def test_correlations_narrow_v1_vs_v5(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)
R_actual, r_actual = wpe.get_correlations_narrow_v5(
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(self):
np_inv_power = wpe.get_power_inverse(self.Y)
np_corr = wpe.get_correlations_narrow(self.Y, np_inv_power, self.K,
self.delay)
with tf.Graph().as_default(), tf.Session() as sess:
tf_signal = tf.placeholder(tf.complex128, shape=[None, None])
tf_inverse_power = tf_wpe.get_power_inverse(tf_signal)
tf_res = tf_wpe.get_correlations_for_single_frequency(
tf_signal, tf_inverse_power, self.K, self.delay)
tf_corr = sess.run(tf_res, {tf_signal: self.Y})
np.testing.assert_allclose(np_corr[0], tf_corr[0])
np.testing.assert_allclose(np_corr[1], tf_corr[1])
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)
