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モジュール mbcp.mp_math.function ​

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func cal_gradient_3vf(func: ThreeSingleVarsFunc, p: Point3, epsilon: float = EPSILON) -> Vector3 ​

説明: 计算三元函数在某点的梯度向量。

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引数:

戻り値: 梯度

ソースコード または GitHubで表示
python
def cal_gradient_3vf(func: ThreeSingleVarsFunc, p: Point3, epsilon: float=EPSILON) -> Vector3:
    dx = (func(p.x + epsilon, p.y, p.z) - func(p.x - epsilon, p.y, p.z)) / (2 * epsilon)
    dy = (func(p.x, p.y + epsilon, p.z) - func(p.x, p.y - epsilon, p.z)) / (2 * epsilon)
    dz = (func(p.x, p.y, p.z + epsilon) - func(p.x, p.y, p.z - epsilon)) / (2 * epsilon)
    return Vector3(dx, dy, dz)

func curry(func: MultiVarsFunc, *args: Var) -> OneVarFunc ​

説明: 对多参数函数进行柯里化。

TIP

有关函数柯里化，可参考函数式编程--柯理化（Currying）

引数:

戻り値: 柯里化后的函数

例:

python
def add(a: int, b: int, c: int) -> int:
    return a + b + c
add_curried = curry(add, 1, 2)
add_curried(3)  # 6
ソースコード または GitHubで表示
python
def curry(func: MultiVarsFunc, *args: Var) -> OneVarFunc:

    def curried_func(*args2: Var) -> Var:
        return func(*args, *args2)
    return curried_func
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