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: Returns a list of all samples that are not currently medoids 
docstr_tok: ["Returns","a","list","of","all","samples","that","are","not","currently","medoids"]
code: def _get_non_medoids(self, X, medoids):
      """Returns a list of all samples that are not currently medoids"""
      non_medoids = []
      for sample in X:
          if not sample in medoids:
              non_medoids.append(sample)
      return non_medoids
  
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 6 for calling the PAM obj's underlying member method code with example usage: obj._get_non_medoids(X, medoids) and returns: non_medoids