def test_fit_remove_add_trend(order, n_instances, n_timepoints):
coefs = np.random.normal(size=order + 1).reshape(-1, 1)
x = np.column_stack([
_generate_polynomial_series(n_timepoints, order, coefs=coefs)
for _ in range(n_instances)
]).T
# assert x.shape == (n_samples, n_obs)
# check shape of fitted coefficients
coefs = _fit_trend(x, order=order)
assert coefs.shape == (n_instances, order + 1)
def _compute_trend(y):
# Trend calculated through least squares regression.
coefs = _fit_trend(y.values.reshape(1, -1), order=1)
return coefs[0, 0] / 2