def test_calculate_or_validate_base_steps_wrong_shape():
base_steps = np.array([0.01, 0.01, 0.01])
min_steps = np.full(3, 1e-8)
with pytest.raises(ValueError):
_calculate_or_validate_base_steps(
base_steps, np.ones(2), "first_derivative", min_steps, scaling_factor=1
)
def test_calculate_or_validate_base_steps_invalid_too_small():
base_steps = np.array([1e-10, 0.01, 0.01])
min_steps = np.full(3, 1e-8)
with pytest.raises(ValueError):
_calculate_or_validate_base_steps(
base_steps, np.ones(3), "first_derivative", min_steps, scaling_factor=1
)
def test_scalars_as_base_steps():
steps_scalar = _calculate_or_validate_base_steps(
0.1, np.ones(3), "first_derivative", None, scaling_factor=1
)
steps_array = _calculate_or_validate_base_steps(
np.full(3, 0.1), np.ones(3), "first_derivative", None, scaling_factor=1
)
aaae(steps_scalar, steps_array)
def test_calculate_or_validate_base_steps_hessian():
x = np.array([0.05, 1, -5])
expected = np.array([0.1, 1, 5]) * np.finfo(float).eps ** (1 / 3)
calculated = _calculate_or_validate_base_steps(
None, x, "second_derivative", 0, scaling_factor=1.0
)
aaae(calculated, expected, decimal=12)
def test_calculate_or_validate_base_steps_jacobian_with_scaling_factor():
x = np.array([0.05, 1, -5])
expected = np.array([0.1, 1, 5]) * np.sqrt(np.finfo(float).eps) * 2
calculated = _calculate_or_validate_base_steps(
None, x, "first_derivative", 0, scaling_factor=2.0
)
aaae(calculated, expected, decimal=12)
def test_calculate_or_validate_base_steps_binding_min_step():
x = np.array([0.05, 1, -5])
expected = np.array([0.1, 1, 5]) * np.sqrt(np.finfo(float).eps)
expected[0] = 1e-8
calculated = _calculate_or_validate_base_steps(
None, x, "first_derivative", 1e-8, scaling_factor=1.0
)
aaae(calculated, expected, decimal=12)
