def test_copy_fit_2d(self): x = 2 * np.pi * np.random.random_sample((20, 2)) y = func_2d(x) eta = 1e-3 for kernel in kernels_dict.values(): k = kernel(2) for tail in tails_dict.values(): t = tail() if t.degree >= k.dmin: model = RBF(k, t, eta) model.fit(x, y) copy_model = model.copy() assert np.allclose(copy_model._X, model._X) assert np.allclose(copy_model._y, model._y) assert copy_model._eta == model._eta assert isinstance(copy_model._kernel, model._kernel.__class__) assert copy_model._kernel.param == model._kernel.param assert isinstance(copy_model._tail, model._tail.__class__) assert np.all( copy_model._tail.params == model._tail.params) assert np.allclose(copy_model._lambda, model._lambda) assert np.allclose(copy_model._LU, model._LU) assert np.allclose(copy_model._piv, model._piv) assert np.allclose(copy_model.loo_residuals, model.loo_residuals)
def test_copy_nofit(self): eta = 1 for kernel in kernels_dict.values(): k = kernel(2) for tail in tails_dict.values(): t = tail() if t.degree >= k.dmin: model = RBF(k, t, eta) copy_model = model.copy() assert copy_model._eta == model._eta assert isinstance(copy_model._kernel, model._kernel.__class__) assert copy_model._kernel.param == model._kernel.param assert isinstance(copy_model._tail, model._tail.__class__) assert np.all( copy_model._tail.params == model._tail.params)