linux命令行---用wget下载电影

实现电视观看互联网影视资源——基于linux+samba+vmware文件共享 tips: Samba可以用于Linux与windows系统直接的文件共享和打印共享,Samba既可以用于windows与Linux之间的文件共享,也可以用于Linux与Linux之间的资源共享,由于NFS(网络文件系统)可以很好的完成Linux与Linux之间的数据共享,因而 Samba较多的用在了Linux与windows之间的数据共享上面。由于电视盒子送的影视会员到期了,那上面很多电影又需要承担“巨额”的会员费用才能观看,所以想通过设备互联观看从互联网下载到电脑里的影视资源。 阅读详情

某些网站通过http header信息限制下载文件,在linux下面可以使用wget来模拟realplay等的,http header信息。

wget --head "ClientID: WinNT_5.0_6.0.12.1056_play32_R30CND_zh-CN_686_axembed" --head "User-Agent: RMA/1.0 (compatible; RealMedia)" http://localhost/head.php 

说明:每一个header信息项用一个 --head参数来指定,可以多次使用 --head参数。

下面是我的http://localhost/head.php获得的header的信息:(关于php操作http header 参见:http://blog.youkuaiyun.com/islq/archive/2006/07/08/893434.aspx)
Array
(
    [User-Agent] => RMA/1.0 (compatible; RealMedia)
    [Accept] => */*
    [Host] => localhost
    [Connection] => Keep-Alive
    [ClientID] => WinNT_5.0_6.0.12.1056_play32_R30CND_zh-CN_686_axembed
)

以下是下载成功和不成功的http链接信息记录:

