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新建一个excel表格（table1.csv）用于案例讲解：
import pandas as pd
import numpy as np
df = pd.read_excel('table1.xlsx') # 相对路径
# df = pd.read_excel(r'E:\Anaconda\hc\dataScience\table1.csv') # 绝对路径
显示数据的行与列数
df.shape
(6, 5)
显示数据格式dtpyes
df.dtypes
Name object
Age int64
Sex int64
Class int64
Score float64
dtype: object
显示列名
df.columns
Index(['Name', 'Age', 'Sex', 'Class', 'Score'], dtype='object')
显示前数据前2行
df.head(2)
显示数据后3行
df.tail(3)
</Linux新手必看：FinalShell连接不上？从检查IP到配置固定地址的完整排错指南
本文为Linux新手提供了FinalShell连接失败的完整排错指南，涵盖IP检查、SSH服务状态、防火墙配置和静态IP设置等关键步骤。通过详细的命令行操作和配置示例，帮助用户快速解决远程连接问题，提升Linux使用效率。
二、ROS2完成Docker容器和宿主机通信，使用ros2 topic list看到，但是无法echo
如果容器和主机权限不一致，则会导致无法通信（我的代码中，docker容器是root，宿主机是普通用户），虽然可以查询到相关话题，但是订阅后没有消息输出。比如，docker容器创建了一个共享内存，其所有者为docker容器用户root，当宿主机去访问的时候，由于用户不同，没有权限访问，也就无法进行信息传递。该方法只是临时修改共享内存的权限，每次运行完，需要重新运行上述命令，才可以进行通信。在运行的过程中，ROS2使用了共享内存机制，会共享内存。运行之后，宿主机和容器可能用户名不同，这是因为。
常见的十大量化投资策略（附源码）
量化投资策略是利用量化的方法，进行金融市场的分析、判断和交易的策略、算法的总称。著名的量化投资策略有以下10种（注：策略源码模板不能直接用于实盘交易，仅供探讨交流） 01、海龟交易策略 海龟交易策略是一套非常完整的趋势跟随型的自动化交易策略。这个复杂的策略在入场条件、仓位控制、资金管理、止损止盈等各个环节，都进行了详细的设计，这基本上可以作为复杂交易策略设计和开发的模板。 海龟交易策略源码：https://www.myquant.cn/docs/python_strategyies/110 0.
UDS服务应该支持哪些否定响应码
UDS服务应该支持哪些否定响应码所有服务都需支持的否定响应码具体的服务需要支持的否定响应码否定响应码定义及其取值 USD诊断服务的否定响应中包含有导致否定响应原因的编码，称为否定响应码(NRC, Negative Response Code)。否定响应码的取值范围为0x00 - 0xFF，被分为三组： 0x00：服务器内部实现否定响应码判断逻辑时使用，表示要给出肯定响应。 0x01 – 0x7F：诊断通信相关的否定响应码。 0x80 – 0xFF：服务器收到诊断服务请求时，由于某些条件不满足要求而给出的否定
【深度学习】什么是自回归模型？
自回归模型是一种重要的时间序列模型，通过利用过去的值来预测当前或未来的值。它在统计学、经济学、自然语言处理等领域有广泛的应用。自回归模型的简单性和可解释性使其成为许多应用的首选，但在更复杂的情况下，可能需要结合其他模型如移动平均模型（MA模型）或混合模型（如ARIMA）来更好地捕捉时间序列中的模式。扩散模型（Diffusion Model）和自回归模型（Autoregressive Model）是两种不同的生成模型，各自有独特的特点和应用场景。
图神经网络在金融风险传播分析中的应用
《图神经网络在金融风险传播分析中的应用》 摘要 本文旨在探讨图神经网络（Graph Neural Networks, GNN）在金融风险传播分析中的应用。首先，本文简要介绍了图神经网络的基本概念、发展历史以及其在各类应用场景中的重要性。接着，详细介绍了图神经网络的核心算法，包括图卷积神经网
【图像超分】论文复现：经典超分 | SAN的Pytorch源码复现，跑通源码，适配高版本的python/pytorch/cuda，使用EDSR-PyTorch进行训练和测试
本文介绍了单幅图像超分辨率重建方法SAN（二阶注意网络）的代码实现。文章详细说明了在高版本Python/Pytorch环境下适配SAN源码的方法，并提供了修改后的完整代码和预训练模型下载链接。 主要亮点包括： 将SAN模型实现整合到EDSR-Pytorch框架中，解决了高版本环境下的兼容性问题 提供了完整的数据集准备指南（DIV2K训练集和5个测试集） 给出了详细的测试命令，涵盖x2、x3、x4不同放大倍率 公开了预训练模型和可视化结果的下载资源 提供了修改后的适配高版本环境的代码。
安路TangDynasty和FD和JTAG驱动器安装
安路FPGA产品矩阵包含SALPHOENIX高性能、SALEAGLE高性价比和SALELF低功耗三大系列，广泛应用于网络通信、消费电子等领域。配套的TangDynasty(TD)集成开发环境支持标准设计输入，提供完整优化流程与调试工具。简要介绍了TD 6.2.1版本的安装步骤。
IMSL CNL 7.0.0 (x86-64)Crack
IMSL CNL 7.0.0 (x86-64)Crack intel的算法库破解文件，这是C++版本的破解文件
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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")
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
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