class_docstr: !
  @brief Defines algorithms that can be used to find optimal number of cluster using Silhouette method.
  
  @see silhouette_ksearch
docstr: !
  @brief Returns algorithm type that corresponds to specified enumeration value.
  
  @return (type) Algorithm type for cluster analysis.
docstr_tok: ["!","@","brief","Returns","algorithm","type","that","corresponds","to","specified","enumeration","value",".","@","return","(","type",")","Algorithm","type","for","cluster","analysis","."]
code: def get_type(self):
      """!
      @brief Returns algorithm type that corresponds to specified enumeration value.
  
      @return (type) Algorithm type for cluster analysis.
  
      """
      if self == silhouette_ksearch_type.KMEANS:
          return kmeans
      elif self == silhouette_ksearch_type.KMEDIANS:
          return kmedians
      elif self == silhouette_ksearch_type.KMEDOIDS:
          return kmedoids
      else:
          return None
  
imports: from enum import IntEnum
  import numpy
  from pyclustering.cluster.kmeans import kmeans
  from pyclustering.cluster.kmedians import kmedians
  from pyclustering.cluster.kmedoids import kmedoids
  from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
  from pyclustering.utils.metric import distance_metric, type_metric
  from pyclustering.core.wrapper import ccore_library
  from pyclustering.core.metric_wrapper import metric_wrapper
  import pyclustering.core.silhouette_wrapper
label_desc: Use image node_id 1 for calling the silhouette_ksearch_type obj's underlying member method code with example usage: obj.get_type() and returns: kmeans, kmedians, kmedoids, None