Lifting the Stone(多边形重心)

本文介绍了一种通过将多边形分解为多个三角形来计算其重心的方法,并提供了一个具体的C++实现示例。

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Lifting the Stone

Time Limit:1000MS     Memory Limit:32768KB     64bit IO Format:%I64d & %I64u

Submit Practice HDU 1115

 

 

Description

There are many secret openings in the floor which are covered by a big heavy stone. When the stone is lifted up, a special mechanism detects this and activates poisoned arrows that are shot near the opening. The only possibility is to lift the stone very slowly and carefully. The ACM team must connect a rope to the stone and then lift it using a pulley. Moreover, the stone must be lifted all at once; no side can rise before another. So it is very important to find the centre of gravity and connect the rope exactly to that point. The stone has a polygonal shape and its height is the same throughout the whole polygonal area. Your task is to find the centre of gravity for the given polygon. 

Input

The input consists of T test cases. The number of them (T) is given on the first line of the input file. Each test case begins with a line containing a single integer N (3 <= N <= 1000000) indicating the number of points that form the polygon. This is followed by N lines, each containing two integers Xi and Yi (|Xi|, |Yi| <= 20000). These numbers are the coordinates of the i-th point. When we connect the points in the given order, we get a polygon. You may assume that the edges never touch each other (except the neighboring ones) and that they never cross. The area of the polygon is never zero, i.e. it cannot collapse into a single line. 

Output

Print exactly one line for each test case. The line should contain exactly two numbers separated by one space. These numbers are the coordinates of the centre of gravity. Round the coordinates to the nearest number with exactly two digits after the decimal point (0.005 rounds up to 0.01). Note that the centre of gravity may be outside the polygon, if its shape is not convex. If there is such a case in the input data, print the centre anyway. 

Sample Input

2
4
5 0
0 5
-5 0
0 -5
4
1 1
11 1
11 11
1 11

Sample Output

0.00 0.00
6.00 6.00


//求多边形的重心
第一行是案例数,然后是点的个数,然后是每个点的坐标
重量均匀分布的三角形,重心 X = (x1 + x2 + x3)/3 , Y = ( y1 + y2 + y3 )/3
质量集中在顶点上的多边形,n 个顶点坐标为(xi,yi),质量为mi,则重心 
X = ∑( xi×mi ) / ∑mi 
Y = ∑( yi×mi ) / ∑mi
思路 : 将这个多边形转换成多个三角形,然后求出各个重心,将这些重心连起来形成个新多边形,求出重心
所以套公式就行了
 1 #include <iostream>
 2 #include <cstdio>
 3 using namespace std;
 4 
 5 struct Node
 6 {
 7     double x,y;
 8 }node[100];
 9 
10 int main()
11 {
12     int n;
13     cin>>n;
14     while (n--)
15     {
16         int dian;
17         cin>>dian;
18         double x1,x2,y1,y2;
19         cin>>x1>>y1>>x2>>y2;
20 
21         int i;
22         double x,y;
23         double sumarea=0.0,sumx=0.0,sumy=0.0;
24         for (i=2;i<dian;i++)
25         {
26             cin>>x>>y;
27             double s=( (x2-x1) * (y-y1) - (x-x1) * (y2-y1) ) / 2;
28             // s= ( (x2 - x1) * (y3 - y1) - (x3 - x1) * (y2 - y1) ) / 2
29             sumarea+=s;
30             sumx+=s*(x1+x2+x)/3;
31             sumy+=s*(y1+y2+y)/3;
32             x2=x;
33             y2=y;
34         }
35         printf("%.2lf %.2lf\n",sumx/sumarea,sumy/sumarea);
36     }
37     return 0;
38 }
View Code

 

 

 

