code: def entropy(labels):
      """Calculates the entropy for a labeling."""
      if len(labels) == 0:
          return 1.0
      label_idx = np.unique(labels, return_inverse=True)[1]
      pi = bincount(label_idx).astype(np.float64)
      pi = pi[pi > 0]
      pi_sum = np.sum(pi)
      # log(a / b) should be calculated as log(a) - log(b) for
      # possible loss of precision
      return -np.sum((pi / pi_sum) * (np.log(pi) - log(pi_sum)))
  
imports: from __future__ import division
  from math import log
  import numpy
  from scipy.misc import comb
  from scipy import sparse
  from .expected_mutual_info_fast import expected_mutual_information
  from ...utils.fixes import bincount
  from ...utils.validation import check_array
label_desc: Use image node_id 13 for calling a global function with example usage: entropy(labels) and returns: unknown, int