src/nupic/algorithms/backtracking_tm.py
def write(self, proto):
    """Populate serialization proto instance.

    :param proto: (BacktrackingTMProto) the proto instance to populate
    """
    proto.version = TM_VERSION
    self._random.write(proto.random)
    proto.numberOfCols = self.numberOfCols
    proto.cellsPerColumn = self.cellsPerColumn
    proto.initialPerm = float(self.initialPerm)
    proto.connectedPerm = float(self.connectedPerm)
    proto.minThreshold = self.minThreshold
    proto.newSynapseCount = self.newSynapseCount
    proto.permanenceInc = float(self.permanenceInc)
    proto.permanenceDec = float(self.permanenceDec)
    proto.permanenceMax = float(self.permanenceMax)
    proto.globalDecay = float(self.globalDecay)
    proto.activationThreshold = self.activationThreshold
    proto.doPooling = self.doPooling
    proto.segUpdateValidDuration = self.segUpdateValidDuration
    proto.burnIn = self.burnIn
    proto.collectStats = self.collectStats
    proto.verbosity = self.verbosity
    proto.pamLength = self.pamLength
    proto.maxAge = self.maxAge
    proto.maxInfBacktrack = self.maxInfBacktrack
    proto.maxLrnBacktrack = self.maxLrnBacktrack
    proto.maxSeqLength = self.maxSeqLength
    proto.maxSegmentsPerCell = self.maxSegmentsPerCell
    proto.maxSynapsesPerSegment = self.maxSynapsesPerSegment
    proto.outputType = self.outputType

    proto.activeColumns = self.activeColumns

    cellListProto = proto.init("cells", len(self.cells))
    for i, columnSegments in enumerate(self.cells):
      columnSegmentsProto = cellListProto.init(i, len(columnSegments))
      for j, cellSegments in enumerate(columnSegments):
        cellSegmentsProto = columnSegmentsProto.init(j, len(cellSegments))
        for k, segment in enumerate(cellSegments):
          segment.write(cellSegmentsProto[k])

    proto.lrnIterationIdx = self.lrnIterationIdx
    proto.iterationIdx = self.iterationIdx
    proto.segID = self.segID
    if self.currentOutput is None:
      proto.currentOutput.none = None
    else:
      proto.currentOutput.list = self.currentOutput.tolist()
    proto.pamCounter = self.pamCounter
    proto.collectSequenceStats = self.collectSequenceStats
    proto.resetCalled = self.resetCalled
    # In case of None, use negative value as placeholder for serialization
    proto.avgInputDensity = self.avgInputDensity or -1.0
    proto.learnedSeqLength = self.learnedSeqLength
    proto.avgLearnedSeqLength = self.avgLearnedSeqLength

    proto.prevLrnPatterns = self._prevLrnPatterns
    proto.prevInfPatterns = self._prevInfPatterns

    segmentUpdatesListProto = proto.init("segmentUpdates",
                                         len(self.segmentUpdates))
    for i, (key, updates) in enumerate(self.segmentUpdates.iteritems()):
      cellSegmentUpdatesProto = segmentUpdatesListProto[i]
      cellSegmentUpdatesProto.columnIdx = key[0]
      cellSegmentUpdatesProto.cellIdx = key[1]
      segmentUpdatesProto = cellSegmentUpdatesProto.init("segmentUpdates",
                                                         len(updates))
      for j, (lrnIterationIdx, segmentUpdate) in enumerate(updates):
        segmentUpdateWrapperProto = segmentUpdatesProto[j]
        segmentUpdateWrapperProto.lrnIterationIdx = lrnIterationIdx
        segmentUpdate.write(segmentUpdateWrapperProto.segmentUpdate)

    # self.cellConfidence
    proto.cellConfidenceT = self.cellConfidence["t"].tolist()
    proto.cellConfidenceT1 = self.cellConfidence["t-1"].tolist()
    proto.cellConfidenceCandidate = self.cellConfidence["candidate"].tolist()

