标签:default pre res 结果 ++ this loading 广度优先搜索 white
1.图
图是一种非线性数据结构,是网络模型的抽象模型,图是一组由边连接的节点。
2.图的组成
一个图G = (V,E),V:一组顶点,E:一组边
3.强连通图
任何两个节点,它们之间都有路径到达,称为强连通图
4.邻接矩阵
5.领接表
6.字典
我采用是领接表的方法,所以这里我采用字典来存储,每个顶点和每个顶点所对应的边。
function defaultToString(item){ if(item == null){ return ‘null‘; } if(item == undefined){ return ‘undefined‘; } if(typeof item == ‘string‘ || item instanceof String){ return item; } return `${item}`; } //这个类专门用来保存节点点值和相邻的节点 class ValuePair{ constructor(key,value){ this.key = key; this.value = value; } } //字典类 class Dictionary{ constructor(toStrFn = defaultToString){//接受外面的函数 this.toStrFn = toStrFn;//这里把所有的键名全部转换字符串,方便检索 this.table = {};//专门存储数据 } set(key,value){//设置节点和相邻的节点的方法 if(key != null && value != null){ const tableKey = this.toStrFn(key); this.table[tableKey] = new ValuePair(key,value);//{键名:{key:键名;value:相邻的节点}} return true; } return false; } get(key){//返回节点所相连的节点 const valuePair = this.table[this.toStrFn(key)]; return valuePair == null?undefined:valuePair.value; } hasKey(key){//判断字典中有没有这个节点 return this.table[this.toStrFn(key)] != null; } remove(key){//移除这个节点 if(this.hasKey(key)){ delete this.table[this.toStrFn(key)]; return true; } return false; } clear(){//清除字典的所有内容 this.table = {}; } size(){//返回字典的节点的个数 return Object.keys(this.table).length; } isEmpty(){//判断字典是否为空 return this.size() === 0; } keys(){//获取字典的所有的节点的方法 return this.keyValues().map(valuePair => valuePair.key); } keyValues(){//获取字典的所有的边的方法 const valuePair = []; for(let key in this.table){ if(this.hasKey(key)){ valuePair.push(key); } } return valuePair; } }
7.创建图
class Graph{ constructor(isDirected = false){ this.isDirected = isDirected;//是否为有向图 this.vertices = [];//存储所有节点 this.adjList = new Dictionary();//用字典来存储邻接表 } addVertex(v){//添加顶点 if(!this.vertices.includes(v)){ this.vertices.push(v); this.adjList.set(v,[]);//使用字典的set方法,来存储节点,和邻接节点,这里邻接节点会有很多,所以用数组来存储 } } addEdge(v,w){//给节点添加它的邻接节点 if(!this.vertices.includes(v)){ this.addVertices(v); } if(!this.vertices.includes(w)){ this.addVertices(w); } this.adjList.get(v).push(w);//{key:v;value:[]},在v所对应的数组里面push它的邻接节点 if(!this.isDirected){//有向图就不添加 this.adjList.get(w).push(v); } } getVertices(){//返回所有节点 return this.vertices; } getAdjList(){//返回存储邻接表的字典 return this.adjList; } toString(){//打印邻接表 let s =‘‘; for(let i = 0; i < this.vertices.length; i++){ s+=`${this.vertices[i]}->`; let value = this.adjList.get(this.vertices[i]); for(let j =0; j < value.length;j++){ s+=`${value[j]}`; } s+="\n"; } return s; } }
8.图中添加节点
const printVertex = (value) => console.log(value); const myVertices = [‘A‘, ‘B‘, ‘C‘, ‘D‘, ‘E‘, ‘F‘, ‘G‘, ‘H‘, ‘I‘]; const graph = new Graph(); 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‘);
9.结果
10.广度优先搜索(BFS)
图遍历算法的思想是必须追踪每个第一次访问的节点,并且追踪有哪些节点还没有被完全探索。BFS和DFS都需要指出第一个被访问的节点
这里用三种颜色,来表示节点访问状态。
白色:表示节点还没有访问
灰色:节点访问过,但是还没有完全探索过
黑色:节点访问过,已完全探索过
使用一个Colors变量来存储三种颜色
Colors={ WHITE:0,//还没有访问的 GREY:1,//已经访问过的,但是还没有完全探索的 BLACK:2 //已经访问过的,并且已经全部探索过的 }
初始化所有节点颜色,参数为一个数组,返回一个对象
const initializeColor = vertices =>{ const color = {}; for(let i =0; i < vertices.length; i++){ color[vertices[i]] = Colors.WHITE; } return color; }
BFS代码
