class_docstr: A simple clustering method that forms k clusters by first assigning
  samples to the closest medoids, and then swapping medoids with non-medoid
  samples if the total distance (cost) between the cluster members and their medoid
  is smaller than prevoisly.
  
  
  Parameters:
  -----------
  k: int
      The number of clusters the algorithm will form.
class_docstr_tok: ["A","simple","clustering","method","that","forms","k","clusters","by","first","assigning","samples","to","the","closest","medoids",",","and","then","swapping","medoids","with","non-medoid","samples","if","the","total","distance","(","cost",")","between","the","cluster","members","and","their","medoid","is","smaller","than","prevoisly",".","Parameters",":","--","--","--","--","--","-","k",":","int","The","number","of","clusters","the","algorithm","will","form","."]
docstr: Classify samples as the index of their clusters 
docstr_tok: ["Classify","samples","as","the","index","of","their","clusters"]
code: def _get_cluster_labels(self, clusters, X):
      """Classify samples as the index of their clusters"""
      # One prediction for each sample
      y_pred = np.zeros(np.shape(X)[0])
      for cluster_i in range(len(clusters)):
          cluster = clusters[cluster_i]
          for sample_i in cluster:
              y_pred[sample_i] = cluster_i
      return y_pred
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/partitioning_around_medoids.py
imports: from __future__ import print_function, division
  import numpy
  from mlfromscratch.utils import normalize, euclidean_distance, Plot
  from mlfromscratch.unsupervised_learning import PCA
label_desc: Use image node_id 7 for calling the PAM obj's underlying member method code with example usage: obj._get_cluster_labels(clusters, X) and returns: y_pred