Ejemplo n.º 1
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def test_multiplication_by_variable():
    x = Reverse(1)
    y = Reverse(2)
    f = x * y
    f.gradient_value = 1.0
    assert f.value == approx(2)
    assert x.get_gradient() == approx(2)
    assert y.get_gradient() == approx(1)
Ejemplo n.º 2
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def test_multiplication():
    x = Reverse(1)
    f = 4 * x
    f.gradient_value = 1.0
    assert f.value == approx(4)
    assert x.get_gradient() == approx(4)

    x = Reverse(1)
    f = x * 4
    f.gradient_value = 1.0
    assert f.value == approx(4)
    assert x.get_gradient() == approx(4)
Ejemplo n.º 3
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def test_division():
    x = Reverse(2)
    f = 4 / x
    f.gradient_value = 1.0
    assert f.value == approx(2)
    assert x.get_gradient() == approx(-1)

    x = Reverse(5)
    f = x / 4
    f.gradient_value = 1.0
    assert f.value == approx(5 / 4)
    assert x.get_gradient() == approx(1 / 4)
Ejemplo n.º 4
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def test_subtraction():
    x = Reverse(5)
    f = 4 * x**2.3 - 3 / x
    f.gradient_value = 1.0
    assert f.value == approx(4 * 5**2.3 - 3 / 5)
    assert x.get_gradient() == approx(4 * 2.3 * 5**1.3 + 3 / (5**2))

    x = Reverse(2)
    f = 4 - x
    f.gradient_value = 1.0
    assert f.value == approx(2)
    assert x.get_gradient() == approx(-1)
Ejemplo n.º 5
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def test_sin():
    x = Reverse(5)
    f = sin(x) + sin(5)
    f.gradient_value = 1.0
    assert f.value == approx(np.sin(5) + np.sin(5))
    assert x.get_gradient() == approx(np.cos(5))

    # Nested case
    x = Reverse(5)
    f = sin(2 * (-x))
    f.gradient_value = 1.0
    assert f.value == approx(np.sin(-2 * 5))
    assert x.get_gradient() == approx(-2 * np.cos(-2 * 5))
Ejemplo n.º 6
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def test_addition():
    x = Reverse(3)
    y = Reverse(3)
    f = 4 * x + y * -6
    f.gradient_value = 1.0
    assert f.value == approx(4 * 3 + 3 * -6)
    assert x.get_gradient() == approx(4)
    assert y.get_gradient() == approx(-6)

    x = Reverse(3)
    f = 4 + x**2
    f.gradient_value = 1.0
    assert f.value == approx(4 + 3**2)
    assert x.get_gradient() == approx(2 * 3)
Ejemplo n.º 7
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def test_cos():
    x = Reverse(5)
    y = Reverse(5)
    f = sin(x) * cos(y) + cos(3)
    f.gradient_value = 1.0
    assert f.value == approx(np.sin(5) * np.cos(5) + np.cos(3))
    assert y.get_gradient() == approx((-np.sin(5)) * np.sin(5))

    # Nested case
    x = Reverse(4)
    y = Reverse(3)
    f = sin(cos(x * y))
    f.gradient_value = 1.0
    assert f.value == approx(np.sin(np.cos(3 * 4)))
    assert x.get_gradient() == approx(-3 * np.sin(12) * np.cos(np.cos(12)))
Ejemplo n.º 8
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def test_pos():
    x = Reverse(3)

    f = +(x * x)
    f.gradient_value = 1

    assert f.value == approx(9)
    assert x.get_gradient() == approx(2 * 3)
Ejemplo n.º 9
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def test_arc_trig_funcs():
    x = Reverse(0.5)
    f = arcsin(x) + arcsin(0.5)
    f.gradient_value = 1.0
    assert f.value == approx(np.arcsin(0.5) + np.arcsin(0.5))
    assert x.get_gradient() == 1 / np.sqrt(1 - 0.5**2)

    x = Reverse(0.5)
    f = arccos(x) + arccos(1)
    f.gradient_value = 1.0
    assert f.value == approx(np.arccos(0.5) + np.arccos(1))
    assert x.get_gradient() == -1 / np.sqrt(1 - 0.5**2)

    x = Reverse(0.2)
    f = arctan(x) + arctan(0.1)
    f.gradient_value = 1.0
    assert f.value == approx(np.arctan(0.2) + np.arctan(0.1))
    assert x.get_gradient() == 1 / (1 + 0.2**2)
Ejemplo n.º 10
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def test_power():
    # Variable root
    x = Reverse(3)
    f = x**5
    f.gradient_value = 1.0
    assert f.value == approx(3**5)
    assert x.get_gradient() == approx(5 * 3**4)

    x = Reverse(2)
    f = x**2.5
    f.gradient_value = 1.0
    assert f.value == approx(2**2.5)
    assert x.get_gradient() == approx(2.5 * 2**1.5)

