def setUp(self):
# Initialize the universe, worlds, and agents
nElements = 5
wEncoders = 7
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=nElements * wEncoders,
wSensor=wEncoders,
nMotor=wEncoders * 7,
wMotor=wEncoders)
self.agents = [
RandomOneDAgent(OneDWorld(universe, range(nElements)),
4,
possibleMotorValues=(-1, 1),
seed=23),
RandomOneDAgent(OneDWorld(universe,
list(reversed(range(nElements)))),
4,
possibleMotorValues=(-1, 1),
seed=23)
]
self.experimentRunner = SensorimotorExperimentRunner(
tmOverrides={
"columnDimensions": [nElements * wEncoders],
"minThreshold": wEncoders * 2,
"maxNewSynapseCount": wEncoders * 2,
"activationThreshold": wEncoders * 2
},
tpOverrides={
"columnDimensions": [512],
"numActiveColumnsPerInhArea": 20
})
def testChooseMotorValue(self):
universe = OneDUniverse(nSensor=100, wSensor=5, nMotor=105, wMotor=5)
world = OneDWorld(universe, [2, 0, 5, 15, 10], 2)
agent = RandomOneDAgent(world,
possibleMotorValues=set(xrange(-10, 10)))
for _ in range(100):
motorValue = agent.chooseMotorValue()
self.assertTrue(-2 <= motorValue <= 2) # bounded by size of world
world.move(-2)
for _ in range(100):
motorValue = agent.chooseMotorValue()
self.assertTrue(0 <= motorValue <= 4) # bounded by size of world
def testChooseMotorValue(self):
universe = OneDUniverse(nSensor=100, wSensor=5,
nMotor=105, wMotor=5)
world = OneDWorld(universe, [2, 0, 5, 15, 10])
agent = RandomOneDAgent(world, 2, possibleMotorValues=set(xrange(-10, 10)))
for _ in range(100):
motorValue = agent.chooseMotorValue()
self.assertTrue(-2 <= motorValue <= 2) # bounded by size of world
agent.move(-2)
for _ in range(100):
motorValue = agent.chooseMotorValue()
self.assertTrue(0 <= motorValue <= 4) # bounded by size of world
def setUp(self):
# Initialize the universe, worlds, and agents
nElements = 4
nWorlds = 3
n = 512
w = 20
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=n,
wSensor=w,
nMotor=n,
wMotor=w)
self.agents = [
RandomOneDAgent(OneDWorld(
universe, range(nElements * world, nElements * (world + 1))),
0,
possibleMotorValues=(-1, 1),
seed=23) for world in xrange(nWorlds)
]
self.experimentRunner = SensorimotorExperimentRunner(
tmOverrides={
"columnDimensions": [n],
"minThreshold": w * 2,
"maxNewSynapseCount": w * 2,
"activationThreshold": w * 2,
"seed": 42
},
tpOverrides={
"columnDimensions": [n],
"numActiveColumnsPerInhArea": w,
"seed": 42
})
def testSingleWorldBasic(self):
"""
Test Sensorimotor Temporal Memory learning in a single world.
Patterns are represented as complete SDRs. No patterns are repeated.
Prediction should be perfect.
"""
self._init()
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=100,
wSensor=10,
nMotor=70,
wMotor=10)
world = OneDWorld(universe, [0, 1, 2, 3], 2)
agent = RandomOneDAgent(world, possibleMotorValues=set(xrange(-3, 4)))
sequence = self._generateSensorimotorSequences(100, [agent])
self._feedTM(sequence)
sequence = self._generateSensorimotorSequences(100, [agent])
stats = self._testTM(sequence)
self._assertAllActiveWerePredicted(stats, universe)
self._assertAllInactiveWereUnpredicted(stats)
self._assertSequencesOnePredictedActiveCellPerColumn(stats)
def testMultipleWorldsSharedPatterns(self):
"""
Test Sensorimotor Temporal Memory learning in multiple separate worlds.
Patterns are represented as complete SDRs. Patterns are shared between
worlds.
All active columns should have been predicted.
