1053 Path of Equal Weight (30 分)

本文介绍了一个复杂的编程问题:在一个加权树中找到所有路径,其权重等于给定的数值。通过深度优先搜索(DFS)算法解决,难点在于如何正确实现DFS以遍历所有可能的路径并返回满足条件的路径。

Given a non-empty tree with root R, and with weight W​i​​ assigned to each tree node T​i​​. The weight of a path from R to L is defined to be the sum of the weights of all the nodes along the path from R to any leaf node L.

Now given any weighted tree, you are supposed to find all the paths with their weights equal to a given number. For example, let's consider the tree showed in the following figure: for each node, the upper number is the node ID which is a two-digit number, and the lower number is the weight of that node. Suppose that the given number is 24, then there exists 4 different paths which have the same given weight: {10 5 2 7}, {10 4 10}, {10 3 3 6 2} and {10 3 3 6 2}, which correspond to the red edges in the figure.

Input Specification:

Each input file contains one test case. Each case starts with a line containing 0<N≤100, the number of nodes in a tree, M (<N), the number of non-leaf nodes, and 0<S<2​30​​, the given weight number. The next line contains N positive numbers where W​i​​ (<1000) corresponds to the tree node T​i​​. Then M lines follow, each in the format:

ID K ID[1] ID[2] ... ID[K]

where ID is a two-digit number representing a given non-leaf node, K is the number of its children, followed by a sequence of two-digit ID's of its children. For the sake of simplicity, let us fix the root ID to be 00.

Output Specification:

For each test case, print all the paths with weight S in non-increasing order. Each path occupies a line with printed weights from the root to the leaf in order. All the numbers must be separated by a space with no extra space at the end of the line.

Note: sequence {A​1​​,A​2​​,⋯,A​n​​} is said to be greater than sequence {B​1​​,B​2​​,⋯,B​m​​} if there exists 1≤k<min{n,m} such that A​i​​=B​i​​ for i=1,⋯,k, and A​k+1​​>B​k+1​​.

Sample Input:

20 9 24
10 2 4 3 5 10 2 18 9 7 2 2 1 3 12 1 8 6 2 2
00 4 01 02 03 04
02 1 05
04 2 06 07
03 3 11 12 13
06 1 09
07 2 08 10
16 1 15
13 3 14 16 17
17 2 18 19

Sample Output:

10 5 2 7
10 4 10
10 3 3 6 2
10 3 3 6 2

写这道题真的是耗费了我大量的脑细胞,大概写了2个小时才写完,主要难点在于DFS怎么写。

  1. ”死胡同“就是到达了叶节点,但同时死胡同也是可能出口(如果权重和==Sum),要写两个if进行判断
  2. 最后输出的路径是权重值的路径,每次迭代也要累加上权重和
  3. vector<path>的存储要特别注意!注意使用完(从迭代里出来后)要及时pop_back()
  4. DFS的参数确定,非常非常重要,一般是会随着迭代(节点选取不同)改变的量
#include<cstdio>
#include<vector>
#include<queue>
#include<algorithm>
using namespace std;
const int maxn = 110;
int weightSum = 0;
int target;
vector<vector<int> > result;
vector<int> path;
struct node {
	int weight;
	vector<int> child;
}Node[maxn];

void DFS(int root, int weightSum) {
	//递归边界

	if (Node[root].child.empty()) {
		if (weightSum == target) {
			result.push_back(path);
		}
		return;
	}

	//不同的分支
	for (int i = 0; i < Node[root].child.size(); i++) {
		if (weightSum + Node[Node[root].child[i]].weight <= target)
		{
			path.push_back(Node[Node[root].child[i]].weight);
			DFS(Node[root].child[i], weightSum + Node[Node[root].child[i]].weight);
			path.pop_back();
		}
	}

