def testWriteRead(self):
c1 = Connections(1024)
# Add data before serializing
s1 = c1.createSegment(0)
c1.createSynapse(s1, 254, 0.1173)
s2 = c1.createSegment(100)
c1.createSynapse(s2, 20, 0.3)
c1.createSynapse(s1, 40, 0.3)
s3 = c1.createSegment(0)
c1.createSynapse(s3, 0, 0.5)
c1.createSynapse(s3, 1, 0.5)
s4 = c1.createSegment(10)
c1.createSynapse(s4, 0, 0.5)
c1.createSynapse(s4, 1, 0.5)
c1.destroySegment(s4)
proto1 = ConnectionsProto_capnp.ConnectionsProto.new_message()
c1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = ConnectionsProto_capnp.ConnectionsProto.read(f)
# Load the deserialized proto
c2 = Connections.read(proto2)
# Check that the two connections objects are functionally equal
self.assertEqual(c1, c2)
def testWriteRead(self):
c1 = Connections(1024)
# Add data before serializing
s1 = c1.createSegment(0)
c1.createSynapse(s1, 254, 0.1173)
s2 = c1.createSegment(100)
c1.createSynapse(s2, 20, 0.3)
c1.createSynapse(s1, 40, 0.3)
s3 = c1.createSegment(0)
c1.createSynapse(s3, 0, 0.5)
c1.createSynapse(s3, 1, 0.5)
s4 = c1.createSegment(10)
c1.createSynapse(s4, 0, 0.5)
c1.createSynapse(s4, 1, 0.5)
c1.destroySegment(s4)
proto1 = ConnectionsProto_capnp.ConnectionsProto.new_message()
c1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = ConnectionsProto_capnp.ConnectionsProto.read(f)
# Load the deserialized proto
c2 = Connections.read(proto2)
# Check that the two connections objects are functionally equal
self.assertEqual(c1, c2)
def read(cls, proto):
"""Deserialize from proto instance.
:param proto: (TemporalMemoryShimProto) the proto instance to read from
"""
tm = super(TemporalMemoryShim, cls).read(proto.baseTM)
tm.predictiveCells = set(proto.predictedState)
tm.connections = Connections.read(proto.conncetions)
def read(cls, proto):
"""Deserialize from proto instance.
:param proto: (TemporalMemoryShimProto) the proto instance to read from
"""
tm = super(TemporalMemoryShim, cls).read(proto.baseTM)
tm.predictiveCells = set(proto.predictedState)
tm.connections = Connections.read(proto.conncetions)
def read(cls, proto):
"""
Reads deserialized data from proto object.
:param proto: (DynamicStructBuilder) Proto object
:returns: (:class:TemporalMemory) TemporalMemory instance
"""
tm = object.__new__(cls)
# capnp fails to save a tuple, so proto.columnDimensions was forced to
# serialize as a list. We prefer a tuple, however, because columnDimensions
# should be regarded as immutable.
tm.columnDimensions = tuple(proto.columnDimensions)
tm.cellsPerColumn = int(proto.cellsPerColumn)
tm.activationThreshold = int(proto.activationThreshold)
tm.initialPermanence = proto.initialPermanence
tm.connectedPermanence = proto.connectedPermanence
tm.minThreshold = int(proto.minThreshold)
tm.maxNewSynapseCount = int(proto.maxNewSynapseCount)
tm.permanenceIncrement = proto.permanenceIncrement
tm.permanenceDecrement = proto.permanenceDecrement
tm.predictedSegmentDecrement = proto.predictedSegmentDecrement
tm.maxSegmentsPerCell = int(proto.maxSegmentsPerCell)
tm.maxSynapsesPerSegment = int(proto.maxSynapsesPerSegment)
tm.connections = Connections.read(proto.connections)
#pylint: disable=W0212
tm._random = Random()
tm._random.read(proto.random)
#pylint: enable=W0212
tm.activeCells = [int(x) for x in proto.activeCells]
tm.winnerCells = [int(x) for x in proto.winnerCells]
flatListLength = tm.connections.segmentFlatListLength()
tm.numActiveConnectedSynapsesForSegment = [0] * flatListLength
tm.numActivePotentialSynapsesForSegment = [0] * flatListLength
tm.lastUsedIterationForSegment = [0] * flatListLength
tm.activeSegments = []
tm.matchingSegments = []
for protoSegment in proto.activeSegments:
tm.activeSegments.append(
tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell))
for protoSegment in proto.matchingSegments:
tm.matchingSegments.append(
tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell))
for protoSegment in proto.numActivePotentialSynapsesForSegment:
segment = tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell)
tm.numActivePotentialSynapsesForSegment[segment.flatIdx] = (int(
protoSegment.number))
tm.iteration = long(proto.iteration)
for protoSegment in proto.lastUsedIterationForSegment:
segment = tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell)
tm.lastUsedIterationForSegment[segment.flatIdx] = (long(
protoSegment.number))
return tm
def read(cls, proto):
"""
Reads deserialized data from proto object.