> >从IE点播的http请求,播放成功。(VOD1.cap)
> >
> >GET /vod2/%5bTV%5dFZXF/01.rmvb HTTP/1.1
> >
> >Accept: */*
> >
> >User-Agent: RMA/1.0 (compatible; RealMedia)
> >
> >Icy-MetaData: 1
> >
> >Bandwidth: 10485800
> >
> >ClientID: WinNT_5.0_6.0.12.1056_play32_R30CND_zh-CN_686_axembed
> >
> >GUID: 00000000-0000-0000-0000-000000000000
> >
> >Language: zh-CN, zh, *
> >
> >RegionData: 0
> >
> >SupportsMaximumASMBandwidth: 1
> >
> >Connection: Keep-Alive
> >
> >Host: 10.146.189.104
> >
> >Accept-Language: zh-CN, zh, *
> >
> >Accept-Encoding: gzip
> >
> >===================================================================
> >从realplayer直接打开对应文件播放成功。(vod2.cap)
> >
> >GET /vod2/%5bTV%5dFZXF/01.rmvb HTTP/1.1
> >
> >Accept: */*
> >
> >User-Agent: RMA/1.0 (compatible; RealMedia)
> >
> >Icy-MetaData: 1
> >
> >Bandwidth: 10485800
> >
> >ClientID: WinNT_5.0_6.0.12.1056_RealPlayer_R30CND_zh-CN_686
> >
> >GUID: 00000000-0000-0000-0000-000000000000
> >
> >Language: zh-CN, zh, *
> >
> >RegionData: 0
> >
> >SupportsMaximumASMBandwidth: 1
> >
> >Connection: Keep-Alive
> >
> >Host: 10.146.189.104
> >
> >Accept-Language: zh-CN, zh, *
> >
> >Accept-Encoding: gzip
> >
> >
> >===================================================================
> >从IE直接下载该文件失败(vod3.cap)
> >GET /vod2/%5bTV%5dFZXF/01.rmvb HTTP/1.1
> >
> >Accept: image/gif, image/x-xbitmap, image/jpeg, image/pjpeg, application/x-shockwave-flash, application/vnd.ms-excel, application/vnd.ms-powerpoint, application/msword, */*
> >
> >Accept-Language: zh-cn
> >
> >Accept-Encoding: gzip, deflate
> >
> >User-Agent: Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.0; MyIE2)
> >
> >Host: 10.146.189.104
> >
> >Connection: Keep-Alive
> >
> >
> >
> >HTTP/1.1 403 Forbidden
> >
> >Date: Tue, 04 Jul 2006 16:33:16 GMT
> >
> >Server: Apache/2.0.52 (Unix) PHP/4.3.10
> >
> >Content-Length: 312
> >
> >Keep-Alive: timeout=15, max=100
> >
> >Connection: Keep-Alive
> >
> >Content-Type: text/html; charset=iso-8859-1
> >
> >
> >
> ><!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN">
> ><html><head>
> ><title>403 Forbidden</title>
> ></head><body>
> ><h1>Forbidden</h1>
> ><p>You don't have permission to access /vod2/[TV]FZXF/01.rmvb
> >on this server.</p>
> ><hr>
> ><address>Apache/2.0.52 (Unix) PHP/4.3.10 Server at 10.146.189.104 Port 80</address>
> ></body></html>
> >
> >
> >===================================================================
> >迅雷下载失败(vod4.cap)
> >GET /vod2/%5bTV%5dFZXF/01.rmvb HTTP/1.1
> >
> >Host: 10.146.189.104
> >
> >Accept: */*
> >
> >User-Agent: Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.0; .NET CLR 1.1.4322)
> >
> >Referer: http://10.146.189.104/vod2/%5bTV%5dFZXF
> >
> >Pragma: no-cache
> >
> >Cache-Control: no-cache
> >
> >Connection: close
> >
> >
> >
> >HTTP/1.1 403 Forbidden
> >
> >Date: Tue, 04 Jul 2006 16:36:20 GMT
> >
> >Server: Apache/2.0.52 (Unix) PHP/4.3.10
> >
> >Content-Length: 312
> >
> >Connection: close
> >
> >Content-Type: text/html; charset=iso-8859-1
> >
> >
> >
> ><!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN">
> ><html><head>
> ><title>403 Forbidden</title>
> ></head><body>
> ><h1>Forbidden</h1>
> ><p>You don't have permission to access /vod2/[TV]FZXF/01.rmvb
> >on this server.</p>
> ><hr>
> ><address>Apache/2.0.52 (Unix) PHP/4.3.10 Server at 10.146.189.104 Port 80</address>
> ></body></html>
> >
> >
> >===================================================================
> >flashget下载失败(vod5.cap)
> >
> >GET /vod2/%5bTV%5dFZXF/01.rmvb HTTP/1.1
> >
> >Host: 10.146.189.104
> >
> >Accept: */*
> >
> >Referer: http://www.freescale.com.cn/Applications/automotive_mpc5200.asp
> >
> >User-Agent: Mozilla/4.0 (compatible; MSIE 5.00; Windows 98)
> >
> >Pragma: no-cache
> >
> >Cache-Control: no-cache
> >
> >Connection: close
> >
> >
> >
> >HTTP/1.1 403 Forbidden
> >
> >Date: Tue, 04 Jul 2006 16:41:39 GMT
> >
> >Server: Apache/2.0.52 (Unix) PHP/4.3.10
> >
> >Content-Length: 312
> >
> >Connection: close
> >
> >Content-Type: text/html; charset=iso-8859-1
> >
> >
> >
> ><!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN">
> ><html><head>
> ><title>403 Forbidden</title>
> ></head><body>
> ><h1>Forbidden</h1>
> ><p>You don't have permission to access /vod2/[TV]FZXF/01.rmvb
> >on this server.</p>
> ><hr>
> ><address>Apache/2.0.52 (Unix) PHP/4.3.10 Server at 10.146.189.104 Port 80</address>
> ></body></html> 

利用宝塔linux面板+苹果CMS搭建电影网站(二)网站的配置+电影资源的爬取上传 在利用宝塔linux面板+苹果CMS-----从0搭建一个电影网站(一)里已经初始创建了一个电影网站的雏形 接下来需要对网站进行编辑修改上传电影资源 达到这一种效果; 网站修改编辑 进入网站后台 ip/admin.php 进入分类管理页面;把电影的各种类别进行修改成常规的影视分类 北斗星资源:https://m3u8.bdxzyapi.com 闪电资源:https://sdzyapi.com/api.php/provide/vod/?ac=list 电影资源网综合资源 https://sdzyapi 阅读详情

相关推荐

Popcorn-Time-0.3.10-Linux-64.tar.xz

linux项下播放在线电影、电视,不用下载完再看,可以看BT资源。在Debian 10 Buster 项下亲测能用。

如何在linux下观看需vodplayer播放的电影

其实这个也可以视为下载需要vodplayer或者叫做宝利通播放器才能观看的在线电影的一个解决方案。宝丽通视频点播服务用在许多宽带服务上。速度较快,但是必须使用其特定的播放器vodplayer才能播放。而这个软件只有windows平台上的版本。在linux或者mac无法使用。除非装虚拟机。那太麻烦。现在提供一个解决方案,其原理如下:首先分析一下,为什么只能用vodplayer播放器才能播

