src/nupic/algorithms/temporal_memory.py
def write(self, proto):
    """
    Writes serialized data to proto object.

    :param proto: (DynamicStructBuilder) Proto object
    """
    # capnp fails to save a tuple.  Let's force columnDimensions to list.
    proto.columnDimensions = list(self.columnDimensions)
    proto.cellsPerColumn = self.cellsPerColumn
    proto.activationThreshold = self.activationThreshold
    proto.initialPermanence = round(self.initialPermanence, EPSILON_ROUND)
    proto.connectedPermanence = round(self.connectedPermanence, EPSILON_ROUND)
    proto.minThreshold = self.minThreshold
    proto.maxNewSynapseCount = self.maxNewSynapseCount
    proto.permanenceIncrement = round(self.permanenceIncrement, EPSILON_ROUND)
    proto.permanenceDecrement = round(self.permanenceDecrement, EPSILON_ROUND)
    proto.predictedSegmentDecrement = self.predictedSegmentDecrement

    proto.maxSegmentsPerCell = self.maxSegmentsPerCell
    proto.maxSynapsesPerSegment = self.maxSynapsesPerSegment

    self.connections.write(proto.connections)
    self._random.write(proto.random)

    proto.activeCells = list(self.activeCells)
    proto.winnerCells = list(self.winnerCells)

    protoActiveSegments = proto.init("activeSegments", len(self.activeSegments))
    for i, segment in enumerate(self.activeSegments):
      protoActiveSegments[i].cell = segment.cell
      idx = self.connections.segmentsForCell(segment.cell).index(segment)
      protoActiveSegments[i].idxOnCell = idx

    protoMatchingSegments = proto.init("matchingSegments",
                                       len(self.matchingSegments))
    for i, segment in enumerate(self.matchingSegments):
      protoMatchingSegments[i].cell = segment.cell
      idx = self.connections.segmentsForCell(segment.cell).index(segment)
      protoMatchingSegments[i].idxOnCell = idx

    protoNumActivePotential = proto.init(
      "numActivePotentialSynapsesForSegment",
      len(self.numActivePotentialSynapsesForSegment))
    for i, numActivePotentialSynapses in enumerate(
        self.numActivePotentialSynapsesForSegment):
      segment = self.connections.segmentForFlatIdx(i)
      if segment is not None:
        protoNumActivePotential[i].cell = segment.cell
        idx = self.connections.segmentsForCell(segment.cell).index(segment)
        protoNumActivePotential[i].idxOnCell = idx
        protoNumActivePotential[i].number = numActivePotentialSynapses

    proto.iteration = self.iteration

    protoLastUsedIteration = proto.init(
      "lastUsedIterationForSegment",
      len(self.numActivePotentialSynapsesForSegment))
    for i, lastUsed in enumerate(self.lastUsedIterationForSegment):
      segment = self.connections.segmentForFlatIdx(i)
      if segment is not None:
        protoLastUsedIteration[i].cell = segment.cell
        idx = self.connections.segmentsForCell(segment.cell).index(segment)
        protoLastUsedIteration[i].idxOnCell = idx
        protoLastUsedIteration[i].number = lastUsed
def write(self, proto):
    """
    Writes serialized data to proto object.

    :param proto: (DynamicStructBuilder) Proto object
    """
    # capnp fails to save a tuple.  Let's force columnDimensions to list.
    proto.columnDimensions = list(self.columnDimensions)
    proto.cellsPerColumn = self.cellsPerColumn
    proto.activationThreshold = self.activationThreshold
    proto.initialPermanence = round(self.initialPermanence, EPSILON_ROUND)
    proto.connectedPermanence = round(self.connectedPermanence, EPSILON_ROUND)
    proto.minThreshold = self.minThreshold
    proto.maxNewSynapseCount = self.maxNewSynapseCount
    proto.permanenceIncrement = round(self.permanenceIncrement, EPSILON_ROUND)
    proto.permanenceDecrement = round(self.permanenceDecrement, EPSILON_ROUND)
    proto.predictedSegmentDecrement = self.predictedSegmentDecrement

    proto.maxSegmentsPerCell = self.maxSegmentsPerCell
    proto.maxSynapsesPerSegment = self.maxSynapsesPerSegment

    self.connections.write(proto.connections)
    self._random.write(proto.random)

    proto.activeCells = list(self.activeCells)
    proto.winnerCells = list(self.winnerCells)

    protoActiveSegments = proto.init("activeSegments", len(self.activeSegments))
    for i, segment in enumerate(self.activeSegments):
      protoActiveSegments[i].cell = segment.cell
      idx = self.connections.segmentsForCell(segment.cell).index(segment)
      protoActiveSegments[i].idxOnCell = idx

