class SequenceMachineTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
def testGenerateFromNumbers(self):
numbers = range(0, 10) + [None] + range(10, 19)
sequence = self.sequenceMachine.generateFromNumbers(numbers)
self.assertEqual(len(sequence), 20)
self.assertEqual(sequence[0], self.patternMachine.get(0))
self.assertEqual(sequence[10], None)
self.assertEqual(sequence[11], self.patternMachine.get(10))
def testAddSpatialNoise(self):
patternMachine = PatternMachine(10000, 1000, num=100)
sequenceMachine = SequenceMachine(patternMachine)
numbers = range(0, 100)
numbers.append(None)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.5)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertTrue(400 < overlap < 600)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.0)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertEqual(overlap, 1000)
def testGenerateNumbers(self):
numbers = self.sequenceMachine.generateNumbers(1, 100)
self.assertEqual(numbers[-1], None)
self.assertEqual(len(numbers), 101)
self.assertFalse(numbers[:-1] == range(0, 100))
self.assertEqual(sorted(numbers[:-1]), range(0, 100))
def testGenerateNumbersMultipleSequences(self):
numbers = self.sequenceMachine.generateNumbers(3, 100)
self.assertEqual(len(numbers), 303)
self.assertEqual(sorted(numbers[0:100]), range(0, 100))
self.assertEqual(sorted(numbers[101:201]), range(100, 200))
self.assertEqual(sorted(numbers[202:302]), range(200, 300))
def testGenerateNumbersWithShared(self):
numbers = self.sequenceMachine.generateNumbers(3, 100, (20, 35))
self.assertEqual(len(numbers), 303)
shared = range(300, 315)
self.assertEqual(numbers[20:35], shared)
self.assertEqual(numbers[20 + 101:35 + 101], shared)
self.assertEqual(numbers[20 + 202:35 + 202], shared)
class ConsecutivePatternMachineTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
def testGet(self):
pattern = self.patternMachine.get(18)
self.assertEqual(len(pattern), 5)
self.assertEqual(pattern, set([90, 91, 92, 93, 94]))
pattern = self.patternMachine.get(19)
self.assertEqual(len(pattern), 5)
self.assertEqual(pattern, set([95, 96, 97, 98, 99]))
def testGetOutOfBounds(self):
args = [20]
self.assertRaises(IndexError, self.patternMachine.get, *args)
class TemporalMemoryMonitorMixinTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
self.tm = MonitoredTemporalMemory(columnDimensions=[100],
cellsPerColumn=4,
initialPermanence=0.6,
connectedPermanence=0.5,
minThreshold=1,
maxNewSynapseCount=6,
permanenceIncrement=0.1,
permanenceDecrement=0.05,
activationThreshold=1)
def testFeedSequence(self):
sequence = self._generateSequence()
sequenceLength = len(sequence) - 3 # without resets
# Replace last pattern (before the None) with an unpredicted one
sequence[-2] = self.patternMachine.get(4)
self._feedSequence(sequence, sequenceLabel="Test")
activeColumnsTrace = self.tm.mmGetTraceActiveColumns()
predictiveCellsTrace = self.tm.mmGetTracePredictiveCells()
sequenceLabelsTrace = self.tm.mmGetTraceSequenceLabels()
resetsTrace = self.tm.mmGetTraceResets()
predictedActiveCellsTrace = self.tm.mmGetTracePredictedActiveCells()
predictedInactiveCellsTrace = self.tm.mmGetTracePredictedInactiveCells()
predictedActiveColumnsTrace = self.tm.mmGetTracePredictedActiveColumns()
predictedInactiveColumnsTrace = self.tm.mmGetTracePredictedInactiveColumns()
unpredictedActiveColumnsTrace = self.tm.mmGetTraceUnpredictedActiveColumns()
self.assertEqual(len(activeColumnsTrace.data), sequenceLength)
self.assertEqual(len(predictiveCellsTrace.data), sequenceLength)
self.assertEqual(len(sequenceLabelsTrace.data), sequenceLength)
self.assertEqual(len(resetsTrace.data), sequenceLength)
self.assertEqual(len(predictedActiveCellsTrace.data), sequenceLength)
