def test_2d_1fi_cokriging(self):
        # CoKrigingSurrogate with one fidelity could be used as a KrigingSurrogate
        # Same test as for KrigingSurrogate...  well with predicted test value adjustment

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

        x = array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5],
                   [-3.5, 6.], [4., 7.5], [-5., 9.], [5.5, 10.5],
                   [10., 12.], [7., 13.5], [2.5, 15.]])
        y = array([branin(case) for case in x])
        krig1 = MultiFiCoKrigingSurrogate()
        krig1.train(x, y)

        mu, sigma = krig1.predict([-2., 0.])
        assert_rel_error(self, mu, branin(x[0]), 1e-5)
        assert_rel_error(self, sigma, 0., 1e-5)

        mu, sigma = krig1.predict([5., 5.])
        assert_rel_error(self, mu, 22, 1)
        assert_rel_error(self, sigma, 13, 1)

        # Test with theta setting instead of estimation
        krig2 = MultiFiCoKrigingSurrogate(theta=[0.1])
        krig1.train(x, y)

        mu, sigma = krig1.predict([-2., 0.])
        assert_rel_error(self, mu, branin(x[0]), 1e-5)
        assert_rel_error(self, sigma, 0., 1e-5)

        mu, sigma = krig1.predict([5., 5.])
        assert_rel_error(self, mu, 22, 1)
        assert_rel_error(self, sigma, 13, 1)
dataset/ETHPy150Open OpenMDAO/OpenMDAO/openmdao/surrogate_models/test/test_multifi_cokriging.py/CoKrigingSurrogateTest.test_2d_1fi_cokriging/swapped operands of `/`