    protoMatchingSegments = proto.init("matchingSegments",
                                       len(self.matchingSegments))
    for i, segment in enumerate(self.matchingSegments):
      protoMatchingSegments[i].cell = segment.cell
      idx = self.connections.segmentsForCell(segment.cell).index(segment)
      protoMatchingSegments[i].idxOnCell = idx

    protoNumActivePotential = proto.init(
      "numActivePotentialSynapsesForSegment",
      len(self.numActivePotentialSynapsesForSegment))
    for i, numActivePotentialSynapses in enumerate(
        self.numActivePotentialSynapsesForSegment):
      segment = self.connections.segmentForFlatIdx(i)
      if segment is not None:
        protoNumActivePotential[i].cell = segment.cell
        idx = self.connections.segmentsForCell(segment.cell).index(segment)
        protoNumActivePotential[i].idxOnCell = idx
        protoNumActivePotential[i].number = numActivePotentialSynapses

    proto.iteration = self.iteration

    protoLastUsedIteration = proto.init(
      "lastUsedIterationForSegment",
      len(self.numActivePotentialSynapsesForSegment))
    for i, lastUsed in enumerate(self.lastUsedIterationForSegment):
      segment = self.connections.segmentForFlatIdx(i)
      if segment is not None:
        protoLastUsedIteration[i].cell = segment.cell
        idx = self.connections.segmentsForCell(segment.cell).index(segment)
        protoLastUsedIteration[i].idxOnCell = idx
        protoLastUsedIteration[i].number = lastUsed
def write ( self , proto ) : # capnp fails to save a tuple.  Let's force columnDimensions to list. proto . columnDimensions = list ( self . columnDimensions ) proto . cellsPerColumn = self . cellsPerColumn proto . activationThreshold = self . activationThreshold proto . initialPermanence = round ( self . initialPermanence , EPSILON_ROUND ) proto . connectedPermanence = round ( self . connectedPermanence , EPSILON_ROUND ) proto . minThreshold = self . minThreshold proto . maxNewSynapseCount = self . maxNewSynapseCount proto . permanenceIncrement = round ( self . permanenceIncrement , EPSILON_ROUND ) proto . permanenceDecrement = round ( self . permanenceDecrement , EPSILON_ROUND ) proto . predictedSegmentDecrement = self . predictedSegmentDecrement proto . maxSegmentsPerCell = self . maxSegmentsPerCell proto . maxSynapsesPerSegment = self . maxSynapsesPerSegment self . connections . write ( proto . connections ) self . _random . write ( proto . random ) proto . activeCells = list ( self . activeCells ) proto . winnerCells = list ( self . winnerCells ) protoActiveSegments = proto . init ( "activeSegments" , len ( self . activeSegments ) ) for i , segment in enumerate ( self . activeSegments ) : protoActiveSegments [ i ] . cell = segment . cell idx = self . connections . segmentsForCell ( segment . cell ) . index ( segment ) protoActiveSegments [ i ] . idxOnCell = idx protoMatchingSegments = proto . init ( "matchingSegments" , len ( self . matchingSegments ) ) for i , segment in enumerate ( self . matchingSegments ) : protoMatchingSegments [ i ] . cell = segment . cell idx = self . connections . segmentsForCell ( segment . cell ) . index ( segment ) protoMatchingSegments [ i ] . idxOnCell = idx protoNumActivePotential = proto . init ( "numActivePotentialSynapsesForSegment" , len ( self . numActivePotentialSynapsesForSegment ) ) for i , numActivePotentialSynapses in enumerate ( self . numActivePotentialSynapsesForSegment ) : segment = self . connections . segmentForFlatIdx ( i ) if segment is not None : protoNumActivePotential [ i ] . cell = segment . cell idx = self . connections . segmentsForCell ( segment . cell ) . index ( segment ) protoNumActivePotential [ i ] . idxOnCell = idx protoNumActivePotential [ i ] . number = numActivePotentialSynapses proto . iteration = self . iteration protoLastUsedIteration = proto . init ( "lastUsedIterationForSegment" , len ( self . numActivePotentialSynapsesForSegment ) ) for i , lastUsed in enumerate ( self . lastUsedIterationForSegment ) : segment = self . connections . segmentForFlatIdx ( i ) if segment is not None : protoLastUsedIteration [ i ] . cell = segment . cell idx = self . connections . segmentsForCell ( segment . cell ) . index ( segment ) protoLastUsedIteration [ i ] . idxOnCell = idx protoLastUsedIteration [ i ] . number = lastUsed
Writes serialized data to proto object.

    :param proto: (DynamicStructBuilder) Proto object
Writes serialized data to proto object .