def test_A(self):
"""Test if A_est is elementwise almost equal A."""
W, A_est, d = calculate_spoc(self.epo)
# A and A_est can have a different scaling, after normalizing
# and correcting for sign, the first pattern should be almost
# equal the source pattern
idx = np.argmax(np.abs(A_est[:, 0]))
A_est[:, 0] /= A_est[idx, 0]
idx = np.argmax(np.abs(self.A[:, 0]))
self.A[:, 0] /= self.A[idx, 0]
# check elementwise if A[:, 0] almost A_est[:, 0]
epsilon = 0.01
diff = self.A[:, 0] - A_est[:, 0]
diff = np.abs(diff)
diff = np.sum(diff) / self.A.shape[0]
self.assertTrue(diff < epsilon)
def test_A(self):
"""Test if A_est is elementwise almost equal A."""
W, A_est, d = calculate_spoc(self.epo)
# A and A_est can have a different scaling, after normalizing
# and correcting for sign, the first pattern should be almost
# equal the source pattern
idx = np.argmax(np.abs(A_est[:, 0]))
A_est[:, 0] /= A_est[idx, 0]
idx = np.argmax(np.abs(self.A[:, 0]))
self.A[:, 0] /= self.A[idx, 0]
# check elementwise if A[:, 0] almost A_est[:, 0]
epsilon = 0.01
diff = self.A[:, 0] - A_est[:, 0]
diff = np.abs(diff)
diff = np.sum(diff) / self.A.shape[0]
self.assertTrue(diff < epsilon)
def test_s(self):
"""Test if s_est is elementwise almost equal s."""
W, A_est, d = calculate_spoc(self.epo)
# applying the filter to X gives us s_est which should be almost
# equal s
s_est = np.empty(self.s.shape[:2])
for i in range(self.EPOCHS):
s_est[i] = np.dot(self.X[i], W[:, 0])
s_true = self.s[..., 0]
epsilon = 0.001
# correct for scale
s_true /= s_true.std()
s_est /= s_est.std()
# correct for sign
s_true = np.abs(s_true)
s_est = np.abs(s_est)
diff = np.sum(s_true - s_est) / (self.s.shape[0] * self.s.shape[1])
self.assertTrue(diff < epsilon)
def test_s(self):
"""Test if s_est is elementwise almost equal s."""
W, A_est, d = calculate_spoc(self.epo)
# applying the filter to X gives us s_est which should be almost
# equal s
s_est = np.empty(self.s.shape[:2])
for i in range(self.EPOCHS):
s_est[i] = np.dot(self.X[i], W[:, 0])
s_true = self.s[..., 0]
epsilon = 0.001
# correct for scale
s_true /= s_true.std()
s_est /= s_est.std()
# correct for sign
s_true = np.abs(s_true)
s_est = np.abs(s_est)
diff = np.sum(s_true - s_est) / (self.s.shape[0] * self.s.shape[1])
self.assertTrue(diff < epsilon)
def test_calculate_signed_r_square_copy(self):
"""caluclate_r_square must not modify argument."""
cpy = self.epo.copy()
calculate_spoc(self.epo)
self.assertEqual(self.epo, cpy)
def test_d(self):
"""Test if the list of lambdas is reverse-sorted and the first one > 0."""
W, A_est, d = calculate_spoc(self.epo)
self.assertTrue(d[0] > 0)
self.assertTrue(np.all(d == np.sort(d)[::-1]))
def test_d(self):
"""Test if the list of lambdas is reverse-sorted and the first one > 0."""
W, A_est, d = calculate_spoc(self.epo)
self.assertTrue(d[0] > 0)
self.assertTrue(np.all(d == np.sort(d)[::-1]))
def test_calculate_signed_r_square_copy(self):
"""caluclate_r_square must not modify argument."""
cpy = self.epo.copy()
calculate_spoc(self.epo)
self.assertEqual(self.epo, cpy)