/*** 字典*/
class Dictionary {constructor() {this.items = {}}// 向字典添加新的元素set(key, value) {this.items[key] = value}// 通过键值从字典移除键值对应元素delete(key) {if (this.has(key)) {delete this.items[key]return true}return false}// 判断某个键值是否存在字典中has(key) {return key in this.items}// 通过键值查找对应值get(key) {return this.has(key) ? this.items[key] : undefined}// 清空字典clear() {this.items = {}}// 返回字典所包含元素的数量size() {return Object.keys(this.items).length}// 将字典所包含所有键名以数组形式返回keys() {return Object.keys(this.items)}// 将字典所包含所有键名对应值以数组形式返回values() {const values = []for (let k in this.items) {if (this.has(k)) {values.push(this.items[k])}}return values}getItems() {return this.items}
}class Queue {constructor() {this.items = []}// 入队enqueue(element) {this.items.push(element)}// 出队dequeue() {return this.items.shift()}// 获取队列第一个元素front() {return this.items[0]}// 是否为空isEmpty() {return !this.items.length}// 返回队列元素个数size() {return this.items.length}print() {console.log(this.items.toString())}
}/*** 图*/
class Graph {constructor() {// 存储顶点的值this.vertices = []// 散列表this.adjList = new Dictionary()}// 添加一个顶点addVertex(v) {this.vertices.push(v)// 初始化顶点的边this.adjList.set(v, [])}// 添加一个边addEdge(v, w) {// 这里是一个无向图this.adjList.get(v).push(w)if (!this.adjList.get(w)) {this.adjList.set(w, [])}this.adjList.get(w).push(v)}toString() {let s = ""for (let i = 0; i < this.vertices.length; i++) {s += this.vertices[i] + "->"const neighbors = this.adjList.get(this.vertices[i])for (let j = 0; j < neighbors.length; j++) {s += neighbors[j] + " "}s += "n"}return s}/*** 白色：表示该顶点还没有被访问* 灰色：表示该顶点被访问过，但是并未搜索过* 黑色：表示该顶点被访问过且完全搜索过*/initializeColor() {let color = []for (let i = 0; i < this.vertices.length; i++) {color[(this, this.vertices[i])] = "white"}return color}/*** 广度优先通过把顶点存入队列中，最先入队的顶点最先被搜索* @param {*} v 起始顶点* @param {*} callback cb*/bfs(v, callback) {const color = this.initializeColor()const queue = new Queue()// 起始点先入队queue.enqueue(v)while (!queue.isEmpty()) {// 出队const u = queue.dequeue()// 访问相邻的顶点[领接表]const neighbors = this.adjList.get(u)color[u] = "grey"for (let i = 0; i < neighbors.length; i++) {const w = neighbors[i]if (color[w] === "white") {color[w] = "grey"queue.enqueue(w)}}color[u] = "black"if (callback) {callback(u)}}}printNode(value) {console.log(`Visited vertex ${value}`)}
}// test case
const graph = new Graph()
const myVertices = ["A", "B", "C", "D", "E", "F", "G", "H", "I"]
for (let i = 0; i < myVertices.length; i++) {graph.addVertex(myVertices[i])
}
graph.addEdge("A", "B")
graph.addEdge("A", "C")
graph.addEdge("A", "D")
graph.addEdge("C", "D")
graph.addEdge("C", "G")
graph.addEdge("D", "G")
graph.addEdge("D", "H")
graph.addEdge("B", "E")
graph.addEdge("B", "F")
graph.addEdge("E", "I")
console.log(graph.toString())
graph.bfs(myVertices[0], graph.printNode)

...
/*** 广度优先通过把顶点存入队列中，最先入队的顶点最先被搜索* @param {*} v 起始顶点* @param {*} callback cb*/
bfs(v, callback) {const color = this.initializeColor()const queue = new Queue()// 记录起始顶点到其他顶点距离const distance = []// 存储前溯顶点,记录搜索路径// pred[w] 存储的是w是从哪个前驱顶点遍历过来的const pred = []// 起始点先入队queue.enqueue(v)// 初始化起始顶点到其他顶点距离for (let j = 0; j < this.vertices.length; j++) {distance[this.vertices[j]] = 0pred[this.vertices[j]] = null}while (!queue.isEmpty()) {// 出队const u = queue.dequeue()// 访问相邻的顶点[领接表]const neighbors = this.adjList.get(u)color[u] = "grey"for (let i = 0; i < neighbors.length; i++) {const w = neighbors[i]if (color[w] === "white") {color[w] = "grey"// 设置w与u的距离，这里是+1。// 如果是加权图，即为边的权值distance[w] = distance[u] + 1// 记录w顶点是从u顶点遍历而来pred[w] = uqueue.enqueue(w)}}color[u] = "black"if (callback) {callback(u)}}return {distance,pred,}
}

bfsVertexPath(callback) {const path = {}const fromVertex = this.vertices[0]const { pred } = this.bfs(fromVertex, callback)        for (let i = 1; i < this.vertices.length; i++) {let curVertex = this.vertices[i]let startKey = curVertexpath[curVertex] = [curVertex]while (pred[startKey]) {path[curVertex].push(pred[startKey])startKey = pred[startKey]}}return path}