def _calc_error(scale, weights, error_input, add_mu=False, add_mu_small=False):
error = utils.l2norm_weighted(error_input, scale, weights)
if add_mu:
error = utils.l2norm(error, MU_ERROR)
if add_mu_small:
error = utils.l2norm(error, MU_ERROR_SMALL)
return error
def _calc_error(
scale: float,
weights: tuple,
error_input: tuple,
add_mu: bool = False,
add_mu_small: bool = False,
) -> ma.MaskedArray:
error = utils.l2norm_weighted(error_input, scale, weights)
if add_mu is True:
error = utils.l2norm(error, MU_ERROR)
if add_mu_small is True:
error = utils.l2norm(error, MU_ERROR_SMALL)
return error
def test_l2_norm_weighted():
x = (2, 3)
weights = (1, 2)
scale = 10
assert_array_almost_equal(utils.l2norm_weighted(x, scale, weights),
10 * np.sqrt([40]))
def _calc_bias(scale, weights):
return utils.l2norm_weighted(bias_input, scale, weights)
def _calc_n_bias():
z_bias = bias_input[0]
dia_bias = db2lin(results['Do_bias'])
return utils.l2norm_weighted((z_bias, dia_bias), 1, (1, 6))
def test_calc_error():
from cloudnetpy.utils import l2norm_weighted
expected = l2norm_weighted(ERROR_INPUT, 1, 1)
testing.assert_almost_equal(de._calc_error(1, 1, ERROR_INPUT), expected)
def _calc_bias(scale: float, weights: tuple) -> ma.MaskedArray:
return utils.l2norm_weighted(bias_input, scale, weights)
def _calc_n_bias() -> ma.MaskedArray:
z_bias = bias_input[0]
dia_bias = db2lin(results["Do_bias"])
return utils.l2norm_weighted((z_bias, dia_bias), 1, (1, 6))
def test_l2_norm_weighted(data, scale, weights, result):
assert_array_almost_equal(utils.l2norm_weighted(data, scale, weights),
result)
