code: def process(self):
          
  
          if len(self.__pointer_data[0]) != len(self.__centers[0]):
              raise ValueError("Dimension of the input data and dimension of the initial cluster centers must be equal.")
  
          if self.__ccore is True:
              self.__process_by_ccore()
          else:
              self.__process_by_python()
  
          return self
docs: !
          @brief Performs cluster analysis in line with rules of K-Means algorithm.
  
          @return (kmeans) Returns itself (K-Means instance).
  
          @remark Results of clustering can be obtained using corresponding get methods.
          
          @see get_clusters()
          @see get_centers()