new_contents: import pytest
  
  import numpy as np
  from sklearn.utils.estimator_checks import check_estimator
  from sklearn.model_selection import train_test_split
  
  import legendary_potato.classifiers as classifiers
  
  
  def classifier_iterator():
      """Return an iterator over classifier.
      """
      return (classifiers.svdd,)
  
  
  def two_class_generator(random_state=1576):
      """Generate classic multidimensional toy set.
  
      For each label, a set of centers are generated following normal
      distribution. A set of samples are generated following anormal distribution
      centered on this centers.
  
      The generated set is a 2-class balanced dataset.
      """
      np.random.seed = random_state
      for dim in range(1, 10):
          orig = np.zeros(dim)
          orig[0] = -1
          a_centers = np.random.normal(orig, 2, size=(3, dim))
          orig[0] = 1
          b_centers = np.random.normal(orig, 2, size=(3, dim))
          a_data = np.vstack(
              np.random.normal(center, 0.5, size=(5, dim))
              for center in a_centers
          )
          b_data = np.vstack(
              np.random.normal(center, 0.5, size=(5, dim))
              for center in b_centers
          )
  
          labels = [1 for _ in range(15)] + [-1 for _ in range(15)]
          data = np.hstack([np.vstack([a_data, b_data]), np.transpose([labels])])
          np.random.shuffle(data)
          yield data[:, 0:-1], data[:, -1]
  
  
  @pytest.mark.parametrize('classifier', classifier_iterator())
  def test_sklearn_compatibility(classifier):
      """Check the compatibility.
      """
      check_estimator(classifier)
  
  
  @pytest.mark.parametrize('classifier', classifier_iterator())
  @pytest.mark.parametrize('dataset', two_class_generator())
  def test_oneclass(classifier, dataset):
      """Perform one class classification.
      """
      X, y = dataset
      X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=False)
      y_train = np.ones(X_train.shape[0])
      classif = classifier()
      classif.fit(X_train, y_train)
      classif.predict(X_test)
  
subject: Add tests for (not yet implemented) classifier