an.tsouchlos/ba-thesis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 1 Wiki Activity

Added unit tests for the calculation of the A nd B sets; Fixed broken unit tests

Browse Source
This commit is contained in:
Andreas Tsouchlos 2022-11-07 14:39:09 +01:00
parent d129a222bc
commit cbb6036beb
2 changed files with 47 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

46
sw/test/test_proximal.py
View File

@ -14,8 +14,12 @@ class CheckParityTestCase(unittest.TestCase):
H = np.array([[1, 0, 1, 0, 1, 0, 1], H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1], [0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]]) [0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
decoder = proximal.ProximalDecoder(H) decoder = proximal.ProximalDecoder(H, R)
d1 = np.array([0, 1, 0, 1]) d1 = np.array([0, 1, 0, 1])
c1 = np.dot(np.transpose(G), d1) % 2 c1 = np.dot(np.transpose(G), d1) % 2
@ -37,19 +41,53 @@ class CheckParityTestCase(unittest.TestCase):
class GradientTestCase(unittest.TestCase): class GradientTestCase(unittest.TestCase):
"""Test case for the calculation of the gradient of the code-constraint-polynomial.""" """Test case for the calculation of the gradient of the code-constraint-polynomial."""
def test_grad_h(self): def test_grad_h(self):
"""Test the gradient of the code-constraint polynomial."""
H = np.array([[1, 0, 0], H = np.array([[1, 0, 0],
[0, 1, 0]]) [0, 1, 0]])
x = np.array([1, 2, 2]) x = np.array([1, 2, 2])
R = np.array([0])
decoder = proximal.ProximalDecoder(H) decoder = proximal.ProximalDecoder(H, R)
grad = decoder._grad_h(x) grad = decoder._grad_h(x)
expected = 4 * (x**2 - 1)*x + 2 / x * np.array([0, 2, 0]) expected = 4 * (x**2 - 1)*x + 2 / x * np.array([0, 2, 0])
print(f"expected: {expected}")
self.assertEqual(np.array_equal(grad, expected), True) self.assertEqual(np.array_equal(grad, expected), True)
def test_gen_A_B(self):
"""Test the generation of the A and B sets used for the gradient calculation."""
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
decoder = proximal.ProximalDecoder(H, R)
expected_A = [np.array([0, 2, 4, 6]),
np.array([1, 2, 5, 6]),
np.array([3, 4, 5, 6])]
expected_B = [np.array([0]),
np.array([1]),
np.array([0, 1]),
np.array([2]),
np.array([0, 2]),
np.array([1, 2]),
np.array([0, 1, 2])]
for A_i, expected_A_i in zip(decoder._A, expected_A):
self.assertEqual(np.array_equal(A_i, expected_A_i), True)
for B_k, expected_B_k in zip(decoder._B, expected_B):
self.assertEqual(np.array_equal(B_k, expected_B_k), True)
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()

6
sw/test/test_soft_decision.py
View File

@ -14,8 +14,12 @@ class CodewordGenerationTestCase(unittest.TestCase):
H = np.array([[1, 0, 1, 0, 1, 0, 1], H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1], [0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]]) [0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
decoder = naive_soft_decision.SoftDecisionDecoder(G, H) decoder = naive_soft_decision.SoftDecisionDecoder(G, H, R)
expected_datawords = np.array([[0, 0, 0, 0], expected_datawords = np.array([[0, 0, 0, 0],
[0, 0, 0, 1], [0, 0, 0, 1],