code: def example_kuncheva():
      distances = np.linspace(1, 15, num=15)
      distances = distances.reshape(1, -1)
  
      # 10 neighbors used in the example
      neighbors = np.linspace(0, 14, num=15, dtype=int)
  
      # target class of each example. independent means that it should be
      # used by the a priori technique
      y_independent = (
          np.array([2, 1, 2, 2, 3, 1, 2, 1, 3, 3, 2, 1, 2, 2, 1]) - 1
      )
  
      # dependent means that it should be used by the a posteriori technique
      y_dependent = np.array(
          [1, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 2, 1, 1, 0]
      )
  
      # Predictions of the base classifier ci. Used to estimate its competence
      # level for the A Posteriori
      classifier_pred = (
          np.array([2, 3, 2, 2, 1, 1, 2, 2, 3, 3, 1, 2, 2, 2, 1]) - 1
      )
  
      # whether or not the base classifier made the correct prediction for each
      # sample in dsel
      dsel_processed = np.transpose(
          np.array([[1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1]])
      )
  
      # In this example we consider that the posteriori is always 1 for the
      # predicted class (15 samples, 1 classifier, 3 classes)
      dsel_scores = np.array(
          [
              [
                  [0.0, 1.0, 0.0],
                  [0.0, 0.0, 1.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [1.0, 0.0, 0.0],
                  [1.0, 0.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 0.0, 1.0],
                  [0.0, 0.0, 1.0],
                  [1.0, 0.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [1.0, 0.0, 0.0],
              ]
          ]
      ).reshape(15, 1, 3)
  
      k = 15
      n_classes = 3
      dict_return = {
          "dsel_processed": dsel_processed,
          "dsel_scores": dsel_scores,
          "distances": distances,
          "neighbors": neighbors,
          "classifier_pred": classifier_pred,
          "y_dependent": y_dependent,
          "y_independent": y_independent,
          "n_classes": n_classes,
          "k": k,
      }
  
      return dict_return
  
file_path: DESlib/deslib/tests/conftest.py
imports: import pytest
  import numpy
  from unittest.mock import MagicMock
  from sklearn.tree import DecisionTreeClassifier
label_desc: Use image node_id 4 for calling a global function with example usage: example_kuncheva() and returns: dict_return