def test_norm_epsilon():
"""Test computation of espilon norm on TF coefficients."""
n_steps = 5
n_freqs = 4
Y = np.zeros(n_steps * n_freqs)
l1_ratio = 0.5
assert_allclose(norm_epsilon(Y, l1_ratio, n_steps), 0.)
Y[0] = 2.
assert_allclose(norm_epsilon(Y, l1_ratio, n_steps), np.max(Y))
l1_ratio = 1.
assert_allclose(norm_epsilon(Y, l1_ratio, n_steps), np.max(Y))
# dummy value without random:
Y = np.arange(n_steps * n_freqs).reshape(-1, )
l1_ratio = 0.
assert_allclose(
norm_epsilon(Y, l1_ratio, n_steps)**2,
stft_norm2(Y.reshape(-1, n_freqs, n_steps)))
def test_norm_epsilon():
"""Test computation of espilon norm on TF coefficients."""
tstep = np.array([2])
wsize = np.array([4])
n_times = 10
n_steps = np.ceil(n_times / tstep.astype(float)).astype(int)
n_freqs = wsize // 2 + 1
n_coefs = n_steps * n_freqs
phi = _Phi(wsize, tstep, n_coefs)
Y = np.zeros(n_steps * n_freqs)
l1_ratio = 0.5
assert_allclose(norm_epsilon(Y, l1_ratio, phi), 0.)
Y[0] = 2.
assert_allclose(norm_epsilon(Y, l1_ratio, phi), np.max(Y))
l1_ratio = 1.
assert_allclose(norm_epsilon(Y, l1_ratio, phi), np.max(Y))
# dummy value without random:
Y = np.arange(n_steps * n_freqs).reshape(-1, )
l1_ratio = 0.
assert_allclose(norm_epsilon(Y, l1_ratio, phi) ** 2,
stft_norm2(Y.reshape(-1, n_freqs[0], n_steps[0])))
def test_norm_epsilon():
"""Test computation of espilon norm on TF coefficients."""
tstep = np.array([2])
wsize = np.array([4])
n_times = 10
n_steps = np.ceil(n_times / tstep.astype(float)).astype(int)
n_freqs = wsize // 2 + 1
n_coefs = n_steps * n_freqs
phi = _Phi(wsize, tstep, n_coefs)
Y = np.zeros(n_steps * n_freqs)
l1_ratio = 0.5
assert_allclose(norm_epsilon(Y, l1_ratio, phi), 0.)
Y[0] = 2.
assert_allclose(norm_epsilon(Y, l1_ratio, phi), np.max(Y))
l1_ratio = 1.
assert_allclose(norm_epsilon(Y, l1_ratio, phi), np.max(Y))
# dummy value without random:
Y = np.arange(n_steps * n_freqs).reshape(-1, )
l1_ratio = 0.
assert_allclose(norm_epsilon(Y, l1_ratio, phi) ** 2,
stft_norm2(Y.reshape(-1, n_freqs[0], n_steps[0])))
def test_norm_epsilon():
"""Test computation of espilon norm on TF coefficients."""
tstep = np.array([2])
wsize = np.array([4])
n_times = 10
n_steps = np.ceil(n_times / tstep.astype(float)).astype(int)
n_freqs = wsize // 2 + 1
n_coefs = n_steps * n_freqs
phi = _Phi(wsize, tstep, n_coefs, n_times)
Y = np.zeros(n_steps * n_freqs)
l1_ratio = 0.03
assert_allclose(norm_epsilon(Y, l1_ratio, phi), 0.)
Y[0] = 2.
assert_allclose(norm_epsilon(Y, l1_ratio, phi), np.max(Y))
l1_ratio = 1.
assert_allclose(norm_epsilon(Y, l1_ratio, phi), np.max(Y))
# dummy value without random:
Y = np.arange(n_steps * n_freqs).reshape(-1, )
l1_ratio = 0.0
assert_allclose(
norm_epsilon(Y, l1_ratio, phi)**2,
stft_norm2(Y.reshape(-1, n_freqs[0], n_steps[0])))
l1_ratio = 0.03
# test that vanilla epsilon norm = weights equal to 1
w_time = np.ones(n_coefs[0])
Y = np.abs(np.random.randn(n_coefs[0]))
assert_allclose(norm_epsilon(Y, l1_ratio, phi),
norm_epsilon(Y, l1_ratio, phi, w_time=w_time))
# scaling w_time and w_space by the same amount should divide
# epsilon norm by the same amount
Y = np.arange(n_coefs) + 1
mult = 2.
assert_allclose(
norm_epsilon(Y, l1_ratio, phi, w_space=1, w_time=np.ones(n_coefs)) /
mult,
norm_epsilon(Y,
l1_ratio,
phi,
w_space=mult,
w_time=mult * np.ones(n_coefs)))