code: def provideObjectsToLearn(self, objectNames=None):
      
      if objectNames is None:
        objectNames = self.objects.keys()
  
      objects = {}
      for name in objectNames:
        objects[name] = [self._getSDRPairs([pair] * self.numColumns) \
                         for pair in self.objects[name]]
  
      self._checkObjectsToLearn(objects)
      return objects
docs: Returns the objects in a canonical format to be sent to an experiment.
  
      The returned format is a a dictionary where the keys are object names, and
      values are lists of sensations, each sensation being a mapping from
      cortical column index to a pair of SDR's (one location and one feature).
  
      returnDict = {
        "objectId1": [
          {
            0: (set([1, 5, 10]), set([6, 12, 52]),  # location, feature for CC0
            1: (set([6, 2, 15]), set([64, 1, 5]),  # location, feature for CC1
          },
          {
            0: (set([5, 46, 50]), set([8, 10, 11]),  # location, feature for CC0
            1: (set([1, 6, 45]), set([12, 17, 23]),  # location, feature for CC1
          },
        ],
        "objectId2": [
          :
        ]
        :
      }
  
  
      Parameters:
      ----------------------------
      @param   objectNames (list)
               List of object names to provide to the experiment