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在linux centos 7下安装mysql-server, yum install mysql-server 遇到报错提示No package mysql-server available，
具体如下截图：
解决方案如下：
在centos7中要安装mysql-server,必须先添加mysql社区repo通过输入命令：sudo rpm -Uvh http://dev.mysql.com/get/mysql-community-release-el7-5.noarch.rpm，
如下截图：
待成功添加完毕后重新使用yum install mysql-server命令安装mysql-server服务即可安装成功，如下截图：
卫星测试科普（三）：卫星载荷的核心测试设备探秘
总的说来，卫星芯片与元器件，部组件与单机，整机与子系统，系统与整星的设计、定型、制造、验证、总装、发射、在轨等环节都离不开各种测试场景，对于这些卫星测试场景而言，“射频三大件”与高速数字示波器是构成强大测试系统的基础。卫星通信信号生成：覆盖Ka、Q等波段，具备扫频、脉冲、数字调制、噪声信号、雷达信号、数据链信号、OFDM 等多种信号库，用于测试和验证卫星电子设备的电气性能、频率响应、抗干扰能力等。卫星的通信需要高效、快速地传输大量数据，高速信号的质量至关重要，如果卫星信号不佳，卫星就会出现故障或者隐患。
No package mysql-server available. Error: Nothing to do
mysql是免费开源的数据库产品，我们在centos mysql安装时报No package mysql-server available错误是因为我们本地yum仓库中没有可用的mysql-server rpm包，因此在yum安装之前先在本地备好rpm软件包。 [root@VM_230_32_centos ~]# yum install mysql-server Loaded plugins:...
第七章-分布式搜索引擎-ES：全文查询、分词查询、精确查询、地理坐标查询、组合查询（bool、funtion_score）以及RestApi
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linux安装mysql提示“No package mysql-server available
在CentOS7上安装mysql时，出现了以下的提示： 原因是： CentOS7带有MariaDB而不是MySQL，MariaDB和MySQL一样也是开元的数据库，您可以使用yum -y installmariadb-server mariadb命令安装 解决方案： 如果必须要安装MySQL，首先必须添加mysql社区repo通过输入命令：sudo rpm -Uvh http://dev.mysql.com/get/mysql-community-release-el7-5.noarch..
非模式物种PySCENIC分析流程
外的物种想要做scenic分析的话, 可以使用同源基因转换后再进行分析, 将要分析的物种的gene Symbol转换为人、小鼠或者是果蝇的gene Symbol后进行分析。odiscus sinensis（中华鳖）的基因-细胞的矩阵，进行pyscenic分析的话，可参考以下示例。目前pyscenic分析支持的物种仅为人、小鼠和果蝇，其他物种暂时还没有相应的数据库，因。本教程基于Linux及服务。SCENIC转录因子分析。器中的Rstudio环境。例如我们有一个Pel。
VHDL六层电梯控制器及仿真
六层电梯电梯 设计要求：设计一个六层电梯控制系统要求有超载、关门中断、提前关门清除报警信电梯外人的上升请求信号,电梯外人的下降请求信号, 电梯外人上升请求信号显示电梯外人下降请求信号显示到达楼层信号 设计思路：输入有clk时钟信号（频率为2Hz） full,deng,quick,clr超载、关门中断、提前关门清除报警c_u1,c_u2,c_u3,c_u4,c_u5电梯外人的上升请求c_d2,c_d3,c_d4,c_d5,c_d6 电梯外人的下降请求 d1,d2,d3,d4,d5,d6电梯内人的...
centos安装mysql-server报错：No package mysql-server available. Error: Nothing to do
CentOS7 安装 mysql 报错：No package mysql-server available. Error: Nothing to do 解决办法：两行命令搞定 wget http://repo.mysql.com/mysql-community-release-el7-5.noarch.rpm rpm -ivh mysql-community-release-el7-5.noarch.rpm 下面再执行安装命令 yum install mysql-server -y，是不是就可以了
第6篇:ESP32连接无源喇叭播放音乐《涛声依旧》
相邻两个音之间的倍数是2^(1/12)，约等于1.0594630943592952645618252949463）。这样一来，高八度的do频率是低八度do的两倍。我们把频率440赫兹作为中央C ，C调的歌就是把频率440赫兹当作do，乐谱中写作：1=C。一个八度分成12份，八个八度的十二平均音率对应的频率如下（从33Hz到7902Hz）。相邻的两个音之间的差一个半音，12345671之间的间隔分别是“全全半全全全半”。D调的歌就是把从440后面第二个音494赫兹当作do，乐谱中写作1=D。
全网最详细的CentOS7里安装MySQL时出现No package mysql-server available错误的解决办法（图文详解）...
不多说，直接上干货！ 直接yum install mysql的话会报错，原因在于yum安装库里没有直接可以用的安装包，此时需要用到MariaDB了，MariaDB是MySQL社区开发的分支，也是一个增强型的替代品。 安装ossec时需要使用到mysql-server，直接安装报错： [root@ossec-server ~]# yum i...
