leetcode:101. Symmetric Tree

@transactional滥用导致数据源连接池耗尽问题 阅读详情

敢不敢把问题理清楚点???懒,写的啥破玩意儿,,,,回来继续写

/**
 * Definition for a binary tree node.
 * struct TreeNode {
 *     int val;
 *     TreeNode *left;
 *     TreeNode *right;
 *     TreeNode(int x) : val(x), left(NULL), right(NULL) {}
 * };
 */

/*
1.判断left 如果不等于right就错
2.分治

3.什么遍历方法?

*/
class Solution {
    TreeNode* travl_left(TreeNode* root){
        TreeNode* tmp=NULL;
        tmp=root->left;
        printf("%d\n",tmp->val);
        return tmp;
    }
    
   TreeNode* travl_right(TreeNode* root){
        TreeNode* tmp=NULL;
        tmp=root->right;
        printf("%d\n",tmp->val);
        return tmp;
    }
    
    void equel(TreeNode* root){
        if(root!=NULL){
        int l = travl_left(root->left)->val;
        int r = travl_right(root->right)->val;
        if(l!=r) return false;
        return true;    
        }
        
    }
    
public:
    bool isSymmetric(TreeNode* root) {
        
       
    
    }
};

1.问题分析:

对称意味着出现偶数次,(非必要条件)

一层一层扫比较保险。每到一层就切换成两路

 

动态2的n次方,,,怎么写?

放弃治疗:看大佬答案去。

 

别人家的代码,,,

 

CPP Iterative and Recursive Solutions (Easy to Understand):
https://leetcode.com/problems/symmetric-tree/discuss/146961/Both-Iterative-and-Recursive-Solutions

 

bestnick

131

December 4, 2018 3:32 PM

Read More

Bonus point

 

Iterative:

 

/**
 * Definition for a binary tree node.
 * struct TreeNode {
 *     int val;
 *     TreeNode *left;
 *     TreeNode *right;
 *     TreeNode(int x) : val(x), left(NULL), right(NULL) {}
 * };
 */
class Solution {
public:
    
    bool isSymmetric(TreeNode* root) {
        if (!root) return true;
        stack<pair<TreeNode*, TreeNode*>> stck;
        stck.push(make_pair(root->left, root->right));
        
        while (!stck.empty()) {
            auto p=stck.top(); stck.pop();
            if (p.first == NULL && p.second == NULL) {
                continue;
            }
            if (p.first == NULL || p.second == NULL) {
                return false;
            }
            
            if (p.first->val != p.second->val) {
                return false;
            }
            
            stck.push(make_pair(p.first->right, p.second->left));
            stck.push(make_pair(p.first->left, p.second->right));
        }
        return true;
    }
};

 

Recursive:

 

/**
 * Definition for a binary tree node.
 * struct TreeNode {
 *     int val;
 *     TreeNode *left;
 *     TreeNode *right;
 *     TreeNode(int x) : val(x), left(NULL), right(NULL) {}
 * };
 */
class Solution {
    bool helper(TreeNode* p, TreeNode* q) {
        if (!p && !q) {
            return true;
        } else if (!p || !q) {
            return false;
        }        
        if (p->val != q->val) {
            return false;
        }        
        return helper(p->left,q->right) && helper(p->right, q->left); 
    }
public:
    bool isSymmetric(TreeNode *root) {
        if (!root) return true;
       return helper(root->left, root->right);
    }
};

 

 

 

sohammehta

572

July 8, 2018 4:04 PM

Read More

 

class Solution {
public:
    
    bool isSymmetric(TreeNode* root) {
    return isMirrorIterative(root);
    //return isMirrorR(root,root);
}

    bool isMirrorR(TreeNode* t1, TreeNode* t2) {
    if (!t1 && !t2) return true;
    if (!t1  || !t2) return false;
    return (t1->val == t2->val) && isMirrorR(t1->right, t2->left)  && isMirrorR(t1->left, t2->right);
}

    bool isMirrorIterative(TreeNode *root) {
        
        if (root == NULL) return true;
        
        queue<TreeNode*> q1, q2;
        q1.push(root->left);
        q2.push(root->right);

        while (!q1.empty() && !q2.empty())
        {
            TreeNode *left = q1.front(); q1.pop();
            TreeNode *right = q2.front(); q2.pop();
            
            if (!left && !right) continue;
            if (!left || !right) return false;
            if (left->val != right->val) return false;
            
            q1.push(left->left);
            q1.push(left->right);
            q2.push(right->right);
            q2.push(right->left);
        }
        
        return true;
    }
    
};

