我的一键个性化系统脚本。

一键下单MT4插件 本来这是某些平台特有的下单插件,这里提取出来给会员免费使用。 功能就是为了手工快速下单,实话说,用起来比看起来还爽! [注意]插件只能用于小数点后为4或2位的MT4平台 立即下载

本文首发于我的个人网站: https://hewanyue.com/
本文作者: Hechao
本文链接: https://hewanyue.com/blog/92724e23.html

  经常要把虚拟机上的系统搞崩,总是各种报错,有次yum安装gcc程序都报错,一旦折腾半天解决不了,只好选择最笨却最有效的方法——还原vrm虚拟机的快照!可是还原快照到干净系统,就导致之前做的喜欢的配置又都没了 还要去慢慢设置开机图案、别名以及各种环境变量比较麻烦,于是就费了点时间,写了下面这个脚本,每次都可以一键实现让新系统恢复自己当初个性化的各种设置。
  先传效果图:
在这里插入图片描述

#!/bin/bash
read -p "please input a name for the address:" USERDIR
#输入用户名,方便在/etc目录下存放生成的配置文件


[ -a /etc/$USERDIR ] && [ -d /etc/$USERDIR ] ||{ \rm -rf /etc/$USERDIR; mkdir /etc/$USERDIR; }
#mkdir /etc/$USERDIR
#生成配置文件目录,若已存在则直接使用此目录


cat > /etc/$USERDIR/motd << END

oooooooooooo              oooooooooo.               ooooooooooooo oooo                     oooooooooo.                         .  
8'   888   '8              '888'   'Y8b              8'   888   '8 '888                     '888'   'Y8b                      .o8  
     888       'ooooo.      888     888  .ooooo.          888       888 .oo.    .ooooo.      888     888  .ooooo.   .oooo.o .o888oo
     888      d88' '88b     888oooo888' d88' '88b         888       888P"Y88b  d88' '88b     888oooo888' d88' '88b d88(  "8   888  
     888      888   888     888    '88b 888ooo888         888       888   888  888ooo888     888    '88b 888ooo888 '"Y88b.    888  
     888      888   888     888    .88P 888    .o         888       888   888  888    .o     888    .88P 888    .o o.  )88b   888 .
    o888o     'Y8bod8P'    o888bood8P'  'Y8bod8P'        o888o     o888o o888o 'Y8bod8P'    o888bood8P'  'Y8bod8P' 8"'888P'   "888"

END
#生成开机欢迎动画,可以随意修改



#创建/etc/$USERDIR/login.sh配置文件
[ -a /etc/$USERDIR ] && [ -d /etc/$USERDIR ]&& [ -w /etc/$USERDIR ]  ||{ \rm -rf /etc/$USERDIR; mkdir /etc/$USERDIR; }
#确认文件目录存在且可用,开始写配置文件。
#因为水平有限,不知道怎么把命令以及变量通过脚本生成到新脚本里
#只能采用最笨的方法——一行一行的写入,希望各位大佬可以指点一二。