}

bool cmp(vector<int> a, vector<int> b) {
	int i = 0;
	for (i = 0;  i < a.size() && i < b.size() && a[i] == b[i] ;i++) {
	}
	if (!(i < a.size() && i < b.size())) {
		return a.size() > b.size();
	}
	return a[i] > b[i];
}

int main() {
	int N, M;
	scanf("%d%d%d", &N, &M, &target);
	for (int i = 0; i < N; i++)
		scanf("%d", &Node[i].weight);
	for (int i = 0; i < M; i++)
	{
		int root, childNum;
		scanf("%d%d", &root, &childNum);
		for (int j = 0; j < childNum; j++) {
			int tmp;
			scanf("%d", &tmp);
			Node[root].child.push_back(tmp);
		}
	}

	path.push_back(Node[0].weight);
	DFS(0, Node[0].weight);
	sort(result.begin(), result.end(), cmp);
	for (int i = 0; i < result.size(); i++)
	{
		for (int j = 0; j < result[i].size(); j++)
		{
			if(j == 0)
				printf("%d", result[i][j]);
			else
				printf(" %d", result[i][j]);
		}
		printf("\n");
	}
	return 0;
}

 

 

机器人路径规划算法是机器人领域的重要研究内容,用于在给定环境中为机器人找到一条从起始点到目标点的最优或可行路径。以下是几种常见的机器人路径规划算法及其细节: ### A*算法 A*算法是一种启发式搜索算法,结合了Dijkstra算法的最优路径搜索特性和贪心最佳优先搜索算法的高效性。它通过维护一个开放列表和一个关闭列表,不断扩展节点直到找到目标节点。其核心公式为 $f(n) = g(n) + h(n)$,其中 $g(n)$ 是从起始节点到当前节点的实际代价,$h(n)$ 是从当前节点到目标节点的启发式估计代价。 ```python import heapq def heuristic(a, b): # 曼哈顿距离作为启发式函数 return abs(a[0] - b[0]) + abs(a[1] - b[1]) def astar(array, start, goal): neighbors = [(0, 1), (0, -1), (1, 0), (-1, 0)] close_set = set() came_from = {} gscore = {start: 0} fscore = {start: heuristic(start, goal)} open_heap = [] heapq.heappush(open_heap, (fscore[start], start)) while open_heap: current = heapq.heappop(open_heap)[1] if current == goal: data = [] while current in came_from: data.append(current) current = came_from[current] return data close_set.add(current) for i, j in neighbors: neighbor = current[0] + i, current[1] + j tentative_g_score = gscore[current] + heuristic(current, neighbor) if 0 <= neighbor[0] < array.shape[0]: if 0 <= neighbor[1] < array.shape[1]: if array[neighbor[0]][neighbor[1]] == 1: continue else: # 越界 continue else: # 越界 continue if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0): continue if tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1] for i in open_heap]: came_from[neighbor] = current gscore[neighbor] = tentative_g_score fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal) heapq.heappush(open_heap, (fscore[neighbor], neighbor)) return None ``` ### Dijkstra算法 Dijkstra算法是一种广度优先搜索算法,用于在加权图中找到从单个源节点到所有其他节点的最短路径。它通过不断扩展距离源节点最近的节点,更新其邻居节点的距离,直到所有可达节点都被访问。 ```python import heapq def dijkstra(graph, start): distances = {node: float('inf') for node in graph} distances[start] = 0 priority_queue = [(0, start)] while priority_queue: current_distance, current_node = heapq.heappop(priority_queue) if current_distance > distances[current_node]: continue for neighbor, weight in graph[current_node].items(): distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(priority_queue, (distance, neighbor)) return distances ``` ### 人工势场法 人工势场法将机器人在环境中的运动看作是在虚拟力场中的运动。目标点对机器人产生引力,障碍物对机器人产生斥力,机器人在合力的作用下向目标点移动。该方法计算简单,实时性好,但容易陷入局部极小值。 ### 遗传算法 遗传算法是一种基于生物进化原理的优化算法。它通过模拟自然选择和遗传机制,对路径进行编码,生成初始种群,然后通过选择、交叉和变异等操作不断进化种群,直到找到最优路径。 ### RRT(快速随机树)算法 RRT算法是一种基于采样的路径规划算法,通过随机采样在状态空间中快速扩展一棵树,直到树的某个节点接近目标点。该算法适用于高维空间和复杂环境,但生成的路径通常不是最优的。 ```python import numpy as np import matplotlib.pyplot as plt class RRT: def __init__(self, start, goal, obstacle_list, rand_area, expand_dis=3.0, path_resolution=0.5, goal_sample_rate=5, max_iter=500): self.start = Node(start[0], start[1]) self.goal = Node(goal[0], goal[1]) self.obstacle_list = obstacle_list self.min_rand = rand_area[0] self.max_rand = rand_area[1] self.expand_dis = expand_dis self.path_resolution = path_resolution self.goal_sample_rate = goal_sample_rate self.max_iter = max_iter self.node_list = [] def planning(self, animation=True): self.node_list = [self.start] for i in range(self.max_iter): rnd_node = self.get_random_node() nearest_node = self.get_nearest_node(self.node_list, rnd_node) new_node = self.steer(nearest_node, rnd_node, self.expand_dis) if self.check_collision(new_node, self.obstacle_list): self.node_list.append(new_node) if animation and i % 5 == 0: self.draw_graph(rnd_node) if self.calc_dist_to_goal(self.node_list[-1].x, self.node_list[-1].y) <= self.expand_dis: final_node = self.steer(self.node_list[-1], self.goal, self.expand_dis) if self.check_collision(final_node, self.obstacle_list): return self.generate_final_course(len(self.node_list) - 1) return None def get_random_node(self): if np.random.randint(0, 100) > self.goal_sample_rate: rnd = Node(np.random.uniform(self.min_rand, self.max_rand), np.random.uniform(self.min_rand, self.max_rand)) else: # goal point sampling rnd = Node(self.goal.x, self.goal.y) return rnd def get_nearest_node(self, node_list, rnd_node): dlist = [(node.x - rnd_node.x) ** 2 + (node.y - rnd_node.y) ** 2 for node in node_list] minind = dlist.index(min(dlist)) return node_list[minind] def steer(self, from_node, to_node, extend_length=float("inf")): new_node = Node(from_node.x, from_node.y) d, theta = self.calc_distance_and_angle(new_node, to_node) new_node.path_x = [new_node.x] new_node.path_y = [new_node.y] if extend_length > d: new_node.x = to_node.x new_node.y = to_node.y else: new_node.x = from_node.x + extend_length * np.cos(theta) new_node.y = from_node.y + extend_length * np.sin(theta) n_expand = int(np.floor(extend_length / self.path_resolution)) for _ in range(n_expand): new_node.path_x.append(new_node.path_x[-1] + self.path_resolution * np.cos(theta)) new_node.path_y.append(new_node.path_y[-1] + self.path_resolution * np.sin(theta)) new_node.path_x.append(new_node.x) new_node.path_y.append(new_node.y) new_node.parent = from_node return new_node def check_collision(self, node, obstacle_list): for (ox, oy, size) in obstacle_list: dx_list = [ox - x for x in node.path_x] dy_list = [oy - y for y in node.path_y] d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)] if min(d_list) <= size ** 2: return False # collision return True # safe def calc_dist_to_goal(self, x, y): dx = x - self.goal.x dy = y - self.goal.y return np.sqrt(dx ** 2 + dy ** 2) def generate_final_course(self, goal_ind): path = [[self.goal.x, self.goal.y]] node = self.node_list[goal_ind] while node.parent is not None: path.append([node.x, node.y]) node = node.parent path.append([node.x, node.y]) return path def draw_graph(self, rnd=None): plt.clf() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect( 'key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: if node.parent is not None: plt.plot(node.path_x, node.path_y, "-g") for (ox, oy, size) in self.obstacle_list: self.plot_circle(ox, oy, size) plt.plot(self.start.x, self.start.y, "xr") plt.plot(self.goal.x, self.goal.y, "xr") plt.axis("equal") plt.axis([-2, 15, -2, 15]) plt.grid(True) plt.pause(0.01) @staticmethod def plot_circle(x, y, size, color="-b"): # pragma: no cover deg = list(range(0, 360, 5)) deg.append(0) xl = [x + size * np.cos(np.deg2rad(d)) for d in deg] yl = [y + size * np.sin(np.deg2rad(d)) for d in deg] plt.plot(xl, yl, color) @staticmethod def calc_distance_and_angle(from_node, to_node): dx = to_node.x - from_node.x dy = to_node.y - from_node.y d = np.sqrt(dx ** 2 + dy ** 2) theta = np.arctan2(dy, dx) return d, theta class Node: def __init__(self, x, y): self.x = x self.y = y self.path_x = [] self.path_y = [] self.parent = None ``` ### 相关问题 1. 这些路径规划算法在不同环境下的性能如何比较? 2. 如何改进A*算法以避免陷入局部最优解? 3. 遗传算法在路径规划中的收敛速度如何提高? 4. RRT算法生成的路径如何进行优化? 5. 人工势场法的局部极小值问题有哪些解决方法?
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