邻接矩阵存储的图

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<span style="font-size:24px;">java实现用邻接矩阵（相邻矩阵）实现图，缺点是矩阵中大量的0元素会耗费大量的存储空间</span>


public class Graph {  
    final int MAX_VERTEX = 10;// 最多10个顶点  
    Vertex[] vertex;// 顶点数组  
    int[][] adjacency;// 邻接矩阵  
    int numOfVertex;// 当前图中顶点的数量  
  
    public Graph() {// 构造器  
        vertex = new Vertex[MAX_VERTEX];  
        adjacency = new int[MAX_VERTEX][MAX_VERTEX];  
        numOfVertex = 0;  
        // 将邻接矩阵初始化  
        for (int i = 0; i < MAX_VERTEX; i++) {  
            for (int j = 0; j < MAX_VERTEX; j++)  
                adjacency[i][j] = 0;  
        }  
    }  
  
    // 添加顶点  
    public void addVertex(char v) {  
        vertex[numOfVertex++] = new Vertex(v);  
    }  
  
    //无向图 添加边  
    public void addEdge(int start, int end) {  
        adjacency[start][end] = 1;  
        adjacency[end][start] = 1;  
    }  
    //有向图添加边  
    public void addEdge1(int start,int end){  
        adjacency[start][end] = 1;  
    }  
    // 打印某个顶点  
    public void showVertex(int index) {  
        System.err.print(vertex[index].label);  
    }  
  
    // 打印邻接矩阵  
    public void show() {  
        for (int i = 0; i < MAX_VERTEX; i++) {  
            for (int j = 0; j < MAX_VERTEX; j++) {  
                if (j == MAX_VERTEX - 1)  
                    System.out.println(adjacency[i][j] + "  ");  
                else  
                    System.out.print(adjacency[i][j] + "  ");  
            }  
        }  
    }  
  
    /** 
     * 找到与某一顶点邻接而未被访问的顶点,如何做？ 
     * 在邻接矩阵中，找到指定顶点所在的行，从第一列开始向后寻找值为1的列，列号是邻接顶点的号码，检查此顶点是否访问过。 
     * 如果该行没有值为1而又未访问过的点，则此顶点的邻接点都访问过了。 
     */  
    public int getUnVisitedVertex(int index) {  
        for (int i = 0; i < numOfVertex; i++)  
            if (adjacency[index][i] == 1 && vertex[i].wasVisited == false)  
                return i;  
        return -1;  
    }  
  
    // 图的深度优先遍历  
    public void dfs() {  
        vertex[0].wasVisited = true;// 从头开始访问  
        showVertex(0);  
        Stack stack = new Stack();  
        stack.push(0);  
        /** 
         * 1.用peek()方法获取栈顶的顶点 2.试图找到这个顶点的未访问过的邻接点 3.如果没有找到这样的顶点，出栈 4.如果找到，访问之，入栈 
         */  
        while (!stack.isEmpty()) {  
            int index = getUnVisitedVertex(stack.peek());  
            if (index == -1)// 没有这个顶点  
                stack.pop();  
            else {  
                vertex[index].wasVisited = true;  
                showVertex(index);  
                stack.push(index);  
            }  
        }  
        // 栈为空，遍历结束，标记位重新初始化  
        for (int i = 0; i < numOfVertex; i++)  
            vertex[i].wasVisited = false;  
    }  
  
    // 图的广度优先遍历  
    public void bfs() {  
        vertex[0].wasVisited = true;  
        showVertex(0);  
        Queue queue = new Queue();  
        queue.insert(0);  
        int v2;  
        while (!queue.isEmpty()) {// 直到队列为空  
            int v1 = queue.remove();  
            // 直到点v1没有未访问过的邻接点  
            while ((v2 = getUnVisitedVertex(v1)) != -1) {  
                // 取到未访问过的点，访问之  
                vertex[v2].wasVisited = true;  
                showVertex(v2);  
                queue.insert(v2);  
            }  
        }  
        for (int i = 0; i < numOfVertex; i++)  
            vertex[i].wasVisited = false;  
    }  
  
    // 最小生成树 minimum spanning tree  
    public void mst() {  
        vertex[0].wasVisited = true;  
        Stack stack = new Stack();  
        stack.push(0);  
        while (!stack.isEmpty()) {  
            int currentVertex = stack.peek();  
            int v = getUnVisitedVertex(currentVertex);  
            if (v == -1)  
                stack.pop();  
            else {  
                vertex[v].wasVisited = true;  
                stack.push(v);  
                //当前顶点与下一个未访问过的邻接点  
                showVertex(currentVertex);  
                showVertex(v);  
                System.out.print("  ");  
            }  
        }  
        for (int i = 0; i < numOfVertex; i++)  
            vertex[i].wasVisited = false;  
    }  
  
    public static void main(String[] args) {  
        Graph graph = new Graph();  
        graph.addVertex('A');  
        graph.addVertex('B');  
        graph.addVertex('C');  
        graph.addVertex('D');  
        graph.addEdge(1, 2);  
        graph.addEdge(0, 1);  
        graph.addEdge(2, 3);  
        graph.show();  
//        graph.dfs();  
        // graph.bfs();  
        //graph.mst();  
    }  
  
}  
  
// 顶点  
class Vertex {  
    char label;// 如A，B,C  
    boolean wasVisited;// 标识是否访问过此顶点  
  
    public Vertex(char vertex) {  
        this.label = vertex;  
        wasVisited = false;  
    }  
}  
  
// 栈  
class Stack {  
    final int MAX_SIZE = 10;  
    int stack[];  
    int top;  
  
    public Stack() {  
        stack = new int[MAX_SIZE];  
        top = -1;  
    }  
  
    public void push(int idata) {  
        stack[++top] = idata;  
    }  
  
    public int pop() {  
        return stack[top--];  
    }  
  
    public int peek() {  
        return stack[top];  
    }  
  
    public boolean isEmpty() {  
        return top == -1;  
    }  
}  
  
// 队列  
class Queue {  
    final int SIZE = 10;  
    int[] qarray;  
    int front;  
    int rear;  
  
    public Queue() {  
        qarray = new int[SIZE];  
        front = 0;  
        rear = -1;  
    }  
  
    // 在队尾追加  
    public void insert(int key) {  
        if (rear == SIZE - 1)  
            rear = -1;  
        qarray[++rear] = key;  
    }  
  
    // 队头删除数据  
    public int remove() {  
        int temp = qarray[front++];  
        if (front == SIZE)  
            front = 0;  
        return temp;  
    }  
  
    public boolean isEmpty() {  
        return rear + 1 == front || front + SIZE - 1 == rear;  
    }  
}

邻接矩阵存储的图,布布扣,bubuko.com

邻接矩阵存储的图

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原文地址：http://blog.csdn.net/li_zhenxing/article/details/28333377