def test_calculating_the_chordal_distance(self):
expected_chord_dist = 0.473867859572
# Test calcChordalDistance
self.assertAlmostEqual(metrics.calc_chordal_distance(self.A, self.B),
expected_chord_dist)
# Test calcChordalDistance2
self.assertAlmostEqual(metrics.calc_chordal_distance_2(self.A, self.B),
expected_chord_dist)
# Test
principal_angles = metrics.calc_principal_angles(self.A, self.B)
self.assertAlmostEqual(
metrics.calc_chordal_distance_from_principal_angles(
principal_angles), expected_chord_dist)
# xxxxxxxxxx Now let's test with complex values xxxxxxxxxxxxxxxxxxx
A = randn_c(3, 2)
B = randn_c(3, 2)
principal_angles2 = metrics.calc_principal_angles(A, B)
dist1 = metrics.calc_chordal_distance(A, B)
dist2 = metrics.calc_chordal_distance_2(A, B)
dist3 = metrics.calc_chordal_distance_from_principal_angles(
principal_angles2)
self.assertAlmostEqual(dist1, dist2)
self.assertAlmostEqual(dist3, dist2)
def test_calculating_the_chordal_distance(self):
expected_chord_dist = np.array([0.473867859572])
# Test calcChordalDistance
np.testing.assert_array_almost_equal(metrics.calc_chordal_distance(self.A, self.B), expected_chord_dist)
# Test calcChordalDistance2
np.testing.assert_array_almost_equal(metrics.calc_chordal_distance_2(self.A, self.B), expected_chord_dist)
# Test
principal_angles = metrics.calc_principal_angles(self.A, self.B)
np.testing.assert_array_almost_equal(
metrics.calc_chordal_distance_from_principal_angles(principal_angles), expected_chord_dist
)
# xxxxxxxxxx Now let's test with complex values xxxxxxxxxxxxxxxxxxx
A = randn_c(3, 2)
B = randn_c(3, 2)
principal_angles2 = metrics.calc_principal_angles(A, B)
dist1 = metrics.calc_chordal_distance(A, B)
dist2 = metrics.calc_chordal_distance_2(A, B)
dist3 = metrics.calc_chordal_distance_from_principal_angles(principal_angles2)
self.assertAlmostEqual(dist1, dist2)
self.assertAlmostEqual(dist3, dist2)
def calc_min_chordal_dist(codebook):
"""
Calculates the minimum chordal distance in the Codebook.
Note that the codebook is a 3-dimensional complex numpy array with
dimension `K x Nt x Ns` (K is the number of precoders in the codebook,
Nt and Ns are the number of rows and columns, respectively, of each
precoder.
Parameters
----------
codebook : np.ndarray
The codebook for which the minimum chordal distance should be
calculated. This is a 3-dimensional (K x Nt x Ns) complex numpy
array.
Returns
-------
(float, np.ndarray)
The tuple (min_dist, principal_angles).
"""
K = codebook.shape[0]
#Se pegar todas as combinacoes possiveis (sem repeticao e sem ligar para
# ordem) vc tera (ncols**2-ncols)/2 possibilidades. Isso Equivale a pegar
# uma matriz matrix.ncols() x matrix.ncols() e contar todos os elementos
# abaixo (ou acima) da diagonal.
num_possibilidades = (K**2 - K) / 2
dists = np.empty(num_possibilidades)
principal_angles = []
index = 0
# for comb in calc_all_comb_indexes(K):
for comb in combinations(range(0, K), 2):
# comb is a tuple with two elements
pa = calc_principal_angles(codebook[comb[0]], codebook[comb[1]])
principal_angles.append(pa)
dists[index] = calc_chordal_distance_from_principal_angles(pa)
index += 1
min_index = dists.argmin() # Index of the minimum distance (in the
# flattened array)
min_dist = dists.flatten()[min_index] # Same as dists.min()
":type: float"
principal_angles = np.array(principal_angles[min_index])
return min_dist, principal_angles
def calc_min_chordal_dist(codebook):
"""Claculates the minimum chordal distance in the Codebook.
Note that the codebook is a 3-dimensional complex numpy array with
dimension `K x Nt x Ns` (K is the number of precoders in the codebook,
Nt and Ns are the number of rows and columns, respectively, of each
precoder.
Parameters
----------
codebook : A 3-dimensional (K x Nt x Ns) complex numpy array
The codebook for which the monimum chordal distance should be
calculated.
"""
K = codebook.shape[0]
#Se pegar todas as combinacoes possiveis (sem repeticao e sem ligar para
# ordem) vc tera (ncols**2-ncols)/2 possibilidades. Isso Equivale a pegar
# uma matriz matrix.ncols() x matrix.ncols() e contar todos os elementos
# abaixo (ou acima) da diagonal.
num_possibilidades = (K ** 2 - K) / 2
dists = np.empty(num_possibilidades)
principal_angles = []
index = 0
# for comb in calc_all_comb_indexes(K):
for comb in combinations(range(0, K), 2):
#comb is a tuple with two elements
pa = calc_principal_angles(codebook[comb[0]], codebook[comb[1]])
principal_angles.append(pa)
dists[index] = calc_chordal_distance_from_principal_angles(pa)
index += 1
min_index = dists.argmin() # Index of the minimum distance (in the
# flattened array)
min_dist = dists.flatten()[min_index] # Same as dists.min()
principal_angles = np.array(principal_angles[min_index])
return min_dist, principal_angles
