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法律研究JavaScript中的图数据结构
图是一种对象的集合的图形表示，其中某些对象对通过链接连接。相互连接的对象由称为**顶点**的点表示，连接顶点的链接称为**边**。
正式地，图是一对集合(V, E)，其中V是顶点的集合，而E是连接顶点对的边的集合。请查看以下图形 -
在上图中，
V = {a, b, c, d, e}
E = {ab, ac, bd, cd, de}术语
数学图可以在数据结构中表示。我们可以使用顶点数组和二维边数组来表示图。在继续之前，让我们熟悉一些重要的术语 -
顶点 - 图的每个节点都表示为顶点。在以下示例中，带标签的圆圈表示顶点。因此，A 到 G 是顶点。我们可以使用数组来表示它们，如以下图像所示。这里 A 可以用索引 0 标识。B 可以用索引 1 标识，依此类推。
边 - 边表示两个顶点之间的路径或两个顶点之间的线。在以下示例中，从 A 到 B、B 到 C 等的线表示边。我们可以使用二维数组来表示数组，如以下图像所示。这里 AB 可以表示为第 0 行、第 1 列的 1，BC 可以表示为第 1 行、第 2 列的 1，依此类推，保持其他组合为 0。
邻接 - 如果两个节点或顶点通过一条边相互连接，则它们是邻接的。在以下示例中，B 与 A 邻接，C 与 B 邻接，依此类推。
路径 - 路径表示两个顶点之间的一系列边。在以下示例中，ABCD 表示从 A 到 D 的路径。
以下是使用 Javascript 实现图类的完整实现。
示例
const Queue = require("./Queue");
const Stack = require("./Stack");
const PriorityQueue = require("./PriorityQueue");
class Graph {
constructor() {
this.edges = {};
this.nodes = [];
}
addNode(node) {
this.nodes.push(node);
this.edges[node] = [];
}
addEdge(node1, node2, weight = 1) {
this.edges[node1].push({ node: node2, weight: weight });
this.edges[node2].push({ node: node1, weight: weight });
}
addDirectedEdge(node1, node2, weight = 1) {
this.edges[node1].push({ node: node2, weight: weight });
}
// addEdge(node1, node2) {
// this.edges[node1].push(node2);
// this.edges[node2].push(node1);
// }
// addDirectedEdge(node1, node2) {
// this.edges[node1].push(node2);
// }
display() {
let graph = "";
this.nodes.forEach(node => {
graph += node + "->" + this.edges[node].map(n => n.node).join(", ") + "
";
});
console.log(graph);
}
BFS(node) {
let q = new Queue(this.nodes.length);
let explored = new Set();
q.enqueue(node);
explored.add(node);
while (!q.isEmpty()) {
let t = q.dequeue();
console.log(t);
this.edges[t].filter(n => !explored.has(n)).forEach(n => {
explored.add(n);
q.enqueue(n);
});
}
}
DFS(node) {
// Create a Stack and add our initial node in it
let s = new Stack(this.nodes.length);
let explored = new Set();
s.push(node);
// Mark the first node as explored
explored.add(node);
// We'll continue till our Stack gets empty
while (!s.isEmpty()) {
let t = s.pop();
// Log every element that comes out of the Stack
console.log(t);
// 1. In the edges object, we search for nodes this node is directly connected to.
// 2. We filter out the nodes that have already been explored.
// 3. Then we mark each unexplored node as explored and push it to the Stack.
this.edges[t].filter(n => !explored.has(n)).forEach(n => {
explored.add(n);
s.push(n);
});
}
}
topologicalSortHelper(node, explored, s) {
explored.add(node);
this.edges[node].forEach(n => {
if (!explored.has(n)) {
this.topologicalSortHelper(n, explored, s);
}
});
s.push(node);
}
topologicalSort() {
// Create a Stack and add our initial node in it
let s = new Stack(this.nodes.length);
let explored = new Set();
this.nodes.forEach(node => {
if (!explored.has(node)) {
this.topologicalSortHelper(node, explored, s);
}
});
while (!s.isEmpty()) {
console.log(s.pop());
}
}
BFSShortestPath(n1, n2) {
let q = new Queue(this.nodes.length);
let explored = new Set();
let distances = { n1: 0 };
q.enqueue(n1);
explored.add(n1);
while (!q.isEmpty()) {
let t = q.dequeue();
this.edges[t].filter(n => !explored.has(n)).forEach(n => {
explored.add(n);
distances[n] = distances[t] == undefined ? 1 : distances[t] + 1;
q.enqueue(n);
});
}
return distances[n2];
}
primsMST() {
// Initialize graph that'll contain the MST
const MST = new Graph();
if (this.nodes.length === 0) {
return MST;
}
// Select first node as starting node
let s = this.nodes[0];
// Create a Priority Queue and explored set
let edgeQueue = new PriorityQueue(this.nodes.length * this.nodes.length);
let explored = new Set();
explored.add(s);
MST.addNode(s);
// Add all edges from this starting node to the PQ taking weights as priority
this.edges[s].forEach(edge => {
edgeQueue.enqueue([s, edge.node], edge.weight);
});
// Take the smallest edge and add that to the new graph
let currentMinEdge = edgeQueue.dequeue();
while (!edgeQueue.isEmpty()) {
// COntinue removing edges till we get an edge with an unexplored node
