class_docstr: A density based clustering method that expands clusters from 
  samples that have more neighbors within a radius specified by eps
  than the value min_samples.
  
  Parameters:
  -----------
  eps: float
      The radius within which samples are considered neighbors
  min_samples: int
      The number of neighbors required for the sample to be a core point. 
docstr: Return the samples labels as the index of the cluster in which they are
  contained 
docstr_tok: ["Return","the","samples","labels","as","the","index","of","the","cluster","in","which","they","are","contained"]
code: def _get_cluster_labels(self):
      """Return the samples labels as the index of the cluster in which they are
      contained"""
      # Set default value to number of clusters
      # Will make sure all outliers have same cluster label
      labels = np.full(
          shape=self.X.shape[0], fill_value=len(self.clusters)
      )
      for cluster_i, cluster in enumerate(self.clusters):
          for sample_i in cluster:
              labels[sample_i] = cluster_i
      return labels
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/dbscan.py
imports: from __future__ import print_function, division
  import numpy
  from mlfromscratch.utils import Plot, euclidean_distance, normalize
label_desc: Use image node_id 4 for calling the DBSCAN obj's underlying member method code with example usage: obj._get_cluster_labels() and returns: labels