def test_2d_2fi_cokriging(self):

        def branin(x):
            x1 = 15*x[0]-5
            x2 = 15*x[1]
            return (x2-(5.1/(4.*pi**2.))*x1**2.+5.*x1/pi-6.)**2.+10.*(1.-1./(8.*pi))*cos(x1)+10.

        # Add a linear error
        def branin_low_fidelity(x):
            return branin(x)+30.*x[1] + 10.

        x = [[[ 0.13073587,  0.24909577],  # expensive (hifi) doe
              [ 0.91915571,  0.4735261 ],
              [ 0.75830543,  0.13321705],
              [ 0.51760477,  0.34594101],
              [ 0.03531219,  0.77765831],
              [ 0.27249206,  0.5306115 ],
              [ 0.62762489,  0.65778471],
              [ 0.3914706 ,  0.09852519],
              [ 0.86565585,  0.85350002],
              [ 0.40806563,  0.91465314]],

             [[ 0.91430235,  0.17029894],  # cheap (lowfi) doe
              [ 0.99329651,  0.76431519],
              [ 0.2012252 ,  0.35006032],
              [ 0.61707854,  0.90210676],
              [ 0.15113004,  0.0133355 ],
              [ 0.07108082,  0.55344447],
              [ 0.4483159 ,  0.52182902],
              [ 0.5926638 ,  0.06595122],
              [ 0.66305449,  0.48579608],
              [ 0.47965045,  0.7407793 ],
              [ 0.13073587,  0.24909577],  # notice hifi doe inclusion
              [ 0.91915571,  0.4735261 ],
              [ 0.75830543,  0.13321705],
              [ 0.51760477,  0.34594101],
              [ 0.03531219,  0.77765831],
              [ 0.27249206,  0.5306115 ],
              [ 0.62762489,  0.65778471],
              [ 0.3914706 ,  0.09852519],
              [ 0.86565585,  0.85350002],
              [ 0.40806563,  0.91465314]]]
        y = array([[branin(case) for case in x[0]],
                   [branin_low_fidelity(case) for case in x[1]]])
        nfi=2
        cokrig = MultiFiCoKrigingSurrogate()
        cokrig.train_multifi(x, y)

        mu, sigma = cokrig.predict([2./3., 1/3.])
        assert_rel_error(self, mu, 26, 0.2)
        assert_rel_error(self, sigma, 0.3, 0.2)

        # Test with theta setting instead of theta estimation
        cokrig2 = MultiFiCoKrigingSurrogate(theta=0.1)
        cokrig2.train_multifi(x, y)

        mu, sigma = cokrig2.predict([2./3., 1/3.])
        assert_rel_error(self, mu, 21.7, 0.1)
        assert_rel_error(self, sigma, 2.29, 0.1)

        # Test with theta setting instead of theta estimation
        cokrig2 = MultiFiCoKrigingSurrogate(theta=[0.1, 10])
        cokrig2.train_multifi(x, y)

        mu, sigma = cokrig2.predict([2./3., 1/3.])
        assert_rel_error(self, mu, 21.01, 0.2)
        assert_rel_error(self, sigma, 2.29, 0.2)

        # Test bad theta setting
        cokrig3 = MultiFiCoKrigingSurrogate(theta=[0.1])
        try:
            cokrig3.train_multifi(x, y)
        except ValueError as err:
            self.assertEqual(str(err),
                "theta must be a list of 2 element(s).")
        else:
            self.fail("ValueError Expected")
dataset/ETHPy150Open OpenMDAO/OpenMDAO/openmdao/surrogate_models/test/test_multifi_cokriging.py/CoKrigingSurrogateTest.test_2d_2fi_cokriging/original