如何更快速地获得数据集中每个点到其他点的平均曼哈顿距离

import numpy as np

# 数据集，假设为一个二维数组 points，每个点表示为 (x, y)
points = np.array([[x1, y1], [x2, y2], [x3, y3], ...])

# 初始化总距离为0
total_distance = 0

# 遍历计算每个点到其他点的曼哈顿距离并累加
for i in range(len(points)):
    for j in range(len(points)):
        if i != j:
            total_distance += abs(points[i][0] - points[j][0]) + abs(points[i][1] - points[j][1])

# 计算平均曼哈顿距离
average_distance = total_distance / (len(points) * (len(points) - 1))

print("平均曼哈顿距离：", average_distance)

import java.util.Arrays;

public class ManhattanDistance {
    public static void main(String[] args) {
        // 数据集，假设为一个二维数组 points，每个点表示为 (x, y)
        int[][] points = {{x1, y1}, {x2, y2}, {x3, y3}, ...};
        
        // 初始化总距离为0
        int totalDistance = 0;
        
        // 遍历计算每个点到其他点的曼哈顿距离并累加
        for (int i = 0; i < points.length; i++) {
            for (int j = 0; j < points.length; j++) {
                if (i != j) {
                    totalDistance += Math.abs(points[i][0] - points[j][0]) + Math.abs(points[i][1] - points[j][1]);
                }
            }
        }
        
        // 计算平均曼哈顿距离
        double averageDistance = (double) totalDistance / (points.length * (points.length - 1));
        
        System.out.println("平均曼哈顿距离：" + averageDistance);
    }
}