vue2/3 - H5 移动端网页中调用手机摄像头扫描二维码 / 条形码功能，在h5手机网页里调起相机摄像头进行二维码或条形码扫描效果详细教程（详细示例组件源码，一键复制开箱即用！）
vue扫描二维码条形码，vue网页调用摄像头扫二维码条形码，vue移动端如何实现调用相机扫描二维码，vue移动端扫一扫功能，vue怎么做移动端h5扫一扫功能，vue识别二维码和条形码，vue实现点击按钮调用摄像头扫码，vue调用本地摄像头实现拍照功能，Vue中如何进行二维码生成与扫描，如何实现vue在手机浏览器中调用摄像头实现扫描二维码功能，VUE项目移动端H5调用摄像头扫码，vue项目中实现H5调取摄像头，网页调用摄像头实现二维码扫描功能，vue调用手机摄像头，vue在调用摄像头扫码，vue自定义打开摄像
C#项目实战——YModem协议文件传输【实例】
1、先由上位机发送*update*的握手信号，下位机返回响应字符‘C’（字符C，ASII码为0x43），之后通信双方开始以YMODEM协议进行数据通信。2、 YMODEM协议格式说明（1）起始帧数据格式帧类型(1byte)帧序号帧序号反码传输的文件名文件大小数据CRCSOH00FFfilenamefileziseNULCRCSOH=0x01，表示这个数据帧中包含着128个字节的数据（STX表示1024字节，初始帧只有128个）；00表示数据帧序号，初始是0，依次向下排。
Centos7安装Mysql8
Centos7安装Mysql8
基于STM32L4XX、HAL库的ICM-42670-P姿态传感器驱动程序设计
sprintf(buffer, "温度: %.1f °C\r\n\r\n", sensor_data.temperature);sprintf(buffer, "陀螺仪: X=%.2f Y=%.2f Z=%.2f rad/s\r\n",sprintf(buffer, "加速度: X=%.2f Y=%.2f Z=%.2f m/s²\r\n",sprintf(buffer, "开始校准IMU，请保持传感器静止...\r\n");sprintf(buffer, "读取传感器数据失败\r\n");
[LINUX]CentOS 7 安装MYSQL时报错“No package mysql-server available“No package zabbix-server-mysql availabl
CentOS7无法安装mysql-server 提示No package mysql-server available.
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")
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
centos7 安装MySQL报错No package mysql-server available
在CentOS7上安装mysql时，如果出现了以下截图的提示： 排错方案有三种：（当前截图属于第三种由于系统原因） 1、没有在线源或者未连接外网2、本地光盘未挂载或者光盘内置包不全3、系统升级是否导致安装包改名或者改安装方式 解决方案：（针对当前问题提供解决方案） 经了解Centos7系统后发现，Centos7带有MariaDB而不是MySQL，MariaDB和MySQL一样也是开元的数据库，同样...
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
全网最有效解决centos7 安装MySQL报错No package mysql-server available
今天想在centos7上面装一个mysql，但是无论我yum就还是wget都装不上，提示No package mysql-server…,然后又度娘又各种查资料，试了好多种方法都没有用， 最后了解Centos7系统后发现，Centos7带有MariaDB而不是MySQL，MariaDB和MySQL一样也是开元的数据库，同样可以使用yum命令安装 ，只不过安装使用的并不是老方式的MySQL，而是默认的MariaDB，并且还需要安装的是mariadb-server，如果想继续使用老方式的MySQL，那么需要清
IDEA的使用指南
IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.
安装mysql-server报错_全网最详细的CentOS7里安装MySQL时出现No package mysql-server available错误的解决办法（图文详解）...
[root@ossec-server Desktop]# yum -y install mariadb-server mariadb mariadb-develLoaded plugins: fastestmirror, langpacksLoading mirror speeds from cached hostfilePackage 1:mariadb-server-5.5.47-1.el7_...
在阿里云服务器CentOS7安装mysql提示“No package mysql-server available上安装mysql遇到的问题...
1：安装mysql的时候：执行以下语句出现错误 yum install mysql-server 提示错误： 原因是： CentOS7带有MariaDB而不是MySQL，MariaDB和MySQL一样也是开元的数据库，您可以使用yum -y installmariadb-server mariadb命令安装 解决方案： 如果必须要安装MySQL，首先必须添加mys...
CenterOS7中解决No package mysql-server available问题
CenterOS7中解决No package mysql-server available 1.使用yum install -y mysql-server报错如下： [root@heyong_jd ~]# yum install -y mysql-server Loaded plugins: fastestmirror, langpacks Loading mirror speeds from cached hostfile No package mysql-server available. Error:
package mysql server_CenterOS7中解决No package mysql-server available.
1.使用yum install -y mysql-server报错如下：[root@heyong_jd ~]# yum install -y mysql-serverLoaded plugins: fastestmirror, langpacksLoading mirror speeds from cached hostfileNo package mysql-server available.E...
全网最详细的CentOS7里如何安装MySQL（得改为替换安装MariaDB）（图文详解）
不多说，直接上干货！ 直接yum install mysql的话会报错，原因在于yum安装库里没有直接可以用的安装包，此时需要用到MariaDB了，MariaDB是MySQL社区开发的分支，也是一个增强型的替代品。 全网最详细的CentOS7里安装MySQL时出现No package mysql-server available错...
CentOS安装配置MySQL
给CentOS配置MySQL踩过好多坑，记录一下。
Percona XtraBackup踩坑实录：从安装报错到恢复失败，我遇到的5个‘坑’及解决办法（CentOS 7 + MySQL 5.7）
本文详细解析了在CentOS 7 + MySQL 5.7环境下使用Percona XtraBackup进行数据库备份与恢复时可能遇到的5个典型问题及解决方案，包括依赖缺失、命令兼容性、数据目录权限、增量备份顺序和备份验证等关键环节，帮助DBA有效规避常见陷阱，提升备份可靠性。
CentOS7下解决yum install mysql-server 异常：No package mysql-server available.问题
yum安装mysql-server没有可用包问题解决方法： step 1: wget http://repo.mysql.com/mysql-community-release-el7-5.noarch.rpm step 2: rpm -ivh mysql-community-release-el7-5.noarch.rpm 经过以上两个步骤后再次执行：yum install mysql-s...
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