    # Numeric root
    x = Reverse(3)
    f = 5**x
    f.gradient_value = 1.0
    assert f.value == approx(5**3)
    assert x.get_gradient() == approx(5**3 * np.log(5))
Ejemplo n.º 11
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def test_log():
    x = Reverse(4)
    f = log(x) + log(5) + ln(5)
    f.gradient_value = 1.0
    assert f.value == approx(np.log(4) + 2 * np.log(5))
    assert x.get_gradient() == approx(1 / 4)

    # Harder case
    x = Reverse(3)
    y = Reverse(7)
    f = log(x**y)
    f.gradient_value = 1.0
    assert f.value == approx(np.log(3**7))
    assert x.get_gradient() == approx((7 * (3**6)) * 1 / (3**7))

    # log2 and log10
    x = Reverse(3)
    f = log2(x) + log10(x)
    f.gradient_value = 1.0
    assert f.value == approx(np.log2(3) + np.log10(3))
    assert x.get_gradient() == approx(1 / (3 * np.log(10)) + 1 /
                                      (3 * np.log(2)))
Ejemplo n.º 12
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def test_other_trig_funcs():
    x = Reverse(2)
    f = sinh(x) + sinh(2)
    f.gradient_value = 1.0
    assert f.value == approx(2 * np.sinh(2))
    assert x.get_gradient() == approx(np.cosh(2))

    x = Reverse(2)
    f = cosh(x) + cosh(2)
    f.gradient_value = 1.0
    assert f.value == approx(2 * np.cosh(2))
    assert x.get_gradient() == approx(np.sinh(2))

    x = Reverse(2)
    f = tanh(x) + tanh(2)
    f.gradient_value = 1.0
    assert f.value == approx(2 * np.tanh(2))
    assert x.get_gradient() == approx(
        (np.cosh(2)**2 - np.sinh(2)**2) / np.cosh(2)**2)

    x = Reverse(2)
    f = sech(x) + sech(2)
    f.gradient_value = 1.0
    assert f.value == approx(2 * 1 / np.cosh(2))
    assert x.get_gradient() == approx(
        (-1 / np.cosh(2)) * (np.sinh(2) / np.cosh(2)))

    x = Reverse(2)
    f = csch(x) + csch(2)
    f.gradient_value = 1.0
    assert f.value == approx(2 * 1 / np.sinh(2))
    assert x.get_gradient() == approx(
        (-1 / np.sinh(2)) * (np.cosh(2) / np.sinh(2)))

    x = Reverse(2)
    f = coth(x) + coth(2)
    f.gradient_value = 1.0
    assert f.value == approx(2 * (np.cosh(2) / np.sinh(2)))
    assert x.get_gradient() == approx(-1 / (np.sinh(2)**2))
Ejemplo n.º 13
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def test_cosec():
    x = Reverse(-1)
    f = csc(x)
    f.gradient_value = 1.0
    assert f.value == approx(1 / np.sin(-1))
    assert x.get_gradient() == approx(-1 / np.tan(-1) / np.sin(-1))
Ejemplo n.º 14
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def test_weirdness():
    x = Reverse(2)
    f = x**x
    f.gradient_value = 1.0
    assert f.value == approx(4)
    assert x.get_gradient() == approx(4 + np.log(16))
Ejemplo n.º 15
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def test_cot():
    x = Reverse(5)
    f = cot(x)
    f.gradient_value = 1.0
    assert f.value == approx(1 / np.tan(5))
    assert x.get_gradient() == approx(-1 / np.sin(5)**2)
Ejemplo n.º 16
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def test_single_var():
    x = Reverse(1) + 2
    f = x
    f.gradient_value = 1.0
    assert f.value == approx(3)
    assert x.get_gradient() == approx(1)
Ejemplo n.º 17
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def test_sec():
    x = Reverse(2)
    f = sec(x)
    f.gradient_value = 1.0
    assert f.value == approx(1 / np.cos(2))
    assert x.get_gradient() == approx(np.tan(2) / np.cos(2))
Ejemplo n.º 18
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def test_sqrt():
    x = Reverse(2)
    f = sqrt(x)
    f.gradient_value = 1.0
    assert f.value == approx(2**0.5)
    assert x.get_gradient() == approx(0.5 * (2**(-0.5)))
Ejemplo n.º 19
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def test_exp():
    x = Reverse(0)
    f = exp(x) + exp(0)
    f.gradient_value = 1.0
    assert f.value == approx(2)
    assert x.get_gradient() == approx(1)