    # self.colConfidence
    proto.colConfidenceT = self.colConfidence["t"].tolist()
    proto.colConfidenceT1 = self.colConfidence["t-1"].tolist()
    proto.colConfidenceCandidate = self.colConfidence["candidate"].tolist()

    # self.lrnActiveState
    proto.lrnActiveStateT = self.lrnActiveState["t"].tolist()
    proto.lrnActiveStateT1 = self.lrnActiveState["t-1"].tolist()

    # self.infActiveState
    proto.infActiveStateT = self.infActiveState["t"].tolist()
    proto.infActiveStateT1 = self.infActiveState["t-1"].tolist()
    proto.infActiveStateBackup = self.infActiveState["backup"].tolist()
    proto.infActiveStateCandidate = self.infActiveState["candidate"].tolist()

    # self.lrnPredictedState
    proto.lrnPredictedStateT = self.lrnPredictedState["t"].tolist()
    proto.lrnPredictedStateT1 = self.lrnPredictedState["t-1"].tolist()

    # self.infPredictedState
    proto.infPredictedStateT = self.infPredictedState["t"].tolist()
    proto.infPredictedStateT1 = self.infPredictedState["t-1"].tolist()
    proto.infPredictedStateBackup = self.infPredictedState["backup"].tolist()
    proto.infPredictedStateCandidate = self.infPredictedState["candidate"].tolist()

    proto.consolePrinterVerbosity = self.consolePrinterVerbosity
def write(self, proto):
    """Populate serialization proto instance.

    :param proto: (BacktrackingTMProto) the proto instance to populate
    """
    proto.version = TM_VERSION
    self._random.write(proto.random)
    proto.numberOfCols = self.numberOfCols
    proto.cellsPerColumn = self.cellsPerColumn
    proto.initialPerm = float(self.initialPerm)
    proto.connectedPerm = float(self.connectedPerm)
    proto.minThreshold = self.minThreshold
    proto.newSynapseCount = self.newSynapseCount
    proto.permanenceInc = float(self.permanenceInc)
    proto.permanenceDec = float(self.permanenceDec)
    proto.permanenceMax = float(self.permanenceMax)
    proto.globalDecay = float(self.globalDecay)
    proto.activationThreshold = self.activationThreshold
    proto.doPooling = self.doPooling
    proto.segUpdateValidDuration = self.segUpdateValidDuration
    proto.burnIn = self.burnIn
    proto.collectStats = self.collectStats
    proto.verbosity = self.verbosity
    proto.pamLength = self.pamLength
    proto.maxAge = self.maxAge
    proto.maxInfBacktrack = self.maxInfBacktrack
    proto.maxLrnBacktrack = self.maxLrnBacktrack
    proto.maxSeqLength = self.maxSeqLength
    proto.maxSegmentsPerCell = self.maxSegmentsPerCell
    proto.maxSynapsesPerSegment = self.maxSynapsesPerSegment
    proto.outputType = self.outputType

    proto.activeColumns = self.activeColumns

    cellListProto = proto.init("cells", len(self.cells))
    for i, columnSegments in enumerate(self.cells):
      columnSegmentsProto = cellListProto.init(i, len(columnSegments))
      for j, cellSegments in enumerate(columnSegments):
        cellSegmentsProto = columnSegmentsProto.init(j, len(cellSegments))
        for k, segment in enumerate(cellSegments):
          segment.write(cellSegmentsProto[k])

    proto.lrnIterationIdx = self.lrnIterationIdx
    proto.iterationIdx = self.iterationIdx
    proto.segID = self.segID
    if self.currentOutput is None:
      proto.currentOutput.none = None
    else:
      proto.currentOutput.list = self.currentOutput.tolist()
    proto.pamCounter = self.pamCounter
    proto.collectSequenceStats = self.collectSequenceStats
    proto.resetCalled = self.resetCalled
    # In case of None, use negative value as placeholder for serialization
    proto.avgInputDensity = self.avgInputDensity or -1.0
    proto.learnedSeqLength = self.learnedSeqLength
    proto.avgLearnedSeqLength = self.avgLearnedSeqLength

