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 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 getPatternMachine(self): return ConsecutivePatternMachine(6, 1)
class TutorialTemporalMemoryTest(AbstractTemporalMemoryTest):
VERBOSITY = 1
DEFAULT_TM_PARAMS = {
"columnDimensions": [6],
"cellsPerColumn": 4,
"initialPermanence": 0.3,
"connectedPermanence": 0.5,
"minThreshold": 1,
"maxNewSynapseCount": 6,
"permanenceIncrement": 0.1,
"permanenceDecrement": 0.05,
"activationThreshold": 1
}
PATTERN_MACHINE = ConsecutivePatternMachine(6, 1)
def testFirstOrder(self):
"""Basic first order sequences"""
self.init()
sequence = self.sequenceMachine.generateFromNumbers([0, 1, 2, 3, None])
self.feedTM(sequence)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 0)
self.feedTM(sequence, num=2)
self.feedTM(sequence)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 1)
self.feedTM(sequence, num=4)
self.feedTM(sequence)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 1)
def testHighOrder(self):
"""High order sequences (in order)"""
self.init()
sequenceA = self.sequenceMachine.generateFromNumbers(
[0, 1, 2, 3, None])
sequenceB = self.sequenceMachine.generateFromNumbers(
[4, 1, 2, 5, None])
self.feedTM(sequenceA, num=5)
self.feedTM(sequenceA, learn=False)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 1)
self.feedTM(sequenceB)
self.feedTM(sequenceB, num=2)
self.feedTM(sequenceB, learn=False)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[1]), 1)
self.feedTM(sequenceB, num=3)
self.feedTM(sequenceB, learn=False)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[2]), 1)
self.feedTM(sequenceB, num=3)
self.feedTM(sequenceB, learn=False)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 1)
self.feedTM(sequenceA, learn=False)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 1)
self.assertEqual(
len(self.tm.mmGetTracePredictedInactiveColumns().data[3]), 1)
self.feedTM(sequenceA, num=10)
self.feedTM(sequenceA, learn=False)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 1)
# TODO: Requires some form of synaptic decay to forget the ABC=>Y
# transition that's initially formed
# self.assertEqual(len(self.tm.mmGetTracePredictedInactiveColumns().data[3]), 0)
def testHighOrderAlternating(self):
"""High order sequences (alternating)"""
self.init()
sequence = self.sequenceMachine.generateFromNumbers([0, 1, 2, 3, None])
sequence += self.sequenceMachine.generateFromNumbers(
[4, 1, 2, 5, None])
self.feedTM(sequence)
self.feedTM(sequence, num=10)
self.feedTM(sequence, learn=False)
# TODO: Requires some form of synaptic decay to forget the
# ABC=>Y and XBC=>D transitions that are initially formed
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[3]), 1)
# self.assertEqual(len(self.tm.mmGetTracePredictedInactiveColumns().data[3]), 0)
self.assertEqual(
len(self.tm.mmGetTracePredictedActiveColumns().data[7]), 1)
# self.assertEqual(len(self.tm.mmGetTracePredictedInactiveColumns().data[7]), 0)
def testEndlesslyRepeating(self):
"""Endlessly repeating sequence of 2 elements"""
self.init({"columnDimensions": [2]})
sequence = self.sequenceMachine.generateFromNumbers([0, 1])
for _ in xrange(7):
self.feedTM(sequence)
self.feedTM(sequence, num=50)
def testEndlesslyRepeatingWithNoNewSynapses(self):
"""Endlessly repeating sequence of 2 elements with maxNewSynapseCount=1"""
self.init({
"columnDimensions": [2],
"maxNewSynapseCount": 1,
"cellsPerColumn": 10
})
sequence = self.sequenceMachine.generateFromNumbers([0, 1])
for _ in xrange(7):
self.feedTM(sequence)
self.feedTM(sequence, num=100)
def testLongRepeatingWithNovelEnding(self):
"""Long repeating sequence with novel pattern at the end"""
self.init({"columnDimensions": [3]})
sequence = self.sequenceMachine.generateFromNumbers([0, 1])
sequence *= 10
sequence += [self.patternMachine.get(2), None]
for _ in xrange(4):
self.feedTM(sequence)
self.feedTM(sequence, num=10)
def testSingleEndlesslyRepeating(self):
"""A single endlessly repeating pattern"""
self.init({"columnDimensions": [1]})
sequence = [self.patternMachine.get(0)]
for _ in xrange(4):
self.feedTM(sequence)
for _ in xrange(2):
self.feedTM(sequence, num=10)
# ==============================
# Overrides
# ==============================
def setUp(self):
super(TutorialTemporalMemoryTest, self).setUp()
print(
"\n"
"======================================================\n"
"Test: {0} \n"
"{1}\n"
"======================================================\n").format(
self.id(), self.shortDescription())
def init(self, *args, **kwargs):
super(TutorialTemporalMemoryTest, self).init(*args, **kwargs)
print "Initialized new TM with parameters:"
print pprint.pformat(self._computeTMParams(kwargs.get("overrides")))
print
def feedTM(self, sequence, learn=True, num=1):
self._showInput(sequence, learn=learn, num=num)
super(TutorialTemporalMemoryTest, self).feedTM(sequence,
learn=learn,
num=num)
print self.tm.mmPrettyPrintTraces(
self.tm.mmGetDefaultTraces(verbosity=2),
breakOnResets=self.tm.mmGetTraceResets())
print
if learn:
print self.tm.mmPrettyPrintConnections()
# ==============================
# Helper functions
# ==============================
def _showInput(self, sequence, learn=True, num=1):
sequenceText = self.sequenceMachine.prettyPrintSequence(
sequence, verbosity=self.VERBOSITY)
learnText = "(learning {0})".format("enabled" if learn else "disabled")
numText = " [{0} times]".format(num) if num > 1 else ""
print "Feeding sequence {0}{1}:\n{2}".format(learnText, numText,
sequenceText)
print
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
def setUp(self):
self.patternMachine = ConsecutivePatternMachine(100, 5)
self.sequenceMachine = SequenceMachine(self.patternMachine)
