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iOS11之前的系统和之后的系统有所不同
if (@available(iOS 11.0, *)) {
str = [NSString stringWithFormat:@"itms-apps://itunes.apple.com/us/app/twitter/id%d?mt=8&action=write-review",1427513484];
} else {
str = [NSString stringWithFormat:@"itms-apps://itunes.apple.com/WebObjects/MZStore.woa/wa/viewContentsUserReviews?type=Purple+Software&id=%d",1427513484];
}
[[UIApplication sharedApplication] openURL:[NSURL URLWithString:str]];
Linux运维人，该醒醒了，2019年要变天了
外界都说运维只是处于被动低效率手工救火的状态，企业对其重视程度不高，提起运维很多人能联想到的字眼就有“苦逼”、“辛苦”、“加班”、“背锅”。在大数据、人工智能、容器技术的强力加持下，运维这个传统的技术工种已经从“机械劳动”这样的刻板印象中蜕变出来，成为了任何一家技术公司所必须依赖和大力投入的核心技术能力。 现在，运维人员的发展前景也开始让很多程序员眼羡，作为Linux运维人员的你：这一年，你的L...
iOS跳转AppStore评价（兼容iOS11）
- (IBAction)commentInAppStore:(id)sender { NSString *str = nil; if (@available(iOS 11.0, *)) { str = [NSString stringWithFormat:@"itms-apps://itunes.apple.com/us/app/twitter/id%@?mt=8&...
rk3326 MIPI休眠唤醒黑屏有背光问题
** 1.问题现象: ** **【开机起来】**开机起来可以正常显示 **【按power按键休眠】**第二次按下Power按键进入休眠，隔10s，再按Power按键唤醒。 **【出现结果】**屏幕黑屏有背光，并提示drm time out错 **【lcd信息】**lcd为480*800 2lane的mipi **【复现概率】**第二次按下休眠唤醒必然复现 【具体错误log如下】 //第一次休眠的log// [ 54.858511] dw-mipi-dsi ff450000.dsi: final DSI-
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现开始总结梳理 关于Appstore应用的关键字规范。 1. 事件：应用名称为 VOA常速新闻广播，但关键词中没有加入“常速”，在appstore中搜索“常速”，全部结果集中，未找到应用。（2015年11月） 总结：用户搜索时，Appstore会首先在关键词中进行一轮查找，后续算法，在此结果之上进行。 2. 是件： 搜索“英语”，voa慢速英语排第五，每日英语听力排第三。搜索“普特”
光电容积脉搏波PPG信号分析笔记
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[Spawn service failed. Exiting] ROS2运行turtlebot3_gazebo例程卡住_报错
[Spawn service failed. Exiting] ROS2运行turtlebot3_gazebo例程卡住_报错
iOS应用内跳转appstore
这里可以搜索应用的链接 https://linkmaker.itunes.apple.com 也可以在itunes里应用详情页面 点击价格旁边的下拉按钮 复制链接即可. 当我们开发的时候需要让用户评价功能或跳转appstore 下载等需求 需要使用app下载地址, 改地址可以在itunesconnect后台中我的应用中, 选中更多点击在apptore 中查看应用,复制链接即可.
Pytorch CIFAR10图像分类 EfficientNet v1篇
EfficientNet源自Google Brain的论文EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 从标题也可以看出，这篇论文最主要的创新点是. 论文提出了，混合缩放，把网络缩放的三种方式：深度、宽度、分辨率，组合起来按照一定规则缩放，从而提高网络的效果。EfficientNet在网络变大时效果提升明显，把精度上限进一步提升，成为了当前最强网络。
iOS应用跳转至app store 评分页（包括iOS7前后两种）
iOS7 由 itms-apps://ax.itunes.apple.com/WebObjects/MZStore.woa/wa/viewContentsUserReviews?type=Purple+Software&id=123456789
利用autojs制作抢购支付宝消费劵的手机脚本
前期准备工作 1.打开支付宝，点击中间的抢消费券的广告 2.点击左上角，将抢购页面添加到首页 3.查看支付宝首页是否出现消费劵 . autojs代码 "auto"; functiontaskstart(){ sleep(1000); launchApp('支付宝'); toast("正在打开支付宝应用"); sleep(1000); varxf=textContains("消费").findOne().bounds(); ...
Prompt提示优化工具
在大模型使用过程中，Prompt尤为重要。一个好的Prompt可以解决很多的问题。那么如何撰写一个完美的Prompt？下面汇总了一些Prompt工具，可以辅助日常Prompt的编写。
ios引导商店评分问题
由于项目有需求在应用内引导评分功能，新手记录开发过程。 iOS引导评分有三种方式： 详见以下博客地址，这位大佬总结的非常到位：https://blog.youkuaiyun.com/jiadabin/article/details/78473927 简述如下： 1、跳转Appstore 可以跳转到App Store评分页面，同时评分及写评论。 2、在自已的应用内打开评分弹框（iOS10.3之后） 不能写评论，...
基于STM32设计的消防小车
当前基于STM32单片机设计了一款智能消防小车；当前小车支持烟雾浓度检测、可燃气体检测，环境温度湿度检测、火焰源检测、能支持远程遥控，方便消防员远程操控进行灭火。小车也支持自动消防，在行驶过程中，消防智能小车通过火焰传感器感应火源以检测火焰具体位置，可以利用算法设计进行循迹灭火，该模式下驱动电机水泵喷水，可以有效控制火源的第二次发生，从而完成消防作业。小车本身带了超声波测距模块，红外壁障模块，在自动或者手动控制行驶过程中可以防止碰到障碍物损伤小车。
ios应用内跳转到appstore里评分
在ios6.0前跳转到appstore评分一般是直接跳转到appstore评分 NSString *evaluateString = [NSString stringWithFormat:@"itms-apps://ax.itunes.apple.com/WebObjects/MZStore.woa/wa/viewContentsUserReviews?type=Purple+Software&
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。
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")
ios android 跳转商店评分
ios android 谷歌Store应用内商店评分，
TMS_WebGMaps_1.9.8.0_Full_Source
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ios应用增加给appstore评分和进入app安装页面代码功能
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温度PID控制PLC程序
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iOS用swift打开appstore的主页或者评分界面
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APP应用评分
示例 /// 应用地址 static NSString *const kAppUrl = @"itms-apps://itunes.apple.com/WebObjects/MZStore.woa/wa/viewContentsUserReviews?type=……"; + (void)showReviewWithTarget:(id)target { if (target ...
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