简简单单,平平淡淡 8371

大模型-高通平台性能测试示例-2

在QNN中,可以使用(genis-t2t-run)和这两个工具来进行推理的性能分析。支持在QNX操作系统或Android操作系统上进行性能分析。:提供基本的性能数据,如运行时间、资源消耗等。:提供更详细的性能数据,可能包括各个操作的执行时间、资源使用情况等。:是指在性能分析的同时进行代码质量检查,以发现潜在的问题。:是指跟踪和分析神经网络中各个操作的性能,以优化网络结构或参数。

点滴的岁月 1491

linux下网络播放器,月亮播放器:Linux无广告看片神器

在 LinuxMint/Ubuntu 系列系统中,使用如下 PPA 安装月亮播放器:sudo add-apt-repository ppa:cos-lyk/moonsoftsudo apt updatesudo apt install moonplayer安装完毕,月亮播放器就在你的主菜单之“影音”类别中等你了。启动月亮播放器,如图所示,站长薄荷君简要介绍其基本按钮。点击资源搜索按钮,如图所示,可...

weixin_36274103的博客 1万+

Win10本地账户密码遗忘?巧用指纹/PIN快速重置(家庭版/专业版通用指南)

本文提供了一份详细的Win10本地账户密码遗忘自救指南,适用于家庭版和专业版用户。核心方法是利用已登录的指纹或PIN码权限,通过创建新的管理员账户或使用系统工具,安全重置遗忘的本地账户密码,无需第三方工具或重装系统,有效保护个人数据。

e6f7g8h9i的博客 576

HI3403烧录全流程:从ToolPlatform安装到eMMC配置避坑指南

本文详细介绍了海思HI3403芯片的完整烧录流程,涵盖从ToolPlatform工具安装、驱动配置到eMMC分区策略的实战指南。文章深入解析了BurnTool工具的使用方法、关键配置项,并提供了烧录过程中的常见问题排查与解决方案,旨在帮助开发者高效、稳定地完成HI3403嵌入式系统的部署。

weixin_29279101的博客 330

linux命令下载电影,Linux命令行用wget下载电影

某些网站通过http header信息限制下载文件,在linux下面可以使用wget来模拟realplay等的,http header信息。wget --head "ClientID: WinNT_5.0_6.0.12.1056_play32_R30CND_zh-CN_686_axembed" --head "User-Agent: RMA/1.0 (compatible; RealMedia)"...

weixin_39723655的博客 591

C# Code-Behind 技术全面解析

Code-Behind 是 ASP.NET Web Forms 中的一种技术模式,通过将用户界面标记(.aspx文件)与程序逻辑代码(.aspx.cs文件)分离,实现表现层与逻辑层的解耦。其核心思想是前端展示与后台逻辑的物理文件分离,通过ASP.NET编译器将两者合并编译成一个Page类并执行。Code-Behind 的优势包括较好的代码组织、较高的开发效率,尤其适合快速开发数据驱动应用。然而,与现代开发模式如ASP.NET MVC、ASP.NET Core和Blazor相比,Code-Behind 在可维

记录学习的过程 956

linux命令下载电影,Linux命令行用wget下载精彩电影

Linux命令行用wget下载精彩电影,某些网站通过http header信息限制下载文件,在linux下面可以使用wget来模拟realplay等的,http h某些网站通过http header信息限制下载文件,在linux下面可以使用wget来模拟realplay等的,http header信息。wget --head "ClientID: WinNT_5.0_6.0.12.1056_pla...

weixin_28973649的博客 945

国产系统迁移实战:在银河麒麟V10上搞定Wine32位支持,让老Windows软件跑起来

本文详细介绍了在银河麒麟V10操作系统上构建Wine32位支持的全过程,帮助用户解决Windows应用迁移难题。从系统架构差异分析到Wine环境配置,再到实战应用部署与优化,提供了一套完整的解决方案,确保老Windows软件在国产系统上稳定运行。

weixin_32288959的博客 648

QGis二次开发 —— 1、win10/win11搭建Vs2017-QGis3.40.12环境(附Vs2017环境效果,附全部问题解决方法)(附:Qt助手加入QGis接口说明文档)

QGis二次开发 —— 1、Windows10搭建Vs2017-QGis环境(附Vs2017环境效果)(附:Qt助手加入QGisA接口说明文档)

1362

linux怎么用命令下电影,linux命令行---用wget下载电影

某些网站通过http header信息限制下载文件,在linux下面可以使用wget来模拟realplay等的,http header信息。wget --head "ClientID: WinNT_5.0_6.0.12.1056_play32_R30CND_zh-CN_686_axembed" --head "User-Agent: RMA/1.0 (compatible; RealMedia)"...