"""
self._init()
universe = OneDUniverse(debugMotor=True,
nSensor=100,
wSensor=10,
nMotor=70,
wMotor=10)
agents = []
numWorlds = 5
for i in xrange(numWorlds):
patterns = range(4)
self._random.shuffle(patterns)
world = OneDWorld(universe, patterns, 2)
agent = RandomOneDAgent(world,
possibleMotorValues=set(xrange(-3, 4)))
agents.append(agent)
sequence = self._generateSensorimotorSequences(150, agents)
self._feedTM(sequence)
sequence = self._generateSensorimotorSequences(100, agents)
stats = self._testTM(sequence)
self._assertAllActiveWerePredicted(stats, universe)
self.assertTrue(0 < stats.predictedInactiveColumns.average < 10)
def testMultipleWorldsBasic(self):
"""
Test Sensorimotor Temporal Memory learning in multiple separate worlds.
Patterns are represented as complete SDRs. No patterns are repeated.
Prediction should be perfect.
"""
self._init()
universe = OneDUniverse(debugMotor=True,
nSensor=100,
wSensor=10,
nMotor=70,
wMotor=10)
agents = []
numWorlds = 5
sequenceLength = 4
for i in xrange(numWorlds):
start = i * sequenceLength
patterns = range(start, start + sequenceLength)
world = OneDWorld(universe, patterns, 2)
agent = RandomOneDAgent(world,
possibleMotorValues=set(xrange(-3, 4)))
agents.append(agent)
sequence = self._generateSensorimotorSequences(150, agents)
self._feedTM(sequence)
sequence = self._generateSensorimotorSequences(100, agents)
stats = self._testTM(sequence)
self._assertAllActiveWerePredicted(stats, universe)
self._assertAllInactiveWereUnpredicted(stats)
self._assertSequencesOnePredictedActiveCellPerColumn(stats)
def testSingleWorldOneBitPerPattern(self):
"""
Test Sensorimotor Temporal Memory learning in a single world.
Patterns (sensor and motor) are represented with one active bit per pattern.
"""
self._init({
"columnDimensions": [4],
"minThreshold": 2,
"activationThreshold": 2
})
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=4,
wSensor=1,
nMotor=3,
wMotor=1)
world = OneDWorld(universe, [0, 1, 2, 3], 2)
agent = RandomOneDAgent(world, possibleMotorValues=set(xrange(-1, 2)))
sequence = self._generateSensorimotorSequences(100, [agent])
self._feedTM(sequence)
sequence = self._generateSensorimotorSequences(20, [agent])
stats = self._testTM(sequence)
self._assertAllActiveWerePredicted(stats, universe)
self._assertAllInactiveWereUnpredicted(stats)
self._assertSequencesOnePredictedActiveCellPerColumn(stats)
def testGenerateSensorimotorSequence(self):
universe = OneDUniverse(nSensor=100, wSensor=5, nMotor=105, wMotor=5)
world = OneDWorld(universe, [2, 0, 5, 15, 10], 2)
agent = RandomOneDAgent(world,
possibleMotorValues=set(xrange(-10, 10)))
sensorSequence, motorSequence, sensorimotorSequence = (
agent.generateSensorimotorSequence(20))
self.assertEqual(len(sensorSequence), 20)
self.assertEqual(len(motorSequence), 20)
self.assertEqual(len(sensorimotorSequence), 20)
# Ensure each encoded pattern has the correct number of ON bits
for i in range(20):
self.assertEqual(len(sensorSequence[i]), 5)
self.assertEqual(len(motorSequence[i]), 5)
self.assertEqual(len(sensorimotorSequence[i]), 10)
def testGenerateSensorimotorSequence(self):
universe = OneDUniverse(nSensor=100, wSensor=5,
nMotor=105, wMotor=5)
world = OneDWorld(universe, [2, 0, 5, 15, 10])
agent = RandomOneDAgent(world, 2, possibleMotorValues=set(xrange(-10, 10)))
sensorSequence, motorSequence, sensorimotorSequence = (
agent.generateSensorimotorSequence(20)
)
self.assertEqual(len(sensorSequence), 20)
self.assertEqual(len(motorSequence), 20)
self.assertEqual(len(sensorimotorSequence), 20)
# Ensure each encoded pattern has the correct number of ON bits
for i in range(20):
self.assertEqual(len(sensorSequence[i]), 5)
