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(self):
np_inv_power = wpe.get_power_inverse(self.Y)
np_filter_matrix = wpe.get_filter_matrix_conj_v5(
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_matrix, tf_vector = tf_wpe.get_correlations_for_single_frequency(
tf_signal, tf_inverse_power, self.K, self.delay)
tf_filter = tf_wpe.get_filter_matrix_conj(tf_signal, tf_matrix,
tf_vector, self.K,
self.delay)
tf_filter_matrix, tf_inv_power_2 = sess.run(
[tf_filter, tf_inverse_power], {tf_signal: self.Y})
np.testing.assert_allclose(np_inv_power, tf_inv_power_2)
np.testing.assert_allclose(np_filter_matrix, tf_filter_matrix)
def test_filter_operation(self):
np_inv_power = wpe.get_power_inverse(self.Y)
np_filter_matrix = wpe.get_filter_matrix_conj_v5(
self.Y, np_inv_power, self.K, self.delay)
np_filter_op = wpe.perform_filter_operation_v4(self.Y,
np_filter_matrix,
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_matrix, tf_vector = tf_wpe.get_correlations_narrow(
tf_signal, tf_inverse_power, self.K, self.delay)
tf_filter = tf_wpe.get_filter_matrix_conj(tf_signal,
tf_inverse_power,
tf_matrix, tf_vector,
self.K, self.delay)
tf_filter_op = tf_wpe.perform_filter_operation(
tf_signal, tf_filter, self.K, self.delay)
tf_filter_op = sess.run(tf_filter_op, {tf_signal: self.Y})
np.testing.assert_allclose(np_filter_op, tf_filter_op)