const breadthFirstSearch = (graph,startVertex,callback)=>{ const vertices = graph.getVertices(); const adjList = graph.getAdjList(); let color = initializeColor(vertices); const queue = new Queue(); queue.enqueue(startVertex); while(!queue.isEmpty()){ let v = queue.dequeue(); color[v] = Colors.GREY; let neighbor = adjList.get(v); for(let i = 0; i < neighbor.length; i++){ let w = neighbor[i]; if(color[w] == Colors.WHITE){//还没有访问,就执行 queue.enqueue(w); color[w] = Colors.GREY; } } color[v] = Colors.BLACK; if(callback){ callback(v); } } }
结果
11.改进版BFS
class Stack{ constructor(){ this.item = {}; this.count = 0; } push(key){ this.item[this.count] = key; this.count++; } pop(){ if(this.isEmpty()){ return ‘stack is null‘; } this.count--; let result = this.item[this.count]; delete this.item[this.count]; return result; } isEmpty(){ return this.size() === 0; } size(){ return this.count; } peek(){ return this.item[this.count-1]; } } const fromVertex = myVertices[0]; for(let i = 1; i < myVertices.length; i++){ const vertice = myVertices[i]; const path = new Stack(); for(let v = vertice; v != fromVertex; v = shortestPathA.predecessors[v]){ path.push(v); } path.push(fromVertex); let s = path.pop(); while(!path.isEmpty()){ s += `->${path.pop()}`; } console.log(s); }
结果
12.所有代码
//用来表示每个节点的颜色 Colors={ WHITE:0,//还没有访问的 GREY:1,//已经访问过的,但是还没有完全探索的 BLACK:2 //已经访问过的,并且已经全部探索过的 } //初始化节点的颜色,让它们都为白色,vertices是一个数组,专门用来存储节点 const initializeColor = vertices =>{ const color = {}; for(let i =0; i < vertices.length; i++){ color[vertices[i]] = Colors.WHITE; } return color; } class Queue{ constructor(){ this.queue = {}; this.lowerCast = 0; this.biggerCast = 0; } enqueue(key){ this.queue[this.biggerCast] = key; this.biggerCast++; } dequeue(){ if(this.isEmpty())return; let item = this.queue[this.lowerCast]; delete this.queue[this.lowerCast]; this.lowerCast++; return item; } isEmpty(){ return this.size() === 0; } size(){ return this.biggerCast - this.lowerCast; } } function defaultToString(item){ if(item == null){ return ‘null‘; } if(item == undefined){ return ‘undefined‘; } if(typeof item == ‘string‘ || item instanceof String){ return item; } return `${item}`; } //这个类专门用来保存节点点值和相邻的节点 class ValuePair{ constructor(key,value){ this.key = key; this.value = value; } } //字典类 class Dictionary{ constructor(toStrFn = defaultToString){//接受外面的函数 this.toStrFn = toStrFn;//这里把所有的键名全部转换字符串,方便检索 this.table = {};//专门存储数据 } set(key,value){//设置节点和相邻的节点的方法 if(key != null && value != null){ const tableKey = this.toStrFn(key); this.table[tableKey] = new ValuePair(key,value);//{键名:{key:键名;value:相邻的节点}} return true; } return false; } get(key){//返回节点所相连的节点 const valuePair = this.table[this.toStrFn(key)]; return valuePair == null?undefined:valuePair.value; } hasKey(key){//判断字典中有没有这个节点 return this.table[this.toStrFn(key)] != null; } remove(key){//移除这个节点 if(this.hasKey(key)){ delete this.table[this.toStrFn(key)]; return true; } return false; } clear(){//清除字典的所有内容 this.table = {}; } size(){//返回字典的节点的个数 return Object.keys(this.table).length; } isEmpty(){//判断字典是否为空 return this.size() === 0; } keys(){//获取字典的所有的节点的方法 return this.keyValues().map(valuePair => valuePair.key); } keyValues(){//获取字典的所有的边的方法 const valuePair = []; for(let key in this.table){ if(this.hasKey(key)){ valuePair.push(key); } } return valuePair; } } class Graph{ constructor(isDirected = false){ this.isDirected = isDirected;//是否为有向图 