def read(cls, proto):
    """
    :param proto: capnp HTMPredictionModelProto message reader
    """
    obj = object.__new__(cls)
    # model.capnp
    super(HTMPredictionModel, obj).__init__(proto=proto.modelBase)

    # HTMPredictionModelProto.capnp
    obj._minLikelihoodThreshold = round(proto.minLikelihoodThreshold,
                                        EPSILON_ROUND)
    obj._maxPredictionsPerStep = proto.maxPredictionsPerStep

    network = Network.read(proto.network)
    obj._hasSP = ("SP" in network.regions)
    obj._hasTP = ("TM" in network.regions)
    obj._hasCL = ("Classifier" in network.regions)
    obj._netInfo = NetworkInfo(net=network, statsCollectors=[])

    obj.__spLearningEnabled = bool(proto.spLearningEnabled)
    obj.__tpLearningEnabled = bool(proto.tpLearningEnabled)
    obj.__numRunCalls = proto.numRunCalls

    obj._classifierInputEncoder = None
    if proto.predictedFieldIdx.which() == "none":
      obj._predictedFieldIdx = None
    else:
      obj._predictedFieldIdx = proto.predictedFieldIdx.value
    if proto.predictedFieldName.which() == "none":
      obj._predictedFieldName = None
    else:
      obj._predictedFieldName = proto.predictedFieldName.value
    obj._numFields = proto.numFields
    if proto.numFields.which() == "none":
      obj._numFields = None
    else:
      obj._numFields = proto.numFields.value
    obj.__trainSPNetOnlyIfRequested = proto.trainSPNetOnlyIfRequested
    obj.__finishedLearning = proto.finishedLearning
    obj._input = None
    sensor = network.regions['sensor'].getSelf()
    sensor.dataSource = DataBuffer()
    network.initialize()


    obj.__logger = initLogger(obj)
    obj.__logger.debug("Instantiating %s." % obj.__myClassName)

    # Mark end of restoration from state
    obj.__restoringFromState = False
    obj.__restoringFromV1 = False

    return obj
def read(cls, proto):
    """
    :param proto: capnp HTMPredictionModelProto message reader
    """
    obj = object.__new__(cls)
    # model.capnp
    super(HTMPredictionModel, obj).__init__(proto=proto.modelBase)

    # HTMPredictionModelProto.capnp
    obj._minLikelihoodThreshold = round(proto.minLikelihoodThreshold,
                                        EPSILON_ROUND)
    obj._maxPredictionsPerStep = proto.maxPredictionsPerStep

    network = Network.read(proto.network)
    obj._hasSP = ("SP" in network.regions)
    obj._hasTP = ("TM" in network.regions)
    obj._hasCL = ("Classifier" in network.regions)
    obj._netInfo = NetworkInfo(net=network, statsCollectors=[])

    obj.__spLearningEnabled = bool(proto.spLearningEnabled)
    obj.__tpLearningEnabled = bool(proto.tpLearningEnabled)
    obj.__numRunCalls = proto.numRunCalls

    obj._classifierInputEncoder = None
    if proto.predictedFieldIdx.which() == "none":
      obj._predictedFieldIdx = None
    else:
      obj._predictedFieldIdx = proto.predictedFieldIdx.value
    if proto.predictedFieldName.which() == "none":
      obj._predictedFieldName = None
    else:
      obj._predictedFieldName = proto.predictedFieldName.value
    obj._numFields = proto.numFields
    if proto.numFields.which() == "none":
      obj._numFields = None
    else:
      obj._numFields = proto.numFields.value
    obj.__trainSPNetOnlyIfRequested = proto.trainSPNetOnlyIfRequested
    obj.__finishedLearning = proto.finishedLearning
    obj._input = None
    sensor = network.regions['sensor'].getSelf()
    sensor.dataSource = DataBuffer()
    network.initialize()


    obj.__logger = initLogger(obj)
    obj.__logger.debug("Instantiating %s." % obj.__myClassName)

    # Mark end of restoration from state
    obj.__restoringFromState = False
    obj.__restoringFromV1 = False

    return obj
def read ( cls , proto ) : obj = object . __new__ ( cls ) # model.capnp super ( HTMPredictionModel , obj ) . __init__ ( proto = proto . modelBase ) # HTMPredictionModelProto.capnp obj . _minLikelihoodThreshold = round ( proto . minLikelihoodThreshold , EPSILON_ROUND ) obj . _maxPredictionsPerStep = proto . maxPredictionsPerStep network = Network . read ( proto . network ) obj . _hasSP = ( "SP" in network . regions ) obj . _hasTP = ( "TM" in network . regions ) obj . _hasCL = ( "Classifier" in network . regions ) obj . _netInfo = NetworkInfo ( net = network , statsCollectors = [ ] ) obj . __spLearningEnabled = bool ( proto . spLearningEnabled ) obj . __tpLearningEnabled = bool ( proto . tpLearningEnabled ) obj . __numRunCalls = proto . numRunCalls obj . _classifierInputEncoder = None if proto . predictedFieldIdx . which ( ) == "none" : obj . _predictedFieldIdx = None else : obj . _predictedFieldIdx = proto . predictedFieldIdx . value if proto . predictedFieldName . which ( ) == "none" : obj . _predictedFieldName = None else : obj . _predictedFieldName = proto . predictedFieldName . value obj . _numFields = proto . numFields if proto . numFields . which ( ) == "none" : obj . _numFields = None else : obj . _numFields = proto . numFields . value obj . __trainSPNetOnlyIfRequested = proto . trainSPNetOnlyIfRequested obj . __finishedLearning = proto . finishedLearning obj . _input = None sensor = network . regions [ 'sensor' ] . getSelf ( ) sensor . dataSource = DataBuffer ( ) network . initialize ( ) obj . __logger = initLogger ( obj ) obj . __logger . debug ( "Instantiating %s." % obj . __myClassName ) # Mark end of restoration from state obj . __restoringFromState = False obj . __restoringFromV1 = False return obj
:param proto: capnp HTMPredictionModelProto message reader
: param proto : capnp HTMPredictionModelProto message reader