code: def test_Gaussian2D_std(self):
      # http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.html
  
      def g(x):
          """The function to predict (classification will then consist in predicting
          whether g(x) <= 0 or not)"""
          return 5.0 - x[:, 1] - 0.5 * x[:, 0] ** 2.0
  
      # Design of experiments
      X = np.array(
          [
              [-4.61611719, -6.00099547],
              [4.10469096, 5.32782448],
              [0.00000000, -0.50000000],
              [-6.17289014, -4.6984743],
              [1.3109306, -6.93271427],
              [-5.03823144, 3.10584743],
              [-2.87600388, 6.74310541],
              [5.21301203, 4.26386883],
          ]
      )
      y = g(X)
  
      df = pdml.ModelFrame(X, target=y)
      gpm1 = df.gaussian_process.GaussianProcessRegressor()
      df.fit(gpm1)
      result, std_result = df.predict(gpm1, return_std=True)
  
      gpm2 = gp.GaussianProcessRegressor()
      gpm2.fit(X, y)
      expected, std_expected = gpm2.predict(X, return_std=True)
  
      self.assert_numpy_array_almost_equal(result.values, expected)
      self.assert_numpy_array_almost_equal(
          std_result.values, std_expected
      )
  
file_path: pandas-ml/pandas_ml/skaccessors/test/test_gaussian_process.py
imports: import numpy
  import sklearn.gaussian_process
  import pandas_ml
  import pandas_ml.util.testing
label_desc: Use image node_id 10 for calling the TestGaussianProcess obj's underlying member method code with example usage: obj.test_Gaussian2D_std() and returns: unknown