mirror of
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703 lines
19 KiB
Markdown
703 lines
19 KiB
Markdown
---
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comments: true
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---
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# 二叉树
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「二叉树 Binary Tree」是一种非线性数据结构,代表着祖先与后代之间的派生关系,体现着 “一分为二” 的分治逻辑。类似于链表,二叉树也是以结点为单位存储的,结点包含「值」和两个「指针」。
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=== "Java"
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```java title=""
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/* 链表结点类 */
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class TreeNode {
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int val; // 结点值
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TreeNode left; // 左子结点指针
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TreeNode right; // 右子结点指针
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TreeNode(int x) { val = x; }
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}
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```
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=== "C++"
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```cpp title=""
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/* 链表结点结构体 */
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struct TreeNode {
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int val; // 结点值
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TreeNode *left; // 左子结点指针
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TreeNode *right; // 右子结点指针
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TreeNode(int x) : val(x), left(nullptr), right(nullptr) {}
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};
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```
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=== "Python"
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```python title=""
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""" 链表结点类 """
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class TreeNode:
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def __init__(self, val=0, left=None, right=None):
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self.val = val # 结点值
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self.left = left # 左子结点指针
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self.right = right # 右子结点指针
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```
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=== "Go"
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```go title=""
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""" 链表结点类 """
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type TreeNode struct {
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Val int
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Left *TreeNode
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Right *TreeNode
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}
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""" 结点初始化方法 """
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func NewTreeNode(v int) *TreeNode {
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return &TreeNode{
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Left: nil,
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Right: nil,
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Val: v,
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}
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}
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```
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=== "JavaScript"
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```js title=""
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/* 链表结点类 */
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function TreeNode(val, left, right) {
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this.val = (val === undefined ? 0 : val); // 结点值
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this.left = (left === undefined ? null : left); // 左子结点指针
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this.right = (right === undefined ? null : right); // 右子结点指针
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}
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```
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=== "TypeScript"
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```typescript title=""
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/* 链表结点类 */
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class TreeNode {
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val: number;
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left: TreeNode | null;
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right: TreeNode | null;
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constructor(val?: number, left?: TreeNode | null, right?: TreeNode | null) {
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this.val = val === undefined ? 0 : val; // 结点值
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this.left = left === undefined ? null : left; // 左子结点指针
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this.right = right === undefined ? null : right; // 右子结点指针
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}
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}
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```
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=== "C"
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```c title=""
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```
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=== "C#"
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```csharp title=""
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```
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结点的两个指针分别指向「左子结点 Left Child Node」和「右子结点 Right Child Node」,并且称该结点为两个子结点的「父结点 Parent Node」。给定二叉树某结点,将左子结点以下的树称为该结点的「左子树 Left Subtree」,右子树同理。
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![binary_tree_definition](binary_tree.assets/binary_tree_definition.png)
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<p align="center"> Fig. 子结点与子树 </p>
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需要注意,父结点、子结点、子树是可以向下递推的。例如,如果将上图的「结点 2」看作父结点,那么其左子结点和右子结点分别为「结点 4」和「结点 5」,左子树和右子树分别为「结点 4 以下的树」和「结点 5 以下的树」。
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## 二叉树常见术语
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二叉树的术语较多,建议尽量理解并记住。后续可能遗忘,可以在需要使用时回来查看确认。