self.assertEqual(len(motorSequence[i]), 5)
self.assertEqual(len(sensorimotorSequence[i]), 10)
def testDistanceToBoundaries(self):
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=100,
wSensor=5,
nMotor=25,
wMotor=5)
world = OneDWorld(universe, [2, 0, 5, 15, 10])
agent = RandomOneDAgent(world, 2)
self.assertEqual(agent.distanceToBoundaries(), (2, 2))
agent.move(-2)
self.assertEqual(agent.distanceToBoundaries(), (0, 4))
agent.move(2)
agent.move(2)
self.assertEqual(agent.distanceToBoundaries(), (4, 0))
def testDistanceToBoundaries(self):
universe = OneDUniverse(debugSensor=True, debugMotor=True,
nSensor=100, wSensor=5,
nMotor=25, wMotor=5)
world = OneDWorld(universe, [2, 0, 5, 15, 10])
agent = RandomOneDAgent(world, 2)
self.assertEqual(agent.distanceToBoundaries(), (2, 2))
agent.move(-2)
self.assertEqual(agent.distanceToBoundaries(), (0, 4))
agent.move(2)
agent.move(2)
self.assertEqual(agent.distanceToBoundaries(), (4, 0))
def testMotion(self):
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=100,
wSensor=5,
nMotor=100,
wMotor=20)
world = OneDWorld(universe, [2, 0, 5, 15, 10])
agent = RandomOneDAgent(world, 2)
self.assertEqual(set(xrange(25, 30)), agent.sense())
self.assertEqual(agent.move(1), set(xrange(60, 80)))
self.assertEqual(set(xrange(75, 80)), agent.sense())
self.assertEqual(agent.move(-2), set(xrange(0, 20)))
self.assertEqual(set(xrange(0, 5)), agent.sense())
self.assertEqual(agent.move(0), set(xrange(40, 60)))
self.assertEqual(set(xrange(0, 5)), agent.sense())
def testMotion(self):
universe = OneDUniverse(debugSensor=True, debugMotor=True,
nSensor=100, wSensor=5,
nMotor=100, wMotor=20)
world = OneDWorld(universe, [2, 0, 5, 15, 10])
agent = RandomOneDAgent(world, 2)
self.assertEqual(set(xrange(25, 30)), agent.sense())
self.assertEqual(agent.move(1), set(xrange(60, 80)))
self.assertEqual(set(xrange(75, 80)), agent.sense())
self.assertEqual(agent.move(-2), set(xrange(0, 20)))
self.assertEqual(set(xrange(0, 5)), agent.sense())
self.assertEqual(agent.move(0), set(xrange(40, 60)))
self.assertEqual(set(xrange(0, 5)), agent.sense())
def testMultipleWorldsSharedPatternsNoSharedSubsequencesWithSelfMovement(
self):
"""Test Sensorimotor Temporal Memory learning in multiple separate worlds.
Patterns are represented as complete SDRs. Patterns are shared between
worlds. Worlds have no shared subsequences. Allows movements with value 0
(self-movements).
All active columns should have been predicted.
All inactive columns should have been unpredicted.
Patterns in different worlds should have different cell representations.
"""
self._init()
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=100,
wSensor=10,
nMotor=70,
wMotor=10)
agents = []
patterns = range(4)
for _ in xrange(2):
world = OneDWorld(universe, patterns)
agent = RandomOneDAgent(world,
2,
possibleMotorValues=set(xrange(-3, 4)))
agents.append(agent)
patterns = list(patterns) # copy
patterns.reverse()
sequence = self._generateSensorimotorSequences(150, agents)
self._feedTM(sequence)
sequence = self._generateSensorimotorSequences(100, agents)
self._testTM(sequence)
self._assertAllActiveWerePredicted(universe)
predictedInactiveColumnsMetric = self.tm.mmGetMetricFromTrace(
self.tm.mmGetTracePredictedInactiveColumns())
# Note: There will be extra predictions because transitions are shared
# between the worlds (the self-movements)
self.assertTrue(0 < predictedInactiveColumnsMetric.mean < 5)
self._assertSequencesOnePredictedActiveCellPerColumn()
self._assertOneSequencePerPredictedActiveCell()
def testMultipleWorldsSharedPatternsNoSharedSubsequences(self):
"""
Test Sensorimotor Temporal Memory learning in multiple separate worlds.