    proto.prevLrnPatterns = self._prevLrnPatterns
    proto.prevInfPatterns = self._prevInfPatterns

    segmentUpdatesListProto = proto.init("segmentUpdates",
                                         len(self.segmentUpdates))
    for i, (key, updates) in enumerate(self.segmentUpdates.iteritems()):
      cellSegmentUpdatesProto = segmentUpdatesListProto[i]
      cellSegmentUpdatesProto.columnIdx = key[0]
      cellSegmentUpdatesProto.cellIdx = key[1]
      segmentUpdatesProto = cellSegmentUpdatesProto.init("segmentUpdates",
                                                         len(updates))
      for j, (lrnIterationIdx, segmentUpdate) in enumerate(updates):
        segmentUpdateWrapperProto = segmentUpdatesProto[j]
        segmentUpdateWrapperProto.lrnIterationIdx = lrnIterationIdx
        segmentUpdate.write(segmentUpdateWrapperProto.segmentUpdate)

    # self.cellConfidence
    proto.cellConfidenceT = self.cellConfidence["t"].tolist()
    proto.cellConfidenceT1 = self.cellConfidence["t-1"].tolist()
    proto.cellConfidenceCandidate = self.cellConfidence["candidate"].tolist()

    # self.colConfidence
    proto.colConfidenceT = self.colConfidence["t"].tolist()
    proto.colConfidenceT1 = self.colConfidence["t-1"].tolist()
    proto.colConfidenceCandidate = self.colConfidence["candidate"].tolist()

    # self.lrnActiveState
    proto.lrnActiveStateT = self.lrnActiveState["t"].tolist()
    proto.lrnActiveStateT1 = self.lrnActiveState["t-1"].tolist()

    # self.infActiveState
    proto.infActiveStateT = self.infActiveState["t"].tolist()
    proto.infActiveStateT1 = self.infActiveState["t-1"].tolist()
    proto.infActiveStateBackup = self.infActiveState["backup"].tolist()
    proto.infActiveStateCandidate = self.infActiveState["candidate"].tolist()

    # self.lrnPredictedState
    proto.lrnPredictedStateT = self.lrnPredictedState["t"].tolist()
    proto.lrnPredictedStateT1 = self.lrnPredictedState["t-1"].tolist()

    # self.infPredictedState
    proto.infPredictedStateT = self.infPredictedState["t"].tolist()
    proto.infPredictedStateT1 = self.infPredictedState["t-1"].tolist()
    proto.infPredictedStateBackup = self.infPredictedState["backup"].tolist()
    proto.infPredictedStateCandidate = self.infPredictedState["candidate"].tolist()

