图的表示：两种表示方法 邻接矩阵和邻接表
无向图
有向图
图的权
连通图
度
图的存储结构
1、邻接矩阵存储
浪费邻接矩阵
2、邻接表存储
2.1、无向图的邻接表
2.2、有向图的邻接表
2.3、带权值的邻接表
代码
1、图的基本操作
package com.dn.dijstra;
import java.util.LinkedList;
public class Graph {
private int vertexSize;//顶点数量
public int getVertexSize() {
return vertexSize;
}
public void setVertexSize(int vertexSize) {
this.vertexSize = vertexSize;
}
private int [] vertexs;//顶点数组
private int[][] matrix;
public int[][] getMatrix() {
return matrix;
}
public void setMatrix(int[][] matrix) {
this.matrix = matrix;
}
private static final int MAX_WEIGHT = 1000;
private boolean [] isVisited;
public Graph(int vertextSize){
this.vertexSize = vertextSize;
matrix = new int[vertextSize][vertextSize];
vertexs = new int[vertextSize];
for(int i = 0;i<vertextSize;i++){
vertexs[i] = i;
}
isVisited = new boolean[vertextSize];
}
/**
* 创建图的过程
*/
public void createGraph(){
int [] a1 = new int[]{0,1,5,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT};
int [] a2 = new int[]{1,0,3,7,5,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT};
int [] a3 = new int[]{5,3,0,MAX_WEIGHT,1,7,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT};
int [] a4 = new int[]{MAX_WEIGHT,7,MAX_WEIGHT,0,2,MAX_WEIGHT,3,MAX_WEIGHT,MAX_WEIGHT};
int [] a5 = new int[]{MAX_WEIGHT,5,1,2,0,3,6,9,MAX_WEIGHT};
int [] a6 = new int[]{MAX_WEIGHT,MAX_WEIGHT,7,MAX_WEIGHT,3,0,MAX_WEIGHT,5,MAX_WEIGHT};
int [] a7 = new int[]{MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,3,6,MAX_WEIGHT,0,2,7};
int [] a8 = new int[]{MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,9,5,2,0,4};
int [] a9 = new int[]{MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,7,4,0};
matrix[0] = a1;
matrix[1] = a2;
matrix[2] = a3;
matrix[3] = a4;
matrix[4] = a5;
matrix[5] = a6;
matrix[6] = a7;
matrix[7] = a8;
matrix[8] = a9;
}
/**
* 获取某个顶点的出度
* @return
*/
public int getOutDegree(int index){
int degree = 0;
for(int j = 0;j<matrix[index].length;j++){
int weight = matrix[index][j];
if(weight!=0&&weight!=MAX_WEIGHT){
degree++;
}
}
return degree;
}
/**
* 入度
* @return
*/
/**
* 获取某个顶点的第一个邻接点
*/
public int getFirstNeighbor(int index){
for(int j = 0;j<vertexSize;j++){
if(matrix[index][j]>0&&matrix[index][j]<MAX_WEIGHT){
return j;
}
}
return -1;
}
/**
* 根据前一个邻接点的下标来取得下一个邻接点
* @param v1表示要找的顶点
* @param v2 表示该顶点相对于哪个邻接点去获取下一个邻接点
*/
public int getNextNeighbor(int v,int index){
for(int j = index+1;j<vertexSize;j++){
if(matrix[v][j]>0&&matrix[v][j]<MAX_WEIGHT){
return j;
}
}
return -1;
}
/**
* 图的深度优先遍历算法
*/
private void depthFirstSearch(int i){
isVisited[i] = true;
int w = getFirstNeighbor(i);//
while(w!=-1){
if(!isVisited[w]){
//需要遍历该顶点
System.out.println("访问到了："+w+"顶点");
depthFirstSearch(w);
}
w = getNextNeighbor(i, w);//第一个相对于w的邻接点
}
}
/**
* 对外公开的深度优先遍历
*/
public void depthFirstSearch(){
isVisited = new boolean[vertexSize];
for(int i = 0;i<vertexSize;i++){
if(!isVisited[i]){
System.out.println("访问到了："+i+"顶点");
depthFirstSearch(i);
}
}
isVisited = new boolean[vertexSize];
}
public void broadFirstSearch(){
isVisited = new boolean[vertexSize];
