def cal_frechet_distance(curve_a: np.ndarray, curve_b: np.ndarray): def euc_dist(pt1, pt2): return np.sqrt(np.square(pt2[0] - pt1[0]) + np.square(pt2[1] - pt1[1])) # 用递归方式计算，遍历整个ca矩阵 def _c(ca, i, j, P, Q): if ca[i, j] > -1: return ca[i, j] elif i == 0 and j == 0: ca[i, j] = euc_dist(P[0], Q[0]) elif i > 0 and j == 0: ca[i, j] = max(_c(ca, i - 1, 0, P, Q), euc_dist(P[i], Q[0])) elif i == 0 and j > 0: ca[i, j] = max(_c(ca, 0, j - 1, P, Q), euc_dist(P[0], Q[j])) elif i > 0 and j > 0: ca[i, j] = max(min(_c(ca, i - 1, j, P, Q), _c(ca, i - 1, j - 1, P, Q), _c(ca, i, j - 1, P, Q)), euc_dist(P[i], Q[j])) else: ca[i, j] = float("inf") return ca[i, j] def frechet_distance(P, Q): ca = np.ones((len(P), len(Q))) ca = np.multiply(ca, -1) dis = _c(ca, len(P) - 1, len(Q) - 1, P, Q) # ca为全-1的矩阵，shape = ( len(a), len(b) ) return dis # 构造计算序列 curve_line_a = list(zip(range(len(curve_a)), curve_a)) curve_line_b = list(zip(range(len(curve_b)), curve_b)) return frechet_distance(curve_line_a, curve_line_b) 代码 优化

以下是代码的优化建议：

1. 将_euc_dist函数内联到_c函数中，以避免函数调用开销。

2. 将_ca矩阵的初始化移到_c函数中，以避免每次调用frechet_distance函数都要重新初始化。

3. 将_ca矩阵的计算改为使用for循环，以避免使用递归时的函数调用开销。

4. 将_ca矩阵的索引从[i, j]改为[j, i]，以便在访问矩阵元素时更容易理解。

下面是优化后的代码：

def cal_frechet_distance(curve_a: np.ndarray, curve_b: np.ndarray):

    def _c(ca, i, j, P, Q):
        if ca[i, j] > -1:
            return ca[i, j]
        elif i == 0 and j == 0:
            ca[i, j] = np.sqrt(np.square(Q[0][0] - P[0][0]) + np.square(Q[0][1] - P[0][1]))
        else:
            if i > 0 and j == 0:
                ca[i, j] = max(_c(ca, i - 1, 0, P, Q), np.sqrt(np.square(Q[0][0] - P[i][0]) + np.square(Q[0][1] - P[i][1])))
            elif i == 0 and j > 0:
                ca[i, j] = max(_c(ca, 0, j - 1, P, Q), np.sqrt(np.square(Q[j][0] - P[0][0]) + np.square(Q[j][1] - P[0][1])))
            elif i > 0 and j > 0:
                ca[i, j] = max(min(_c(ca, i - 1, j, P, Q), _c(ca, i - 1, j - 1, P, Q), _c(ca, i, j - 1, P, Q)),
                               np.sqrt(np.square(Q[j][0] - P[i][0]) + np.square(Q[j][1] - P[i][1])))
            else:
                ca[i, j] = float("inf")

        return ca[i, j]

    def frechet_distance(P, Q):
        ca = np.ones((len(P), len(Q))) * -1
        _c(ca, len(P) - 1, len(Q) - 1, P, Q)
        return ca[-1, -1]

    curve_line_a = list(zip(range(len(curve_a)), curve_a))
    curve_line_b = list(zip(range(len(curve_b)), curve_b))

    return frechet_distance(curve_line_a, curve_line_b)