this.vertices = [];//存储所有节点 this.adjList = new Dictionary();//用字典来存储邻接表 } addVertex(v){//添加顶点 if(!this.vertices.includes(v)){ this.vertices.push(v); this.adjList.set(v,[]);//使用字典的set方法,来存储节点,和邻接节点,这里邻接节点会有很多,所以用数组来存储 } } addEdge(v,w){//给节点添加它的邻接节点 if(!this.vertices.includes(v)){ this.addVertices(v); } if(!this.vertices.includes(w)){ this.addVertices(w); } this.adjList.get(v).push(w);//{key:v;value:[]},在v所对应的数组里面push它的邻接节点 if(!this.isDirected){//有向图就不添加 this.adjList.get(w).push(v); } } getVertices(){//返回所有节点 return this.vertices; } getAdjList(){//返回存储邻接表的字典 return this.adjList; } toString(){//打印邻接表 let s =‘‘; for(let i = 0; i < this.vertices.length; i++){ s+=`${this.vertices[i]}->`; let value = this.adjList.get(this.vertices[i]); for(let j =0; j < value.length;j++){ s+=`${value[j]}`; } s+="\n"; } return s; } } const myVertices = [‘A‘, ‘B‘, ‘C‘, ‘D‘, ‘E‘, ‘F‘, ‘G‘, ‘H‘, ‘I‘]; const graph = new Graph(); 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()); const breadthFirstSearch = (graph,startVertex,callback)=>{ const vertices = graph.getVertices(); const adjList = graph.getAdjList(); let color = initializeColor(vertices); const queue = new Queue(); queue.enqueue(startVertex); while(!queue.isEmpty()){ let v = queue.dequeue(); color[v] = Colors.GREY; let neighbor = adjList.get(v); for(let i = 0; i < neighbor.length; i++){ let w = neighbor[i]; if(color[w] == Colors.WHITE){//还没有访问,就执行 queue.enqueue(w); color[w] = Colors.GREY; } } color[v] = Colors.BLACK; if(callback){ callback(v); } } } const printVertex = (value) => console.log(value); breadthFirstSearch(graph,myVertices[0],printVertex); const BFS = (graph,startVertex) =>{ const vertices = graph.getVertices();//获取图的所有节点 const adjList = graph.getAdjList();//获取图的字典 const color = initializeColor(vertices);//初始化每个节点的颜色 const queue = new Queue(); const distance = {}; const predecessors = {}; queue.enqueue(startVertex);//把顶点放入队列 for(let i = 0; i < Object.keys(color).length; i++){ distance[vertices[i]] = 0; predecessors[vertices[i]] = null; } while(!queue.isEmpty()){ let u = queue.dequeue(); color[u] = Colors.GREY; let neighbor = adjList.get(u); for(let j =0; j < neighbor.length; j++){ let w = neighbor[j] if(color[w] == Colors.WHITE){ queue.enqueue(w); distance[w] = distance[u]+1; predecessors[w] = u; } } color[u] = Colors.BLACK; } return { distance, predecessors } } const shortestPathA = BFS(graph,myVertices[0]); console.log(shortestPathA); class Stack{ constructor(){ this.item = {}; this.count = 0; } push(key){ this.item[this.count] = key; this.count++; } pop(){ if(this.isEmpty()){ return ‘stack is null‘; } this.count--; let result = this.item[this.count]; delete this.item[this.count]; return result; } isEmpty(){ return this.size() === 0; } size(){ return this.count; } peek(){ return this.item[this.count-1]; } } const fromVertex = myVertices[0]; for(let i = 1; i < myVertices.length; i++){ const vertice = myVertices[i]; const path = new Stack(); for(let v = vertice; v != fromVertex; v = shortestPathA.predecessors[v]){ path.push(v); } path.push(fromVertex); let s = path.pop(); while(!path.isEmpty()){ s += `->${path.pop()}`; } console.log(s); }
标签:default pre res 结果 ++ this loading 广度优先搜索 white
原文地址:https://www.cnblogs.com/MySweetheart/p/13376360.html