前言
本文中将要介绍的是二叉堆,它是一种特殊的二叉树,也就是堆数据结构,也叫做二叉堆.它能快速高效地找出最大值和最小值,常被用于优先队列. (关于二叉树的相关文章之后会写)
定义
- 它是一棵完全二叉树,表示树的每一层都有左侧和右侧子节点(除了最后一层的叶节点),并且最后一层的叶节点尽可能都是左侧子节点,这叫做结构特性
- 二叉堆不是最小堆就是最大堆.最小堆允许你快速的导出树的最小值,最大值允许你快速导出树的最大值.所有的节点都大于等于(最大堆) 或小于等于(最小堆)每个它的子节点.这叫作堆特性
实现(最小堆)
主要方法
- insert (value): 向方法中插入一个新的值.如果插入成功,返回true,否则返回false
- extract(): 这个方法移除最小值(最小堆)或最大值(最大堆),并返回这个值
- findMinimum():这个方法返回最小值(最小堆)或最大值(最大堆)且不会移除这个值
获取节点 使用一个数组,通过索引值检索父节点,左侧和右侧节点的值.
- getLeftIndex(index): 获取它的左侧子节点位置, 操作逻辑 2 * index + 1
- getRightIndex(index): 获取它的右侧子节点位置, 操作逻辑 2 * index + 1
- getParentIndex(index) : 获取父节点位置,操作逻辑 index / 2
辅助方法
- size(): 获取长度
- isEmpty(): 判断是为空
const Compare = {
LESS_THAN: -1,
BIGGER_THAN: 1,
EQUALS: 0
};
function defaultCompare(a, b) {
if (a === b) {
return Compare.EQUALS;
}
return a < b ? Compare.LESS_THAN : Compare.BIGGER_THAN;
}
function swap(array, a, b) {
[array[a], array[b]] = [array[b], array[a]];
}
class MinHeap {
constructor() {
this.compareFn = defaultCompare;
this.heap = [];
}
getLeftIndex(index) {
return (2 * index) + 1;
}
getRightIndex(index) {
return (2 * index) + 2;
}
getParentIndex(index) {
if (index === 0) {
return undefined;
}
return Math.floor((index - 1) / 2);
}
size() {
return this.heap.length;
}
isEmpty() {
return this.size() <= 0;
}
clear() {
this.heap = [];
}
findMinimum() {
return this.isEmpty() ? undefined : this.heap[0];
}
insert(value) {
if (value != null) {
const index = this.heap.length;
this.heap.push(value);
this.siftUp(index);
return true;
}
return false;
}
siftDown(index) {
let element = index;
const left = this.getLeftIndex(index); //获取左侧子节点
const right = this.getRightIndex(index); //获取右侧子节点
const size = this.size();
if (
left < size &&
this.compareFn(this.heap[element], this.heap[left]) === Compare.BIGGER_THAN
) {
element = left; //交换元素
}
if (
right < size &&
this.compareFn(this.heap[element], this.heap[right]) === Compare.BIGGER_THAN
) {
element = right; //交换元素
}
if (index !== element) {
swap(this.heap, index, element); //交换位置
this.siftDown(element);
}
}
siftUp(index) { //上移操作
let parent = this.getParentIndex(index);
while (
index > 0 &&
this.compareFn(this.heap[parent], this.heap[index]) === Compare.BIGGER_THAN
) {
swap(this.heap, parent, index);
index = parent;
parent = this.getParentIndex(index);
}
}
extract() {
if (this.isEmpty()) {
return undefined;
}
if (this.size() === 1) {
return this.heap.shift();
}
const removedValue = this.heap[0];
this.heap[0] = this.heap.pop();
this.siftDown(0);
return removedValue;
}
getAsArray() {
return this.heap;
}
}
let heap = new MinHeap();
heap.insert(2);
heap.insert(3);
heap.insert(4);
heap.insert(5);
heap.insert(2);
console.log(heap.getAsArray());
console.log('Heap size: ', heap.size()); // 5
console.log('Heap is empty: ', heap.isEmpty()); // false
console.log('Heap min value: ', heap.findMinimum()); // 1
heap = new MinHeap();
for (let i = 1; i < 10; i++) {
heap.insert(i);
}
console.log(heap.getAsArray());
console.log('Extract minimum: ', heap.extract()); // 1
console.log(heap.getAsArray()); // [2, 4, 3, 8, 5, 6, 7, 9]

实现(最大堆)
MaxHeap 类的算法与MinHeap的算法一模一样.不同之处在于 我们要把所有的> (大于)的比较换成<(小于)的比较
function reverseCompare(compareFn){
return (a,b)=> compareFn(b,a)
}
class MaxHeap extends MinHeap{
constructor(){
super()
this.compareFn = reverseCompare(defaultCompare);
}
}
const maxHeap = new MaxHeap();
maxHeap.insert(2);
maxHeap.insert(3);
maxHeap.insert(4);
maxHeap.insert(5);
maxHeap.insert(1);
console.log(maxHeap.getAsArray());
console.log('Heap size: ', maxHeap.size()); // 5
console.log('Heap is empty: ', maxHeap.isEmpty()); // false
console.log('Heap min value: ', maxHeap.findMinimum()); // 5
maxHeap.insert(6);
maxHeap.insert(9);
maxHeap.insert(10);
maxHeap.insert(14);
console.log(maxHeap.getAsArray()); // [14, 10, 6, 9, 1, 3, 5, 2, 4]
console.log('Extract minimum: ', maxHeap.extract()); // 14
console.log(maxHeap.getAsArray()); // [10, 9, 6, 4, 1, 3, 5, 2]
结语
本次分享到这里了 与君共勉. 前端界的一枚小学生!!!