 64 * 64 * 32)
def testCellsForColumn2D(self):
tm = ExtendedTemporalMemory(columnDimensions=[64, 64],
cellsPerColumn=4)
expectedCells = set([256, 257, 258, 259])
self.assertEqual(tm.cellsForColumn(64), expectedCells)
def testCellsForColumn1D(self):
tm = ExtendedTemporalMemory(columnDimensions=[2048], cellsPerColumn=5)
expectedCells = set([5, 6, 7, 8, 9])
self.assertEqual(tm.cellsForColumn(1), expectedCells)
def testlearnOnOneCellParam(self):
tm = self.tm
self.assertFalse(tm.learnOnOneCell)
tm = ExtendedTemporalMemory(learnOnOneCell=True)
self.assertTrue(tm.learnOnOneCell)
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 setUp(self):
self.tm = ExtendedTemporalMemory(learnOnOneCell=False)
