Esempio n. 1
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def test_norms():
    # matrix norms
    A = matrix([[1, -2], [-3, -1], [2, 1]])
    assert mnorm_1(A) == 6
    assert mnorm_oo(A) == 4
    assert mnorm_F(A) == sqrt(20)
    # vector norms
    x = [1, -2, 7, -12]
    assert norm_p(x, 1) == 22
    assert round(norm_p(x, 2), 10) == 14.0712472795
    assert round(norm_p(x, 10), 10) == 12.0054633727
    assert norm_p(x, inf) == 12
Esempio n. 2
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def test_norms():
    # matrix norms
    A = matrix([[1, -2], [-3, -1], [2, 1]])
    assert mnorm_1(A) == 6
    assert mnorm_oo(A) == 4
    assert mnorm_F(A) == sqrt(20)
    # vector norms
    x = [1, -2, 7, -12]
    assert norm_p(x, 1) == 22
    assert round(norm_p(x, 2), 10) == 14.0712472795
    assert round(norm_p(x, 10), 10) == 12.0054633727
    assert norm_p(x, inf) == 12
Esempio n. 3
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def test_inverse():
    for A in [A1, A2, A5]:
        inv = inverse(A)
        assert mnorm_1(A*inv - eye(A.rows)) < 1.e-14
Esempio n. 4
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def test_precision():
    A = randmatrix(10, 10)
    assert mnorm_1(inverse(inverse(A)) - A) < 1.e-45
Esempio n. 5
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def test_factorization():
    A = randmatrix(5)
    P, L, U = lu(A)
    assert mnorm_1(P*A - L*U) < 1.e-15
Esempio n. 6
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def test_inverse():
    for A in [A1, A2, A5]:
        inv = inverse(A)
        assert mnorm_1(A * inv - eye(A.rows)) < 1.e-14
Esempio n. 7
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def test_precision():
    A = randmatrix(10, 10)
    assert mnorm_1(inverse(inverse(A)) - A) < 1.e-45
Esempio n. 8
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def test_factorization():
    A = randmatrix(5)
    P, L, U = lu(A)
    assert mnorm_1(P * A - L * U) < 1.e-15