class_docstr: !
  @brief Class represents BSAS clustering algorithm - basic sequential algorithmic scheme.
  @details Algorithm has two mandatory parameters: maximum allowable number of clusters and threshold
            of dissimilarity or in other words maximum distance between points. Distance metric also can
            be specified using 'metric' parameters, by default 'Manhattan' distance is used.
            BSAS using following rule for updating cluster representative:
  
  [
  ec{m}_{C_{k}}^{new}=rac{ \left ( n_{C_{k}^{new}} - 1 
  ight )ec{m}_{C_{k}}^{old} + ec{x} }{n_{C_{k}^{new}}}
  ]
  
  Clustering results of this algorithm depends on objects order in input data.
  
  Example:
  @code
      from pyclustering.cluster.bsas import bsas, bsas_visualizer
      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 BSAS algorithm.
      bsas_instance = bsas(sample, max_clusters, threshold)
      bsas_instance.process()
  
      # Get clustering results.
      clusters = bsas_instance.get_clusters()
      representatives = bsas_instance.get_representatives()
  
      # Display results.
      bsas_visualizer.show_clusters(sample, clusters, representatives)
  @endcode
  
  Implementation based on paper @cite book::pattern_recognition::2009.
  
  @see pyclustering.cluster.mbsas, pyclustering.cluster.ttsas
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docstr: !
  @brief Returns list of representatives of allocated clusters.
  
  @see process()
  @see get_clusters()
docstr_tok: ["!","@","brief","Returns","list","of","representatives","of","allocated","clusters",".","@","see","process","(",")","@","see","get_clusters","(",")"]
code: def get_representatives(self):
      """!
      @brief Returns list of representatives of allocated clusters.
  
      @see process()
      @see get_clusters()
  
      """
      return self._representatives
  
file_path: pyclustering/pyclustering/cluster/bsas.py
imports: from pyclustering.core.wrapper import ccore_library
  from pyclustering.core.bsas_wrapper import bsas
  from pyclustering.core.metric_wrapper import metric_wrapper
  from pyclustering.cluster import cluster_visualizer
  from pyclustering.cluster.encoder import type_encoding
  from pyclustering.utils.metric import type_metric, distance_metric
label_desc: Use image node_id 6 for calling the bsas obj's underlying member method code with example usage: obj.get_representatives() and returns: self