def test_to_dict_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)
output_dict = model.to_dict()
assert "X" in output_dict
assert "y" in output_dict
assert "eta" in output_dict
assert "kernel" in output_dict
assert "tail" in output_dict
assert "lambda" in output_dict
assert "LU" in output_dict
assert "piv" in output_dict
assert "loo" in output_dict
assert np.allclose(np.array(output_dict["X"]), model._X)
assert np.allclose(np.array(output_dict["y"]), model._y)
assert output_dict["eta"] == model._eta
assert output_dict["kernel"] == model._kernel.to_dict()
assert output_dict["tail"] == model._tail.to_dict()
assert np.allclose(np.array(output_dict["lambda"]),
model._lambda)
assert np.allclose(np.array(output_dict["LU"]), model._LU)
assert np.allclose(np.array(output_dict["piv"]),
model._piv)
assert np.allclose(np.array(output_dict["loo"]),
model.loo_residuals)
def test_to_dict_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)
output_dict = model.to_dict()
assert "eta" in output_dict
assert "kernel" in output_dict
assert "tail" in output_dict
assert output_dict["eta"] == model._eta
assert output_dict["kernel"] == model._kernel.to_dict()
assert output_dict["tail"] == model._tail.to_dict()