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
docstr: !
  @brief Performs cluster analysis in line with MBSAS algorithm.
  
  @return (mbsas) Returns itself (MBSAS instance).
  
  @see get_clusters()
  @see get_representatives()
code: def process(self):
      """!
      @brief Performs cluster analysis in line with MBSAS algorithm.
  
      @return (mbsas) Returns itself (MBSAS instance).
  
      @see get_clusters()
      @see get_representatives()
  
      """
  
      if self._ccore is True:
          self.__process_by_ccore()
      else:
          self.__prcess_by_python()
  
      return self
  
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 2 for calling the mbsas obj's underlying member method code with example usage: obj.process() and returns: self