补一下queue 和stack

 

 

LM Studio Bionic:本地AI智能体从部署到实战完整指南 AI智能体作为能够自主规划、执行多步任务的人工智能系统,其核心原理在于结合大语言模型的推理能力与工具调用机制。通过状态保持和任务分解算法,智能体实现了从单次问答到持续协作的技术跃迁。在工程实践中,这种架构显著降低了复杂任务自动化的门槛,特别适用于数据敏感场景和定制化需求。以Tesla V100等专业硬件为例,本地部署能充分发挥其大显存优势,支持更高精度的模型量化格式。在实际应用层面,智能体可无缝集成到代码审查、文档生成等开发工作流中,而LM Studio Bionic通过统一界面整合了模型服务、任务规划器等 阅读详情

相关推荐

从零理解域随机化:通过Isaac Lab的events.py掌握Sim-to-Real关键技术

本文深入解析域随机化技术如何通过Isaac Lab的events.py实现Sim-to-Real迁移,提升机器人策略在真实世界的鲁棒性。从刚体属性到环境条件的系统级随机化,详细介绍了工程实践中的关键参数配置与调试技巧,帮助开发者有效缩小仿真与现实的差距。

weixin_29062613的博客 260

入坑LeetCode: 101. Symmetric Tree

入坑LeetCode: 101. Symmetric Tree 1、解题思路 判断一棵二叉树是否对称,其实就是递归判断,层层判断,将左二子与右儿子比较,将左二子的右儿子与右儿子的左二子比较,如果始终相同,则返回true。 2、代码 bool isymmetric(struct TreeNode* left,struct TreeNode* right){ if(left == NULL &...

chicwzh的博客 257

【Linux】常见指令的使用

Linux常用指令

qq_74897518的博客 1479

leetcode 101 : Symmetric Tree

问题描述 java实现 //way1 递归 /** * Definition for a binary tree node. * public class TreeNode { * int val; * TreeNode left; * TreeNode right; * TreeNode(int x) { val = x; } * } */ ...

zero 217

从Power到HO:一文搞懂主流脑图谱在DPABI里怎么选,让你的rs-fMRI分析更精准

本文深入解析了rs-fMRI分析中主流脑图谱(如Power、HO、AAL等)在DPABI中的科学选择策略,帮助研究者根据不同的神经科学研究目标优化分析流程。从解剖学图谱到功能连接图谱的特性对比,到DPABI实操中的多图谱分析工作流,提供了全面的方法论指导,助力提升静息态功能磁共振成像(rs-fMRI)研究的精准度和可重复性。

weixin_30654583的博客 403

【ESP32】1.编译、烧录、创建工程

本文介绍了在VSCode中搭建ESP32开发环境并烧录"Hello World"工程的过程。首先通过串口工具成功下载固件后,解决了OpenOCD报错问题。针对头文件无法识别的问题,修改了c_cpp_properties.json配置文件,调整了编译器路径和IntelliSense模式。文章还详细说明了创建新工程的步骤,包括选择芯片型号和项目模板,并验证了串口打印功能。最后提醒在项目构建时要等待完成,避免文件缺失。整个流程涵盖了环境搭建、代码烧录、调试及新项目创建等关键环节。

梁山1号 1485

LeetCode 101: Symmetric Tree

Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree [1,2,2,3,4,4,3] is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3 But ...

forever_zzx的专栏 202

【ESP 保姆级教程】第三方库篇 ——TimeLib库,一个非常Nice的时间库,结合第三方进行校正时间

Time,这是一个为Arduino提供时间支持的库。 这个库的例子中会教会我们多种方式去获取时间: - 一个`RTC`时钟 - 网络`NTP`时间 - `GPS`时间

单片机菜鸟哥的博客 3674

Leetcode 101:Symmetric Tree

Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree [1,2,2,3,4,4,3] is symmetric: But the following [1,2,2,null,3,null,3] is n...

qq_34314736的博客 143

第1章:认识OpenClaw

OpenClaw是一个开源的 AI 智能体 Gateway 网关项目名称演变:OpenClaw 原名 Clawdbot,因 Anthropic 商标顾虑在 2026年1月27日更名为 Moltbot(过渡名),最终在1月30日确定为 OpenClaw。三个名字本质是同一个项目,功能完全一致。🏠在你的电脑上运行:完全本地部署,数据隐私有保障📁访问本地文件:可以搜索、读取、编辑你电脑上的文件🔌扩展能力:通过Skills(技能)系统无限扩展功能💬多平台使用。

