给出基于轨迹段的豪斯多夫距离算法计算轨迹之间的相似性python代码

以下是基于轨迹段的豪斯多夫距离算法计算轨迹之间的相似性的Python代码：

import numpy as np

def hausdorff_distance(traj1, traj2):
    """计算基于轨迹段的豪斯多夫距离"""
    n1, n2 = len(traj1), len(traj2)
    # 计算距离矩阵
    dist_mat = np.zeros((n1, n2))
    for i in range(n1):
        for j in range(n2):
            dist_mat[i][j] = np.sqrt(np.sum(np.square(traj1[i] - traj2[j])))
    # 计算轨迹1到轨迹2的距离
    min_dist1 = np.min(dist_mat, axis=1)
    max_dist1 = np.max(min_dist1)
    # 计算轨迹2到轨迹1的距离
    min_dist2 = np.min(dist_mat, axis=0)
    max_dist2 = np.max(min_dist2)
    # 取两个方向距离的最大值作为豪斯多夫距离
    return max(max_dist1, max_dist2)

traj1 = np.array([[0, 0], [1, 1], [2, 2], [3, 3]])
traj2 = np.array([[0, 0], [0, 1], [0, 2], [0, 3]])
distance = hausdorff_distance(traj1, traj2)
print("轨迹1与轨迹2的相似性距离为：", distance)