for(int i =0;i<vertexSize;i++){
if(!isVisited[i]){
broadFirstSearch(i);
}
}
}
/**
* 实现广度优先遍历
* @param i
*/
private void broadFirstSearch(int i) {
int u,w;
LinkedList<Integer> queue = new LinkedList<Integer>();
System.out.println("访问到："+i+"顶点");
isVisited[i] = true;
queue.add(i);//第一次把v0加到队列
while(!queue.isEmpty()){
u = (Integer)(queue.removeFirst()).intValue();
w = getFirstNeighbor(u);
while(w!=-1){
if(!isVisited[w]){
System.out.println("访问到了："+w+"顶点");
isVisited[w] = true;
queue.add(w);
}
w = getNextNeighbor(u, w);
}
}
}
/**
* prim 普里姆算法
*/
public void prim(){
int [] lowcost = new int[vertexSize];//最小代价顶点权值的数组,为0表示已经获取最小权值
int [] adjvex = new int[vertexSize];//放顶点权值
int min,minId,sum = 0;
for(int i = 1;i<vertexSize;i++){
lowcost[i] = matrix[0][i];
}
for(int i = 1;i<vertexSize;i++){
min = MAX_WEIGHT;
minId = 0;
for(int j = 1;j<vertexSize;j++){
if(lowcost[j]<min&&lowcost[j]>0){
min = lowcost[j];
minId = j;
}
}
System.out.println("顶点："+adjvex[minId]+"权值："+min);
sum+=min;
lowcost[minId] = 0;
for(int j = 1;j<vertexSize;j++){
if(lowcost[j]!=0&&matrix[minId][j]<lowcost[j]){
lowcost[j] = matrix[minId][j];
adjvex[j] = minId;
}
}
}
System.out.println("最小生成树权值和:"+sum);
}
/**
* 图的广度优先搜索算法
*/
/**
* 获取两个顶点之间的权值
* @return
*/
public int getWeight(int v1,int v2){
int weight = matrix[v1][v2];
return weight == 0?0:(weight == MAX_WEIGHT?-1:weight);
}
public int[] getVertexs() {
return vertexs;
}
public void setVertexs(int[] vertexs) {
this.vertexs = vertexs;
}
public static void main(String [] args){
Graph graph = new Graph(9);
int [] a1 = new int[]{0,10,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,11,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT};
int [] a2 = new int[]{10,0,18,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,16,MAX_WEIGHT,12};
int [] a3 = new int[]{MAX_WEIGHT,MAX_WEIGHT,0,22,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,8};
int [] a4 = new int[]{MAX_WEIGHT,MAX_WEIGHT,22,0,20,MAX_WEIGHT,MAX_WEIGHT,16,21};
int [] a5 = new int[]{MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,20,0,26,MAX_WEIGHT,7,MAX_WEIGHT};
int [] a6 = new int[]{11,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,26,0,17,MAX_WEIGHT,MAX_WEIGHT};
int [] a7 = new int[]{MAX_WEIGHT,16,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,17,0,19,MAX_WEIGHT};
int [] a8 = new int[]{MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,16,7,MAX_WEIGHT,19,0,MAX_WEIGHT};
int [] a9 = new int[]{MAX_WEIGHT,12,8,21,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,MAX_WEIGHT,0};
graph.matrix[0] = a1;
graph.matrix[1] = a2;
graph.matrix[2] = a3;
graph.matrix[3] = a4;
graph.matrix[4] = a5;
graph.matrix[5] = a6;
graph.matrix[6] = a7;
graph.matrix[7] = a8;
graph.matrix[8] = a9;
// int degree = graph.getOutDegree(4);
// System.out.println("vo的出度:"+degree);
// System.out.println("权值："+graph.getWeight(2,3));
// graph.depthFirstSearch();
// graph.broadFirstSearch();
graph.prim();
}
}
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