class_docstr: !
  @brief Class represents MBSAS (Modified Basic Sequential Algorithmic Scheme).
  @details Interface of MBSAS algorithm is the same as for BSAS. This algorithm performs clustering in two steps.
            The first - is determination of amount of clusters. The second - is assignment of points that were not
            marked as a cluster representatives to clusters.
  
  Code example of MBSAS usage:
  @code
      from pyclustering.cluster.bsas import bsas_visualizer
      from pyclustering.cluster.mbsas import mbsas
      from pyclustering.utils import read_sample
      from pyclustering.samples.definitions import SIMPLE_SAMPLES
  
      # Read data sample from 'Simple02.data'.
      sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
  
      # Prepare algorithm's parameters.
      max_clusters = 3
      threshold = 1.0
  
      # Create instance of MBSAS algorithm.
      mbsas_instance = mbsas(sample, max_clusters, threshold)
      mbsas_instance.process()
  
      # Get clustering results.
      clusters = mbsas_instance.get_clusters()
      representatives = mbsas_instance.get_representatives()
  
      # Display results.
      bsas_visualizer.show_clusters(sample, clusters, representatives)
  @endcode
  
  @see pyclustering.cluster.bsas, pyclustering.cluster.ttsas
code: def __prcess_by_python(self):
      self._clusters.append([0])
      self._representatives.append(self._data[0])
  
      skipped_objects = []
  
      for i in range(1, len(self._data)):
          point = self._data[i]
          index_cluster, distance = self._find_nearest_cluster(point)
  
          if (distance > self._threshold) and (
              len(self._clusters) < self._amount
          ):
              self._representatives.append(point)
              self._clusters.append([i])
          else:
              skipped_objects.append(i)
  
      for i in skipped_objects:
          point = self._data[i]
          index_cluster, _ = self._find_nearest_cluster(point)
  
          self._clusters[index_cluster].append(i)
          self._update_representative(index_cluster, point)
  
file_path: pyclustering/pyclustering/cluster/mbsas.py
imports: from pyclustering.core.mbsas_wrapper import mbsas
  from pyclustering.core.metric_wrapper import metric_wrapper
  from pyclustering.cluster.bsas import bsas
label_desc: Use image node_id 4 for calling the mbsas obj's underlying member method code with example usage: obj.__prcess_by_python() without return types