def test_correlations_narrow_v1_vs_v6(self):
inverse_power = wpe.get_power_inverse(self.Y)
R_desired, r_desired = wpe.get_correlations_narrow(
self.Y, inverse_power, self.K, self.delay)
s = [Ellipsis, slice(self.delay + self.K - 1, None)]
Y_tilde = wpe.build_y_tilde(self.Y, self.K, self.delay)
R_actual, r_actual = wpe.get_correlations_v6(self.Y[s], Y_tilde[s],
inverse_power[s])
tc.assert_allclose(R_actual.conj(), R_desired)
tc.assert_allclose(r_actual.conj(),
np.swapaxes(r_desired, 1,
2).reshape(-1, r_desired.shape[-1]),
rtol=1e-5,
atol=1e-5)
def test_filter_matrix_conj_v1_vs_v7(self):
inverse_power = wpe.get_power_inverse(self.Y)
correlation_matrix, correlation_vector = wpe.get_correlations(
self.Y, inverse_power, self.K, self.delay)
desired = wpe.get_filter_matrix_conj(correlation_matrix,
correlation_vector, self.K,
self.D)
s = [Ellipsis, slice(self.delay + self.K - 1, None)]
Y_tilde = wpe.build_y_tilde(self.Y, self.K, self.delay)
actual = wpe.get_filter_matrix_v7(
self.Y,
Y_tilde=Y_tilde,
inverse_power=inverse_power,
)
tc.assert_allclose(actual.conj(),
np.swapaxes(desired, 1,
2).reshape(-1, desired.shape[-1]),
atol=1e-10)