Patterns are represented as complete SDRs. Patterns are shared between
worlds. Worlds have no shared subsequences.
All active columns should have been predicted.
Patterns in different worlds should have different cell representations
"""
self._init()
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=100,
wSensor=10,
nMotor=70,
wMotor=10)
agents = []
patterns = range(4)
for _ in xrange(2):
world = OneDWorld(universe, patterns, 2)
agent = RandomOneDAgent(world,
possibleMotorValues=set(
[-3, -2, -1, 1, 2, 3]))
agents.append(agent)
patterns = list(patterns) # copy
patterns.reverse()
sequence = self._generateSensorimotorSequences(150, agents)
self._feedTM(sequence)
sequence = self._generateSensorimotorSequences(100, agents)
stats = self._testTM(sequence)
self._assertAllActiveWerePredicted(stats, universe)
self.assertTrue(0 < stats.predictedInactiveColumns.average < 5)
self._assertSequencesOnePredictedActiveCellPerColumn(stats)
self._assertOneSequencePerPredictedActiveCell(stats)
def setupExperiment(n, w, numElements, numWorlds, tmParams, tpParams):
print "Setting up experiment..."
universe = OneDUniverse(nSensor=n, wSensor=w,
nMotor=n, wMotor=w)
runner = SensorimotorExperimentRunner(tmOverrides=tmParams,
tpOverrides=tpParams,
seed=RANDOM_SEED)
exhaustiveAgents = []
randomAgents = []
for world in xrange(numWorlds):
elements = range(world * numElements, world * numElements + numElements)
# agent = ExhaustiveOneDAgent(OneDWorld(universe, elements), 0)
# exhaustiveAgents.append(agent)
possibleMotorValues = range(-numElements, numElements + 1)
possibleMotorValues.remove(0)
agent = RandomOneDAgent(OneDWorld(universe, elements), numElements / 2,
possibleMotorValues=possibleMotorValues,
seed=RANDOM_SEED)
randomAgents.append(agent)
print "Done setting up experiment."
print
return runner, exhaustiveAgents, randomAgents
def testMultipleWorldsSharedPatterns(self):
"""Test Sensorimotor Temporal Memory learning in multiple separate worlds.
Patterns are represented as complete SDRs. Patterns are shared between
worlds.
All active columns should have been predicted.
"""
self._init()
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=100,
wSensor=10,
nMotor=70,
wMotor=10)
agents = []
patternSets = [[3, 2, 1, 0], [0, 2, 1, 3], [1, 2, 0, 3], [3, 0, 2, 1],
[1, 0, 2, 3]]
for patterns in patternSets:
world = OneDWorld(universe, patterns)
agent = RandomOneDAgent(world,
2,
possibleMotorValues=set(
[-3, -2, -1, 1, 2, 3]))
agents.append(agent)
sequence = self._generateSensorimotorSequences(150, agents)
self._feedTM(sequence)
sequence = self._generateSensorimotorSequences(100, agents)
self._testTM(sequence)
self._assertAllActiveWerePredicted(universe)
predictedInactiveColumnsMetric = self.tm.mmGetMetricFromTrace(
self.tm.mmGetTracePredictedInactiveColumns())
self.assertTrue(0 < predictedInactiveColumnsMetric.mean < 5)
exhaustiveAgents = []
randomAgents = []
completeSequenceLength = numElements**2
for world in xrange(numWorlds):
elements = range(world * numElements,
world * numElements + numElements)
exhaustiveAgents.append(
ExhaustiveOneDAgent(OneDWorld(universe, elements), 0))
possibleMotorValues = range(-numElements, numElements + 1)
possibleMotorValues.remove(0)
randomAgents.append(
RandomOneDAgent(OneDWorld(universe, elements),
numElements / 2,
possibleMotorValues=possibleMotorValues))
print "Training (worlds: {0}, elements: {1})...".format(
numWorlds, numElements)
