code: def cluster_accuracy_cost_function(
      input_data, ground_truth, connectivity=2
  ):
      """Computes a function to see how many clusters that exist in the ground truth
          data have overlapping segments in the input data. Note that this does not account
          for extraneous segmentations in the input data that do not correspond to any
          clusters in the ground truth data.
  
      Parameters
      ----------
      input_data, ground_truth : NumPy
          Arrays to be compared.
      connectivity : int, optional
          Description
  
      Returns
      -------
      float
          Cluster accuracy metric.
      """
  
      if input_data.shape[-1] != 1:
          raise NotImplementedError(
              "Cluster accuracy not implemented for data with multiple channels."
          )
  
      input_data = input_data[0, ..., 0]
      ground_truth = ground_truth[0, ..., 0]
  
      overlapping_map = np.logical_and(input_data, ground_truth)
      connected_components = label(
          ground_truth, connectivity=connectivity
      )
      total_components = np.max(connected_components)
  
      overlapping_components = 0
  
      for i in range(1, total_components + 1):
          individual_component = np.copy(connected_components)
          individual_component[individual_component != i] == 0
          if (
              np.sum(
                  np.logical_and(
                      overlapping_map, individual_component.astype(bool)
                  )
              )
              != 0
          ):
              overlapping_components += 1
  
      return overlapping_components / total_components
  
file_path: DeepNeuro/deepneuro/postprocessing/statistics.py
imports: import csv
  import numpy
  from skimage.measure import label
  from deepneuro.postprocessing.postprocessor import Postprocessor
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 3 for calling a global function with example usage: cluster_accuracy_cost_function(input_data, ground_truth, connectivity) and returns: unknown