[TopCoder] SRM 578 DIV 2, Goose In Zoo, Solution

本文介绍了一个有趣的算法问题:在动物园的鹅笼中寻找鹅的位置。通过分析问题特点,将其转化为图论中的连通区域问题,并给出了解决方案。文章详细解释了如何利用曼哈顿距离确定鹅的位置,并通过计算连通区域数量来确定可能的鹅群组合。

Problem Statement

     Crow Keith is looking at the goose cage in the zoo. The bottom of the cage is divided into a grid of square cells. There are some birds sitting on those cells (with at most one bird per cell). Some of them are geese and all the others are ducks. Keith wants to know which birds are geese. He knows the following facts about them:
  • There is at least one goose in the cage.
  • Each bird within Manhattan distance dist of any goose is also a goose.

You are given a vector <string> field and the int dist. The array field describes the bottom of the cage. Each character of each element of field describes one of the cells. The meaning of individual characters follows.

  • The character ‘v’ represents a cell that contains a bird.
  • The character ‘.’ represents an empty cell.

Return the number of possible sets of geese in the cage, modulo 1,000,000,007. Note that for some of the test cases there can be no possible sets of geese.

Definition

    
Class: GooseInZooDivTwo
Method: count
Parameters: vector <string>, int
Returns: int
Method signature: int count(vector <string> field, int dist)
(be sure your method is public)
    

Notes

- The Manhattan distance between cells (a,b) and (c,d) is |a-c| + |b-d|, where || denotes absolute value. In words, the Manhattan distance is the smallest number of steps needed to get from one cell to the other, given that in each step you can move to a cell that shares a side with your current cell.

Constraints

- field will contain between 1 and 50 elements, inclusive.
- Each element of field will contain between 1 and 50 characters, inclusive.
- Each element of field will contain the same number of characters.
- Each character of each element of field will be ‘v’ or ‘.’.
- dist will be between 0 and 100, inclusive.

Examples

0)
    
{"vvv"}
0
Returns: 7
There are seven possible sets of positions of geese: “ddg”, “dgd”, “dgg”, “gdd”, “gdg”, “ggd”, “ggg” (‘g’ are geese and ‘d’ are ducks).
1)
    
{"."}
100
Returns: 0
The number of geese must be positive, but there are no birds in the cage.
2)
    
{"vvv"}
1
Returns: 1

[Thoughts]
这道题非常有意思。刚拿到题的时候,第一个想法就是,这不是八皇后的变形吗? DFS一通到底就好了。但是细细的品味之后,发现这个不是这么简单。这道题其实是图论中连通区域的变形。
在题目中已经说了,给定任意一个点,如果该节点是一只鹅,那么所有与该鹅在曼哈顿距离以内的节点都是鹅。换句话说,所有与该鹅在曼哈顿距离以内的,都是连通的,可以收缩成一个节点,因为他们的行为时一致的,要么都是鹅,要么都不是鹅。
到这里,题目就变形为,在一个二维数组里面,找出连通区域的个数。然后对连通区域数求排列(这里就是2的幂数)。
计算大数取余的时候,要考虑溢出,通过迭代法计算。
(a*b)%m=(a%m*b%m )%m;
[Code]
懒得自己写了,偷用Zhongwen的 code
1:  #define pb push_back  
2: #define INF 100000000000
3: #define L(s) (int)((s).size())
4: #define FOR(i,a,b) for (int _n(b), i(a); i<=_n; i++)
5: #define rep(i,n) FOR(i,1,(n))
6: #define rept(i,n) FOR(i,0,(n)-1)
7: #define C(a) memset((a), 0, sizeof(a))
8: #define ll long long
9: #define VI vector<int>
10: #define ppb pop_back
11: #define mp make_pair
12: #define MOD 1000000007
13: struct Node {
14: int x;
15: int y;
16: Node(int a, int b) : x(a), y(b) { }
17: };
18: int toInt(string s){ istringstream sin(s); int t; sin>>t;return t;}
19: vector<Node> GooseInZooDivTwo::flood(vector<string> &field, vector<vector<bool> > &visit, int x, int y, int dist, int m, int n)
20: {
21: vector<Node> ret;
22: queue<Node> S;
23: visit[x][y] = true;
24: S.push(Node(x, y));
25: while (!S.empty())
26: {
27: Node cur = S.front();
28: ret.pb(S.front());
29: S.pop();
30: for (int i = max(0, cur.x-dist); i <= min(m-1, cur.x+dist); i++)
31: {
32: for (int j = max(0, cur.y-dist); j <= min(n-1, cur.y+dist); j++)
33: {
34: if (field[i][j] == 'v' && !visit[i][j] && (abs(i-cur.x)+abs(j-cur.y) <=dist))
35: {
36: S.push(Node(i, j));
37: visit[i][j] = true;
38: }
39: }
40: }
41: }
42: return ret;
43: }
44: int GooseInZooDivTwo::count(vector <string> field, int dist) {
45: int m = L(field);
46: if (!m) return 0;
47: int n = L(field[0]);
48: vector<vector<bool> > visit(m, vector<bool>(n, false));
49: vector<vector<Node> > ret;
50: rept(i, m)
51: {
52: rept(j, n)
53: {
54: if (field[i][j] == 'v' && !visit[i][j])
55: {
56: ret.pb(flood(field, visit, i, j, dist, m, n));
57: }
58: }
59: }
60: if (!L(ret)) return 0;
61: long num=1;
62: for(int i =0; i< L(ret); i++) //要考虑排列溢出的情况
63: {
64: num*=2;
65: if(num> MOD)
66: {
67: num = num % MOD;
68: }
69: }
70: return num-1;
71: }

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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