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- 「根结点 Root Node」:二叉树最顶层的结点,其没有父结点;
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- 「叶结点 Leaf Node」:没有子结点的结点,其两个指针都指向 $\text{null}$ ;
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- 结点所处「层 Level」:从顶置底依次增加,根结点所处层为 1 ;
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- 结点「度 Degree」:结点的子结点数量,二叉树中度的范围是 0, 1, 2 ;
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- 「边 Edge」:连接两个结点的边,即结点指针;
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- 二叉树「高度」:二叉树中根结点到最远叶结点走过边的数量;
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- 结点「深度 Depth」 :根结点到该结点走过边的数量;
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- 结点「高度 Height」:最远叶结点到该结点走过边的数量;
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![binary_tree_terminology](binary_tree.assets/binary_tree_terminology.png)
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<p align="center"> Fig. 二叉树的常见术语 </p>
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!!! tip "高度与深度的定义"
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值得注意,我们通常将「高度」和「深度」定义为“走过边的数量”,而有些题目或教材会将其定义为“走过结点的数量”,此时高度或深度都需要 + 1 。
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## 二叉树最佳和最差结构
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当二叉树的每层的结点都被填满时,达到「完美二叉树」;而当所有结点都偏向一边时,二叉树退化为「链表」。
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![binary_tree_corner_cases](binary_tree.assets/binary_tree_corner_cases.png)
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<p align="center"> Fig. 二叉树的最佳和最差结构 </p>
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如下表所示,在最佳和最差结构下,二叉树的叶结点数量、结点总数、高度等达到极大或极小值。
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<div class="center-table" markdown>
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| | 完美二叉树 | 链表 |
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| ----------------------------- | ---------- | ---------- |
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| 第 $i$ 层的结点数量 | $2^{i-1}$ | $1$ |
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| 树的高度为 $h$ 时的叶结点数量 | $2^h$ | $1$ |
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| 树的高度为 $h$ 时的结点总数 | $2^{h+1} - 1$ | $h + 1$ |
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| 树的结点总数为 $n$ 时的高度 | $\log_2 (n+1) - 1$ | $n - 1$ |
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</div>
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## 二叉树基本操作
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**初始化二叉树。** 与链表类似,先初始化结点,再构建引用指向(即指针)。
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=== "Java"
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```java title="binary_tree.java"
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// 初始化结点
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TreeNode n1 = new TreeNode(1);
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TreeNode n2 = new TreeNode(2);
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TreeNode n3 = new TreeNode(3);
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TreeNode n4 = new TreeNode(4);
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TreeNode n5 = new TreeNode(5);
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// 构建引用指向(即指针)
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n1.left = n2;
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n1.right = n3;
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n2.left = n4;
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n2.right = n5;
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```
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=== "C++"
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```cpp title="binary_tree.cpp"
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/* 初始化二叉树 */
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// 初始化结点
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TreeNode* n1 = new TreeNode(1);
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TreeNode* n2 = new TreeNode(2);
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TreeNode* n3 = new TreeNode(3);
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TreeNode* n4 = new TreeNode(4);
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TreeNode* n5 = new TreeNode(5);
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// 构建引用指向(即指针)
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n1->left = n2;
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n1->right = n3;
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n2->left = n4;
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n2->right = n5;
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```
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=== "Python"
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```python title="binary_tree.py"
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```
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=== "Go"
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```go title="binary_tree.go"
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/* 初始化二叉树 */
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// 初始化结点
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n1 := NewTreeNode(1)
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n2 := NewTreeNode(2)
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n3 := NewTreeNode(3)
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n4 := NewTreeNode(4)
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n5 := NewTreeNode(5)
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// 构建引用指向(即指针)
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n1.Left = n2
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n1.Right = n3
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n2.Left = n4
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n2.Right = n5
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```
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=== "JavaScript"
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```js title="binary_tree.js"
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/* 初始化二叉树 */
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// 初始化结点
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let n1 = new TreeNode(1),
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n2 = new TreeNode(2),
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n3 = new TreeNode(3),
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n4 = new TreeNode(4),
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n5 = new TreeNode(5);
