class_docstr: !
  @brief Observer of K-Means algorithm that is used to collect information about clustering process on each iteration of the algorithm.
  
  @see kmeans
docstr: !
  @brief Set evolution of changes of centers during clustering process.
  
  @param[in] evolution_centers (array_like): Evolution of changes of centers during clustering process.
docstr_tok: ["!","@","brief","Set","evolution","of","changes","of","centers","during","clustering","process",".","@","param","[","in","]","evolution_centers","(","array_like",")",":","Evolution","of","changes","of","centers","during","clustering","process","."]
code: def set_evolution_centers(self, evolution_centers):
      """!
      @brief Set evolution of changes of centers during clustering process.
  
      @param[in] evolution_centers (array_like): Evolution of changes of centers during clustering process.
  
      """
      self.__evolution_centers = evolution_centers
  
imports: import copy
  import numpy
  import matplotlib.pyplot
  import matplotlib.animation
  import pyclustering.core.kmeans_wrapper
  from pyclustering.core.wrapper import ccore_library
  from pyclustering.core.metric_wrapper import metric_wrapper
  from pyclustering.cluster.encoder import type_encoding
  from pyclustering.cluster import cluster_visualizer
  from pyclustering.utils.metric import distance_metric, type_metric
label_desc: Use image node_id 4 for calling the kmeans_observer obj's underlying member method code with example usage: obj.set_evolution_centers(evolution_centers) without return types