echo '#!/bin/bash '>/etc/$USERDIR/login.sh
echo 'cat /etc/$USERDIR/motd'>>/etc/$USERDIR/login.sh
echo 'date=`date "+%F %T"`'>>/etc/$USERDIR/login.sh
echo 'head="System information as of: $date"'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo 'kernel=`uname -r`'>>/etc/$USERDIR/login.sh
echo 'hostname=`echo $HOSTNAME`'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo '#Cpu load'>>/etc/$USERDIR/login.sh
echo 'load1=`cat /proc/loadavg | awk '"'"'{print $1}'"'"'`'>>/etc/$USERDIR/login.sh
echo 'load5=`cat /proc/loadavg | awk '"'"'{print $2}'"'"'`'>>/etc/$USERDIR/login.sh
echo 'load15=`cat /proc/loadavg | awk '"'"'{print $3}'"'"'`'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo '#System uptime'>>/etc/$USERDIR/login.sh
echo 'uptime=`cat /proc/uptime | cut -f1 -d.`'>>/etc/$USERDIR/login.sh
echo 'upDays=$((uptime/60/60/24))'>>/etc/$USERDIR/login.sh
echo 'upHours=$((uptime/60/60%24))'>>/etc/$USERDIR/login.sh
echo 'upMins=$((uptime/60%60))'>>/etc/$USERDIR/login.sh
echo 'upSecs=$((uptime%60))'>>/etc/$USERDIR/login.sh
echo 'up_lastime=`date -d "$(awk -F. '"'"'{print $1}'"'"' /proc/uptime) second ago" +"%Y-%m-%d %H:%M:%S"`'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo '#Memory Usage'>>/etc/$USERDIR/login.sh
echo 'mem_usage=`free -m | awk '"'"'/Mem:/{total=$2} /buffers\/cache/ {used=$3} END {printf("%3.2f%%",used/total*100)}'"'"'`'>>/etc/$USERDIR/login.sh
echo 'swap_usage=`free -m | awk '"'"'/Swap/{printf "%.2f%",$3/$2*100}'"'"'`'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo '#Processes'>>/etc/$USERDIR/login.sh
echo 'processes=`ps aux | wc -l`'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo '#User'>>/etc/$USERDIR/login.sh
echo 'users=`users | wc -w`'>>/etc/$USERDIR/login.sh
echo 'USER=`whoami`'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo '#System fs usage'>>/etc/$USERDIR/login.sh
echo 'Filesystem=$(df -h | awk '"'"'/^\/dev/{print $6}'"'"')'>>/etc/$USERDIR/login.sh
echo ''>>/etc/$USERDIR/login.sh
echo '#Interfaces'>>/etc/$USERDIR/login.sh
echo 'INTERFACES=$(ip -4 ad | grep '"'"'state '"'"' | awk -F":" '"'"'!/^[0-9]*: ?lo/ {print $2}'"'"'|grep e)'>>/etc/$USERDIR/login.sh
echo 'echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"'>>/etc/$USERDIR/login.sh
echo 'echo "$head"'>>/etc/$USERDIR/login.sh
echo 'echo "----------------------------------------------"'>>/etc/$USERDIR/login.sh
echo 'printf "Kernel Version:\t%s\n" $kernel'>>/etc/$USERDIR/login.sh
echo 'printf "HostName:\t%s\n" $hostname'>>/etc/$USERDIR/login.sh
echo 'printf "System Load:\t%s %s %s\n" $load1, $load5, $load15'>>/etc/$USERDIR/login.sh
echo 'printf "System Uptime:\t%s "days" %s "hours" %s "min" %s "sec"\n" $upDays $upHours $upMins $upSecs'>>/etc/$USERDIR/login.sh
echo 'printf "Memory Usage:\t%s\t\t\tSwap Usage:\t%s\n" $mem_usage $swap_usage'>>/etc/$USERDIR/login.sh
echo 'printf "Login Users:\t%s\t\t\tWhoami:\t\t%s\n" $users $USER'>>/etc/$USERDIR/login.sh
echo 'printf "Processes:\t%s\n" $processes'>>/etc/$USERDIR/login.sh
echo 'printf "\n"'>>/etc/$USERDIR/login.sh
echo 'printf "Filesystem\tUsage\n"'>>/etc/$USERDIR/login.sh
echo 'for f in $Filesystem'>>/etc/$USERDIR/login.sh
echo 'do'>>/etc/$USERDIR/login.sh
echo '    Usage=$(df -h | awk '"'"'{if($NF=="'"'"''"'"''"'"'$f'"'"''"'"''"'"'") print $5}'"'"')'>>/etc/$USERDIR/login.sh
echo '    echo -e "$f\t\t$Usage"'>>/etc/$USERDIR/login.sh
echo 'done'>>/etc/$USERDIR/login.sh
echo 'printf "\n"'>>/etc/$USERDIR/login.sh
echo 'printf "Interface\tMAC Address\t\tIP Address\n"'>>/etc/$USERDIR/login.sh
echo 'for i in $INTERFACES'>>/etc/$USERDIR/login.sh
echo 'do'>>/etc/$USERDIR/login.sh
echo '    MAC=$(ip ad show dev $i | grep "link/ether" | awk '"'"'{print $2}'"'"')'>>/etc/$USERDIR/login.sh
echo '    IP=$(ip ad show dev $i | awk '"'"'/inet / {print $2}'"'"')'>>/etc/$USERDIR/login.sh
echo '    printf $i"\t\t"$MAC"\t$IP\n"'>>/etc/$USERDIR/login.sh
echo 'done'>>/etc/$USERDIR/login.sh
echo 'echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"'>>/etc/$USERDIR/login.sh
echo 'echo'>>/etc/$USERDIR/login.sh

#生成脚本设置常用命令符以及别名及命令历史
cat > /etc/profile.d/env.sh << END
PS1="\e[1;36m[\u@\h \W]\\\\$\e[0m"
HISTTIMEFORMAT="%F %T "
HISTCONTROL=ignoreboth
alias rm='mkdir -p /date/delete/`date +%F`;\mv -t /date/delete/`date +%F` -f'
bash /etc/$USERDIR/login.sh
END