self.assertEqual(len(predictedInactiveCellsTrace.data), sequenceLength)
self.assertEqual(len(predictedActiveColumnsTrace.data), sequenceLength)
self.assertEqual(len(predictedInactiveColumnsTrace.data), sequenceLength)
self.assertEqual(len(unpredictedActiveColumnsTrace.data), sequenceLength)
self.assertEqual(activeColumnsTrace.data[-1], self.patternMachine.get(4))
self.assertEqual(sequenceLabelsTrace.data[-1], "Test")
self.assertEqual(resetsTrace.data[0], True)
self.assertEqual(resetsTrace.data[1], False)
self.assertEqual(resetsTrace.data[10], True)
self.assertEqual(resetsTrace.data[-1], False)
self.assertEqual(len(predictedActiveCellsTrace.data[-2]), 5)
self.assertEqual(len(predictedActiveCellsTrace.data[-1]), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data[-2]), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data[-1]), 5)
self.assertEqual(len(predictedActiveColumnsTrace.data[-2]), 5)
self.assertEqual(len(predictedActiveColumnsTrace.data[-1]), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data[-2]), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data[-1]), 5)
self.assertEqual(len(unpredictedActiveColumnsTrace.data[-2]), 0)
self.assertEqual(len(unpredictedActiveColumnsTrace.data[-1]), 5)
def testClearHistory(self):
sequence = self._generateSequence()
self._feedSequence(sequence, sequenceLabel="Test")
self.tm.mmClearHistory()
activeColumnsTrace = self.tm.mmGetTraceActiveColumns()
predictiveCellsTrace = self.tm.mmGetTracePredictiveCells()
sequenceLabelsTrace = self.tm.mmGetTraceSequenceLabels()
resetsTrace = self.tm.mmGetTraceResets()
predictedActiveCellsTrace = self.tm.mmGetTracePredictedActiveCells()
predictedInactiveCellsTrace = self.tm.mmGetTracePredictedInactiveCells()
predictedActiveColumnsTrace = self.tm.mmGetTracePredictedActiveColumns()
predictedInactiveColumnsTrace = self.tm.mmGetTracePredictedInactiveColumns()
unpredictedActiveColumnsTrace = self.tm.mmGetTraceUnpredictedActiveColumns()
self.assertEqual(len(activeColumnsTrace.data), 0)
self.assertEqual(len(predictiveCellsTrace.data), 0)
self.assertEqual(len(sequenceLabelsTrace.data), 0)
self.assertEqual(len(resetsTrace.data), 0)
self.assertEqual(len(predictedActiveCellsTrace.data), 0)
self.assertEqual(len(predictedInactiveCellsTrace.data), 0)
self.assertEqual(len(predictedActiveColumnsTrace.data), 0)
self.assertEqual(len(predictedInactiveColumnsTrace.data), 0)
self.assertEqual(len(unpredictedActiveColumnsTrace.data), 0)
def testSequencesMetrics(self):
sequence = self._generateSequence()
self._feedSequence(sequence, "Test1")
sequence.reverse()
sequence.append(sequence.pop(0)) # Move None (reset) to the end
self._feedSequence(sequence, "Test2")
sequencesPredictedActiveCellsPerColumnMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsPerColumn()
sequencesPredictedActiveCellsSharedMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsShared()
self.assertEqual(sequencesPredictedActiveCellsPerColumnMetric.mean, 1)
self.assertEqual(sequencesPredictedActiveCellsSharedMetric.mean, 1)
self._feedSequence(sequence, "Test3")
sequencesPredictedActiveCellsPerColumnMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsPerColumn()
sequencesPredictedActiveCellsSharedMetric = \
self.tm.mmGetMetricSequencesPredictedActiveCellsShared()
self.assertEqual(sequencesPredictedActiveCellsPerColumnMetric.mean, 1)
self.assertTrue(sequencesPredictedActiveCellsSharedMetric.mean > 1)
# ==============================
# Helper functions
# ==============================
def _generateSequence(self):
numbers = range(0, 10)
sequence = self.sequenceMachine.generateFromNumbers(numbers)
sequence.append(None)
sequence *= 3
return sequence
def _feedSequence(self, sequence, sequenceLabel=None):
for pattern in sequence:
if pattern is None:
self.tm.reset()
