def test_train_2d(self):
N = 20
x = 2 * np.pi * np.random.random_sample((N, 2))
y = func_2d(x)
for kernel in kernels_dict.values():
k = kernel(30)
for tail in tails_dict.values():
t = tail()
if t.degree >= k.dmin:
model = RBF(k, t)
model.train(x, y, method="Bounded", bounds=[1e-5, 20])
assert model._kernel.param >= 1e-5
assert model._kernel.param <= 20
assert model._is_fitted()
def test_train_1d(self):
N = 8
x = np.linspace(0, 2 * np.pi, num=N)
y = func_1d(x)
for kernel in kernels_dict.values():
k = kernel(30)
for tail in tails_dict.values():
t = tail()
if t.degree >= k.dmin:
model = RBF(k, t)
model.train(x, y, method="Bounded", bounds=[1e-5, 20])
assert model._kernel.param >= 1e-5
assert model._kernel.param <= 20
assert model._is_fitted()