weixin_29391747的博客 1252

Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Mode

多项选择题(MCQ)在几乎所有级别的教育中都无处不在,因为它们易于管理、评分,并且是评估和实践中可靠的格式。MCQ最重要的方面之一是干扰因素,即旨在针对真实学生常见错误或误解的不正确选项。迄今为止,对于教师和学习内容设计师来说,制作高质量的干扰物的任务在很大程度上仍然是一个劳动和时间密集型的过程,其可扩展性有限。在这项工作中,我们研究了数学MCQ领域的自动干扰物生成任务,并探索了从上下文学习到微调的各种基于大型语言模型(LLM)的方法。

c_cpp_csharp的专栏 317

linux命令下载电影,linux命令行---用wget下载电影

某些网站通过http header信息限制下载文件,在linux下面可以使用wget来模拟realplay等的,http header信息。wget --head "ClientID: WinNT_5.0_6.0.12.1056_play32_R30CND_zh-CN_686_axembed" --head "User-Agent: RMA/1.0 (compatible; RealMedia)"...

weixin_39773239的博客 585

SentEval学习笔记

SentEval allows you to evaluate your sentence embeddings as features for the followingdownstreamtasks: Task Type #train #test needs_train set_classifier MR movie review(影评) 11k 11k 1 1 CR product review(产品评述) ...

AlphaFinance 803

win10 跳过U8环境检测的IE WebControls.rar

win10 安装u8的时候 提示IE WebControls 不通过,这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后,重新检测通过。

linux版软件在哪下载电影,linux命令行---用wget下载电影

某些网站通过http header信息限制下载文件,在linux下面可以使用wget来模拟realplay等的,http header信息。wget --head "ClientID: WinNT_5.0_6.0.12.1056_play32_R30CND_zh-CN_686_axembed" --head "User-Agent: RMA/1.0 (compatible; RealMedia)"...

weixin_33831334的博客 489

jdk-8u144-linux-x64.tar.gz.

该资源为搭建Linux系统Java环境用,资源的后缀为tar.gz格式的压缩包

Linux卸载cmatrix,Linux下 cmatrix的安装和使用

Linux下 cmatrix安装过程 wget http://www.asty.org/cmatrix/dist/cmatrix-1.2a.tar.gz tar xvf cmatrix-1.2a.tar.gz cd cmatrix-1.2a yum install ncurses-devel ./configure make make install cmatrix使用说明 cmatrix [-a...

weixin_42140716的博客 351

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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

[转载]命令行也强大之下载迅雷资源的方法

我给迅雷发过邮件,问他们什么时候开发linux版的迅雷,但几个月过了,我还是没收到任何回音但是下载东西的时候,经常会遇到以"thunder://"的链接,比如:Quote:thunder://QUFmdHA6Ly9keWdvZDE6ZHlnb2QxQGQxMzEuZHlnb2QuY246MzA0OS/pnZ7or5rli7/mibBEVkRzY3IvW+eUteW9seWkqeWggnd3dy5k

linuxchyu的专栏 7978

使用wget下载电影

1.程序的安装: Wget是常用工具,系统自带。 2.Wget的使用: wget [参数] URL,如:在提示符下输入wget-c ftp://61.132.72.33/1202/gugong/01.avi 即可。假设该FTP存在密码,假设密码为password,用户名为user,你只需要将以上命令改成wget-c ftp://use

2691

关于Linux下载工具

P2P协议(magnet,BT和ed2k)放在一起比较,linux下浩如烟海的下载软件,除了非P2P下载的比如wget(通过http和ftp),其他不过是基于上面这三种下载协议写成的下载软件.

Silbert Monaphia 3万+

Linux文件服务配置FTP服务_linux ftp配置

之前学习了网络管理,在平时我们去各网站下载电影或者文件,比如去电影天堂下载电影的时候,细心的会发现网站的前缀是ftp://,这篇文章就开始学习怎么配置ftp服务器供客户端进行下载文件。FTP(File Transfer Protocol,文件传输协议)是TCP/IP协议组中的协议之一;作用:提供文件共享服务,互联网上多的媒体资源和软件资源。绝大部分都是通过FTP服务器传递。

m0_60388292的博客 1298

Linux文件服务配置FTP服务

了解ftp文件服务,学习配置ftp服务,以及通过客户端怎么访问并下载服务器的分享文件的三种方法

s1429583654的博客 6118

Linux命令实战

使用wget从网上下载资源 wget http://download...... 使用 wget 将下载的资源保存到指定文件 wget -o wangzi.tar.zip http://.....

王子老师 234
上一篇: 关于php操作http header
下一篇: bash 脚本学习笔记
islq
博客等级 码龄21年 26粉丝 65原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值