def setParameter(self, name, index, value):
    """
    Overrides :meth:`nupic.bindings.regions.PyRegion.PyRegion.setParameter`.
    """
    if name == "trainRecords":
      # Ensure that the trainRecords can only be set to minimum of the ROWID in
      # the saved states
      if not (isinstance(value, float) or isinstance(value, int)):
        raise HTMPredictionModelInvalidArgument("Invalid argument type \'%s\'. threshold "
          "must be a number." % (type(value)))

      if len(self._recordsCache) > 0 and value < self._recordsCache[0].ROWID:
        raise HTMPredictionModelInvalidArgument("Invalid value. autoDetectWaitRecord "
          "value must be valid record within output stream. Current minimum "
          " ROWID in output stream is %d." % (self._recordsCache[0].ROWID))

      self.trainRecords = value
      # Remove any labels before the first cached record (wont be used anymore)
      self._deleteRangeFromKNN(0, self._recordsCache[0].ROWID)
      # Reclassify all states
      self._classifyStates()
    elif name == "anomalyThreshold":
      if not (isinstance(value, float) or isinstance(value, int)):
        raise HTMPredictionModelInvalidArgument("Invalid argument type \'%s\'. threshold "
          "must be a number." % (type(value)))
      self.anomalyThreshold = value
      self._classifyStates()
    elif name == "classificationMaxDist":
      if not (isinstance(value, float) or isinstance(value, int)):
        raise HTMPredictionModelInvalidArgument("Invalid argument type \'%s\'. "
          "classificationMaxDist must be a number." % (type(value)))
      self._classificationMaxDist = value
      self._classifyStates()
    elif name == "activeColumnCount":
      self._activeColumnCount = value
    else:
      return PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
    """
    Overrides :meth:`nupic.bindings.regions.PyRegion.PyRegion.setParameter`.
    """
    if name == "trainRecords":
      # Ensure that the trainRecords can only be set to minimum of the ROWID in
      # the saved states
      if not (isinstance(value, float) or isinstance(value, int)):
        raise HTMPredictionModelInvalidArgument("Invalid argument type \'%s\'. threshold "
          "must be a number." % (type(value)))

      if len(self._recordsCache) > 0 and value < self._recordsCache[0].ROWID:
        raise HTMPredictionModelInvalidArgument("Invalid value. autoDetectWaitRecord "
          "value must be valid record within output stream. Current minimum "
          " ROWID in output stream is %d." % (self._recordsCache[0].ROWID))

      self.trainRecords = value
      # Remove any labels before the first cached record (wont be used anymore)
      self._deleteRangeFromKNN(0, self._recordsCache[0].ROWID)
      # Reclassify all states
      self._classifyStates()
    elif name == "anomalyThreshold":
      if not (isinstance(value, float) or isinstance(value, int)):
        raise HTMPredictionModelInvalidArgument("Invalid argument type \'%s\'. threshold "
          "must be a number." % (type(value)))
      self.anomalyThreshold = value
      self._classifyStates()
    elif name == "classificationMaxDist":
      if not (isinstance(value, float) or isinstance(value, int)):
        raise HTMPredictionModelInvalidArgument("Invalid argument type \'%s\'. "
          "classificationMaxDist must be a number." % (type(value)))
      self._classificationMaxDist = value
      self._classifyStates()
    elif name == "activeColumnCount":
      self._activeColumnCount = value
    else:
      return PyRegion.setParameter(self, name, index, value)
def setParameter ( self , name , index , value ) : if name == "trainRecords" : # Ensure that the trainRecords can only be set to minimum of the ROWID in # the saved states if not ( isinstance ( value , float ) or isinstance ( value , int ) ) : raise HTMPredictionModelInvalidArgument ( "Invalid argument type \'%s\'. threshold " "must be a number." % ( type ( value ) ) ) if len ( self . _recordsCache ) > 0 and value < self . _recordsCache [ 0 ] . ROWID : raise HTMPredictionModelInvalidArgument ( "Invalid value. autoDetectWaitRecord " "value must be valid record within output stream. Current minimum " " ROWID in output stream is %d." % ( self . _recordsCache [ 0 ] . ROWID ) ) self . trainRecords = value # Remove any labels before the first cached record (wont be used anymore) self . _deleteRangeFromKNN ( 0 , self . _recordsCache [ 0 ] . ROWID ) # Reclassify all states self . _classifyStates ( ) elif name == "anomalyThreshold" : if not ( isinstance ( value , float ) or isinstance ( value , int ) ) : raise HTMPredictionModelInvalidArgument ( "Invalid argument type \'%s\'. threshold " "must be a number." % ( type ( value ) ) ) self . anomalyThreshold = value self . _classifyStates ( ) elif name == "classificationMaxDist" : if not ( isinstance ( value , float ) or isinstance ( value , int ) ) : raise HTMPredictionModelInvalidArgument ( "Invalid argument type \'%s\'. " "classificationMaxDist must be a number." % ( type ( value ) ) ) self . _classificationMaxDist = value self . _classifyStates ( ) elif name == "activeColumnCount" : self . _activeColumnCount = value else : return PyRegion . setParameter ( self , name , index , value )
Overrides :meth:`nupic.bindings.regions.PyRegion.PyRegion.setParameter`.
Overrides : meth : nupic . bindings . regions . PyRegion . PyRegion . setParameter .