def test_correlations_v1_vs_v2(self):
inverse_power = wpe.get_power_inverse(self.Y)
R_desired, r_desired = wpe.get_correlations(self.Y, inverse_power,
self.K, self.delay)
R_actual, r_actual = wpe.get_correlations_v2(self.Y, inverse_power,
self.K, self.delay)
tc.assert_allclose(R_actual, R_desired)
tc.assert_allclose(r_actual, r_desired)
def test_correlations_v1_vs_v2_toy_example(self):
K = 3
delay = 1
Y = np.asarray([[11, 12, 13, 14], [41, 22, 23, 24]], dtype=np.float32)
inverse_power = wpe.get_power_inverse(Y)
R_desired, r_desired = wpe.get_correlations(Y, inverse_power, K, delay)
R_actual, r_actual = wpe.get_correlations_v2(Y, inverse_power, K,
delay)
tc.assert_allclose(R_actual, R_desired)
tc.assert_allclose(r_actual, r_desired)
def test_filter_matrix_conj_v1_vs_v5(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)
actual = wpe.get_filter_matrix_conj_v5(self.Y, inverse_power, self.K,
self.delay)
tc.assert_allclose(actual, desired, atol=1e-10)
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)
