class_docstr: A simple clustering method that forms k clusters by iteratively reassigning
  samples to the closest centroids and after that moves the centroids to the center
  of the new formed clusters.
  
  
  Parameters:
  -----------
  k: int
      The number of clusters the algorithm will form.
  max_iterations: int
      The number of iterations the algorithm will run for if it does
      not converge before that. 
class_docstr_tok: ["A","simple","clustering","method","that","forms","k","clusters","by","iteratively","reassigning","samples","to","the","closest","centroids","and","after","that","moves","the","centroids","to","the","center","of","the","new","formed","clusters",".","Parameters",":","--","--","--","--","--","-","k",":","int","The","number","of","clusters","the","algorithm","will","form",".","max_iterations",":","int","The","number","of","iterations","the","algorithm","will","run","for","if","it","does","not","converge","before","that","."]
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, cluster in enumerate(clusters):
          for sample_i in cluster:
              y_pred[sample_i] = cluster_i
      return y_pred
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/k_means.py
imports: from __future__ import print_function, division
  import numpy
  from mlfromscratch.utils import normalize, euclidean_distance, Plot
  from mlfromscratch.unsupervised_learning import *
label_desc: Use image node_id 6 for calling the KMeans obj's underlying member method code with example usage: obj._get_cluster_labels(clusters, X) and returns: y_pred