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Ubuntu下安装Python的详细步骤
Python是一种广泛使用的编程语言，它在各个领域都有广泛的应用。在Ubuntu操作系统上安装Python非常简单，下面是详细的安装步骤：
步骤 1：更新软件包列表
在开始安装Python之前，我们首先要确保系统的软件包列表是最新的。打开终端，使用以下命令执行更新操作：
sudo apt update
该命令将更新软件包列表并获取最新的软件包信息。
步骤 2：安装Python
在Ubuntu上，Python的默认版本是Python 2.x系列。然而，我们推荐安装Python 3.x系列的最新版本，因为它是当前主要的Python版本。使用以下命令安装Python 3：
sudo apt install python3
执行此命令后，系统将提示您输入管理员密码进行确认，然后安装将自动开始。
步骤 3：验证安装
安装完成后，我们可以验证Python是否成功安装。在终端中输入以下命令：
在Ubuntu上安装Python
确保系统是最新的、安装Python 3.x版本并验证安装后，您就可以开始使用Python编写和运行应用程序了。在Ubuntu操作系统上安装Python非常简单，下面我将为您提供详细的步骤和相应的源代码。Ubuntu默认安装了Python 2.x版本，但我们建议安装Python 3.x版本，因为它是Python的最新稳定版本。在安装Python之前，我们需要确保系统是最新的状态。现在，您已经成功在Ubuntu上安装了Python。安装完成后，我们可以验证Python是否成功安装。步骤2：安装Python。
在Ubuntu中安装Python
然后，验证安装是否成功，最后可以选择安装pip来管理Python包。安装完成后，你就可以开始在Ubuntu上使用Python进行开发了。如果你看到了一个类似于"Python 3.x.x"的输出，那么说明Python已经安装在你的系统中。现在你已经成功安装了Python，你可以在终端中直接运行Python解释器来执行Python代码。在安装Python之前，首先检查是否已经安装了Python。你应该能够看到类似于"Python 3.x.x"的输出，这表明Python已经成功安装在你的系统中。
Android音频学习(一)——音频基础
在系统的学习Android音频之前，先了解音频的基础知识。
51单片机———UART串口通信
传统理解就是信息的传输与交换。单片机与单片机之间，单片机与电脑之间，以及单片机与各式各样的模块之间都可以进行通信。如果没有通信，那么单片机的功能仅仅局限于它本身，它没办法从其他设备获取有用信息，也无法将自己产生的信息告诉其他设备，这样子单片机与外围器件的合作就会收到限制，没办法完成更多的功能，就像一个没办法拓展业务的公司。：实现我们程序当中写的P0=0xFC,就是一次同时对8位进行信号的输出；习惯上，我们还称P0、P1、P2、P3为单片机的4组并行总线：实现低位先发 高位在后。
QLIB使用tushare更新每日行情
0. 介绍 qlib每日数据的源是yahoo，更新速度较慢并且不稳定。tushare每日行情是免费接口，速度较快。 1. 获取行情数据，保存为csv格式。 def GetDataFromTushare(self, today, fpath): pro = ts.pro_api() df = pro.daily(trade_date=today) for index, row in df.iterrows(): code = row['ts_code']
springcloud+nacos+gateway+oauth2小聚会
在微服务工程中，Nacos作为目前主流的注册中心和配置中心，Spring Cloud Gateway作为目前主流的网关，下面引入spring security+oauth2+jwt作为认证和授权中心，进行简单的聊聊。
箱式连续提升机
箱式连续提升机，是对具有高度差较大（＞1500mm）的输送线之间进行连接，将箱式物资进行连续输送的提升（或下降）的输送设备，可根据输送线的具体输送要求作相应的参数设计。目前的箱式连续提升机种类比较多，常用的形式主要有Z型和C型，工作原理相同，只是入口和出口分别在两侧和同侧。
Far planner 代码系列（14）
MatchContourWithNavGraph（3） this->EnclosePolygonsCheck(); //可能有些ctnode的is_contour_necessary属性变成了true // 这说明我的contour_graph是一个local层级的 才会从这里面 get new vertices new_convex_vertices.clear(); //! 把contour_graph_里符
使用Python爬虫抓取公共交通网站的时刻表
Python爬虫是一种通过自动化方式从网页中抓取数据的程序。爬虫通过模拟用户请求，获取网站的HTML页面内容，并通过解析这些HTML内容，提取出我们需要的信息。requests：用于向网站发送HTTP请求并获取响应。：用于解析HTML文档，提取数据。Selenium：用于处理动态加载的网页，模拟浏览器操作。pandas：用于数据处理和分析，方便存储和操作抓取到的数据。本文介绍了如何使用Python爬虫抓取公共交通网站的时刻表，并提取其中的关键信息。我们使用了requestsSelenium和。
置顶必读(1) |《滚雪球学SpringBoot 3.x》· 高质量专栏介绍，你值得拥有！
《滚雪球学SpringBoot 3.x》 并不是普通的技术讲解合集，它是专为开发者精心设计的系统化学习路径。通过不断“滚动学习”，我们将带领你从基础入门到高阶应用，从理解每一行代码到设计和优化大规模分布式系统，最终帮助你成长为一位精通 spring架构 的工程师。
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")
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
C# WinForm 基于DirectShow.dll编写的程序，可边预览边保存视频，音频和图片
基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择（默认选中第一个）2.可录制wmv和avi两种格式的视频并且都带有音频，avi可以录制的分辨率和帧数进行选择，wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照，也可边录制视频边拍照4.添加了视频和音频的压缩功能（酌情使用）这次二次开发发现C#对DirectShow的相关信息太少，这次做完也不敢藏私共享出来，还望大家指正
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
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