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102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
import unittest
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import numpy as np # For testing ONLY
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from richardson_abstract import modified_richardson
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class TestModifiedRichardson(unittest.TestCase):
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def setUp(self):
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self.A_2x2 = np.random.rand(2, 2).tolist()
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self.b_2x2 = np.random.rand(2).tolist()
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self.x0_2x2 = np.random.rand(2).tolist()
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self.alpha_2x2 = 0.1
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self.A_3x3 = np.random.rand(3, 3).tolist()
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self.b_3x3 = np.random.rand(3).tolist()
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self.x0_3x3 = np.random.rand(3).tolist()
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self.alpha_3x3 = 0.15
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def test_convergence_2x2(self):
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print("Testing 2x2 Convergence")
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print(f"A: {self.A_2x2}")
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print(f"b: {self.b_2x2}")
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print(f"x0: {self.x0_2x2}")
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richardson = modified_richardson(self.A_2x2, self.b_2x2, self.x0_2x2, self.alpha_2x2)
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result = richardson()
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# result = modified_richardson(self.A_2x2, self.b_2x2, self.x0_2x2, self.alpha_2x2)
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expected_solution = np.linalg.solve(np.array(self.A_2x2), np.array(self.b_2x2))
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print(f"Result: {result}")
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print(f"Expected: {expected_solution}")
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for r, e in zip(result, expected_solution):
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self.assertAlmostEqual(r, e, places=4)
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def test_convergence_3x3(self):
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print("Testing 3x3 Convergence")
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print(f"A: {self.A_3x3}")
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print(f"b: {self.b_3x3}")
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print(f"x0: {self.x0_3x3}")
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richardson = modified_richardson(self.A_3x3, self.b_3x3, self.x0_3x3, self.alpha_3x3)
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result = richardson()
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# result = modified_richardson(self.A_3x3, self.b_3x3, self.x0_3x3, self.alpha_3x3)
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expected_solution = np.linalg.solve(np.array(self.A_3x3), np.array(self.b_3x3))
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print(f"Result: {result}")
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print(f"Expected: {expected_solution}")
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for r, e in zip(result, expected_solution):
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self.assertAlmostEqual(r, e, places=2)
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def test_invalid_alpha(self):
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richardson = modified_richardson(self.A_2x2, self.b_2x2, self.x0_2x2, alpha=-0.1)
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with self.assertRaises(ValueError):
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richardson()
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# modified_richardson(self.A_2x2, self.b_2x2, self.x0_2x2, alpha=-0.1)
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def test_non_square_matrix(self):
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A = [[1, 2, 3], [4, 5, 6]] # Not a square matrix
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b = [7, 8]
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richardson = modified_richardson(A, b, self.x0_2x2, self.alpha_2x2)
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with self.assertRaises(ValueError):
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richardson()
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# modified_richardson(A, b, self.x0_2x2, self.alpha_2x2)
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def test_dimension_mismatch(self):
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b = [1, 2, 3] # Length mismatch with A_2x2
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richardson = modified_richardson(self.A_2x2, b, self.x0_2x2, self.alpha_2x2)
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with self.assertRaises(ValueError):
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richardson()
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# modified_richardson(self.A_2x2, b, self.x0_2x2, self.alpha_2x2)
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def test_zero_matrix(self):
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A = [[0, 0], [0, 0]]
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b = [0, 0]
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richardson = modified_richardson(A, b, self.x0_2x2, self.alpha_2x2)
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result = richardson()
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# result = modified_richardson(A, b, self.x0_2x2, self.alpha_2x2)
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# Solution should be [0, 0]
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print("Testing Zero Matrix")
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print(f"A: {A}")
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print(f"b: {b}")
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print(f"Result: {result}")
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self.assertEqual(result, [0, 0])
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def test_large_system(self):
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# A large test case designed to take a long time to converge
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size = 10 #1000
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A = np.random.rand(size, size) + size * np.eye(size) # Large diagonally dominant matrix
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b = np.random.rand(size)
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x0 = np.random.rand(size)
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alpha = 0.01 / size # Small alpha to ensure convergence
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print("Testing Large System")
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#print(f"A: {A}")
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#print(f"b: {b}")
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#print(f"x0: {x0}")
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richardson = modified_richardson(A.tolist(), b.tolist(), x0.tolist(), alpha, tol=1e-6, max_iter=500000)
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result = richardson()
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# result = modified_richardson(A.tolist(), b.tolist(), x0.tolist(), alpha, tol=1e-6, max_iter=500000)
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expected_solution = np.linalg.solve(A, b)
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print(f"Result: {result}")
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print(f"Expected: {expected_solution}")
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for r, e in zip(result, expected_solution):
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self.assertAlmostEqual(r, e, places=2)
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if __name__ == '__main__':
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unittest.main()
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