content: # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #         Fabian Pedregosa <fabian.pedregosa@inria.fr>
  #
  # License: BSD 3 clause
  
  import numpy as np
  
  from sklearn.utils.testing import assert_array_equal
  from sklearn.utils.testing import SkipTest
  from sklearn.linear_model.bayes import BayesianRidge, ARDRegression
  from sklearn import datasets
  
  from sklearn.utils.testing import assert_array_almost_equal
  
  
  def test_bayesian_on_diabetes():
      # Test BayesianRidge on diabetes
      raise SkipTest("XFailed Test")
      diabetes = datasets.load_diabetes()
      X, y = diabetes.data, diabetes.target
  
      clf = BayesianRidge(compute_score=True)
  
      # Test with more samples than features
      clf.fit(X, y)
      # Test that scores are increasing at each iteration
      assert_array_equal(np.diff(clf.scores_) > 0, True)
  
      # Test with more features than samples
      X = X[:5, :]
      y = y[:5]
      clf.fit(X, y)
      # Test that scores are increasing at each iteration
      assert_array_equal(np.diff(clf.scores_) > 0, True)
  
  
  def test_toy_bayesian_ridge_object():
      # Test BayesianRidge on toy
      X = np.array([[1], [2], [6], [8], [10]])
      Y = np.array([1, 2, 6, 8, 10])
      clf = BayesianRidge(compute_score=True)
      clf.fit(X, Y)
  
      # Check that the model could approximately learn the identity function
      test = [[1], [3], [4]]
      assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
  
  
  def test_toy_ard_object():
      # Test BayesianRegression ARD classifier
      X = np.array([[1], [2], [3]])
      Y = np.array([1, 2, 3])
      clf = ARDRegression(compute_score=True)
      clf.fit(X, Y)
  
      # Check that the model could approximately learn the identity function
      test = [[1], [3], [4]]
      assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
  