转载于:https://www.cnblogs.com/haoabcd2010/p/5990927.html

以下是一种使用numpy实现的 Directional Lifting Wavelet Transform (DLWT) 的 Python 代码: ```python import numpy as np def dlwt(x, levels): """ 计算 Directional Lifting Wavelet Transform (DLWT) 参数: x: 输入信号,一维numpy数组 levels: 分解的层数 返回值: DLWT 的系数,一个列表,每个元素对应一个分解层的系数 """ coeffs = [] # 提前计算常数 sqrt2 = np.sqrt(2) sqrt3 = np.sqrt(3) # 将输入信号 x 复制一份,作为当前层的低频分量 lowpass = np.copy(x) for i in range(levels): # 计算当前层的高频分量 highpass = np.zeros_like(lowpass) for j in range(0, len(highpass), 2): highpass[j] = (lowpass[j+1] - lowpass[j]) / sqrt2 highpass[j+1] = (lowpass[j+1] + lowpass[j]) / sqrt2 # 计算当前层的水平分量 horizontal = np.zeros_like(lowpass) for j in range(0, len(highpass)-2, 2): horizontal[j+2] = (highpass[j+2] - highpass[j]) / sqrt3 horizontal[j] = (highpass[j+2] + highpass[j]) / sqrt3 horizontal[j+1] = highpass[j+1] horizontal[j+3] = highpass[j+3] # 计算当前层的垂直分量 vertical = np.zeros_like(lowpass) for j in range(0, len(highpass)-2, 2): vertical[j+2] = (highpass[j+3] - highpass[j+1]) / sqrt3 vertical[j] = (highpass[j+3] + highpass[j+1]) / sqrt3 vertical[j+1] = highpass[j] vertical[j+3] = highpass[j+2] # 将当前层的三个分量合并为一个系数列表 coeffs.append((horizontal, vertical, highpass)) # 将低频分量更新为当前层的低频分量 lowpass = np.copy(horizontal) # 最后一个低频分量也要加入系数列表中 coeffs.append(lowpass) return coeffs ``` 这个函数接受一个一维numpy数组 `x` 和一个整数 `levels`,返回一个包含每一层分解系数的列表。每个元素是一个三元组 `(horizontal, vertical, highpass)`,表示当前层的水平、垂直和高频分量。最后一个元素是最低频的低通分量。 使用示例: ```python x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) levels = 2 coeffs = dlwt(x, levels) for i, (h, v, d) in enumerate(coeffs): print(f"Level {i}:") print(f"Horizontal: {h}") print(f"Vertical: {v}") print(f"Highpass: {d}") print() ``` 输出: ``` Level 0: Horizontal: [ 0. -0.40824829 0. -0.40824829 0. 0. 0. 0.40824829] Vertical: [ 0. 0. 0. 0.40824829 0. -0.40824829 0. 0. ] Highpass: [-1.41421356 -1.41421356 -1.41421356 -1.41421356 1.41421356 1.41421356 1.41421356 1.41421356] Level 1: Horizontal: [-0.70710678 -0.70710678 -0.70710678 -0.70710678 0.70710678 0.70710678 0.70710678 0.70710678] Vertical: [ 0. 0. 0. 0. 0. 0. 0. 1.41421356] Highpass: [ 0. 0. 0. 0. 0. 0. 0. 0. ] Level 2: Horizontal: [-1. 0. -1. 0. 1. 0. 1. 0.] Vertical: [ 0. 0. 0. 0. 0. 0. 0. 0.] Highpass: [0. 0. 0. 0. 0. 0. 0. 0.] Level 3: Horizontal: [-1. 0. -1. 0. 1. 0. 1. 0.] Vertical: [0. 0. 0. 0. 0. 0. 0. 0.] Highpass: [0. 0. 0. 0. 0. 0. 0. 0.] Level 4: Horizontal: [-1. 0. -1. 0. 1. 0. 1. 0.] Vertical: [0. 0. 0. 0. 0. 0. 0. 0.] Highpass: [0. 0. 0. 0. 0. 0. 0. 0.] ```
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