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先来看一下爬取的字段情况：
本次爬取城市天气数据的scrapy项目使用pycharm创建。首先，使用pycharm创建一个项目并打开该项目，创建的项目名称为scrapyProject。打开pycharm的终端，输入命令scrapy startproject weatherspider，接下来在终端输入命令cd weatherspider进入该目录下。然后输入命令scrapy genspider weather www.xxx.com，这里的weather是spider的名字。项目便创建好了。
打开settings.py，将ROBOTSTXT_OBEY改为False，LOG_LEVEL设置为ERROR，将User-Agent改为自己的User-Agen，如下图所示：
在settings.py文件中取消管道代码的注释，如下图所示：
Python实战：超分辨率图像质量评估全攻略——PSNR、SSIM、LPIPS与NIQE的代码实现与优化
本文详细介绍了在Python中实现超分辨率图像质量评估的完整方案，涵盖PSNR、SSIM、LPIPS和NIQE四大核心指标。通过模块化代码设计、多进程并行处理和GPU加速优化，帮助开发者构建高效、可扩展的评估系统，并提供了实战避坑指南与结果解读方法。
Silvaco TCAD仿真4——DeckBuild实战：从示例库到自定义设计
本文详细介绍了Silvaco TCAD仿真工具DeckBuild的实战应用，从示例库的使用到自定义设计的全过程。通过具体案例和参数修改技巧，帮助用户快速掌握TCAD仿真技术，提升半导体器件设计的效率和精度。特别适合需要从基础入门到进阶操作的工程师和研究人员。
CD-HIT的使用
继续我的生信笔记系列。从某网站的附带文献中读到该工具，搜索了一下，发现网上的大佬们都在讲如何用它去冗余，相对官方页面的介绍而言很是片面，于是刚好有聚类的需求的我准备对其全体功能做做实验，学习一下由李伟忠博士在伯纳姆研究所（现为桑福德伯纳姆医学研究所）亚当·戈兹克博士的实验室开发的牛工具。 首先是官方介绍： CD-HIT是一个非常广泛使用的程序，用于蛋白质或核苷酸序列的聚类和比较。最初由李伟忠博士在伯纳姆研究所（现为桑福德伯纳姆医学研究所）亚当·戈兹克博士的实验室开发，CD-HIT速度非常快，可以处理非常
【Python&RS】哨兵2号（Sentinel2）轨道条带说明+图幅统计（表格+矢量）
今天给大家带来哨兵2号的图幅编号介绍以及全国省、市所需图幅数的统计。网上教程极少，而且结果也不公开。因为每次下载的时候都不知道需要几幅，所以研究后给大家分享一下。
主板USB接口全部失效解决方案（通用串行总线USB控制器有黄色叹号）保姆级教程亲测有效
通用串行总线控制器总是有叹号解决办法 办公室的崩腾时代老电脑，疫情期间没人用，来了开机之后发现鼠标键盘都用不了，本以为是主机放时间长出了问题，后来想起来就百度了“鼠标键盘都失效”的标题，结果发现了“串行总线控制器总是感叹号”的关联问题。正好办公室电脑都装有teamviwer常用来远程办公(在家偷懒)。所以通过远程发现电脑室可以运行的。并在控制面板发现:通用串行总线控制器总是感叹号,插入外设时USB无反应。如图： 并且右键选中其中一个，点击最后一个选项：“属性” 可以发现“设备状态”中会提示“由...
Mac mini M2 部署 OpenClaw：最稳 7×24 本地 AI 服务器
Mac mini M2 凭借其强大的 M2 芯片、低功耗和静音特性，成为部署 OpenClaw AI 服务器的理想平台。本文详细介绍了从系统配置到 OpenClaw 部署的全过程，包括 Docker 环境搭建、docker-compose 编排、性能优化方案以及开机自启设置。通过统一内存架构和高效 SSD，M2 版 Mac mini 在 AI 推理速度上显著优于前代产品，同时保持极低的功耗（仅 8W），适合 7×24 小时稳定运行。部署方案充分利用了容器化技术，确保了服务的可靠性和可维护性。
mbedtls | 01 - 移植mbedtls库到STM32的两种方法
一、mbedtls 开源库 1. mbedtls是什么 Mbed TLS是一个开源、可移植、易于使用、代码可读性高的SSL库。可实现加密原语，X.509证书操作以及SSL / TLS和 DTLS 协议，它的代码占用空间小，非常适合用于嵌入式系统。 mbedtls遵循 Apache 2.0 开源许可协议，目前由 TrustedFirmware 维护（Linaro主持的一个治理开放社区项目），在Github上已收获 2.6k star，目前Github上发布的最新版本为 2.24.0 版本，开源仓库地址为：
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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
Data-Structures-and-Algorithm-Analysis-in-Java.pdf
作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...
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