class PatternMachineTest(unittest.TestCase): def setUp(self): self.patternMachine = PatternMachine(10000, 5, num=50) def testGet(self): patternA = self.patternMachine.get(48) self.assertEqual(len(patternA), 5) patternB = self.patternMachine.get(49) self.assertEqual(len(patternB), 5) self.assertEqual(patternA & patternB, set()) def testGetOutOfBounds(self): args = [50] self.assertRaises(IndexError, self.patternMachine.get, *args) def testAddNoise(self): patternMachine = PatternMachine(10000, 1000, num=1) pattern = patternMachine.get(0) noisy = patternMachine.addNoise(pattern, 0.0) self.assertEqual(len(pattern & noisy), 1000) noisy = patternMachine.addNoise(pattern, 0.5) self.assertTrue(400 < len(pattern & noisy) < 600) noisy = patternMachine.addNoise(pattern, 1.0) self.assertTrue(50 < len(pattern & noisy) < 150) def testNumbersForBit(self): pattern = self.patternMachine.get(49) for bit in pattern: self.assertEqual(self.patternMachine.numbersForBit(bit), set([49])) def testNumbersForBitOutOfBounds(self): args = [10000] self.assertRaises(IndexError, self.patternMachine.numbersForBit, *args) def testNumberMapForBits(self): pattern = self.patternMachine.get(49) numberMap = self.patternMachine.numberMapForBits(pattern) self.assertEqual(numberMap.keys(), [49]) self.assertEqual(numberMap[49], pattern) def testWList(self): w = [4, 7, 11] patternMachine = PatternMachine(100, w, num=50) widths = dict((el, 0) for el in w) for i in range(50): pattern = patternMachine.get(i) width = len(pattern) self.assertTrue(width in w) widths[len(pattern)] += 1 for i in w: self.assertTrue(widths[i] > 0)
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 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 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 testAddNoise(self): patternMachine = PatternMachine(10000, 1000, num=1) pattern = patternMachine.get(0) noisy = patternMachine.addNoise(pattern, 0.0) self.assertEqual(len(pattern & noisy), 1000) noisy = patternMachine.addNoise(pattern, 0.5) self.assertTrue(400 < len(pattern & noisy) < 600) noisy = patternMachine.addNoise(pattern, 1.0) self.assertTrue(50 < len(pattern & noisy) < 150)
def testWList(self): w = [4, 7, 11] patternMachine = PatternMachine(100, w, num=50) widths = dict((el, 0) for el in w) for i in range(50): pattern = patternMachine.get(i) width = len(pattern) self.assertTrue(width in w) widths[len(pattern)] += 1 for i in w: self.assertTrue(widths[i] > 0)
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 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)
class ExtensiveColumnPoolerTest(unittest.TestCase): """ Algorithmic tests for the ColumnPooler region. Each test actually tests multiple aspects of the algorithm. For more atomic tests refer to column_pooler_unit_test. The notation for objects is the following: object{patternA, patternB, ...} In these tests, the proximally-fed SDR's are simulated as unique (location, feature) pairs regardless of actual locations and features, unless stated otherwise. """ inputWidth = 2048 * 8 numInputActiveBits = int(0.02 * inputWidth) outputWidth = 2048 numOutputActiveBits = 40 seed = 42 def testNewInputs(self): """ Checks that the behavior is correct when facing unseed inputs. """ self.init() # feed the first input, a random SDR should be generated initialPattern = self.generateObject(1) self.learn(initialPattern, numRepetitions=1, newObject=True) representation = self._getActiveRepresentation() self.assertEqual( len(representation), self.numOutputActiveBits, "The generated representation is incorrect" ) # feed a new input for the same object, the previous SDR should persist newPattern = self.generateObject(1) self.learn(newPattern, numRepetitions=1, newObject=False) newRepresentation = self._getActiveRepresentation() self.assertNotEqual(initialPattern, newPattern) self.assertEqual( newRepresentation, representation, "The SDR did not persist when learning the same object" ) # without sensory input, the SDR should persist as well emptyPattern = [set()] self.learn(emptyPattern, numRepetitions=1, newObject=False) newRepresentation = self._getActiveRepresentation() self.assertEqual( newRepresentation, representation, "The SDR did not persist after an empty input." ) def testLearnSinglePattern(self): """ A single pattern is learnt for a single object. Objects: A{X, Y} """ self.init() object = self.generateObject(1) self.learn(object, numRepetitions=1, newObject=True) # check that the active representation is sparse representation = self._getActiveRepresentation() self.assertEqual( len(representation), self.numOutputActiveBits, "The generated representation is incorrect" ) # check that the pattern was correctly learnt self.infer(feedforwardPattern=object[0]) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) # present new pattern for same object # it should be mapped to the same representation newPattern = [self.generatePattern()] self.learn(newPattern, numRepetitions=1, newObject=False) # check that the active representation is sparse newRepresentation = self._getActiveRepresentation() self.assertEqual( newRepresentation, representation, "The new pattern did not map to the same object representation" ) # check that the pattern was correctly learnt and is stable self.infer(feedforwardPattern=object[0]) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) def testLearnSingleObject(self): """ Many patterns are learnt for a single object. Objects: A{P, Q, R, S, T} """ self.init() object = self.generateObject(numPatterns=5) self.learn(object, numRepetitions=1, randomOrder=True, newObject=True) representation = self._getActiveRepresentation() # check that all patterns map to the same object for pattern in object: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) # if activity stops, check that the representation persists self.infer(feedforwardPattern=set()) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation did not persist" ) def testLearnTwoObjectNoCommonPattern(self): """ Same test as before, using two objects, without common pattern. Objects: A{P, Q, R, S,T} B{V, W, X, Y, Z} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # check that all patterns map to the same object for pattern in objectA: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns map to the same object for pattern in objectB: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # feed union of patterns in object A pattern = objectA[0] | objectA[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns in objects A and B pattern = objectA[0] | objectB[0] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) def testLearnTwoObjectsOneCommonPattern(self): """ Same test as before, except the two objects share a pattern Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # feed shared pattern pattern = objectA[0] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns in objects A and B pattern = objectA[1] | objectB[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) def testLearnThreeObjectsOneCommonPattern(self): """ Same test as before, with three objects Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} C{W, H, I, K, L} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() objectC = self.generateObject(numPatterns=5) objectC[0] = objectB[1] self.learn(objectC, numRepetitions=3, randomOrder=True, newObject=True) representationC = self._getActiveRepresentation() self.assertNotEquals(representationA, representationB, representationC) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) self.assertLessEqual(len(representationB & representationC), 3) self.assertLessEqual(len(representationA & representationC), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[2:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectC[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationC, "The pooled representation for the third object is not stable" ) # feed shared pattern between A and B pattern = objectA[0] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed shared pattern between B and C pattern = objectB[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB | representationC, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns to activate all objects pattern = objectA[1] | objectB[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB | representationC, "The active representation is incorrect" ) def testLearnThreeObjectsOneCommonPatternSpatialNoise(self): """ Same test as before, with three objects Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} C{W, H, I, K, L} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() objectC = self.generateObject(numPatterns=5) objectC[0] = objectB[1] self.learn(objectC, numRepetitions=3, randomOrder=True, newObject=True) representationC = self._getActiveRepresentation() self.assertNotEquals(representationA, representationB, representationC) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) self.assertLessEqual(len(representationB & representationC), 3) self.assertLessEqual(len(representationA & representationC), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[2:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectC[1:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationC, "The pooled representation for the third object is not stable" ) # feed shared pattern between A and B pattern = objectA[0] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed shared pattern between B and C pattern = objectB[1] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationB | representationC, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns to activate all objects pattern = objectA[1] | objectB[1] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB | representationC, "The active representation is incorrect" ) def testLearnOneObjectInTwoColumns(self): """Learns one object in two different columns.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) objectARepresentations = self._getActiveRepresentations() for pooler in self.poolers: pooler.reset() for patterns in objectA: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) if i > 0: self.assertEqual(activeRepresentations, self._getActiveRepresentations()) self.assertEqual(objectARepresentations, self._getActiveRepresentations()) def testLearnTwoObjectsInTwoColumnsNoCommonPattern(self): """Learns two objects in two different columns.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True, ) activeRepresentationsB = self._getActiveRepresentations() for pooler in self.poolers: pooler.reset() # check inference for object A # for the first pattern, the distal predictions won't be correct firstPattern = True for patternsA in objectA: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsA, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) if firstPattern: firstPattern = False else: self.assertEqual( activeRepresentationsA, self._getPredictedActiveCells() ) self.assertEqual( activeRepresentationsA, self._getActiveRepresentations() ) for pooler in self.poolers: pooler.reset() # check inference for object B firstPattern = True for patternsB in objectB: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsB, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices ) if firstPattern: firstPattern = False else: self.assertEqual( activeRepresentationsB, self._getPredictedActiveCells() ) self.assertEqual( activeRepresentationsB, self._getActiveRepresentations() ) def testLearnTwoObjectsInTwoColumnsOneCommonPattern(self): """Learns two objects in two different columns, with a common pattern.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) # second pattern in column 0 is shared objectB[1][0] = objectA[1][0] # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # check inference for object A # for the first pattern, the distal predictions won't be correct # for the second one, the prediction will be unique thanks to the # distal predictions from the other column which has no ambiguity for pooler in self.poolers: pooler.reset() firstPattern = True for patternsA in objectA: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsA, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) if firstPattern: firstPattern = False else: self.assertEqual( activeRepresentationsA, self._getPredictedActiveCells() ) self.assertEqual( activeRepresentationsA, self._getActiveRepresentations() ) for pooler in self.poolers: pooler.reset() # check inference for object B firstPattern = True for patternsB in objectB: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsB, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices ) if firstPattern: firstPattern = False else: self.assertEqual( activeRepresentationsB, self._getPredictedActiveCells() ) self.assertEqual( activeRepresentationsB, self._getActiveRepresentations() ) def testLearnTwoObjectsInTwoColumnsOneCommonPatternEmptyFirstInput(self): """Learns two objects in two different columns, with a common pattern.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) # second pattern in column 0 is shared objectB[1][0] = objectA[1][0] # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # check inference for object A for pooler in self.poolers: pooler.reset() firstPattern = True for patternsA in objectA: activeRepresentations = self._getActiveRepresentations() if firstPattern: self.inferMultipleColumns( feedforwardPatterns=[set(), patternsA[1]], activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) desiredRepresentation = [set(), activeRepresentationsA[1]] else: self.inferMultipleColumns( feedforwardPatterns=patternsA, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) desiredRepresentation = activeRepresentationsA self.assertEqual( desiredRepresentation, self._getActiveRepresentations() ) def testPersistence(self): """After learning, representation should persist in L2 without input.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) objectARepresentations = self._getActiveRepresentations() for pooler in self.poolers: pooler.reset() for patterns in objectA: for i in xrange(3): # replace third pattern for column 2 by empty pattern if i == 2: patterns[1] = set() activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) if i > 0: self.assertEqual(activeRepresentations, self._getActiveRepresentations()) self.assertEqual(objectARepresentations, self._getActiveRepresentations()) def testLateralDisambiguation(self): """Lateral disambiguation using a constant simulated distal input.""" self.init() objectA = self.generateObject(numPatterns=5) lateralInputA = [None] + [self.generatePattern() for _ in xrange(4)] self.learn(objectA, lateralPatterns=lateralInputA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[3] = objectA[3] lateralInputB = [None] + [self.generatePattern() for _ in xrange(4)] self.learn(objectB, lateralPatterns=lateralInputB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) # no ambiguity with lateral input for pattern in objectA: self.infer(feedforwardPattern=pattern, lateralInput=lateralInputA[-1]) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # no ambiguity with lateral input for pattern in objectB: self.infer(feedforwardPattern=pattern, lateralInput=lateralInputB[-1]) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) @unittest.skip("Fails, need to discuss") def testMultiColumnCompetition(self): """Competition between multiple conflicting lateral inputs.""" self.init(numCols=4) neighborsIndices = [[1, 2, 3], [0, 2, 3], [0, 1, 3], [0, 1, 2]] objectA = self.generateObject(numPatterns=5, numCols=4) objectB = self.generateObject(numPatterns=5, numCols=4) # second pattern in column 0 is shared objectB[1][0] = objectA[1][0] # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # check inference for object A # for the first pattern, the distal predictions won't be correct # for the second one, the prediction will be unique thanks to the # distal predictions from the other column which has no ambiguity for pooler in self.poolers: pooler.reset() # sensed patterns will be mixed sensedPatterns = objectA[1][:-1] + [objectA[1][-1] | objectB[1][-1]] # feed sensed patterns first time # every one feels the correct object, except first column which feels # the union (reminder: lateral input are delayed) activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) firstSensedRepresentations = [ activeRepresentationsA[0] | activeRepresentationsB[0], activeRepresentationsA[1], activeRepresentationsA[2], activeRepresentationsA[3] | activeRepresentationsB[3] ] self.assertEqual( firstSensedRepresentations, self._getActiveRepresentations() ) # feed sensed patterns second time # the distal predictions are still ambiguous in C1, but disambiguated # in C4 activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) secondSensedRepresentations = [ activeRepresentationsA[0] | activeRepresentationsB[0], activeRepresentationsA[1], activeRepresentationsA[2], activeRepresentationsA[3] ] self.assertEqual( secondSensedRepresentations, self._getActiveRepresentations() ) # feed sensed patterns third time # this time, it is all disambiguated activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) self.assertEqual( activeRepresentationsA, self._getActiveRepresentations() ) def testMutualDisambiguationThroughUnions(self): """ Learns three object in two different columns. Feed ambiguous sensations, A u B and B u C. The system should narrow down to B. """ self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) objectC = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectC, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsC = self._getActiveRepresentations() # create sensed patterns (ambiguous) sensedPatterns = [objectA[1][0] | objectB[1][0], objectB[2][1] | objectC[2][1]] for pooler in self.poolers: pooler.reset() # feed sensed patterns first time # the L2 representations should be ambiguous activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) firstRepresentations = [ activeRepresentationsA[0] | activeRepresentationsB[0], activeRepresentationsB[1] | activeRepresentationsC[1] ] self.assertEqual( firstRepresentations, self._getActiveRepresentations() ) # feed a second time # the L2 representations should be ambiguous activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) self.assertEqual( firstRepresentations, self._getActiveRepresentations() ) # feed a third time, distal predictions should disambiguate # we are using the third time because there is an off-by-one in pooler activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) # check that representations are unique, being slightly tolerant self.assertLessEqual( len(self._getActiveRepresentations()[0] - activeRepresentationsB[0]), 5, ) self.assertLessEqual( len(self._getActiveRepresentations()[1] - activeRepresentationsB[1]), 5, ) self.assertGreaterEqual( len(self._getActiveRepresentations()[0] & activeRepresentationsB[0]), 35, ) self.assertGreaterEqual( len(self._getActiveRepresentations()[1] & activeRepresentationsB[1]), 35, ) self.assertEqual( self._getActiveRepresentations(), self._getPredictedActiveCells(), ) def setUp(self): """ Sets up the test. """ # single column case self.pooler = None # multi column case self.poolers = [] # create pattern machine self.proximalPatternMachine = PatternMachine( n=self.inputWidth, w=self.numOutputActiveBits, num=200, seed=self.seed ) self.patternId = 0 np.random.seed(self.seed) # Wrappers around ColumnPooler API def learn(self, feedforwardPatterns, lateralPatterns=None, numRepetitions=1, randomOrder=True, newObject=True): """ Parameters: ---------------------------- Learns a single object, with the provided patterns. @param feedforwardPatterns (list(set)) List of proximal input patterns @param lateralPatterns (list(list(set))) List of distal input patterns, or None. If no lateral input is used. The outer list is expected to have the same length as feedforwardPatterns, whereas each inner list's length is the number of cortical columns which are distally connected to the pooler. @param numRepetitions (int) Number of times the patterns will be fed @param randomOrder (bool) If true, the order of patterns will be shuffled at each repetition """ if newObject: self.pooler.mmClearHistory() self.pooler.reset() # set-up indices = range(len(feedforwardPatterns)) if lateralPatterns is None: lateralPatterns = [None] * len(feedforwardPatterns) for _ in xrange(numRepetitions): if randomOrder: np.random.shuffle(indices) for idx in indices: self.pooler.compute(feedforwardPatterns[idx], activeExternalCells=lateralPatterns[idx], learn=True) def infer(self, feedforwardPattern, lateralInput=None, printMetrics=False): """ Feeds a single pattern to the column pooler (as well as an eventual lateral pattern). Parameters: ---------------------------- @param feedforwardPattern (set) Input proximal pattern to the pooler @param lateralPatterns (list(set)) Input dislal patterns to the pooler (one for each neighboring CC's) @param printMetrics (bool) If true, will print cell metrics """ self.pooler.compute(feedforwardPattern, activeExternalCells=lateralInput, learn=False) if printMetrics: print self.pooler.mmPrettyPrintMetrics( self.pooler.mmGetDefaultMetrics() ) # Helper functions def generatePattern(self): """ Returns a random proximal input pattern. """ pattern = self.proximalPatternMachine.get(self.patternId) self.patternId += 1 return pattern def generateObject(self, numPatterns, numCols=1): """ Creates a list of patterns, for a given object. If numCols > 1 is given, a list of list of patterns will be returned. """ if numCols == 1: return [self.generatePattern() for _ in xrange(numPatterns)] else: patterns = [] for i in xrange(numPatterns): patterns.append([self.generatePattern() for _ in xrange(numCols)]) return patterns def init(self, overrides=None, numCols=1): """ Creates the column pooler with specified parameter overrides. Except for the specified overrides and problem-specific parameters, used parameters are implementation defaults. """ params = { "inputWidth": self.inputWidth, "numActiveColumnsPerInhArea": self.numOutputActiveBits, "columnDimensions": (self.outputWidth,), "seed": self.seed, "initialPermanence": 0.51, "connectedPermanence": 0.6, "permanenceIncrement": 0.1, "permanenceDecrement": 0.02, "minThreshold": 10, "predictedSegmentDecrement": 0.004, "activationThreshold": 10, "maxNewSynapseCount": 20, "maxSegmentsPerCell": 255, "maxSynapsesPerSegment": 255, } if overrides is None: overrides = {} params.update(overrides) if numCols == 1: self.pooler = MonitoredColumnPooler(**params) else: # TODO: We need a different seed for each pooler otherwise each one # outputs an identical representation. Use random seed for now but ideally # we would set different specific seeds for each pooler params['seed']=0 self.poolers = [MonitoredColumnPooler(**params) for _ in xrange(numCols)] def _getActiveRepresentation(self): """ Retrieves the current active representation in the pooler. """ if self.pooler is None: raise ValueError("No pooler has been instantiated") return set(self.pooler.getActiveCells()) # Multi-column testing def learnMultipleColumns(self, feedforwardPatterns, numRepetitions=1, neighborsIndices=None, randomOrder=True, newObject=True): """ Learns a single object, feeding it through the multiple columns. Parameters: ---------------------------- Learns a single object, with the provided patterns. @param feedforwardPatterns (list(list(set))) List of proximal input patterns (one for each pooler). @param neighborsIndices (list(list)) List of column indices each column received input from. @param numRepetitions (int) Number of times the patterns will be fed @param randomOrder (bool) If true, the order of patterns will be shuffled at each repetition """ if newObject: for pooler in self.poolers: pooler.mmClearHistory() pooler.reset() # use different set of pattern indices to allow random orders indices = [range(len(feedforwardPatterns))] * len(self.poolers) representations = [set()] * len(self.poolers) # by default, all columns are neighbors if neighborsIndices is None: neighborsIndices = [ range(i) + range(i+1, len(self.poolers)) for i in xrange(len(self.poolers)) ] for _ in xrange(numRepetitions): # independently shuffle pattern orders if necessary if randomOrder: for idx in indices: np.random.shuffle(idx) for i in xrange(len(indices[0])): # get union of relevant lateral representations lateralInputs = [] for col in xrange(len(self.poolers)): lateralInputsCol = set() for idx in neighborsIndices[col]: lateralInputsCol = lateralInputsCol.union(representations[idx]) lateralInputs.append(lateralInputsCol) # Train each column for col in xrange(len(self.poolers)): self.poolers[col].compute( feedforwardInput=feedforwardPatterns[indices[col][i]][col], activeExternalCells=lateralInputs[col], learn=True ) # update active representations representations = self._getActiveRepresentations() for i in xrange(len(representations)): representations[i] = set([i * self.outputWidth + k \ for k in representations[i]]) def inferMultipleColumns(self, feedforwardPatterns, activeRepresentations=None, neighborsIndices=None, printMetrics=False, reset=False): """ Feeds a single pattern to the column pooler (as well as an eventual lateral pattern). Parameters: ---------------------------- @param feedforwardPattern (list(set)) Input proximal patterns to the pooler (one for each column) @param activeRepresentations (list(set)) Active representations in the columns at the previous step. @param neighborsIndices (list(list)) List of column indices each column received input from. @param printMetrics (bool) If true, will print cell metrics """ if reset: for pooler in self.poolers: pooler.reset() # create copy of activeRepresentations to not mutate it representations = [None] * len(self.poolers) # by default, all columns are neighbors if neighborsIndices is None: neighborsIndices = [ range(i) + range(i+1, len(self.poolers)) for i in xrange(len(self.poolers)) ] for i in xrange(len(self.poolers)): if activeRepresentations[i] is not None: representations[i] = set(i * self.outputWidth + k \ for k in activeRepresentations[i]) for col in range(len(self.poolers)): lateralInputs = [representations[idx] for idx in neighborsIndices[col]] if len(lateralInputs) > 0: lateralInputs = set.union(*lateralInputs) else: lateralInputs = set() self.poolers[col].compute( feedforwardPatterns[col], activeExternalCells=lateralInputs, learn=False ) if printMetrics: for pooler in self.poolers: print pooler.mmPrettyPrintMetrics( pooler.mmGetDefaultMetrics() ) def _getActiveRepresentations(self): """ Retrieves the current active representations in the poolers. """ if len(self.poolers) == 0: raise ValueError("No pooler has been instantiated") return [set(pooler.getActiveCells()) for pooler in self.poolers] def _getPredictedActiveCells(self): """ Retrieves the current active representations in the poolers. """ if len(self.poolers) == 0: raise ValueError("No pooler has been instantiated") return [set(pooler.getActiveCells()) & set(pooler.tm.getPredictiveCells())\ for pooler in self.poolers]
class ExtensiveColumnPoolerTest(unittest.TestCase): """ Algorithmic tests for the ColumnPooler region. Each test actually tests multiple aspects of the algorithm. For more atomic tests refer to column_pooler_unit_test. The notation for objects is the following: object{patternA, patternB, ...} In these tests, the proximally-fed SDR's are simulated as unique (location, feature) pairs regardless of actual locations and features, unless stated otherwise. """ inputWidth = 2048 * 8 numInputActiveBits = int(0.02 * inputWidth) outputWidth = 2048 numOutputActiveBits = 40 seed = 42 def testNewInputs(self): """ Checks that the behavior is correct when facing unseed inputs. """ self.init() # feed the first input, a random SDR should be generated initialPattern = self.generateObject(1) self.learn(initialPattern, numRepetitions=1, newObject=True) representation = self._getActiveRepresentation() self.assertEqual( len(representation), self.numOutputActiveBits, "The generated representation is incorrect" ) # feed a new input for the same object, the previous SDR should persist newPattern = self.generateObject(1) self.learn(newPattern, numRepetitions=1, newObject=False) newRepresentation = self._getActiveRepresentation() self.assertNotEqual(initialPattern, newPattern) self.assertEqual( newRepresentation, representation, "The SDR did not persist when learning the same object" ) # without sensory input, the SDR should persist as well emptyPattern = [set()] self.learn(emptyPattern, numRepetitions=1, newObject=False) newRepresentation = self._getActiveRepresentation() self.assertEqual( newRepresentation, representation, "The SDR did not persist after an empty input." ) def testLearnSinglePattern(self): """ A single pattern is learnt for a single object. Objects: A{X, Y} """ self.init() object = self.generateObject(1) self.learn(object, numRepetitions=1, newObject=True) # check that the active representation is sparse representation = self._getActiveRepresentation() self.assertEqual( len(representation), self.numOutputActiveBits, "The generated representation is incorrect" ) # check that the pattern was correctly learnt self.infer(feedforwardPattern=object[0]) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) # present new pattern for same object # it should be mapped to the same representation newPattern = [self.generatePattern()] self.learn(newPattern, numRepetitions=1, newObject=False) # check that the active representation is sparse newRepresentation = self._getActiveRepresentation() self.assertEqual( newRepresentation, representation, "The new pattern did not map to the same object representation" ) # check that the pattern was correctly learnt and is stable self.infer(feedforwardPattern=object[0]) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) def testLearnSingleObject(self): """ Many patterns are learnt for a single object. Objects: A{P, Q, R, S, T} """ self.init() object = self.generateObject(numPatterns=5) self.learn(object, numRepetitions=1, randomOrder=True, newObject=True) representation = self._getActiveRepresentation() # check that all patterns map to the same object for pattern in object: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) # if activity stops, check that the representation persists self.infer(feedforwardPattern=set()) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation did not persist" ) def testLearnTwoObjectNoCommonPattern(self): """ Same test as before, using two objects, without common pattern. Objects: A{P, Q, R, S,T} B{V, W, X, Y, Z} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=1, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) self.learn(objectB, numRepetitions=1, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # check that all patterns map to the same object for pattern in objectA: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns map to the same object for pattern in objectB: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # feed union of patterns in object A pattern = objectA[0] | objectA[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns in objects A and B pattern = objectA[0] | objectB[0] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) def testLearnTwoObjectsOneCommonPattern(self): """ Same test as before, except the two objects share a pattern Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=1, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=1, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # feed shared pattern pattern = objectA[0] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns in objects A and B pattern = objectA[1] | objectB[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) def testLearnThreeObjectsOneCommonPattern(self): """ Same test as before, with three objects Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} C{W, H, I, K, L} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=1, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=1, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() objectC = self.generateObject(numPatterns=5) objectC[0] = objectB[1] self.learn(objectC, numRepetitions=1, randomOrder=True, newObject=True) representationC = self._getActiveRepresentation() self.assertNotEquals(representationA, representationB, representationC) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) self.assertLessEqual(len(representationB & representationC), 3) self.assertLessEqual(len(representationA & representationC), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[2:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectC[1:]: