def test_from_data_qr(self):
t = np.linspace(0, 1, 5)
x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t)
basis = BSpline((0, 1), n_basis=5)
np.testing.assert_array_almost_equal(
FDataBasis.from_data(x, t, basis,
method='qr').coefficients.round(2),
np.array([[1., 2.78, -3., -0.78, 1.]]))
def test_from_data_qr(self):
t = np.linspace(0, 1, 5)
x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t)
basis = BSpline((0, 1), nbasis=5)
np.testing.assert_array_almost_equal(
FDataBasis.from_data(x, t, basis, smoothness_parameter=10,
penalty_degree=2, method='qr'
).coefficients.round(2),
np.array([[0.60, 0.47, 0.20, -0.07, -0.20]])
)
def test_monomial_smoothing(self):
# It does not have much sense to apply smoothing in this basic case
# where the fit is very good but its just for testing purposes
t = np.linspace(0, 1, 5)
x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t)
basis = Monomial(nbasis=4)
fd = FDataBasis.from_data(x, t, basis,
penalty_degree=2,
smoothness_parameter=1)
# These results where extracted from the R package fda
np.testing.assert_array_almost_equal(
fd.coefficients.round(2), np.array([[0.61, -0.88, 0.06, 0.02]]))
