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class Solution {
public:
TreeNode* invertTree(TreeNode* root)
{
if(root==NULL)
return root;
swap(root);
invertTree(root->left);
invertTree(root->right);
return root;
}
void swap(TreeNode *root)
{
TreeNode *temp=root->left;
root->left=root->right;
root->right=temp;
}
};
LeetCodeView
解决ublox无法定位问题（一直输出$GxTXT）
最近开始研究有关RTK技术的项目，于是先购买了两块ublox公司的NEO-M8T-0型号芯片。（注意NEO-M8T还细分为NEO-M8T-0和NEO-M8T-2，前者不支持RTCM输出，后者支持RTCM输出） 首先都是按常规步骤，直接把芯片模块串口接电脑调试，这边推荐是用官网的u-center调试软件。GPS芯片大多数都是上电后串口自动发送定位等信息，这两块芯片有一块正常情...
leetcode226 Invert Binary Tree
226. Invert Binary Tree Total Accepted: 73962 Total Submissions: 167537 Difficulty: Easy Invert a binary tree. 4 / \ 2 7 / \ / \ 1 3 6 9 to 4 /
计算机视觉三大顶会（ICCV/CVPR/ECCV）投稿指南与趋势解析
本文详细解析计算机视觉三大顶会（ICCV/CVPR/ECCV）的投稿指南与最新趋势。从基础认知到实战经验，涵盖时间线管理、选题策略、论文写作技巧及实验设计要点，帮助研究者提升投稿成功率。特别关注2024年技术融合、评估维度和伦理要求等前沿趋势，为计算机视觉领域的研究者提供实用建议。
226. Invert Binary Tree
nvert a binary tree. 4 / \ 2 7 / \ / \ 1 3 6 9 to 4 / \ 7 2 / \ / \ 9 6 3 1 Trivia: This problem was inspired by this original tweet by Max Howel
Adobe软件非正版弹窗终极解决方案：PS/Ai/PR/AE禁用提示一键清除指南
本文提供了Adobe软件非正版弹窗的终极解决方案，包括PS、Ai、PR、AE等软件的禁用提示一键清除指南。通过专业清理工具和手动修改系统文件的方法，帮助用户彻底解决Adobe弹窗问题，提升使用体验。适用于Windows和Mac系统用户，操作简单高效。
保姆级教程：用Zemax 18.9复现单模光纤耦合仿真（附康宁SMF-28e参数）
本文提供了一份详细的Zemax 18.9单模光纤耦合仿真教程，以康宁SMF-28e为例，涵盖从软件设置、材料参数输入到系统构建和优化的全流程。通过光束耦合分析和物理光学传播(POP)技术，帮助用户准确模拟光纤耦合效率，并解决常见问题，适用于光学设计初学者和专业人士。
leetcode 226. Invert Binary Tree
Invert a binary tree. 4 / \ 2 7 / \ / \ 1 3 6 9 to 4 / \ 7 2 / \ / \ 9 6 3 1 Trivia: This problem was inspired by this original tweet by Max Howe
U2Net网络实战：从原理到代码实现（附背景去除应用）
本文深入解析了U2Net网络的原理与代码实现，重点剖析了其独特的嵌套U型结构（RSU模块）和显著图融合机制。通过实战演示，指导读者如何加载预训练模型并应用于精准的图像背景去除任务，为电商、摄影等领域提供高效的自动化抠图解决方案。
226. Invert Binary Tree | 翻转二叉树
Invert a binary tree. 4 / \ 2 7 / \ / \ 1 3 6 9 to 4 / \ 7 2 / \ / \ 9 6 3 1 Trivia: This problem was inspired by this original tweet by Max Howe
从零到一：手把手教你用海康VisionMaster完成第一个字符识别项目（附完整流程与避坑点）
本文详细介绍了如何使用海康VisionMaster完成首个字符识别项目，涵盖从环境搭建、图像预处理到系统集成的完整流程。特别分享了Blob分析配置、字符识别优化等实战技巧，帮助开发者避开常见陷阱，快速掌握VisionMaster的基本操作，提升工业自动化场景下的字符识别效率。
扩充两个三维体积：ITK实现
在医学图像处理中，经常需要进行 3D 体积的操作，比如合并、分割等。而 ITK (Insight Segmentation and Registration Toolkit) 是一个常用的开源库，可以用于实现各种医学图像处理算法。这篇文章将介绍如何使用 ITK 实现扩充两个三维体积的操作。不过以上代码已经足够简单明了地演示了如何使用 ITK 实现扩充两个三维体积的操作。首先，我们需要准备两个三维体积的数据。这里假设这两个体积大小相同，并且已经加载到内存中了。来读取和保存体积数据，使用了。
LeetCode 226：Invert Binary Tree