self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationC, "The pooled representation for the third object is not stable" ) # feed shared pattern between A and B pattern = objectA[0] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed shared pattern between B and C pattern = objectB[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB | representationC, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns to activate all objects pattern = objectA[1] | objectB[1] self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB | representationC, "The active representation is incorrect" ) def testLearnThreeObjectsOneCommonPatternSpatialNoise(self): """ Same test as before, with three objects Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} C{W, H, I, K, L} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=1, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=1, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() objectC = self.generateObject(numPatterns=5) objectC[0] = objectB[1] self.learn(objectC, numRepetitions=1, randomOrder=True, newObject=True) representationC = self._getActiveRepresentation() self.assertNotEquals(representationA, representationB, representationC) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) self.assertLessEqual(len(representationB & representationC), 3) self.assertLessEqual(len(representationA & representationC), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[2:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectC[1:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationC, "The pooled representation for the third object is not stable" ) # feed shared pattern between A and B pattern = objectA[0] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed shared pattern between B and C pattern = objectB[1] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationB | representationC, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns to activate all objects pattern = objectA[1] | objectB[1] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB | representationC, "The active representation is incorrect" ) def setUp(self): """ Sets up the test. """ self.pooler = None self.proximalPatternMachine = PatternMachine( n=self.inputWidth, w=self.numOutputActiveBits, num=200, seed=self.seed ) self.patternId = 0 np.random.seed(self.seed) # Wrappers around ColumnPooler API def learn(self, feedforwardPatterns, lateralPatterns=None, numRepetitions=1, randomOrder=True, newObject=True): """ Parameters: ---------------------------- Learns a single object, with the provided patterns. @param feedforwardPatterns (list(set)) List of proximal input patterns @param lateralPatterns (list(list(set))) List of distal input patterns, or None. If no lateral input is used. The outer list is expected to have the same length as feedforwardPatterns, whereas each inner list's length is the number of cortical columns which are distally connected to the pooler. @param numRepetitions (int) Number of times the patterns will be fed @param randomOrder (bool) If true, the order of patterns will be shuffled at each repetition """ if newObject: self.pooler.mmClearHistory() self.pooler.reset() # set-up indices = range(len(feedforwardPatterns)) if lateralPatterns is None: lateralPatterns = [None] * len(feedforwardPatterns) for _ in xrange(numRepetitions): if randomOrder: np.random.shuffle(indices) for idx in indices: self.pooler.compute(feedforwardPatterns[idx], activeExternalCells=lateralPatterns[idx], learn=True) def infer(self, feedforwardPattern, lateralPatterns=None, printMetrics=False): """ Feeds a single pattern to the column pooler (as well as an eventual lateral pattern). Parameters: ---------------------------- @param feedforwardPattern (set) Input proximal pattern to the pooler @param lateralPatterns (list(set)) Input dislal patterns to the pooler (one for each neighboring CC's) @param printMetrics (bool) If true, will print cell metrics """ self.pooler.compute(feedforwardPattern, activeExternalCells=lateralPatterns, learn=False) if printMetrics: print self.pooler.mmPrettyPrintMetrics( self.pooler.mmGetDefaultMetrics() ) # Helper functions def generatePattern(self): """ Returns a random proximal input pattern. """ pattern = self.proximalPatternMachine.get(self.patternId) self.patternId += 1 return pattern def generateObject(self, numPatterns): """ Creates a list of patterns, for a given object. """ return [self.generatePattern() for _ in xrange(numPatterns)] def init(self, overrides=None): """ Creates the column pooler with specified parameter overrides. Except for the specified overrides and problem-specific parameters, used parameters are implementation defaults. """ params = { "inputWidth": self.inputWidth, "numActivecolumnsPerInhArea": self.numOutputActiveBits, "columnDimensions": (self.outputWidth,), "seed": self.seed, "learnOnOneCell": False } if overrides is None: overrides = {} params.update(overrides) self.pooler = MonitoredColumnPooler(**params) def _getActiveRepresentation(self): """ Retrieves the current active representation in the pooler. """ if self.pooler is None: raise ValueError("No pooler has been instantiated") return set(self.pooler.getActiveCells())
class ExtensiveColumnPoolerTest(unittest.TestCase): """ Algorithmic tests for the ColumnPooler region. Each test actually tests multiple aspects of the algorithm. For more atomic tests refer to column_pooler_unit_test. The notation for objects is the following: object{patternA, patternB, ...} In these tests, the proximally-fed SDR's are simulated as unique (location, feature) pairs regardless of actual locations and features, unless stated otherwise. """ inputWidth = 2048 * 8 numInputActiveBits = int(0.02 * inputWidth) outputWidth = 4096 numOutputActiveBits = 40 seed = 42 def testNewInputs(self): """ Checks that the behavior is correct when facing unseed inputs. """ self.init() # feed the first input, a random SDR should be generated initialPattern = self.generateObject(1) self.learn(initialPattern, numRepetitions=1, newObject=True) representation = self._getActiveRepresentation() self.assertEqual( len(representation), self.numOutputActiveBits, "The generated representation is incorrect" ) # feed a new input for the same object, the previous SDR should persist newPattern = self.generateObject(1) self.learn(newPattern, numRepetitions=1, newObject=False) newRepresentation = self._getActiveRepresentation() self.assertNotEqual(initialPattern, newPattern) self.assertEqual( newRepresentation, representation, "The SDR did not persist when learning the same object" ) # without sensory input, the SDR should persist as well emptyPattern = [set()] self.learn(emptyPattern, numRepetitions=1, newObject=False) newRepresentation = self._getActiveRepresentation() self.assertEqual( newRepresentation, representation, "The SDR did not persist after an empty input." ) def testLearnSinglePattern(self): """ A single pattern is learnt for a single object. Objects: A{X, Y} """ self.init() object = self.generateObject(1) self.learn(object, numRepetitions=2, newObject=True) # check that the active representation is sparse representation = self._getActiveRepresentation() self.assertEqual( len(representation), self.numOutputActiveBits, "The generated representation is incorrect" ) # check that the pattern was correctly learnt self.pooler.reset() self.infer(feedforwardPattern=object[0]) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) # present new pattern for same object # it should be mapped to the same representation newPattern = [self.generatePattern()] self.learn(newPattern, numRepetitions=2, newObject=False) # check that the active representation is sparse newRepresentation = self._getActiveRepresentation() self.assertEqual( newRepresentation, representation, "The new pattern did not map to the same object representation" ) # check that the pattern was correctly learnt and is stable self.pooler.reset() self.infer(feedforwardPattern=object[0]) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) def testLearnSingleObject(self): """ Many patterns are learnt for a single object. Objects: A{P, Q, R, S, T} """ self.init() object = self.generateObject(numPatterns=5) self.learn(object, numRepetitions=2, randomOrder=True, newObject=True) representation = self._getActiveRepresentation() # check that all patterns map to the same object for pattern in object: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation is not stable" ) # if activity stops, check that the representation persists self.infer(feedforwardPattern=set()) self.assertEqual( self._getActiveRepresentation(), representation, "The pooled representation did not persist" ) def testLearnTwoObjectNoCommonPattern(self): """ Same test as before, using two objects, without common pattern. Objects: A{P, Q, R, S,T} B{V, W, X, Y, Z} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # check that all patterns map to the same object for pattern in objectA: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns map to the same object for pattern in objectB: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # feed union of patterns in object A pattern = objectA[0] | objectA[1] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns in objects A and B pattern = objectA[0] | objectB[0] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) def testLearnTwoObjectsOneCommonPattern(self): """ Same test as before, except the two objects share a pattern Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[1:]: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # feed shared pattern pattern = objectA[0] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns in objects A and B pattern = objectA[1] | objectB[1] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) def testLearnThreeObjectsOneCommonPattern(self): """ Same test as before, with three objects Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} C{W, H, I, K, L} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() objectC = self.generateObject(numPatterns=5) objectC[0] = objectB[1] self.learn(objectC, numRepetitions=3, randomOrder=True, newObject=True) representationC = self._getActiveRepresentation() self.assertNotEquals(representationA, representationB, representationC) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) self.assertLessEqual(len(representationB & representationC), 3) self.assertLessEqual(len(representationA & representationC), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[2:]: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectC[1:]: self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationC, "The pooled representation for the third object is not stable" ) # feed shared pattern between A and B pattern = objectA[0] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed shared pattern between B and C pattern = objectB[1] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationB | representationC, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns to activate all objects pattern = objectA[1] | objectB[1] self.pooler.reset() self.infer(feedforwardPattern=pattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB | representationC, "The active representation is incorrect" ) def testLearnThreeObjectsOneCommonPatternSpatialNoise(self): """ Same test as before, with three objects Objects: A{P, Q, R, S,T} B{P, W, X, Y, Z} C{W, H, I, K, L} """ self.init() objectA = self.generateObject(numPatterns=5) self.learn(objectA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[0] = objectA[0] self.learn(objectB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() objectC = self.generateObject(numPatterns=5) objectC[0] = objectB[1] self.learn(objectC, numRepetitions=3, randomOrder=True, newObject=True) representationC = self._getActiveRepresentation() self.assertNotEquals(representationA, representationB, representationC) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) self.assertLessEqual(len(representationB & representationC), 3) self.assertLessEqual(len(representationA & representationC), 3) # check that all patterns except the common one map to the same object for pattern in objectA[1:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.pooler.reset() self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectB[2:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.pooler.reset() self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) # check that all patterns except the common one map to the same object for pattern in objectC[1:]: noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.pooler.reset() self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationC, "The pooled representation for the third object is not stable" ) # feed shared pattern between A and B pattern = objectA[0] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.pooler.reset() self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB, "The active representation is incorrect" ) # feed shared pattern between B and C pattern = objectB[1] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.pooler.reset() self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationB | representationC, "The active representation is incorrect" ) # feed union of patterns in object A pattern = objectA[1] | objectA[2] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.pooler.reset() self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA, "The active representation is incorrect" ) # feed unions of patterns to activate all objects pattern = objectA[1] | objectB[1] noisyPattern = self.proximalPatternMachine.addNoise(pattern, 0.05) self.pooler.reset() self.infer(feedforwardPattern=noisyPattern) self.assertEqual( self._getActiveRepresentation(), representationA | representationB | representationC, "The active representation is incorrect" ) def testInferObjectOverTime(self): """Infer an object after touching only ambiguous points.""" self.init() patterns = [self.generatePattern() for _ in xrange(3)] objectA = [patterns[0], patterns[1]] objectB = [patterns[1], patterns[2]] objectC = [patterns[2], patterns[0]] self.learn(objectA, numRepetitions=3, newObject=True) representationA = set(self.pooler.getActiveCells()) self.learn(objectB, numRepetitions=3, newObject=True) representationB = set(self.pooler.getActiveCells()) self.learn(objectC, numRepetitions=3, newObject=True) representationC = set(self.pooler.getActiveCells()) self.pooler.reset() self.infer(patterns[0]) self.assertEqual(set(self.pooler.getActiveCells()), representationA | representationC) self.infer(patterns[1]) self.assertEqual(set(self.pooler.getActiveCells()), representationA) def testLearnOneObjectInTwoColumns(self): """Learns one object in two different columns.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) objectARepresentations = self._getActiveRepresentations() for pooler in self.poolers: pooler.reset() for patterns in objectA: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) if i > 0: self.assertEqual(activeRepresentations, self._getActiveRepresentations()) self.assertEqual(objectARepresentations, self._getActiveRepresentations()) def testLearnTwoObjectsInTwoColumnsNoCommonPattern(self): """Learns two objects in two different columns.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True, ) activeRepresentationsB = self._getActiveRepresentations() for pooler in self.poolers: pooler.reset() # check inference for object A for patternsA in objectA: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsA, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) self.assertEqual( activeRepresentationsA, self._getActiveRepresentations() ) for pooler in self.poolers: pooler.reset() # check inference for object B for patternsB in objectB: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsB, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices ) self.assertEqual( activeRepresentationsB, self._getActiveRepresentations() ) def testLearnTwoObjectsInTwoColumnsOneCommonPattern(self): """Learns two objects in two different columns, with a common pattern.