def testChooseMotorValueStartAt2(self):
numElements = 5
universe = OneDUniverse(nSensor=100, wSensor=5,
nMotor=100, wMotor=5)
world = OneDWorld(universe, range(numElements))
agent = ExhaustiveOneDAgent(world, 2)
motorValues = []
for _ in range(numElements ** 2):
motorValue = agent.chooseMotorValue()
agent.move(motorValue)
motorValues.append(motorValue)
self.assertEqual(motorValues,
[-2, 2, -1, 1, 1, -1, 2, -2, -2, 1, -1, 3, -3, 4, -4, 1, 2, -2, 3, -3, 2, 1, -1, 1, -2])
def testChooseMotorValueStartAt2(self):
numElements = 5
universe = OneDUniverse(nSensor=100, wSensor=5, nMotor=100, wMotor=5)
world = OneDWorld(universe, range(numElements))
agent = ExhaustiveOneDAgent(world, 2)
motorValues = []
for _ in range(numElements**2):
motorValue = agent.chooseMotorValue()
agent.move(motorValue)
motorValues.append(motorValue)
self.assertEqual(motorValues, [
-2, 2, -1, 1, 1, -1, 2, -2, -2, 1, -1, 3, -3, 4, -4, 1, 2, -2, 3,
-3, 2, 1, -1, 1, -2
])
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 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...")
[universe.nSensor],
"numActiveColumnsPerInhArea":
universe.wSensor
})
print "Done setting up experiment.\n"
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,