docs: def nne(dim_red, true_labels):
      """
      Calculates the nearest neighbor accuracy (basically leave-one-out cross
      validation with a 1NN classifier).
  
      Args:
          dim_red (array): dimensions (k, cells)
          true_labels (array): 1d array of integers
  
      Returns:
          Nearest neighbor accuracy - fraction of points for which the 1NN
          1NN classifier returns the correct value.
      """
      # use sklearn's BallTree
      bt = BallTree(dim_red.T)
      correct = 0
      for i, l in enumerate(true_labels):
          dist, ind = bt.query([dim_red[:,i]], k=2)
          closest_cell = ind[0, 1]
          if true_labels[closest_cell] == l:
              correct += 1
      return float(correct)/len(true_labels)