else:
self.tm.compute(pattern, sequenceLabel=sequenceLabel)
class SequenceMachineTest(unittest.TestCase):
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
def testGenerateFromNumbers(self):
numbers = range(0, 10) + [None] + range(10, 19)
sequence = self.sequenceMachine.generateFromNumbers(numbers)
self.assertEqual(len(sequence), 20)
self.assertEqual(sequence[0], self.patternMachine.get(0))
self.assertEqual(sequence[10], None)
self.assertEqual(sequence[11], self.patternMachine.get(10))
def testAddSpatialNoise(self):
patternMachine = PatternMachine(10000, 1000, num=100)
sequenceMachine = SequenceMachine(patternMachine)
numbers = range(0, 100)
numbers.append(None)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.5)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertTrue(400 < overlap < 600)
sequence = sequenceMachine.generateFromNumbers(numbers)
noisy = sequenceMachine.addSpatialNoise(sequence, 0.0)
overlap = len(noisy[0] & patternMachine.get(0))
self.assertEqual(overlap, 1000)
def testGenerateNumbers(self):
numbers = self.sequenceMachine.generateNumbers(1, 100)
self.assertEqual(numbers[-1], None)
self.assertEqual(len(numbers), 101)
self.assertFalse(numbers[:-1] == range(0, 100))
self.assertEqual(sorted(numbers[:-1]), range(0, 100))
def testGenerateNumbersMultipleSequences(self):
numbers = self.sequenceMachine.generateNumbers(3, 100)
self.assertEqual(len(numbers), 303)
self.assertEqual(sorted(numbers[0:100]), range(0, 100))
self.assertEqual(sorted(numbers[101:201]), range(100, 200))
self.assertEqual(sorted(numbers[202:302]), range(200, 300))
def testGenerateNumbersWithShared(self):
numbers = self.sequenceMachine.generateNumbers(3, 100, (20, 35))
self.assertEqual(len(numbers), 303)
shared = range(300, 315)
self.assertEqual(numbers[20:35], shared)
self.assertEqual(numbers[20+101:35+101], shared)
self.assertEqual(numbers[20+202:35+202], shared)
class OneDUniverse(AbstractUniverse):
def __init__(self, debugSensor=False, debugMotor=False, **kwargs):
"""
@param debugSensor (bool) Controls whether sensor encodings are contiguous
@param debugMotor (bool) Controls whether motor encodings are contiguous
"""
super(OneDUniverse, self).__init__(**kwargs)
self.debugSensor = debugSensor
self.debugMotor = debugMotor
self.sensorPatternMachine = ConsecutivePatternMachine(self.nSensor,
self.wSensor)
self.sensorEncoder = SDRCategoryEncoder(self.nSensor, self.wSensor,
forced=True)
self.motorPatternMachine = ConsecutivePatternMachine(self.nMotor,
self.wMotor)
self.motorEncoder = SDRCategoryEncoder(self.nMotor, self.wMotor,
forced=True)
# This pool is a human friendly representation of sensory values
self.elementCodes = (
range(0x0041, 0x005A+1) + # A-Z
range(0x0061, 0x007A+1) + # a-z
range(0x0030, 0x0039+1) + # 0-9
range(0x00C0, 0x036F+1) # Many others
)
self.numDecodedElements = len(self.elementCodes)
def encodeSensorValue(self, sensorValue):
"""
@param sensorValue (object) Sensor value
@return (set) Sensor pattern
"""
if self.debugSensor:
return self.sensorPatternMachine.get(sensorValue)
else:
return set(self.sensorEncoder.encode(sensorValue).nonzero()[0])
def decodeSensorValue(self, sensorValue):
"""
@param sensorValue (object) Sensor value
@return (string) Human viewable representation of sensorValue
"""
if sensorValue < len(self.elementCodes):
return unichr(self.elementCodes[sensorValue])
else:
return unichr(0x003F) # Character: ?
def encodeMotorValue(self, motorValue):
"""
@param motorValue (object) Motor value
@return (set) Motor pattern
"""
if self.debugMotor:
numMotorValues = self.nMotor / self.wMotor
motorRadius = (numMotorValues - 1) / 2
return self.motorPatternMachine.get(motorValue + motorRadius)
else:
return set(self.motorEncoder.encode(motorValue).nonzero()[0])