:param proto: (DynamicStructBuilder) Proto object
:returns: (:class:TemporalMemory) TemporalMemory instance
"""
tm = object.__new__(cls)
# capnp fails to save a tuple, so proto.columnDimensions was forced to
# serialize as a list. We prefer a tuple, however, because columnDimensions
# should be regarded as immutable.
tm.columnDimensions = tuple(proto.columnDimensions)
tm.cellsPerColumn = int(proto.cellsPerColumn)
tm.activationThreshold = int(proto.activationThreshold)
tm.initialPermanence = round(proto.initialPermanence, EPSILON_ROUND)
tm.connectedPermanence = round(proto.connectedPermanence, EPSILON_ROUND)
tm.minThreshold = int(proto.minThreshold)
tm.maxNewSynapseCount = int(proto.maxNewSynapseCount)
tm.permanenceIncrement = round(proto.permanenceIncrement, EPSILON_ROUND)
tm.permanenceDecrement = round(proto.permanenceDecrement, EPSILON_ROUND)
tm.predictedSegmentDecrement = round(proto.predictedSegmentDecrement,
EPSILON_ROUND)
tm.maxSegmentsPerCell = int(proto.maxSegmentsPerCell)
tm.maxSynapsesPerSegment = int(proto.maxSynapsesPerSegment)
tm.connections = Connections.read(proto.connections)
#pylint: disable=W0212
tm._random = Random()
tm._random.read(proto.random)
#pylint: enable=W0212
tm.activeCells = [int(x) for x in proto.activeCells]
tm.winnerCells = [int(x) for x in proto.winnerCells]
flatListLength = tm.connections.segmentFlatListLength()
tm.numActiveConnectedSynapsesForSegment = [0] * flatListLength
tm.numActivePotentialSynapsesForSegment = [0] * flatListLength
tm.lastUsedIterationForSegment = [0] * flatListLength
tm.activeSegments = []
tm.matchingSegments = []
for protoSegment in proto.activeSegments:
tm.activeSegments.append(
tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell))
for protoSegment in proto.matchingSegments:
tm.matchingSegments.append(
tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell))
for protoSegment in proto.numActivePotentialSynapsesForSegment:
segment = tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell)
tm.numActivePotentialSynapsesForSegment[segment.flatIdx] = (
int(protoSegment.number))
tm.iteration = long(proto.iteration)
for protoSegment in proto.lastUsedIterationForSegment:
segment = tm.connections.getSegment(protoSegment.cell,
protoSegment.idxOnCell)
tm.lastUsedIterationForSegment[segment.flatIdx] = (
long(protoSegment.number))
return tm
def read(cls, proto):
"""
Reads deserialized data from proto object.
