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))
presynaptic_input_set = set(presynaptic_input)
inputSDR = SDR(1024)
inputSDR.sparse = presynaptic_input
connections = Connections(NUM_CELLS, 0.3)
for i in range(NUM_CELLS):
seg = 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 testDestroySegment(self):
co = Connections(NUM_CELLS, 0.51)
self.assertEqual(co.numSegments(), 0, "there are zero segments yet")
# successfully remove
seg = co.createSegment(1, 20)
self.assertEqual(co.numSegments(), 1)
n = co.numConnectedSynapses(seg) #uses dataForSegment()
co.destroySegment(seg)
self.assertEqual(co.numSegments(), 0, "segment should have been removed")
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):
seg = 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 testAdaptShouldRemoveSegments(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, 0.51)
for i in range(NUM_CELLS):
seg = connections.createSegment(i, 1)
for cell in active_cells:
segments = connections.segmentsForCell(cell)
self.assertEqual(len(segments), 1,
"Segments were prematurely destroyed.")
segment = segments[0]
connections.adaptSegment(segment, inputSDR, 0.1, 0.001, True)
segments = connections.segmentsForCell(cell)
self.assertEqual(len(segments), 0, "Segments were not destroyed.")
def testAdaptShouldRemoveSegments(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, 0.51)
for i in range(NUM_CELLS):
seg = connections.createSegment(i, 2)
seg = connections.createSegment(i, 2) #create 2 segments on each cell
for cell in active_cells:
segments = connections.segmentsForCell(cell)
self.assertEqual(len(segments), 2, "Segments were prematurely destroyed.")
segment = segments[0]
numSynapsesOnSegment = len(segments)
connections.adaptSegment(segment, inputSDR, 0.1, 0.001, pruneZeroSynapses=True, segmentThreshold=1) #set to =1 so that segments get always deleted in this test
segments = connections.segmentsForCell(cell)
self.assertEqual(len(segments), 1, "Segments were not destroyed.")
def testDestroySynapse(self):
# empty connections, create segment seg and a synapse syn
co = Connections(NUM_CELLS, 0.51)
seg = co.createSegment(NUM_CELLS - 1, 1)
syn1 = co.createSynapse(seg, NUM_CELLS - 1, 0.52)
# destroy the synapse
co.destroySynapse(syn1)
self.assertEqual(co.numSynapses(), 0)
with pytest.raises(
RuntimeError
): # NTA_CHECK, data for removed synapse must not be accessible!
permRemoved = co.permanenceForSynapse(syn1)
assert permRemoved == perm1
# double remove should be just ignored
co.destroySynapse(syn1)
def testDestroySynapse(self):
# empty connections, create segment seg and a synapse syn
co = Connections(NUM_CELLS, 0.51)
seg = co.createSegment(NUM_CELLS-1, 1)
syn1 = co.createSynapse(seg, NUM_CELLS-1, 0.52)
# destroy the synapse
co.destroySynapse(syn1)
self.assertEqual(co.numSynapses(), 0)
def LoadConnections(self, connectionType, regionName,
iteration): # loads connections (can be more then one)
self.dumpFolderPath = os.path.splitext(
self.databaseFilePath)[0] + "_dumpData"
if connectionType != '':
connectionType = "_" + connectionType
try:
with open(
os.path.join(self.dumpFolderPath,
str(regionName) + "_" + str(iteration)) +
connectionType + ".dump", "rb") as f:
connections = Connections.load(f.read())
except FileNotFoundError:
connections = None
return connections
def testComputeActivityUnion(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_input1 = list(range(0, 10))
presynaptic_input1_set = set(presynaptic_input1)
presynaptic_input2 = list(range(10, 20))
presynaptic_input2_set = set(presynaptic_input1)
connections = Connections(NUM_CELLS, 0.51, False)
for i in range(NUM_CELLS):
seg = connections.createSegment(i, 1)
self._learn(connections, active_cells, presynaptic_input1)
self._learn(connections, active_cells, presynaptic_input2)
numSynapses = connections.numSynapses()
self.assertNotEqual(
numSynapses, 40,
"There should be a synapse for each presynaptic cell")
active_cells_set = set(active_cells)
inputSDR = SDR(1024)
inputSDR.sparse = presynaptic_input1
numActiveConnectedSynapsesForSegment = connections.computeActivity(
inputSDR, False)
for cell, count in enumerate(numActiveConnectedSynapsesForSegment):
if cell in active_cells_set:
self.assertNotEqual(count, 0, "Segment should be active")
inputSDR.sparse = presynaptic_input2
numActiveConnectedSynapsesForSegment = connections.computeActivity(
inputSDR, False)
for cell, count in enumerate(numActiveConnectedSynapsesForSegment):
if cell in active_cells_set:
self.assertNotEqual(count, 0, "Segment should be active")
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))
presynaptic_input_set = set(presynaptic_input)
inputSDR = SDR(1024)
inputSDR.sparse = presynaptic_input
l = len(presynaptic_input)
connections = Connections(NUM_CELLS, 0.51, False)
for i in range(NUM_CELLS):
seg = 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")
def testConnectedThreshold(self):
conn = Connections(1024, 0.123, False)
self.assertAlmostEqual(conn.connectedThreshold, 0.123, places=4)
data = []
for cell in range(connections.numCells()):
for seg in connections.segmentsForCell(cell):
for syn in connections.synapsesForSegment(seg):
datum = connections.permanenceForSynapse(syn)
data.append(datum)
plt.figure("Histogram of Synapse Permanences")
plt.hist(data, bins=100, range=(0, 1))
plt.axvline(connections.connectedThreshold, color='red')
plt.title("Histogram of Synapse Permanences")
plt.ylabel("Number of Synapses")
plt.xlabel("Permanence of Synapse")
if show:
plt.show()
if __name__ == '__main__':
import argparse
# Accept location of data dump from command line
parser = argparse.ArgumentParser()
parser.add_argument('connections_save_file')
args = parser.parse_args()