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// 构建引用指向(即指针)
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n1.left = n2;
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n1.right = n3;
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n2.left = n4;
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n2.right = n5;
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```
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=== "TypeScript"
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```typescript title="binary_tree.ts"
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/* 初始化二叉树 */
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// 初始化结点
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let n1 = new TreeNode(1),
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n2 = new TreeNode(2),
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n3 = new TreeNode(3),
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n4 = new TreeNode(4),
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n5 = new TreeNode(5);
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// 构建引用指向(即指针)
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n1.left = n2;
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n1.right = n3;
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n2.left = n4;
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n2.right = n5;
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```
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=== "C"
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```c title="binary_tree.c"
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```
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=== "C#"
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```csharp title="binary_tree.cs"
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```
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**插入与删除结点。** 与链表类似,插入与删除结点都可以通过修改指针实现。
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![binary_tree_add_remove](binary_tree.assets/binary_tree_add_remove.png)
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<p align="center"> Fig. 在二叉树中插入与删除结点 </p>
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=== "Java"
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```java title="binary_tree.java"
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TreeNode P = new TreeNode(0);
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// 在 n1 -> n2 中间插入结点 P
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n1.left = P;
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P.left = n2;
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// 删除结点 P
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n1.left = n2;
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```
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=== "C++"
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```cpp title="binary_tree.cpp"
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/* 插入与删除结点 */
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TreeNode* P = new TreeNode(0);
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// 在 n1 -> n2 中间插入结点 P
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n1->left = P;
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P->left = n2;
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// 删除结点 P
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n1->left = n2;
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```
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=== "Python"
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```python title="binary_tree.py"
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```
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=== "Go"
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```go title="binary_tree.go"
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/* 插入与删除结点 */
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// 在 n1 -> n2 中间插入结点 P
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p := NewTreeNode(0)
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n1.Left = p
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p.Left = n2
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// 删除结点 P
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n1.Left = n2
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```
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=== "JavaScript"
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```js title="binary_tree.js"
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/* 插入与删除结点 */
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let P = new TreeNode(0);
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// 在 n1 -> n2 中间插入结点 P
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n1.left = P;
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P.left = n2;
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// 删除结点 P
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n1.left = n2;
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```
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=== "TypeScript"
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```typescript title="binary_tree.ts"
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/* 插入与删除结点 */
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const P = new TreeNode(0);
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// 在 n1 -> n2 中间插入结点 P
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n1.left = P;
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P.left = n2;
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// 删除结点 P
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n1.left = n2;
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```
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=== "C"
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```c title="binary_tree.c"
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```
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=== "C#"
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```csharp title="binary_tree.cs"
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```
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!!! note
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插入结点会改变二叉树的原有逻辑结构,删除结点往往意味着删除了该结点的所有子树。因此,二叉树中的插入与删除一般都是由一套操作配合完成的,这样才能实现有意义的操作。
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## 二叉树遍历
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非线性数据结构的遍历操作比线性数据结构更加复杂,往往需要使用搜索算法来实现。常见的二叉树遍历方式有层序遍历、前序遍历、中序遍历、后序遍历。
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### 层序遍历