    proto.consolePrinterVerbosity = self.consolePrinterVerbosity
def write ( self , proto ) : proto . version = TM_VERSION self . _random . write ( proto . random ) proto . numberOfCols = self . numberOfCols proto . cellsPerColumn = self . cellsPerColumn proto . initialPerm = float ( self . initialPerm ) proto . connectedPerm = float ( self . connectedPerm ) proto . minThreshold = self . minThreshold proto . newSynapseCount = self . newSynapseCount proto . permanenceInc = float ( self . permanenceInc ) proto . permanenceDec = float ( self . permanenceDec ) proto . permanenceMax = float ( self . permanenceMax ) proto . globalDecay = float ( self . globalDecay ) proto . activationThreshold = self . activationThreshold proto . doPooling = self . doPooling proto . segUpdateValidDuration = self . segUpdateValidDuration proto . burnIn = self . burnIn proto . collectStats = self . collectStats proto . verbosity = self . verbosity proto . pamLength = self . pamLength proto . maxAge = self . maxAge proto . maxInfBacktrack = self . maxInfBacktrack proto . maxLrnBacktrack = self . maxLrnBacktrack proto . maxSeqLength = self . maxSeqLength proto . maxSegmentsPerCell = self . maxSegmentsPerCell proto . maxSynapsesPerSegment = self . maxSynapsesPerSegment proto . outputType = self . outputType proto . activeColumns = self . activeColumns cellListProto = proto . init ( "cells" , len ( self . cells ) ) for i , columnSegments in enumerate ( self . cells ) : columnSegmentsProto = cellListProto . init ( i , len ( columnSegments ) ) for j , cellSegments in enumerate ( columnSegments ) : cellSegmentsProto = columnSegmentsProto . init ( j , len ( cellSegments ) ) for k , segment in enumerate ( cellSegments ) : segment . write ( cellSegmentsProto [ k ] ) proto . lrnIterationIdx = self . lrnIterationIdx proto . iterationIdx = self . iterationIdx proto . segID = self . segID if self . currentOutput is None : proto . currentOutput . none = None else : proto . currentOutput . list = self . currentOutput . tolist ( ) proto . pamCounter = self . pamCounter proto . collectSequenceStats = self . collectSequenceStats proto . resetCalled = self . resetCalled # In case of None, use negative value as placeholder for serialization proto . avgInputDensity = self . avgInputDensity or - 1.0 proto . learnedSeqLength = self . learnedSeqLength proto . avgLearnedSeqLength = self . avgLearnedSeqLength proto . prevLrnPatterns = self . _prevLrnPatterns proto . prevInfPatterns = self . _prevInfPatterns segmentUpdatesListProto = proto . init ( "segmentUpdates" , len ( self . segmentUpdates ) ) for i , ( key , updates ) in enumerate ( self . segmentUpdates . iteritems ( ) ) : cellSegmentUpdatesProto = segmentUpdatesListProto [ i ] cellSegmentUpdatesProto . columnIdx = key [ 0 ] cellSegmentUpdatesProto . cellIdx = key [ 1 ] segmentUpdatesProto = cellSegmentUpdatesProto . init ( "segmentUpdates" , len ( updates ) ) for j , ( lrnIterationIdx , segmentUpdate ) in enumerate ( updates ) : segmentUpdateWrapperProto = segmentUpdatesProto [ j ] segmentUpdateWrapperProto . lrnIterationIdx = lrnIterationIdx segmentUpdate . write ( segmentUpdateWrapperProto . segmentUpdate ) # self.cellConfidence proto . cellConfidenceT = self . cellConfidence [ "t" ] . tolist ( ) proto . cellConfidenceT1 = self . cellConfidence [ "t-1" ] . tolist ( ) proto . cellConfidenceCandidate = self . cellConfidence [ "candidate" ] . tolist ( ) # self.colConfidence proto . colConfidenceT = self . colConfidence [ "t" ] . tolist ( ) proto . colConfidenceT1 = self . colConfidence [ "t-1" ] . tolist ( ) proto . colConfidenceCandidate = self . colConfidence [ "candidate" ] . tolist ( ) # self.lrnActiveState proto . lrnActiveStateT = self . lrnActiveState [ "t" ] . tolist ( ) proto . lrnActiveStateT1 = self . lrnActiveState [ "t-1" ] . tolist ( ) # self.infActiveState proto . infActiveStateT = self . infActiveState [ "t" ] . tolist ( ) proto . infActiveStateT1 = self . infActiveState [ "t-1" ] . tolist ( ) proto . infActiveStateBackup = self . infActiveState [ "backup" ] . tolist ( ) proto . infActiveStateCandidate = self . infActiveState [ "candidate" ] . tolist ( ) # self.lrnPredictedState proto . lrnPredictedStateT = self . lrnPredictedState [ "t" ] . tolist ( ) proto . lrnPredictedStateT1 = self . lrnPredictedState [ "t-1" ] . tolist ( ) # self.infPredictedState proto . infPredictedStateT = self . infPredictedState [ "t" ] . tolist ( ) proto . infPredictedStateT1 = self . infPredictedState [ "t-1" ] . tolist ( ) proto . infPredictedStateBackup = self . infPredictedState [ "backup" ] . tolist ( ) proto . infPredictedStateCandidate = self . infPredictedState [ "candidate" ] . tolist ( ) proto . consolePrinterVerbosity = self . consolePrinterVerbosity
Populate serialization proto instance.

    :param proto: (BacktrackingTMProto) the proto instance to populate
Populate serialization proto instance .