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) # second pattern in column 0 is shared objectB[1][0] = objectA[1][0] # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # check inference for object A # for the first pattern, the distal predictions won't be correct # for the second one, the prediction will be unique thanks to the # distal predictions from the other column which has no ambiguity for pooler in self.poolers: pooler.reset() for patternsA in objectA: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsA, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) self.assertEqual( activeRepresentationsA, self._getActiveRepresentations() ) for pooler in self.poolers: pooler.reset() # check inference for object B for patternsB in objectB: for i in xrange(3): activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patternsB, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices ) self.assertEqual( activeRepresentationsB, self._getActiveRepresentations() ) def testLearnTwoObjectsInTwoColumnsOneCommonPatternEmptyFirstInput(self): """Learns two objects in two different columns, with a common pattern.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) # second pattern in column 0 is shared objectB[1][0] = objectA[1][0] # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # check inference for object A for pooler in self.poolers: pooler.reset() firstPattern = True for patternsA in objectA: activeRepresentations = self._getActiveRepresentations() if firstPattern: self.inferMultipleColumns( feedforwardPatterns=[set(), patternsA[1]], activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) desiredRepresentation = [set(), activeRepresentationsA[1]] else: self.inferMultipleColumns( feedforwardPatterns=patternsA, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) desiredRepresentation = activeRepresentationsA self.assertEqual( desiredRepresentation, self._getActiveRepresentations() ) def testPersistence(self): """After learning, representation should persist in L2 without input.""" self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) objectARepresentations = self._getActiveRepresentations() for pooler in self.poolers: pooler.reset() for patterns in objectA: for i in xrange(3): # replace third pattern for column 2 by empty pattern if i == 2: patterns[1] = set() activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=patterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) if i > 0: self.assertEqual(activeRepresentations, self._getActiveRepresentations()) self.assertEqual(objectARepresentations, self._getActiveRepresentations()) def testLateralDisambiguation(self): """Lateral disambiguation using a constant simulated distal input.""" self.init(overrides={ "lateralInputWidths": [self.inputWidth], }) objectA = self.generateObject(numPatterns=5) lateralInputA = [[set()]] + [[self.generatePattern()] for _ in xrange(4)] self.learn(objectA, lateralPatterns=lateralInputA, numRepetitions=3, randomOrder=True, newObject=True) representationA = self._getActiveRepresentation() objectB = self.generateObject(numPatterns=5) objectB[3] = objectA[3] lateralInputB = [[set()]] + [[self.generatePattern()] for _ in xrange(4)] self.learn(objectB, lateralPatterns=lateralInputB, numRepetitions=3, randomOrder=True, newObject=True) representationB = self._getActiveRepresentation() self.assertNotEqual(representationA, representationB) # very small overlap self.assertLessEqual(len(representationA & representationB), 3) # no ambiguity with lateral input for pattern in objectA: self.pooler.reset() self.infer(feedforwardPattern=pattern, lateralInputs=lateralInputA[-1]) self.assertEqual( self._getActiveRepresentation(), representationA, "The pooled representation for the first object is not stable" ) # no ambiguity with lateral input for pattern in objectB: self.pooler.reset() self.infer(feedforwardPattern=pattern, lateralInputs=lateralInputB[-1]) self.assertEqual( self._getActiveRepresentation(), representationB, "The pooled representation for the second object is not stable" ) def testLateralContestResolved(self): """ Infer an object via lateral disambiguation even if some other columns have similar ambiguity. """ self.init(overrides={"lateralInputWidths": [self.inputWidth, self.inputWidth]}) patterns = [self.generatePattern() for _ in xrange(3)] objectA = [patterns[0], patterns[1]] objectB = [patterns[1], patterns[2]] lateralInput1A = self.generatePattern() lateralInput2A = self.generatePattern() lateralInput1B = self.generatePattern() lateralInput2B = self.generatePattern() self.learn(objectA, lateralPatterns=[[lateralInput1A, lateralInput2A]]*2, numRepetitions=3, newObject=True) representationA = set(self.pooler.getActiveCells()) self.learn(objectB, lateralPatterns=[[lateralInput1B, lateralInput2B]]*2, numRepetitions=3, newObject=True) representationB = set(self.pooler.getActiveCells()) self.pooler.reset() # This column will say A | B # One lateral column says A | B # Another lateral column says A self.infer(patterns[1], lateralInputs=[(), ()]) self.infer(patterns[1], lateralInputs=[lateralInput1A | lateralInput1B, lateralInput2A]) self.assertEqual(set(self.pooler.getActiveCells()), representationA) @unittest.skip("Fails, need to discuss") def testMultiColumnCompetition(self): """Competition between multiple conflicting lateral inputs.""" self.init(numCols=4) neighborsIndices = [[1, 2, 3], [0, 2, 3], [0, 1, 3], [0, 1, 2]] objectA = self.generateObject(numPatterns=5, numCols=4) objectB = self.generateObject(numPatterns=5, numCols=4) # second pattern in column 0 is shared objectB[1][0] = objectA[1][0] # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # check inference for object A # for the first pattern, the distal predictions won't be correct # for the second one, the prediction will be unique thanks to the # distal predictions from the other column which has no ambiguity for pooler in self.poolers: pooler.reset() # sensed patterns will be mixed sensedPatterns = objectA[1][:-1] + [objectA[1][-1] | objectB[1][-1]] # feed sensed patterns first time # every one feels the correct object, except first column which feels # the union (reminder: lateral input are delayed) activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) firstSensedRepresentations = [ activeRepresentationsA[0] | activeRepresentationsB[0], activeRepresentationsA[1], activeRepresentationsA[2], activeRepresentationsA[3] | activeRepresentationsB[3] ] self.assertEqual( firstSensedRepresentations, self._getActiveRepresentations() ) # feed sensed patterns second time # the distal predictions are still ambiguous in C1, but disambiguated # in C4 activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) secondSensedRepresentations = [ activeRepresentationsA[0] | activeRepresentationsB[0], activeRepresentationsA[1], activeRepresentationsA[2], activeRepresentationsA[3] ] self.assertEqual( secondSensedRepresentations, self._getActiveRepresentations() ) # feed sensed patterns third time # this time, it is all disambiguated activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) self.assertEqual( activeRepresentationsA, self._getActiveRepresentations() ) def testMutualDisambiguationThroughUnions(self): """ Learns three object in two different columns. Feed ambiguous sensations, A u B and B u C. The system should narrow down to B. """ self.init(numCols=2) neighborsIndices = [[1], [0]] objectA = self.generateObject(numPatterns=5, numCols=2) objectB = self.generateObject(numPatterns=5, numCols=2) objectC = self.generateObject(numPatterns=5, numCols=2) # learn object self.learnMultipleColumns( objectA, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsA = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectB, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsB = self._getActiveRepresentations() # learn object self.learnMultipleColumns( objectC, numRepetitions=3, neighborsIndices=neighborsIndices, randomOrder=True, newObject=True ) activeRepresentationsC = self._getActiveRepresentations() # create sensed patterns (ambiguous) sensedPatterns = [objectA[1][0] | objectB[1][0], objectB[2][1] | objectC[2][1]] for pooler in self.poolers: pooler.reset() # feed sensed patterns first time # the L2 representations should be ambiguous activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) firstRepresentations = [ activeRepresentationsA[0] | activeRepresentationsB[0], activeRepresentationsB[1] | activeRepresentationsC[1] ] self.assertEqual( firstRepresentations, self._getActiveRepresentations() ) # feed a second time, distal predictions should disambiguate activeRepresentations = self._getActiveRepresentations() self.inferMultipleColumns( feedforwardPatterns=sensedPatterns, activeRepresentations=activeRepresentations, neighborsIndices=neighborsIndices, ) # check that representations are unique, being slightly tolerant self.assertLessEqual( len(self._getActiveRepresentations()[0] - activeRepresentationsB[0]), 5, ) self.assertLessEqual( len(self._getActiveRepresentations()[1] - activeRepresentationsB[1]), 5, ) self.assertGreaterEqual( len(self._getActiveRepresentations()[0] & activeRepresentationsB[0]), 35, ) self.assertGreaterEqual( len(self._getActiveRepresentations()[1] & activeRepresentationsB[1]), 35, ) def setUp(self): """ Sets up the test. """ # single column case self.pooler = None # multi column case self.poolers = [] # create pattern machine self.proximalPatternMachine = PatternMachine( n=self.inputWidth, w=self.numOutputActiveBits, num=200, seed=self.seed ) self.patternId = 0 np.random.seed(self.seed) # Wrappers around ColumnPooler API def learn(self, feedforwardPatterns, lateralPatterns=None, numRepetitions=1, randomOrder=True, newObject=True): """ Parameters: ---------------------------- Learns a single object, with the provided patterns. @param feedforwardPatterns (list(set)) List of proximal input patterns @param lateralPatterns (list(list(iterable))) List of distal input patterns, or None. If no lateral input is used. The outer list is expected to have the same length as feedforwardPatterns, whereas each inner list's length is the number of cortical columns which are distally connected to the pooler. @param numRepetitions (int) Number of times the patterns will be fed @param randomOrder (bool) If true, the order of patterns will be shuffled at each repetition """ if newObject: self.pooler.mmClearHistory() self.pooler.reset() # set-up indices = range(len(feedforwardPatterns)) if lateralPatterns is None: lateralPatterns = [[] for _ in xrange(len(feedforwardPatterns))] for _ in xrange(numRepetitions): if randomOrder: np.random.shuffle(indices) for idx in indices: self.pooler.compute(sorted(feedforwardPatterns[idx]), [sorted(lateralPattern) for lateralPattern in lateralPatterns[idx]], learn=True) def infer(self, feedforwardPattern, lateralInputs=(), printMetrics=False): """ Feeds a single pattern to the column pooler (as well as an eventual lateral pattern). Parameters: ---------------------------- @param feedforwardPattern (set) Input proximal pattern to the pooler @param lateralInputs (list(set)) Input distal patterns to the pooler (one for each neighboring CC's) @param printMetrics (bool) If true, will print cell metrics """ self.pooler.compute(sorted(feedforwardPattern), [sorted(lateralInput) for lateralInput in lateralInputs], learn=False) if printMetrics: print self.pooler.mmPrettyPrintMetrics( self.pooler.mmGetDefaultMetrics() ) # Helper functions def generatePattern(self): """ Returns a random proximal input pattern. """ pattern = self.proximalPatternMachine.get(self.patternId) self.patternId += 1 return pattern def generateObject(self, numPatterns, numCols=1): """ Creates a list of patterns, for a given object. If numCols > 1 is given, a list of list of patterns will be returned. """ if numCols == 1: return [self.generatePattern() for _ in xrange(numPatterns)] else: patterns = [] for i in xrange(numPatterns): patterns.append([self.generatePattern() for _ in xrange(numCols)]) return patterns def init(self, overrides=None, numCols=1): """ Creates the column pooler with specified parameter overrides. Except for the specified overrides and problem-specific parameters, used parameters are implementation defaults. """ params = { "inputWidth": self.inputWidth, "lateralInputWidths": [self.outputWidth]*(numCols-1), "cellCount": self.outputWidth, "sdrSize": self.numOutputActiveBits, "minThresholdProximal": 10, "sampleSizeProximal": 20, "connectedPermanenceProximal": 0.6, "initialDistalPermanence": 0.51, "activationThresholdDistal": 10, "sampleSizeDistal": 20, "connectedPermanenceDistal": 0.6, "seed": self.seed, } if overrides is None: overrides = {} params.update(overrides) if numCols == 1: self.pooler = MonitoredColumnPooler(**params) else: # TODO: We need a different seed for each pooler otherwise each one # outputs an identical representation. Use random seed for now but ideally # we would set different specific seeds for each pooler params['seed']=0 self.poolers = [MonitoredColumnPooler(**params) for _ in xrange(numCols)] def _getActiveRepresentation(self): """ Retrieves the current active representation in the pooler. """ if self.pooler is None: raise ValueError("No pooler has been instantiated") return set(self.pooler.getActiveCells()) # Multi-column testing def learnMultipleColumns(self, feedforwardPatterns, numRepetitions=1, neighborsIndices=None, randomOrder=True, newObject=True): """ Learns a single object, feeding it through the multiple columns. Parameters: ---------------------------- Learns a single object, with the provided patterns. @param feedforwardPatterns (list(list(set))) List of proximal input patterns (one for each pooler). @param neighborsIndices (list(list)) List of column indices each column received input from. @param numRepetitions (int) Number of times the patterns will be fed @param randomOrder (bool) If true, the order of patterns will be shuffled at each repetition """ if newObject: for pooler in self.poolers: pooler.mmClearHistory() pooler.reset() # use different set of pattern indices to allow random orders indices = [range(len(feedforwardPatterns))] * len(self.poolers) prevActiveCells = [set() for _ in xrange(len(self.poolers))] # by default, all columns are neighbors if neighborsIndices is None: neighborsIndices = [ range(i) + range(i+1, len(self.poolers)) for i in xrange(len(self.poolers)) ] for _ in xrange(numRepetitions): # independently shuffle pattern orders if necessary if randomOrder: for idx in indices: np.random.shuffle(idx) for i in xrange(len(indices[0])): # Train each column for col, pooler in enumerate(self.poolers): # get union of relevant lateral representations lateralInputs = [sorted(activeCells) for presynapticCol, activeCells in enumerate(prevActiveCells) if col != presynapticCol] pooler.compute(sorted(feedforwardPatterns[indices[col][i]][col]), lateralInputs, learn=True) prevActiveCells = self._getActiveRepresentations() def inferMultipleColumns(self, feedforwardPatterns, activeRepresentations, neighborsIndices=None, printMetrics=False, reset=False): """ Feeds a single pattern to the column pooler (as well as an eventual lateral pattern). Parameters: ---------------------------- @param feedforwardPattern (list(set)) Input proximal patterns to the pooler (one for each column) @param activeRepresentations (list(set)) Active representations in the columns at the previous step. @param neighborsIndices (list(list)) List of column indices each column received input from. @param printMetrics (bool) If true, will print cell metrics """ if reset: for pooler in self.poolers: pooler.reset() # by default, all columns are neighbors if neighborsIndices is None: neighborsIndices = [ range(i) + range(i+1, len(self.poolers)) for i in xrange(len(self.poolers)) ] for col, pooler in enumerate(self.poolers): # get union of relevant lateral representations lateralInputs = [sorted(activeCells) for presynapticCol, activeCells in enumerate(activeRepresentations) if col != presynapticCol] pooler.compute(sorted(feedforwardPatterns[col]), lateralInputs, learn=False) if printMetrics: for pooler in self.poolers: print pooler.mmPrettyPrintMetrics( pooler.mmGetDefaultMetrics() ) def _getActiveRepresentations(self): """ Retrieves the current active representations in the poolers. """ if len(self.poolers) == 0: raise ValueError("No pooler has been instantiated") return [set(pooler.getActiveCells()) for pooler in self.poolers]
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)