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)