@param proto (DynamicStructBuilder) Proto object
@return (TemporalMemory) TemporalMemory instance
"""
tm = object.__new__(cls)
# capnp fails to save a tuple, so proto.columnDimensions was forced to
# serialize as a list. We prefer a tuple, however, because columnDimensions
# should be regarded as immutable.
tm.columnDimensions = tuple(proto.columnDimensions)
tm.cellsPerColumn = int(proto.cellsPerColumn)
tm.activationThreshold = int(proto.activationThreshold)
tm.initialPermanence = proto.initialPermanence
tm.connectedPermanence = proto.connectedPermanence
tm.minThreshold = int(proto.minThreshold)
tm.maxNewSynapseCount = int(proto.maxNewSynapseCount)
tm.permanenceIncrement = proto.permanenceIncrement
tm.permanenceDecrement = proto.permanenceDecrement
tm.predictedSegmentDecrement = proto.predictedSegmentDecrement
tm.connections = Connections.read(proto.connections)
#pylint: disable=W0212
tm._random = Random()
tm._random.read(proto.random)
#pylint: enable=W0212
tm.activeCells = [int(x) for x in proto.activeCells]
tm.winnerCells = [int(x) for x in proto.winnerCells]
flatListLength = tm.connections.segmentFlatListLength()
tm.numActiveConnectedSynapsesForSegment = [0] * flatListLength
tm.numActivePotentialSynapsesForSegment = [0] * flatListLength
tm.activeSegments = []
tm.matchingSegments = []
for i in xrange(len(proto.activeSegmentOverlaps)):
protoSegmentOverlap = proto.activeSegmentOverlaps[i]
segment = tm.connections.getSegment(protoSegmentOverlap.cell,
protoSegmentOverlap.segment)
tm.activeSegments.append(segment)
overlap = protoSegmentOverlap.overlap
tm.numActiveConnectedSynapsesForSegment[segment.flatIdx] = overlap
for i in xrange(len(proto.matchingSegmentOverlaps)):
protoSegmentOverlap = proto.matchingSegmentOverlaps[i]
segment = tm.connections.getSegment(protoSegmentOverlap.cell,
protoSegmentOverlap.segment)
tm.matchingSegments.append(segment)
overlap = protoSegmentOverlap.overlap
tm.numActivePotentialSynapsesForSegment[segment.flatIdx] = overlap
return tm
def read(cls, proto):
"""
Reads deserialized data from proto object.
@param proto (DynamicStructBuilder) Proto object
@return (TemporalMemory) TemporalMemory instance
"""
tm = object.__new__(cls)
# capnp fails to save a tuple, so proto.columnDimensions was forced to
# serialize as a list. We prefer a tuple, however, because columnDimensions
# should be regarded as immutable.
tm.columnDimensions = tuple(proto.columnDimensions)
tm.cellsPerColumn = int(proto.cellsPerColumn)
tm.activationThreshold = int(proto.activationThreshold)
tm.initialPermanence = proto.initialPermanence
tm.connectedPermanence = proto.connectedPermanence
tm.minThreshold = int(proto.minThreshold)
tm.maxNewSynapseCount = int(proto.maxNewSynapseCount)
tm.permanenceIncrement = proto.permanenceIncrement
tm.permanenceDecrement = proto.permanenceDecrement
tm.predictedSegmentDecrement = proto.predictedSegmentDecrement
tm.connections = Connections.read(proto.connections)
#pylint: disable=W0212
tm._random = Random()
tm._random.read(proto.random)
#pylint: enable=W0212
tm.activeCells = [int(x) for x in proto.activeCells]
tm.winnerCells = [int(x) for x in proto.winnerCells]
flatListLength = tm.connections.segmentFlatListLength()
tm.numActiveConnectedSynapsesForSegment = [0] * flatListLength
tm.numActivePotentialSynapsesForSegment = [0] * flatListLength
tm.activeSegments = []
tm.matchingSegments = []
for i in xrange(len(proto.activeSegmentOverlaps)):
protoSegmentOverlap = proto.activeSegmentOverlaps[i]
segment = tm.connections.getSegment(protoSegmentOverlap.cell,
protoSegmentOverlap.segment)
tm.activeSegments.append(segment)
overlap = protoSegmentOverlap.overlap
tm.numActiveConnectedSynapsesForSegment[segment.flatIdx] = overlap
for i in xrange(len(proto.matchingSegmentOverlaps)):
protoSegmentOverlap = proto.matchingSegmentOverlaps[i]
segment = tm.connections.getSegment(protoSegmentOverlap.cell,
protoSegmentOverlap.segment)
tm.matchingSegments.append(segment)
overlap = protoSegmentOverlap.overlap
tm.numActivePotentialSynapsesForSegment[segment.flatIdx] = overlap
return tm