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)