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想写一个游戏的辅助脚本（或者外挂? ）很久了。这几天工作、考试都完成的差不多了，闲下来写一个王者荣耀的刷金币脚本，供大家参考。
很多人学习python，不知道从何学起。
很多人学习python，掌握了基本语法过后，不知道在哪里寻找案例上手。
很多已经做案例的人，却不知道如何去学习更加高深的知识。
那么针对这三类人，我给大家提供一个好的学习平台，免费领取视频教程，电子书籍，以及课程的源代码！??¤
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我想，游戏的脚本应该都是差不多的，我们玩游戏的流程是这样的：
那么用脚本玩游戏呢，完全可以省略中间的步骤：
所以我们只要：在特定的时刻，特定的位置，按键，就可以了。
1、ADB工具（让电脑使用手机）
下载地址：https://adbshell.com/downloads/
如果慢的话找其它资源也好
下载配置不再赘述了… 最后出现类似下图即可。
2、python与IDE
下载配置不再赘述了… 最后出现类似下图即可。
1、数据线链接电脑，进入手机开发者模式
2、启动adb
错误一：5037端口被占用，关闭相应进程即可
LangChain实战：Plan and Execute代理在智能预算规划中的应用
本文详细介绍了如何利用LangChain框架中的Plan and Execute代理实现智能预算规划。通过拆解复杂财务问题、调用多工具链协作，该代理能自动完成从个人月度预算到家庭年度综合预算的智能规划，并具备消费习惯学习和多目标优化等进阶功能，帮助用户实现精准财务管理和决策优化。
5种隐藏代码到图片的实用技巧：从Notepad++到Linux命令全解析
本文详细解析了5种将代码或文本隐藏到图片中的实用数据隐写技巧。从Notepad++尾部追加、Windows命令行合并，到编辑图像元数据、使用十六进制编辑器，再到Linux的cat/dd命令，全面覆盖了从图形界面到命令行的工程化实践方法，帮助开发者和安全研究人员掌握隐蔽信息传输与存储的核心技术。
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爬取B站评论+AI情感分析：Python批量抓数据+BERT正负向分类（全流程）
B站作为年轻群体聚集的内容平台，评论区蕴含海量用户真实态度（如好评、吐槽、中立观点），是情感分析的优质数据源！本文分享完整流程：批量爬取B站视频评论→数据清洗去噪→BERT模型情感分类→可视化分析，基于预训练BERT模型实现高精度正负向判断，新手也能快速复刻情感分析系统！ 前置配置（关键！获取B站API访问权限） B站评论通过官方API接口返回，无需复杂解析，只需配置用户Cookie即可正常请求： 2. 完整代码（直接复制运行） 三、使用步骤（5步搞定，新手友好） 步骤1：配置关键参数 打开，在类中修改
YOLOv11 知识蒸馏完整实战：Teacher–Student 训练策略与性能飞跃
在self.alpha = alpha # 蒸馏损失权重self.temperature = temperature # 温度系数，控制分布平滑度# 输出分布蒸馏（KL散度损失）# 中间特征蒸馏（MSE损失，对齐中间层特征）# 总蒸馏损失知识蒸馏是YOLOv11在“小模型高性能部署”场景的“破局之法”——它让小模型能“站在大模型的肩膀上”，兼具速度与精度。从损失函数定义到多阶段训练，再到实战落地，本教程已为你铺就了一条清晰的路径。
ubuntu-emto-cpa 测试和使用(单核运行)
ubuntu-emto-cpa 测试和使用(单核运行)
诊断数据库ODX标准协议ISO22901定义组成部分
综上所述，简略描述了ODX数据库中UML/XML/Text/Check rules具体功能以及详细内容。
【2026年阿里巴巴集团暑期实习- 4月25日-算法岗-第二题- 矩阵计数】（题目+思路+Java&C++&Python解析+在线测试)
摘要： 给定两个非负整数序列a和b，构造n×m矩阵C，其中C[i][j]=a[i]×b[j]。统计矩阵中元素≥k的数量。解法是先对数组b排序，然后对于每个a[i]，若k=0则直接累加m；若a[i]=0且k>0则跳过；否则计算需要的最小b[j]值，在排序后的b中使用二分查找确定满足条件的元素数量。时间复杂度为O((n+m)log m)，空间复杂度O(1)。代码实现了排序和二分查找的高效组合。
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。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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