# Load and run the connections object.
with open(args.connections_save_file, 'rb') as data_file:
data = data_file.read()
# TODO: Try both pickle and load, use whichever works.
C = Connections.load(data)
main(C)
def testDestroySegment(self):
co = Connections(NUM_CELLS, 0.51)
self.assertEqual(co.numSegments(), 0, "there are zero segments yet")
# removing while no segments exist
co.destroySegment(1)
# successfully remove
seg = co.createSegment(1, 20)
self.assertEqual(co.numSegments(), 1)
n = co.numConnectedSynapses(seg) #uses dataForSegment()
co.destroySegment(seg)
self.assertEqual(co.numSegments(), 0,
"segment should have been removed")
with pytest.raises(RuntimeError):
n2 = co.numConnectedSynapses(seg)
def testCreateSegment(self):
co = Connections(NUM_CELLS, 0.51)
self.assertEqual(co.numSegments(), 0, "there are zero segments yet")
# create segment
co.createSegment(NUM_CELLS - 1, 20)
self.assertEqual(co.numSegments(), 1, "created 1 new segment")
# wrong param
with pytest.raises(RuntimeError):
co.createSegment(1, 0) #wrong param maxSegmentsPerCell "0"
# wrong param - OOB cell
with pytest.raises(RuntimeError):
co.createSegment(NUM_CELLS + 22, 1)
# another segment
co.createSegment(NUM_CELLS - 1, 20)
self.assertEqual(co.numSegments(), 2)
# segment pruning -> reduce to 1 seg per cell
co.createSegment(NUM_CELLS - 1, 1)
self.assertEqual(co.numSegments(), 1)
def testCreateSynapse(self):
# empty connections, create segment and a synapse
co = Connections(NUM_CELLS, 0.51)
self.assertEqual(co.numSynapses(), 0)
self.assertEqual(co.numSegments(), 0)
# 1st, create a segment
seg = co.createSegment(NUM_CELLS - 1, 1)
self.assertEqual(co.numSegments(), 1)
#1. create a synapse on that segment
syn1 = co.createSynapse(seg, NUM_CELLS - 1, 0.52)
self.assertEqual(pytest.approx(co.permanenceForSynapse(syn1)), 0.52)
self.assertEqual(co.numSynapses(), 1)
#2. creating a duplicit synapse should not crash!
syn2 = co.createSynapse(seg, NUM_CELLS - 1, 0.52)
self.assertEqual(syn1, syn2,
"creating duplicate synapses should return the same")
self.assertEqual(co.numSynapses(), 1,
"Duplicate synapse, number should not increase")
#3. create a different synapse
syn3 = co.createSynapse(seg, 1, 0.52)
self.assertNotEqual(
syn1, syn3, "creating a different synapse must create a new one")
self.assertEqual(co.numSynapses(), 2,
"New synapse should increase the number")
#4. create existing synapse with a new value -> should update permanence
#4.a lower permanence -> keep max()
syn4 = co.createSynapse(
seg, NUM_CELLS - 1,
0.11) #all the same just permanence is a lower val
self.assertEqual(syn1, syn4, "just updating existing syn")
self.assertEqual(co.numSynapses(), 2,
"Duplicate synapse, number should not increase")
self.assertEqual(pytest.approx(co.permanenceForSynapse(syn1)), 0.52,
"update keeps the larger value")
#4.b higher permanence -> update
syn5 = co.createSynapse(
seg, NUM_CELLS - 1,
0.99) #all the same just permanence is a higher val
self.assertEqual(syn1, syn5, "just updating existing syn")
self.assertEqual(co.numSynapses(), 2,
"Duplicate synapse, number should not increase")
self.assertEqual(pytest.approx(co.permanenceForSynapse(syn1)), 0.99,
"updated to the larger permanence value")