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「层序遍历 Hierarchical-Order Traversal」从顶至底、一层一层地遍历二叉树,并在每层中按照从左到右的顺序访问结点。
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层序遍历本质上是「广度优先搜索 Breadth-First Traversal」,其体现着一种 “一圈一圈向外” 的层进遍历方式。
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![binary_tree_bfs](binary_tree.assets/binary_tree_bfs.png)
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<p align="center"> Fig. 二叉树的层序遍历 </p>
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广度优先遍历一般借助「队列」来实现。队列的规则是 “先进先出” ,广度优先遍历的规则是 ”一层层平推“ ,两者背后的思想是一致的。
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=== "Java"
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```java title="binary_tree_bfs.java"
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/* 层序遍历 */
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List<Integer> hierOrder(TreeNode root) {
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// 初始化队列,加入根结点
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Queue<TreeNode> queue = new LinkedList<>() {{ add(root); }};
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// 初始化一个列表,用于保存遍历序列
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List<Integer> list = new ArrayList<>();
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while (!queue.isEmpty()) {
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TreeNode node = queue.poll(); // 队列出队
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list.add(node.val); // 保存结点值
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if (node.left != null)
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queue.offer(node.left); // 左子结点入队
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if (node.right != null)
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queue.offer(node.right); // 右子结点入队
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}
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return list;
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}
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```
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=== "C++"
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```cpp title="binary_tree_bfs.cpp"
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/* 层序遍历 */
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vector<int> hierOrder(TreeNode* root) {
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// 初始化队列,加入根结点
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queue<TreeNode*> queue;
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queue.push(root);
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// 初始化一个列表,用于保存遍历序列
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vector<int> vec;
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while (!queue.empty()) {
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TreeNode* node = queue.front();
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queue.pop(); // 队列出队
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vec.push_back(node->val); // 保存结点
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if (node->left != nullptr)
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queue.push(node->left); // 左子结点入队
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if (node->right != nullptr)
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queue.push(node->right); // 右子结点入队
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}
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return vec;
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}
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```
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=== "Python"
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```python title="binary_tree_bfs.py"
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```
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=== "Go"
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```go title="binary_tree_bfs.go"
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/* 层序遍历 */
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func levelOrder(root *TreeNode) []int {
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// 初始化队列,加入根结点
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queue := list.New()
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queue.PushBack(root)
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// 初始化一个切片,用于保存遍历序列
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nums := make([]int, 0)
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for queue.Len() > 0 {
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// poll
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node := queue.Remove(queue.Front()).(*TreeNode)
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// 保存结点
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nums = append(nums, node.Val)
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if node.Left != nil {
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// 左子结点入队
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queue.PushBack(node.Left)
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}
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if node.Right != nil {
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// 右子结点入队
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queue.PushBack(node.Right)
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}
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}
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return nums
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}
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```
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=== "JavaScript"
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```js title="binary_tree_bfs.js"
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/* 层序遍历 */
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function hierOrder(root) {
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// 初始化队列,加入根结点
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let queue = [root];
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// 初始化一个列表,用于保存遍历序列
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let list = [];
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while (queue.length) {
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let node = queue.shift(); // 队列出队
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list.push(node.val); // 保存结点
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if (node.left)