Invert a binary tree. 4 / \ 2 7 / \ / \ 1 3 6 9 to 4 / \ 7 2 / \ / \ 9 6 3 1 Trivia: This problem was inspired by this original tweet by Max Howe
代码复现1——Matterport3d数据集下载
进入“Dataset Download”部分，下载申请书，填写之后发送到指定邮箱（matterport3d@googlegroups.com），之后会收到回信，回信中附有一个download_mp.py的脚本文件。，大小约15G，并不是所有的Mp3d datasets，所有大小约1.3T。下载完成后如下图所示：我这边显示大小为16.1G。
LeetCode-226. Invert Binary Tree
Invert a binary tree. 4 / \ 2 7 / \ / \ 1 3 6 9 to 4 / \ 7 2 / \ / \ 9 6 3 1 Trivia: This problem was inspired by this original tweet by Max Howe
温度PID控制PLC程序
温度PID稳定控制程序，适用于欧姆龙PLC，对其他PLC也具有很大参考价值
数学建模模型总结
这是本人在两年内准备参加数学建模竞赛搜集到的模型资料，已经经过了本人的筛选和整理，希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助！
工作中使用到的单词（软件开发）
１．エイリアス alias 别名 powershell使用 get-help 命令时，会得到与此命令 相关（别名）的命令 ２．プロキシ proxy 代 理 ３．プロモート promote 促进，提升 开发的系统新功能上线发布，有时会使用到这个词，比如 STプロモート 还有使用リリース这个词的。比如STリリース。个人认为两者含义相同。 ４．見逃し み................................................................
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
see-88. Merge Sorted Array
利用array1多余的位置，从后向前遍历二个数组： class Solution { public: void merge(vector<int>& nums1, int m, vector<int>& nums2, int n) { int total=m+n-1; m--; n--; ...
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');```[](https://bitdeli.com/free "Bitdeli Badge")
see-1. Two Sum
python版： class Solution: def twoSum(self, nums, target): """ :type nums: List[int] :type target: int :rtype: List[int] """ ans = list() ...
see-123. Best Time to Buy and Sell Stock III
Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may complete at most two transactions. Note: You may not e...
see-169. Majority Element
寻找数组中出现次数超过元素个数一半的那个元素：若数组中存在一个元素的累计次数大于元素总个数的一般，那么不断消除数组中二个互异的元素，那么数组中最终剩余的元素即为符合条件的元素。 class Solution { public: int majorityElement(vector<int>& nums) { int flag=1; i...
【位运算】338. Counting Bits
任意一个整数左移一位后，1的个数不变，而且扩大一倍，变为偶数（末位必定为0），因此偶数中1的个数等于偶数的一半的数中1的个数； 对于奇数而言，末位必定为1，相当于前一位的偶数在末位添加一个'1' vector<int> countBits(int num) { vector<int> table; table.push_back(0);...
see-66. Plus One
注意进位: class Solution { public: vector<int> plusOne(vector<int>& digits) { int i=digits.size()-1,flag=0,sum; vector<int> res; vector<int> an...
【贪心】see-122. Best Time to Buy and Sell Stock II
贪心算法求解: class Solution: def maxProfit(self, prices): """ :type prices: List[int] :rtype: int """ if prices == [] or len(prices) == 1: return...
see-120. Triangle
动态规划: triangle[i][j]=min(triangle[i][j]+triangle[i+1][j],triangle[i][j]+triangle[i+1][j+1]); return triangle[0][0]; 2 2 2 ...
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