Maynor的博客 2834

Google Earth Engine:LandTrendr (Landsat Collection 2)全更新使用(代码调用+UI交互)

本文介绍了使用Landsat Collection 2进行连续变化监测的分析方法,包含代码实现和可视化界面两部分。代码部分通过Google Earth Engine平台运行LandTrendr算法,设置参数包括时间范围、光谱指数、变化检测阈值等,最终生成变化幅度和检测年份的可视化图层,并将结果导出至Google Drive。可视化界面部分展示了基于LandTrendr的土地覆盖变化结果,结合Landsat影像进行分析。整个方法实现了从数据处理到结果展示的完整流程,适用于森林覆盖等环境变化监测。

此星光明博客 211

leetcode[101]:Symmetric Tree

Symmetric TreeGiven a binary tree, check whether it is a mirror of itself (ie, symmetric around its center).Note: Bonus points if you could solve it both recursively and iteratively./** * Definition

hahubaba的博客 448

SPRD Android 14 通过属性控制系统设置显示双栏或者单栏

SPRD Android 14 通过属性控制系统设置显示双栏或者单栏

m0_60898338的博客 1118

LeetCode : 101. Symmetric Tree

LeetCode 101 对称树 比较简单的题目 利用递归-分治方法解决就ok了(虽然递归很占内存...) 直接看代码吧public int depth(TreeNode root) { if(root == null) { return 0; }else { int leftLength = depth(root.

JZ(Bao哥)的Galaxy 400

VC中sqlite的使用

在VC中对sqlite的使用,实现数据库大批数据的快速插入,读取数据库中的数据在ListCtrl中显示

大中型超市收银系统(可直接运行)

光步前台收银系统系统功能:商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传,基本资料下载,开钱箱、小票打印、收款结算,支持多币种付款方式(现金、储值卡、礼券、微信等),支持多种促销。运行环境:.net 4.0登陆信息用户名:1001 密码:123456内置商品信息,商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开

LeetCode 101:Symmetric Tree

Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3 But the f

henryzhihua 1919

计算机控制技术及应用

《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

leetcode 101: Symmetric Tree

解题思路: 先比较根节点的左子节点和右子节点,然后递归判断,左子节点的左子树与右子节点的右子树,左子节点的右子树与右子节点的左子树原题目:Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center).For example, this binary tree [1,2,2,

永远不要给自己找借口 352

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

LeetCode: 101. Symmetric Tree

LeetCode: 101. Symmetric Tree 题目描述 Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree [1,2,2,3,4,4,3] is symmetric: ...

杨领well的专栏 739

Leetcode: 101. Symmetric Tree

题目 Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree [1,2,2,3,4,4,3] is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3 B...

liveway6的博客 159

LeetCode101:Symmetric Tree

Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree [1,2,2,3,4,4,3] is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3 ...

卷卷萌的博客 217

Leetcode学习笔记:#101. Symmetric Tree

Leetcode学习笔记:#101. Symmetric Tree Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree [1,2,2,3,4,4,3] is symmetric: 1 / 2 2...

ccystewart的博客 241

LeetCode: Symmetric Tree [101]

【题目】 Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3 But the following is not: 1 / \ 2 2 \ \ 3 3 No

Harry Huang 1161

Leetcode 101: Symmetric Tree

问题描述: Given the root of a binary tree, check whether it is a mirror of itself (i.e., symmetric around its center). 思路: (1)若根节点不存在,则以其为根的子树不存在,则认为是对称 (2)若根节点存在,且 (1) 左子树和右子树均不存在,认为是对称 (2) 左子树和右子树仅存其一,认为是不对称 (3) 左子树与右子树均存在 (1)值相等, 递归比较 (2)值不相等,则不对称 class Sol

weixin_51401145的博客 122

LeetCode笔记:101. Symmetric Tree

判断一个二叉树是不是镜像对称的

Minecraft 710

Leetcode-101: Symmetric Tree

这题也是经典题,需要注意。我的解法也是参考了网上的答案。 解法1: 递归 bool isMirror(TreeNode* r1, TreeNode* r2) { if (!r1 &amp;&amp; !r2) return true; if (!r1 || !r2) return false; if (r1-&gt;val!=r2-&gt;val) return f...

roufoo的博客 205

Leetcode:101. Symmetric Tree(JAVA)

【问题描述】 Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3

莫名的程序猿 730

leetcode 101:Symmetric Tree

题目描述:Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center).For example, this binary tree is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3 But the following is not:

a253664942的专栏 495
上一篇: android:bootloader
下一篇: 面:24点游戏
nedwons
博客等级 码龄9年 311粉丝 572原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值