== universe.wSensor * 199 * len(agents)):
print "Test successful!!"
else:
print "Test unsuccessful"
print "Training TP on sequences"
l3NumColumns = 512
l3NumActiveColumnsPerInhArea = 20
tp = TemporalPooler(inputDimensions=[tm.connections.numberOfCells()],
columnDimensions=[l3NumColumns],
potentialRadius=tm.connections.numberOfCells(),
globalInhibition=True,
numActiveColumnsPerInhArea=l3NumActiveColumnsPerInhArea,
synPermInactiveDec=0,
synPermActiveInc=0.001,
synPredictedInc=0.5,
maxBoost=1.0,
seed=4,
potentialPct=0.9,
stimulusThreshold=2,
useBurstingRule=False,
minPctActiveDutyCycle=0.1,
synPermConnected=0.3,
initConnectedPct=0.2,
spVerbosity=0)
print "Testing TM on sequences"
sequences = generateSequences(10, agents, verbosity=1)
stats = feedTMTP(tm, tp, sequences=sequences, verbosity=2)
# Inputs to the layer 3 temporal pooler are the cells from the temporal pooler
# Temporal pooler cell [column c, cell i] corresponds to input
# c * cellsPerCol + i
print "Initializing Temporal Pooler"
l3NumColumns = 512
l3NumActiveColumnsPerInhArea = 20
l3InputSize = tm.numberOfCols * tm.cellsPerColumn
l3sp = TemporalPooler(inputDimensions=[l3InputSize],
columnDimensions=[l3NumColumns],
potentialRadius=l3InputSize,
globalInhibition=True,
numActiveColumnsPerInhArea=l3NumActiveColumnsPerInhArea,
synPermInactiveDec=0,
synPermActiveInc=0.001,
synPredictedInc=0.5,
boostStrength=0.0,
seed=4,
potentialPct=0.9,
stimulusThreshold=2,
useBurstingRule=False,
synPermConnected=0.3,
initConnectedPct=0.2,
spVerbosity=0)
print "Layer 3 Temporal Pooler parameters:"
l3sp.printParameters()
#######################################################################
#
# Step 4: Train temporal pooler (layer 3) to form stable and distinct
# representation for sensorimotor sequence
