text: # -*- coding: utf-8 -*-
  """TimeSeriesForestClassifier test code."""
  import numpy as np
  from numpy import testing
  
  from sktime.classification.interval_based import TimeSeriesForestClassifier
  from sktime.datasets import load_unit_test
  
  
  def test_tsf_on_unit_test_data():
      """Test of TimeSeriesForestClassifier on unit test data."""
      # load unit test data
      X_train, y_train = load_unit_test(split="train", return_X_y=True)
      X_test, y_test = load_unit_test(split="test", return_X_y=True)
      indices = np.random.RandomState(0).choice(len(y_train), 10, replace=False)
  
      # train TSF
      tsf = TimeSeriesForestClassifier(n_estimators=10, random_state=0)
      tsf.fit(X_train, y_train)
  
      # assert probabilities are the same
      probas = tsf.predict_proba(X_test.iloc[indices])
      testing.assert_array_equal(probas, tsf_unit_test_probas)
  
  
  tsf_unit_test_probas = np.array(
      [
          [
              0.1,
              0.9,
          ],
          [
              0.7,
              0.3,
          ],
          [
              0.0,
              1.0,
          ],
          [
              1.0,
              0.0,
          ],
          [
              0.8,
              0.2,
          ],
          [
              1.0,
              0.0,
          ],
          [
              0.8,
              0.2,
          ],
          [
              0.0,
              1.0,
          ],
          [
              0.8,
              0.2,
          ],
          [
              0.9,
              0.1,
          ],
      ]
  )
  
  # def print_array(array):
  #     print('[')
  #     for sub_array in array:
  #         print('[')
  #         for value in sub_array:
  #             print(value.astype(str), end='')
  #             print(', ')
  #         print('],')
  #     print(']')
  #
  #
  # if __name__ == "__main__":
  #     X_train, y_train = load_unit_test(split="train", return_X_y=True)
  #     X_test, y_test = load_unit_test(split="test", return_X_y=True)
  #     indices = np.random.RandomState(0).choice(len(y_train), 10, replace=False)
  #
  #     tsf = TimeSeriesForestClassifier(n_estimators=10, random_state=0)
  #
  #     tsf.fit(X_train, y_train)
  #     probas = tsf.predict_proba(X_test.iloc[indices])
  #     print_array(probas)
  