while (!edgeQueue.isEmpty() && explored.has(currentMinEdge.data[1])) {
currentMinEdge = edgeQueue.dequeue();
}
let nextNode = currentMinEdge.data[1];
// Check again as queue might get empty without giving back unexplored element
if (!explored.has(nextNode)) {
MST.addNode(nextNode);
MST.addEdge(currentMinEdge.data[0], nextNode, currentMinEdge.priority);
// Again add all edges to the PQ
this.edges[nextNode].forEach(edge => {
edgeQueue.enqueue([nextNode, edge.node], edge.weight);
});
// Mark this node as explored
explored.add(nextNode);
s = nextNode;
}
}
return MST;
}
kruskalsMST() {
// Initialize graph that'll contain the MST
const MST = new Graph();
this.nodes.forEach(node => MST.addNode(node));
if (this.nodes.length === 0) {
return MST;
}
// Create a Priority Queue
let edgeQueue = new PriorityQueue(this.nodes.length * this.nodes.length);
// Add all edges to the Queue:
for (let node in this.edges) {
this.edges[node].forEach(edge => {
edgeQueue.enqueue([node, edge.node], edge.weight);
});
}
let uf = new UnionFind(this.nodes);
// Loop until either we explore all nodes or queue is empty
while (!edgeQueue.isEmpty()) {
// Get the edge data using destructuring
let nextEdge = edgeQueue.dequeue();
let nodes = nextEdge.data;
let weight = nextEdge.priority;
if (!uf.connected(nodes[0], nodes[1])) {
MST.addEdge(nodes[0], nodes[1], weight);
uf.union(nodes[0], nodes[1]);
}
}
return MST;
}
djikstraAlgorithm(startNode) {
let distances = {};
// Stores the reference to previous nodes
let prev = {};
let pq = new PriorityQueue(this.nodes.length * this.nodes.length);
// Set distances to all nodes to be infinite except startNode
distances[startNode] = 0;
pq.enqueue(startNode, 0);
this.nodes.forEach(node => {
if (node !== startNode) distances[node] = Infinity;
prev[node] = null;
});
while (!pq.isEmpty()) {
let minNode = pq.dequeue();
let currNode = minNode.data;
let weight = minNode.priority;
this.edges[currNode].forEach(neighbor => {
let alt = distances[currNode] + neighbor.weight;
if (alt < distances[neighbor.node]) {
distances[neighbor.node] = alt;
prev[neighbor.node] = currNode;
pq.enqueue(neighbor.node, distances[neighbor.node]);
}
});
}
return distances;
}
floydWarshallAlgorithm() {
let dist = {};
for (let i = 0; i < this.nodes.length; i++) {
dist[this.nodes[i]] = {};
// For existing edges assign the dist to be same as weight
this.edges[this.nodes[i]].forEach(e => (dist[this.nodes[i]][e.node] = e.weight));
this.nodes.forEach(n => {
// For all other nodes assign it to infinity
if (dist[this.nodes[i]][n] == undefined)
dist[this.nodes[i]][n] = Infinity;
// For self edge assign dist to be 0
if (this.nodes[i] === n) dist[this.nodes[i]][n] = 0;
});
}
this.nodes.forEach(i => {
this.nodes.forEach(j => {
this.nodes.forEach(k => {
// Check if going from i to k then from k to j is better
// than directly going from i to j. If yes then update
// i to j value to the new value
if (dist[i][k] + dist[k][j] < dist[i][j])
dist[i][j] = dist[i][k] + dist[k][j];
});
});
});
return dist;
}
}
class UnionFind {
constructor(elements) {
// Number of disconnected components
this.count = elements.length;
// Keep Track of connected components
this.parent = {};
// Initialize the data structure such that all elements have themselves as parents
elements.forEach(e => (this.parent[e] = e));
}
union(a, b) {
let rootA = this.find(a);
let rootB = this.find(b);
// Roots are same so these are already connected.
if (rootA === rootB) return;
// Always make the element with smaller root the parent.
if (rootA < rootB) {
if (this.parent[b] != b) this.union(this.parent[b], a);
this.parent[b] = this.parent[a];
} else {
if (this.parent[a] != a) this.union(this.parent[a], b);
this.parent[a] = this.parent[b];
}
}
// Returns final parent of a node
find(a) {
while (this.parent[a] !== a) {
a = this.parent[a];
}
return a;
}
// Checks connectivity of the 2 nodes
connected(a, b) {
return this.find(a) === this.find(b);
}
}
更新于: 2020年6月15日
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