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queue.push(node.left); // 左子结点入队
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if (node.right)
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queue.push(node.right); // 右子结点入队
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}
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return list;
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}
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```
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=== "TypeScript"
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```typescript title="binary_tree_bfs.ts"
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/* 层序遍历 */
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function hierOrder(root: TreeNode | null): number[] {
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// 初始化队列,加入根结点
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const queue = [root];
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// 初始化一个列表,用于保存遍历序列
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const list: number[] = [];
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while (queue.length) {
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let node = queue.shift() as TreeNode; // 队列出队
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list.push(node.val); // 保存结点
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if (node.left) {
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queue.push(node.left); // 左子结点入队
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}
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if (node.right) {
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queue.push(node.right); // 右子结点入队
|
||
}
|
||
}
|
||
return list;
|
||
}
|
||
```
|
||
|
||
=== "C"
|
||
|
||
```c title="binary_tree_bfs.c"
|
||
|
||
```
|
||
|
||
=== "C#"
|
||
|
||
```csharp title="binary_tree_bfs.cs"
|
||
|
||
```
|
||
|
||
### 前序、中序、后序遍历
|
||
|
||
相对地,前、中、后序遍历皆属于「深度优先遍历 Depth-First Traversal」,其体现着一种 “先走到尽头,再回头继续” 的回溯遍历方式。
|
||
|
||
如下图所示,左侧是深度优先遍历的的示意图,右上方是对应的递归实现代码。深度优先遍历就像是绕着整个二叉树的外围 “走” 一圈,走的过程中,在每个结点都会遇到三个位置,分别对应前序遍历、中序遍历、后序遍历。
|
||
|
||
![binary_tree_dfs](binary_tree.assets/binary_tree_dfs.png)
|
||
|
||
<p align="center"> Fig. 二叉树的前 / 中 / 后序遍历 </p>
|
||
|
||
<div class="center-table" markdown>
|
||
|
||
| 位置 | 含义 | 此处访问结点时对应 |
|
||
| ---------- | ------------------------------------ | ----------------------------- |
|
||
| 橙色圆圈处 | 刚进入此结点,即将访问该结点的左子树 | 前序遍历 Pre-Order Traversal |
|
||
| 蓝色圆圈处 | 已访问完左子树,即将访问右子树 | 中序遍历 In-Order Traversal |
|
||
| 紫色圆圈处 | 已访问完左子树和右子树,即将返回 | 后序遍历 Post-Order Traversal |
|
||
|
||
</div>
|
||
|
||
=== "Java"
|
||
|
||
```java title="binary_tree_dfs.java"
|
||
/* 前序遍历 */
|
||
void preOrder(TreeNode root) {
|
||
if (root == null) return;
|
||
// 访问优先级:根结点 -> 左子树 -> 右子树
|
||
list.add(root.val);
|
||
preOrder(root.left);
|
||
preOrder(root.right);
|
||
}
|
||
|
||
/* 中序遍历 */
|
||
void inOrder(TreeNode root) {
|
||
if (root == null) return;
|
||
// 访问优先级:左子树 -> 根结点 -> 右子树
|
||
inOrder(root.left);
|
||
list.add(root.val);
|
||
inOrder(root.right);
|
||
}
|
||
|
||
/* 后序遍历 */
|
||
void postOrder(TreeNode root) {
|
||
if (root == null) return;
|
||
// 访问优先级:左子树 -> 右子树 -> 根结点
|
||
postOrder(root.left);
|
||
postOrder(root.right);
|
||
list.add(root.val);
|
||
}
|
||
```
|
||
|
||
=== "C++"
|
||
|
||
```cpp title="binary_tree_dfs.cpp"
|
||
/* 前序遍历 */
|
||
void preOrder(TreeNode* root) {
|
||
if (root == nullptr) return;
|
||
// 访问优先级:根结点 -> 左子树 -> 右子树
|
||
vec.push_back(root->val);
|
||
preOrder(root->left);
|
||
preOrder(root->right);
|
||
}
|
||
|
||
/* 中序遍历 */
|
||
void inOrder(TreeNode* root) {
|
||
if (root == nullptr) return;
|
||
// 访问优先级:左子树 -> 根结点 -> 右子树
|
||
inOrder(root->left);
|
||
vec.push_back(root->val);
|
||
inOrder(root->right);
|
||
}
|
||
|
||
/* 后序遍历 */
|
||
void postOrder(TreeNode* root) {
|
||
if (root == nullptr) return;
|
||
// 访问优先级:左子树 -> 右子树 -> 根结点
|
||
postOrder(root->left);
|
||
postOrder(root->right);
|
||
vec.push_back(root->val);
|
||
}
|
||
```
|
||
|
||
=== "Python"
|
||
|
||
```python title="binary_tree_dfs.py"
|
||
|
||
```
|
||
|
||
=== "Go"
|
||
|
||
```go title="binary_tree_dfs.go"
|
||
/* 前序遍历 */
|
||
func preOrder(node *TreeNode) {
|
||
if node == nil {
|
||
return
|
||
}
|
||
// 访问优先级:根结点 -> 左子树 -> 右子树
|
||
nums = append(nums, node.Val)
|
||
preOrder(node.Left)
|
||
preOrder(node.Right)
|
||
}
|
||
|
||
/* 中序遍历 */
|
||
func inOrder(node *TreeNode) {
|
||
if node == nil {
|
||
return
|
||
}
|
||
// 访问优先级:左子树 -> 根结点 -> 右子树
|
||
inOrder(node.Left)
|
||
nums = append(nums, node.Val)
|
||
inOrder(node.Right)
|
||
}
|
||
|
||
/* 后序遍历 */
|
||
func postOrder(node *TreeNode) {
|
||
if node == nil {
|
||
return
|
||
}
|
||
// 访问优先级:左子树 -> 右子树 -> 根结点
|
||
postOrder(node.Left)
|
||
postOrder(node.Right)
|
||
nums = append(nums, node.Val)
|
||
}
|
||
```
|
||
|
||
=== "JavaScript"
|
||
|
||
```js title="binary_tree_dfs.js"
|
||
/* 前序遍历 */
|
||
function preOrder(root){
|
||
if (root === null) return;
|
||
// 访问优先级:根结点 -> 左子树 -> 右子树
|
||
list.push(root.val);
|
||
preOrder(root.left);
|
||
preOrder(root.right);
|
||
}
|
||
|
||
/* 中序遍历 */
|
||
function inOrder(root) {
|
||
if (root === null) return;
|
||
// 访问优先级:左子树 -> 根结点 -> 右子树
|
||
inOrder(root.left);
|
||
list.push(root.val);
|
||
inOrder(root.right);
|
||
}
|
||
|
||
/* 后序遍历 */
|
||
function postOrder(root) {
|
||
if (root === null) return;
|
||
// 访问优先级:左子树 -> 右子树 -> 根结点
|
||
postOrder(root.left);
|
||
postOrder(root.right);
|
||
list.push(root.val);
|
||
}
|
||
```
|
||
|
||
=== "TypeScript"
|
||
|
||
```typescript title="binary_tree_dfs.ts"
|
||
/* 前序遍历 */
|
||
function preOrder(root: TreeNode | null): void {
|
||
if (root === null) {
|
||
return;
|
||
}
|
||
// 访问优先级:根结点 -> 左子树 -> 右子树
|
||
list.push(root.val);
|
||
preOrder(root.left);
|
||
preOrder(root.right);
|
||
}
|
||
|
||
/* 中序遍历 */
|
||
function inOrder(root: TreeNode | null): void {
|
||
if (root === null) {
|
||
return;
|
||
}
|
||
// 访问优先级:左子树 -> 根结点 -> 右子树
|
||
inOrder(root.left);
|
||
list.push(root.val);
|
||
inOrder(root.right);
|
||
}
|
||
|
||
/* 后序遍历 */
|
||
function postOrder(root: TreeNode | null): void {
|
||
if (root === null) {
|
||
return;
|
||
}
|
||
// 访问优先级:左子树 -> 右子树 -> 根结点
|
||
postOrder(root.left);
|
||
postOrder(root.right);
|
||
list.push(root.val);
|
||
}
|
||
```
|
||
|
||
=== "C"
|
||
|
||
```c title="binary_tree_dfs.c"
|
||
|
||
```
|
||
|
||
=== "C#"
|
||
|
||
```csharp title="binary_tree_dfs.cs"
|
||
|
||
```
|
||
|
||
!!! note
|
||
|
||
使用循环一样可以实现前、中、后序遍历,但代码相对繁琐,有兴趣的同学可以自行实现。
|