def test_vanilla_bq_loop_initial_state():
    x_init = np.random.rand(5, 2)
    y_init = np.random.rand(5, 1)
    bounds = [((- 1), 1), (0, 1)]
    gpy_model = GPy.models.GPRegression(X=x_init, Y=y_init, kernel=GPy.kern.RBF(input_dim=x_init.shape[1], lengthscale=1.0, variance=1.0))
    emukit_qrbf = QuadratureRBF(RBFGPy(gpy_model.kern), integral_bounds=bounds)
    emukit_model = BaseGaussianProcessGPy(kern=emukit_qrbf, gpy_model=gpy_model)
    emukit_method = VanillaBayesianQuadrature(base_gp=emukit_model)
    emukit_loop = VanillaBayesianQuadratureLoop(model=emukit_method)
    assert_array_equal(emukit_loop.loop_state.X, x_init)
    assert_array_equal(emukit_loop.loop_state.Y, y_init)
    assert (emukit_loop.loop_state.iteration == 0)
def test_vanilla_bq_loop_initial_state():
    x_init = np.random.rand(5, 2)
    y_init = np.random.rand(5, 1)
    bounds = [((- 1), 1), (0, 1)]
    gpy_model = GPy.models.GPRegression(X=x_init, Y=y_init, kernel=GPy.kern.RBF(input_dim=x_init.shape[1], lengthscale=1.0, variance=1.0))
    emukit_qrbf = QuadratureRBF(RBFGPy(gpy_model.kern), integral_bounds=bounds)
    emukit_model = BaseGaussianProcessGPy(kern=emukit_qrbf, gpy_model=gpy_model)
    emukit_method = VanillaBayesianQuadrature(base_gp=emukit_model)
    emukit_loop = VanillaBayesianQuadratureLoop(model=emukit_method)
    assert_array_equal(emukit_loop.loop_state.X, x_init)
    assert_array_equal(emukit_loop.loop_state.Y, y_init)
    assert (emukit_loop.loop_state.iteration == 0)