def _input_check(t: Tuple[ndarray, ndarray]) -> None:
direct1, direct2 = t
assert len(direct1.shape) == 1
assert len(direct2.shape) == 1
assert direct1.shape == direct2.shape
assertNoInfNaN(direct1)
assertNoInfNaN(direct2)
def __init__(self, *, g: ndarray, H: ndarray) -> None:
(n, ) = g.shape
assert H.shape == (n, n)
assert numpy.all(H.T == H)
assertNoInfNaN(g)
assertNoInfNaN(H)
self.__g = g.copy()
self.__H = H.copy()
def make_status(
x: ndarray,
angle: float,
flag: Flag,
delta: float,
qpval: QuadEvaluator,
) -> Status:
assertNoInfNaN(x)
size = norm_l2(x)
assert size / delta < 1.0 + 1e-6
return Status(x=x, fval=qpval(x), angle=angle, flag=flag, size=size)
def constraint_check(constraints: Tuple[ndarray, ndarray, ndarray, ndarray],
*,
theta: Optional[ndarray] = None) -> None:
A, b, lb, ub = constraints
if theta is not None:
assertNoInfNaN(theta)
assertNoInfNaN(A)
assertNoInfNaN(b)
assertNoNaN(lb)
assertNoNaN(ub)
def _input_check(
t: Tuple[ndarray, ndarray, ndarray, Tuple[ndarray, ndarray, ndarray,
ndarray], float]
) -> None:
x, g, H, _, delta = t
assert len(x.shape) == 2
n, _ = x.shape
assert g.shape == (n, )
assert H.shape == (n, n)
assertNoInfNaN(x)
assertNoInfNaN(g)
assertNoInfNaN(H)
assertNoInfNaN_float(delta)
def test_输入负无穷(self) -> None:
ss = "出现了Inf或NaN"
assert (cap_except(lambda: shortcuts.assertNoInfNaN(
numpy.array([-numpy.inf]))).args[0][0] == ss)
assert (cap_except(lambda: shortcuts.assertNoInfNaN(
numpy.array([-math.inf]))).args[0][0] == ss)
def test_输入NaN(self) -> None:
ss = "出现了Inf或NaN"
assert (cap_except(lambda: shortcuts.assertNoInfNaN(
numpy.array([numpy.nan]))).args[0][0] == ss)
assert (cap_except(lambda: shortcuts.assertNoInfNaN(
numpy.array([math.nan]))).args[0][0] == ss)
def _output_check_x(x: ndarray) -> None:
assert len(x.shape) == 1
assertNoInfNaN(x)
def _output_check(output: Trust_Region_Result) -> None:
assertNoInfNaN(output.x)
def __call__(self, x: ndarray) -> float:
assertNoInfNaN(x)
return 0.5 * float(x @ (self.__H @ x)) + float(self.__g @ x)