sequences = runner.generateSequences(completeSequenceLength * 2,
exhaustiveAgents,
verbosity=VERBOSITY)
runner.feedLayers(sequences,
tmLearn=True,
tpLearn=True,
verbosity=VERBOSITY,
showProgressInterval=SHOW_PROGRESS_INTERVAL)
print "Done training.\n"
print "Testing (worlds: {0}, elements: {1})...".format(
############################################################
# Initialize the universe, worlds, and agents
nElements = 20
wEncoders = 21
universe = OneDUniverse(debugSensor=False,
debugMotor=False,
nSensor=512,
wSensor=wEncoders,
nMotor=wEncoders * 7,
wMotor=wEncoders)
# Initialize a bunch of worlds, each with at most 8 elements
agents = [
RandomOneDAgent(OneDWorld(universe, range(8), 4),
possibleMotorValues=(-2, -1, 1, 2),
seed=23),
RandomOneDAgent(OneDWorld(universe, range(8 - 1, -1, -1), 4),
possibleMotorValues=(-2, -1, 1, 2),
seed=42),
RandomOneDAgent(OneDWorld(universe, range(0, 16, 2), 4),
possibleMotorValues=(-2, -1, 1, 2),
seed=10),
RandomOneDAgent(OneDWorld(universe, range(0, 15, 3), 2),
possibleMotorValues=(-2, -1, 1, 2),
seed=5),
RandomOneDAgent(OneDWorld(universe, range(0, 20, 4), 2),
possibleMotorValues=(-2, -1, 1, 2),
seed=5),
RandomOneDAgent(OneDWorld(universe, [0, 8, 3, 1, 6], 2),
possibleMotorValues=(-2, -1, 1, 2),
return tm.getStatistics()
# Initialize the universe, worlds, and agents
nElements = 10
wEncoders = 7
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=nElements * wEncoders,
wSensor=wEncoders,
nMotor=wEncoders * 7,
wMotor=wEncoders)
agents = [
RandomOneDAgent(OneDWorld(universe, range(nElements)),
4,
possibleMotorValues=(-1, 1),
seed=23),
RandomOneDAgent(OneDWorld(universe, range(nElements - 1, -1, -1)),
4,
possibleMotorValues=(-1, 1),
seed=42),
RandomOneDAgent(OneDWorld(universe, range(0, nElements, 2)),
4,
possibleMotorValues=(-1, 1),
seed=10),
RandomOneDAgent(OneDWorld(universe, range(0, nElements, 3)),
2,
possibleMotorValues=(-1, 1),
seed=5),
]
def run(numWorlds, numElements, outputDir, params=DEFAULTS):
# Extract params
n = params["n"]
w = params["w"]
tmParams = params["tmParams"]
tpParams = params["tpParams"]
# Initialize output
if not os.path.exists(outputDir):
os.makedirs(outputDir)
csvFilePath = os.path.join(outputDir,
"{0}x{1}.csv".format(numWorlds, numElements))
# Initialize experiment
start = time.time()
universe = OneDUniverse(nSensor=n, wSensor=w, nMotor=n, wMotor=w)
# Run the experiment
with open(csvFilePath, 'wb') as csvFile:
csvWriter = csv.writer(csvFile)
print(
"Experiment parameters: "
"(# worlds = {0}, # elements = {1}, n = {2}, w = {3})".format(
numWorlds, numElements, n, w))
print "Temporal memory parameters: {0}".format(tmParams)
print "Temporal pooler parameters: {0}".format(tpParams)
print
print "Setting up experiment..."
runner = SensorimotorExperimentRunner(tmOverrides=tmParams,
tpOverrides=tpParams)
print "Done setting up experiment."
print
exhaustiveAgents = []
randomAgents = []
completeSequenceLength = numElements**2
for world in xrange(numWorlds):
elements = range(world * numElements,
world * numElements + numElements)
exhaustiveAgents.append(
ExhaustiveOneDAgent(OneDWorld(universe, elements), 0))
possibleMotorValues = range(-numElements, numElements + 1)
possibleMotorValues.remove(0)
randomAgents.append(
RandomOneDAgent(OneDWorld(universe, elements),
numElements / 2,
possibleMotorValues=possibleMotorValues))
print "Training (worlds: {0}, elements: {1})...".format(
numWorlds, numElements)
print
print "Training temporal memory..."
