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: Return the index of the closest medoid to the sample 
docstr_tok: ["Return","the","index","of","the","closest","medoid","to","the","sample"]
code: def _closest_medoid(self, sample, medoids):
      """Return the index of the closest medoid to the sample"""
      closest_i = None
      closest_distance = float("inf")
      for i, medoid in enumerate(medoids):
          distance = euclidean_distance(sample, medoid)
          if distance < closest_distance:
              closest_i = i
              closest_distance = distance
      return closest_i
  
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 3 for calling the PAM obj's underlying member method code with example usage: obj._closest_medoid(sample, medoids) and returns: closest_i