tm = TM(columnDimensions = (50,),
cellsPerColumn=2,
initialPermanence=0.5,
connectedPermanence=0.5,
minThreshold=8,
maxNewSynapseCount=20,
permanenceIncrement=0.1,
permanenceDecrement=0.0,
activationThreshold=8,
)
# Step 2: create input vectors to feed to the temporal memory. Each input vector
# must be numberOfCols wide. Here we create a simple sequence of 5 vectors
# representing the sequence A -> B -> C -> D -> E
x = np.zeros((5, tm.numberOfColumns()), dtype="uint32")
x[0, 0:10] = 1 # Input SDR representing "A", corresponding to columns 0-9
x[1, 10:20] = 1 # Input SDR representing "B", corresponding to columns 10-19
x[2, 20:30] = 1 # Input SDR representing "C", corresponding to columns 20-29
x[3, 30:40] = 1 # Input SDR representing "D", corresponding to columns 30-39
x[4, 40:50] = 1 # Input SDR representing "E", corresponding to columns 40-49
# Step 3: send this simple sequence to the temporal memory for learning
# We repeat the sequence 10 times
for i in range(10):
# Send each letter in the sequence in order
for j in range(5):
activeColumns = set([i for i, j in zip(count(), x[j]) if j == 1])
tm = TM(
columnDimensions=(50, ),
cellsPerColumn=2,
initialPermanence=0.5,
connectedPermanence=0.5,
minThreshold=8,
maxNewSynapseCount=20,
permanenceIncrement=0.1,
permanenceDecrement=0.0,
activationThreshold=8,
)
# Step 2: create input vectors to feed to the temporal memory. Each input vector
# must be numberOfCols wide. Here we create a simple sequence of 5 vectors
# representing the sequence A -> B -> C -> D -> E
x = numpy.zeros((5, tm.numberOfColumns()), dtype="uint32")
x[0, 0:10] = 1 # Input SDR representing "A", corresponding to columns 0-9
x[1, 10:20] = 1 # Input SDR representing "B", corresponding to columns 10-19
x[2, 20:30] = 1 # Input SDR representing "C", corresponding to columns 20-29
x[3, 30:40] = 1 # Input SDR representing "D", corresponding to columns 30-39
x[4, 40:50] = 1 # Input SDR representing "E", corresponding to columns 40-49
# Step 3: send this simple sequence to the temporal memory for learning
# We repeat the sequence 10 times
for i in range(10):
# Send each letter in the sequence in order
for j in range(5):
activeColumns = set([i for i, j in zip(count(), x[j]) if j == 1])
# The compute method performs one step of learning and/or inference. Note:
tm = TM(columnDimensions = (50,),
cellsPerColumn=2,
initialPermanence=0.5,
connectedPermanence=0.5,
minThreshold=8,
maxNewSynapseCount=20,
permanenceIncrement=0.1,
permanenceDecrement=0.0,
activationThreshold=8,
)
# Step 2: create input vectors to feed to the temporal memory. Each input vector
# must be numberOfCols wide. Here we create a simple sequence of 5 vectors
# representing the sequence A -> B -> C -> D -> E
x = numpy.zeros((5, tm.numberOfColumns()), dtype="uint32")
x[0, 0:10] = 1 # Input SDR representing "A", corresponding to columns 0-9
x[1, 10:20] = 1 # Input SDR representing "B", corresponding to columns 10-19
x[2, 20:30] = 1 # Input SDR representing "C", corresponding to columns 20-29
x[3, 30:40] = 1 # Input SDR representing "D", corresponding to columns 30-39
x[4, 40:50] = 1 # Input SDR representing "E", corresponding to columns 40-49
# Step 3: send this simple sequence to the temporal memory for learning
# We repeat the sequence 10 times
for i in range(10):
# Send each letter in the sequence in order
for j in range(5):
activeColumns = set([i for i, j in zip(count(), x[j]) if j == 1])
def testNumberOfColumns(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=32
)
self.assertEqual(tm.numberOfColumns(), 64 * 64)
GPIO.setmode(GPIO.BCM)
mode = 0
tm = TemporalMemory(
columnDimensions=(200, ),
cellsPerColumn=32,
initialPermanence=0.5,
connectedPermanence=0.5,
minThreshold=10,
maxNewSynapseCount=32,
permanenceIncrement=0.1,
permanenceDecrement=0.1,
activationThreshold=13,
)
inputSDRs = numpy.zeros((5, tm.numberOfColumns()), dtype="uint32")
for x in range(5):
inputSDRs[x, (x * 40):((x + 1) * 40)] = 1
inputColumns = []
for x in range(5):
inputColumns.append(set([c for c, i in enumerate(inputSDRs[x]) if i == 1]))
ledPins = [26, 19, 17, 27, 22]
leds = []
for x in range(5):
leds.append(Led(ledPins[x]))
tones = [0, 220, 262, 330, 440]
speaker = Speaker(13)
def testNumberOfColumns(self):
tm = TemporalMemory(
columnDimensions=[64, 64],
cellsPerColumn=32
)
self.assertEqual(tm.numberOfColumns(), 64 * 64)
