zoukankan      html  css  js  c++  java
  • 图 的矩阵表示 和邻接表表示

    代码

    邻接矩阵

    package Graph;
    
    import java.util.ArrayList;
    import java.util.LinkedList;
    import java.util.Queue;
    
    public class Graph {
    
    	final int Maxsize=10;
    	final int Max=999;
    	int[][] arcs;
    	int[] v;
    	int e;
    	int n;
    	boolean[] found;
    	public Graph() {
    		v=new int[Maxsize];
    		arcs=new int[Maxsize][Maxsize];
    	}
    	public Graph(int n,int e) {
    		this();	
    		this.e=e;
    		this.n=n;
    		found=new boolean[n];
    		
    	}
    	//增加无向边
    	public void addEdge(String str) {	
    		int v1=str.charAt(0)-48;
    		int v2=str.charAt(1)-48;
    		arcs[v1][v2]=1;
    		arcs[v2][v1]=1;
    		
    	}
    	//增加有向边
    	public void addDirectedEdge(String str) {
    		int v1=str.charAt(0)-48;
    		int v2=str.charAt(1)-48;
    		arcs[v1][v2]=1;
    		
    	}
    	public void DisplayAdjMatrix() {
    		int i,j;
    		System.out.println("图的邻接矩阵表示:");
    		for(i=0;i<n;i++) {
    			for(j=0;j<n;j++) {
    				System.out.print(" "+arcs[i][j]);
    			}
    			System.out.println();
    		}
    	}
    	public void BFS(int s) {
    		boolean visited[] =new boolean[n];
    		visited[s]=true;
    		Queue<Integer> queue=new LinkedList<Integer>();
    		queue.add(s);
    		while(!queue.isEmpty()) {
    			int w=queue.poll();
    			System.out.print(w+" ");
    			for (int i = 0; i < n; i++) {
    				if(arcs[w][i]==1 && visited[i]==false) {
    					visited[i]=true;
    					queue.add(i);
    				}
    			}
    		}
    	}
    	public void DFS(int s) {
    		found[s]=true;
    		System.out.print(s+" ");
    		for(int i=0;i<n;i++) {
    			if(arcs[s][i]==1 && found[i]==false) {
    				DFS(i);
    			}
    		}
    	}
    	//测试
    	public static void main(String[] args) {
    		Graph G=new Graph(5,8);
    		String[] string = new String[] {
    				"01","03","04","12","13","23","24","34"
    		};
    		for(int i=0;i<string.length;i++) {
    			G.addEdge(string[i]);
    		}
    		G.DisplayAdjMatrix();
    		G.BFS(0);
    		System.out.println();
    		G.DFS(0);
    	}
    
    }
    
    

    邻接表

    package Graph;
    
    import java.util.ArrayList;
    import java.util.LinkedList;
    import java.util.Queue;
    
    public class ADJGraph {
    	
    	private int v;
    	private LinkedList<Integer> adj[];
    	private boolean[] found;
    	public ADJGraph(int n) {
    		this.v=n;
    		adj=new LinkedList[n];
    		found=new boolean[n];
    		for(int i=0;i<v;i++) {
    			adj[i]=new LinkedList<Integer>();
    		}
    	}
    	public void addEdge(String str) {
    		int s = str.charAt(0)-48;
    		int t = str.charAt(1)-48;
    		adj[s].add(t);
    		adj[t].add(s);
    	}
    	public void addDirectedEdge(String str) {
    		int s = str.charAt(0)-48;
    		int t = str.charAt(1)-48;
    		adj[s].add(t);
    	}
    	public void BFS(int s) {
    		boolean visited[] =new boolean[v];
    		visited[s]=true;
    		System.out.print(s+" ");
    		Queue<Integer> queue=new LinkedList<Integer>();
    		queue.add(s);
    		while(!queue.isEmpty()) {
    			int w=queue.poll();
    			for (int i = 0; i < adj[w].size(); i++) {
    				int q=adj[w].get(i);
    				if(!visited[q]) {
    					visited[q]=true;
    					System.out.print(q+" ");
    					queue.add(q);
    				}
    			}
    		}
    	}
    	public void DFS(int s) {
    		found[s]=true;
    		System.out.print(s+" ");
    		for (int i = 0; i < adj[s].size(); i++) {
    			int q=adj[s].get(i);
    			if(!found[q]) {
    				DFS(q);
    			}
    		}
    	}
    	
    	public static void main(String[] args) {
    		ADJGraph graph = new ADJGraph(5);
    		String[] string = new String[] {
    				"01","03","04","12","13","23","24","34"
    		};
    		for(int i=0;i<string.length;i++) {
    			graph.addEdge(string[i]);
    		}
    		for (int i = 0; i < graph.v; i++) {
    			System.out.println(graph.adj[i]);
    		}
    		graph.BFS(0);
    		System.out.println();
    		graph.DFS(0);
    		
    	}
    	
    
    }
    
    
  • 相关阅读:
    多传感器融合(三)
    多传感器融合(二)
    多传感器融合(一)
    3D点云完美匹配
    Geo-CNN的三维点云
    3D点云几何拟合
    BAD SLAM:捆绑束调整直接RGB-D SLAM
    三维视觉惯性SLAM的有效Schmidt-EKF
    RGB-D相机视觉SLAM
    Visual SLAM
  • 原文地址:https://www.cnblogs.com/nmydt/p/14256355.html
Copyright © 2011-2022 走看看