def testAdaptShouldIncrementSynapses(self):
"""
Test that connections are generated on predefined segments.
"""
random = Random(1981)
active_cells = np.array(random.sample(
np.arange(0, NUM_CELLS, 1, dtype="uint32"), 40),
dtype="uint32")
active_cells.sort()
presynaptic_input = list(range(0, 10))
presynaptic_input_set = set(presynaptic_input)
inputSDR = SDR(1024)
inputSDR.sparse = presynaptic_input
connections = Connections(NUM_CELLS, 0.51)
for i in range(NUM_CELLS):
connections.createSegment(i, 1)
for cell in active_cells:
segments = connections.segmentsForCell(cell)
segment = segments[0]
for c in presynaptic_input:
connections.createSynapse(segment, c, 0.1)
connections.adaptSegment(segment, inputSDR, 0.1, 0.001, True)
presynamptic_cells = self._getPresynapticCells(
connections, segment, 0.2)
self.assertEqual(presynamptic_cells, presynaptic_input_set,
"Missing synapses")
presynamptic_cells = self._getPresynapticCells(
connections, segment, 0.3)
self.assertEqual(presynamptic_cells, set(), "Too many synapses")
def testNumSynapses(self):
"""
Test that connections are generated on predefined segments.
"""
random = Random(1981)
active_cells = np.array(random.sample(
np.arange(0, NUM_CELLS, 1, dtype="uint32"), 40),
dtype="uint32")
active_cells.sort()
presynaptic_input = list(range(0, 10))
inputSDR = SDR(1024)
inputSDR.sparse = presynaptic_input
connections = Connections(NUM_CELLS, 0.3)
for i in range(NUM_CELLS):
connections.createSegment(i, 1)
for cell in active_cells:
segments = connections.segmentsForCell(cell)
segment = segments[0]
for c in presynaptic_input:
connections.createSynapse(segment, c, 0.1)
connections.adaptSegment(segment, inputSDR, 0.1, 0.0, False)
num_synapses = connections.numSynapses(segment)
self.assertEqual(num_synapses, len(presynaptic_input),
"Missing synapses")
self.assertEqual(connections.numSynapses(),
len(presynaptic_input) * 40, "Missing synapses")
def testComputeActivity(self):
"""
Test that connections are generated on predefined segments.
"""
random = Random(1981)
active_cells = np.array(random.sample(
np.arange(0, NUM_CELLS, 1, dtype="uint32"), 40),
dtype="uint32")
active_cells.sort()
presynaptic_input = list(range(0, 10))
inputSDR = SDR(1024)
inputSDR.sparse = presynaptic_input
l = len(presynaptic_input)
connections = Connections(NUM_CELLS, 0.51, False)
for i in range(NUM_CELLS):
connections.createSegment(i, 1)
numActiveConnectedSynapsesForSegment = connections.computeActivity(
inputSDR, False)
for count in numActiveConnectedSynapsesForSegment:
self.assertEqual(count, 0, "Segment should not be active")
for cell in active_cells:
segments = connections.segmentsForCell(cell)
segment = segments[0]
for c in presynaptic_input:
connections.createSynapse(segment, c, 0.1)
numActiveConnectedSynapsesForSegment = connections.computeActivity(
inputSDR, False)
for count in numActiveConnectedSynapsesForSegment:
self.assertEqual(count, 0, "Segment should not be active")
for cell in active_cells:
segments = connections.segmentsForCell(cell)
segment = segments[0]
connections.adaptSegment(segment, inputSDR, 0.5, 0.0, False)
active_cells_set = set(active_cells)
numActiveConnectedSynapsesForSegment = connections.computeActivity(
inputSDR, False)
for cell, count in enumerate(numActiveConnectedSynapsesForSegment):
if cell in active_cells_set:
self.assertEqual(count, l, "Segment should be active")
else:
self.assertEqual(count, 0, "Segment should not be active")