class_docstr: !
  @brief Class represents TTSAS (Two-Threshold Sequential Algorithmic Scheme).
  @details Clustering results of BSAS and MBSAS are strongly dependent on the order in which the points in data.
            TTSAS helps to overcome this shortcoming by using two threshold parameters. The first - if the distance
            to the nearest cluster is less than the first threshold then point is assigned to the cluster. The
            second - if distance to the nearest cluster is greater than the second threshold then new cluster is
            allocated.
  
  Code example of TTSAS usage:
  @code
      from pyclustering.cluster.bsas import bsas_visualizer
      from pyclustering.cluster.ttsas import ttsas
      from pyclustering.samples.definitions import SIMPLE_SAMPLES
      from pyclustering.utils import read_sample
  
      # Read data sample from 'Simple03.data'.
      sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
  
      # Prepare algorithm's parameters.
      threshold1 = 1.0
      threshold2 = 2.0
  
      # Create instance of TTSAS algorithm.
      ttsas_instance = ttsas(sample, threshold1, threshold2)
      ttsas_instance.process()
  
      # Get clustering results.
      clusters = ttsas_instance.get_clusters()
      representatives = ttsas_instance.get_representatives()
  
      # Display results using BSAS visualizer.
      bsas_visualizer.show_clusters(sample, clusters, representatives)
  @endcode
  
  @see pyclustering.cluster.bsas, pyclustering.cluster.mbsas
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code: def __process_objects(self, changes):
      index_point = self._skipped_objects.index(True)
  
      if changes == 0:
          self.__allocate_cluster(index_point, self._data[index_point])
          index_point += 1
  
      for i in range(index_point, len(self._data)):
          if self._skipped_objects[i] is True:
              self.__process_skipped_object(i)
  
file_path: pyclustering/pyclustering/cluster/ttsas.py
imports: from pyclustering.core.ttsas_wrapper import ttsas
  from pyclustering.core.metric_wrapper import metric_wrapper
  from pyclustering.cluster.bsas import bsas
label_desc: Use image node_id 5 for calling the ttsas obj's underlying member method code with example usage: obj.__process_objects(changes) without return types