sed -i 's/^alias rm/#&/' ~/.bashrc
#取消用户定义的alias rm。

#生成VIM的格式脚本
cat > ~/.vimrc << END
#!/bin/bash
""新建.c,.h,.sh,.java文件,自动插入文件头 
autocmd BufNewFile *.cpp,*.[ch],*.sh,*.java exec ":call SetTitle()" 
""定义函数SetTitle,自动插入文件头 
func SetTitle()
""如果文件类型为.sh文件 
	if &filetype == 'sh'
	call setline(1,"\##########################################################################") 
	call append(line("."), "\# File Name:                  ".expand("%"))
	call append(line(".")+1,"\# Author:                       Name ")
	call append(line(".")+2,"\# QQ:	                      xxxxxxxxx") 
	call append(line(".")+3,"\# mail:                       xxxxxxxxxx@xxx.com") 
	call append(line(".")+4,"\# Description:                The test script") 
	call append(line(".")+5,"\# Created Time:               ".strftime("%F %H:%M:%S"))
	call append(line(".")+6,"\# Copyright(C):             ".strftime("%Y")." All rights reserved")
	call append(line(".")+7,"\##########################################################################") 
	call append(line(".")+8,"\#!/bin/bash")
	call append(line(".")+9,"")
	else 
	call setline(1, "/*************************************************************************") 
	call append(line("."), "         > File Name: ".expand("%")) 
	call append(line(".")+1, "       > Author:   Name") 
	call append(line(".")+2, "       > Mail: xxxxxxxxxx@xxx.com") 
	call append(line(".")+3, "       > Created Time: ".strftime("%c")) 
	call append(line(".")+4, "************************************************************************/") 
	call append(line(".")+5, "")
endif
	if &filetype == 'cpp'
	call append(line(".")+6, "#include<iostream>")
	call append(line(".")+7, "using namespace std;")
	call append(line(".")+8, "")
endif
	if &filetype == 'c'
	call append(line(".")+6, "#include<stdio.h>")
	call append(line(".")+7, "")
endif
	if &filetype == 'java'
	call append(line(".")+6,"public class ".expand("%"))
	call append(line(".")+7,"")
endif
""新建文件后,自动定位到文件末尾
endfunc
autocmd BufNewFile * normal G
END



mkdir -p /etc/yum.repos.d/backup
\mv -f /etc/yum.repos.d/*.repo /etc/yum.repos.d/backup/
#禁用已有其他epel源,并做备份。



umount /dev/cdrom
#[ `cat /etc/fstab|grep -o iso` ]||sed -i '/iso/c\' /etc/fstab   
                                                          #解挂其他光盘


CDUUID=`blkid|sed -nr '/sr0/s/.*\bUUID="([^"]+).*"/\1/p'`
CDTYPE=`blkid|sed -nr '/sr0/s/.*\bTYPE="([^"]+).*"/\1/p'`
[ `cat /etc/fstab|grep -o iso` ]||mkdir -p  /cdrom        #创建光盘目录
[ `cat /etc/fstab|grep -o iso` ]||echo -e "UUID=$CDUUID              /cdrom                  $CDTYPE defaults       0 0" >>/etc/fstab
                                                       #修改fstab文件,方便以后自动挂载光盘
#[ `cat /etc/fstab|grep -o iso` ]|| mount -o ro /dev/sr0  /cdrom   #挂载光盘
#mount -a   #挂载光盘
mount -o ro /dev/sr0  /cdrom   #挂载光盘


#生成常用yum源和epel源,这里选用阿里云的
cat > /etc/yum.repos.d/cdrom.repo << END
[cdrom]
name=cdrom-repo
baseurl=file:///cdrom
gpgcheck=0
enabled=1
END
cat > /etc/yum.repos.d/aliyun.repo << "END"
[aliyun]
name=aliyun-epel
baseurl=https://mirrors.aliyun.com/epel/$releasever/$basearch/
gpgcheck=1
gpgkey=https://mirrors.aliyun.com/epel/RPM-GPG-KEY-EPEL-7
enabled=1
END
#新建本地及阿里云epel源.

#修改网卡配置,根据个人情况修改
cat > /etc/sysconfig/network-scripts/ifcfg-ens33 << END
TYPE=Ethernet
PROXY_METHOD=none
BROWSER_ONLY=no
BOOTPROTO=none
DEFROUTE=yes
IPV4_FAILURE_FATAL=no
IPV6INIT=yes
IPV6_AUTOCONF=yes
IPV6_DEFROUTE=yes
IPV6_FAILURE_FATAL=no
IPV6_ADDR_GEN_MODE=stable-privacy
NAME=ens33
UUID=29126291-418f-4a08-b33e-c5cfa659d9b8
DEVICE=ens33
ONBOOT=yes
IPADDR=172.18.32.7
PREFIX=16
GATEWAY=172.18.0.1
DNS1=114.114.114.114
END


systemctl restart network.service
#重启网络服务,让刚才修改的配置生效

mkdir -p /date/apps

[ -z `cat ~/.bash_profile|grep apps` ] && echo 'PATH=/date/apps:$PATH' >>~/.bash_profile
#修改PAT变量,方便以后装软件。

  水平有限,都是简单代码,留存记录,方便日后查看整理改进。

MT4 脚本 一键开仓双向单 MT4 脚本文件,可以方便进行锁仓操作,一键开仓多空两单,默认手数0.01 立即下载

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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]); 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