sequences = runner.generateSequences(completeSequenceLength * 2,
exhaustiveAgents,
verbosity=VERBOSITY)
runner.feedLayers(sequences,
tmLearn=True,
tpLearn=False,
verbosity=VERBOSITY,
showProgressInterval=SHOW_PROGRESS_INTERVAL)
print
print MonitorMixinBase.mmPrettyPrintMetrics(
runner.tp.mmGetDefaultMetrics() + runner.tm.mmGetDefaultMetrics())
print
print "Training temporal pooler..."
sequences = runner.generateSequences(completeSequenceLength * 1,
exhaustiveAgents,
verbosity=VERBOSITY)
runner.feedLayers(sequences,
tmLearn=False,
tpLearn=True,
verbosity=VERBOSITY,
showProgressInterval=SHOW_PROGRESS_INTERVAL)
print
print "Done training."
print
print MonitorMixinBase.mmPrettyPrintMetrics(
runner.tp.mmGetDefaultMetrics() + runner.tm.mmGetDefaultMetrics())
print
if PLOT >= 1:
runner.tp.mmGetPlotConnectionsPerColumn(
title="worlds: {0}, elements: {1}".format(
numWorlds, numElements))
print "Testing (worlds: {0}, elements: {1})...".format(
numWorlds, numElements)
sequences = runner.generateSequences(completeSequenceLength / 4,
randomAgents,
verbosity=VERBOSITY,
numSequences=4)
runner.feedLayers(sequences,
tmLearn=False,
tpLearn=False,
verbosity=VERBOSITY,
showProgressInterval=SHOW_PROGRESS_INTERVAL)
print "Done testing.\n"
if VERBOSITY >= 2:
print "Overlap:"
print
print runner.tp.mmPrettyPrintDataOverlap()
print
print MonitorMixinBase.mmPrettyPrintMetrics(
runner.tp.mmGetDefaultMetrics() + runner.tm.mmGetDefaultMetrics())
print
elapsed = int(time.time() - start)
print "Total time: {0:2} seconds.".format(elapsed)
header = ["# worlds", "# elements", "duration"]
row = [numWorlds, numElements, elapsed]
for metric in (runner.tp.mmGetDefaultMetrics() +
runner.tm.mmGetDefaultMetrics()):
header += [
"{0} ({1})".format(metric.prettyPrintTitle(), x)
for x in ["min", "max", "sum", "mean", "stddev"]
]
row += [
metric.min, metric.max, metric.sum, metric.mean,
metric.standardDeviation
]
csvWriter.writerow(header)
csvWriter.writerow(row)
csvFile.flush()
if PLOT >= 1:
raw_input("Press any key to exit...")
return tm.getStatistics()
# Initialize the universe, worlds, and agents
nElements = 5
wEncoders = 7
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=nElements * wEncoders,
wSensor=wEncoders,
nMotor=wEncoders * 7,
wMotor=wEncoders)
agents = [
RandomOneDAgent(OneDWorld(universe, range(nElements), 4),
possibleMotorValues=(-1, 1),
seed=23),
]
# The TM parameters
DEFAULT_TM_PARAMS = {
"columnDimensions": [nElements * wEncoders],
"cellsPerColumn": 8,
"initialPermanence": 0.5,
"connectedPermanence": 0.6,
"minThreshold": wEncoders * 2,
"maxNewSynapseCount": wEncoders * 2,
"permanenceIncrement": 0.1,
"permanenceDecrement": 0.02,
"activationThreshold": wEncoders * 2
}
the number of predicted columns matches the actual columns.
"""
############################################################
# Initialize the universe, worlds, and agents
nElements = 5
wEncoders = 7
universe = OneDUniverse(debugSensor=True,
debugMotor=True,
nSensor=nElements * wEncoders,
wSensor=wEncoders,
nMotor=wEncoders * 7,
wMotor=wEncoders)
agents = [
RandomOneDAgent(OneDWorld(universe, range(nElements)),
4,
possibleMotorValues=(-1, 1),
seed=23),
RandomOneDAgent(OneDWorld(universe, list(reversed(range(nElements)))),
4,
possibleMotorValues=(-1, 1),
seed=23),
]
l3NumColumns = 512
l3NumActiveColumnsPerInhArea = 20
############################################################
# Initialize the experiment runner with relevant parameters
smer = SensorimotorExperimentRunner(tmOverrides={
"columnDimensions": [nElements * wEncoders],
"minThreshold": wEncoders * 2,
