墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
700+累计阅读
759平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
天气预报是人们日常生活中非常重要的一部分，而在现代化的网页应用中，将天气预报以直观、美观的方式展示给用户是一项常见的需求。在前端开发中，Echarts是一个功能强大且易于使用的可视化库，它能够帮助我们以图表的形式展示数据。本文将介绍如何使用Echarts制作自定义的天气预报界面，并提供相应的源代码。
首先，我们需要准备好所需的HTML和JavaScript文件，并引入Echarts库。以下是一个基本的HTML结构，用于容纳天气预报图表：
<!DOCTYPE html>
<html>
<head>
react echarts制作曲线图(以天气预报为例)
在React中使用echarts图表，制作一张24小时天气曲线图，显示温度，湿度曲线和各个小时的天气情况和具体温度； 描述： 设置了3个x轴，设置了时间，温度，天气 效果图： 1. 安装： npm install --save echarts-for-react npm install echarts --save 2. 在图表页面引入Echarts import React from 'react'; import ReactEcharts from 'echarts-for-r.
未来天气预报展示与图表轮播效果及图标插入（使用ECharts）
概述： 在本文中，我们将介绍如何使用ECharts库创建一个动态的未来天气预报应用程序，其中包括图表轮播效果和插入自定义的SVG或图片图标。我们将使用JavaScript和HTML来实现该应用程序，并使用ECharts库来生成图表和图形。步骤1：设置HTML结构 首先，我们需要设置一个基本的HTML结构，以便容纳天气预报和图表。 步骤2：引入ECharts库和数据 在我们的HTML文件中，我们需要引入ECharts库和相关的数据。您可以从ECharts官方网站下载最新版本的库文件，并将其链接到您的HTML文
mui+echart+天气预报
跨域得到天气预报信息，然后利用 mui+echart+天气预报
eCharts实现七天天气预报（整合）
eCharts实现七天天气预报（整合）
软件工程实验报告--用Visio绘制数据流图、ER图、状态图、软件结构图、程序流程图、系统流程图
用Visio绘制数据流图、ER图、状态图、软件结构图、程序流程图、系统流程图
链式中介效应结果解读：从Bootstrap置信区间到实践意义
本文深入解析链式中介效应结果的解读方法，从Bootstrap置信区间的应用到实践意义的阐述。通过实际案例展示如何分析总效应、直接效应和间接效应，并强调效应量评估的重要性。文章还提供了构建逻辑解释的三步法，以及敏感性分析和报告撰写的实用技巧，帮助研究者准确解读链式中介效应。
UCD9081EVM电源时序管理：硬件配置与GUI操作全解析
电源时序管理是复杂电子系统设计的核心环节，它通过精确控制多路电源的上电/下电顺序，确保核心芯片免受浪涌电流或电压倒灌的损害。其原理基于状态机逻辑与电压监控，技术价值在于将硬连线时序转化为可编程配置，极大提升了系统可靠性与设计灵活性。在服务器、通信设备、工控系统等高可靠性应用场景中，时序控制器是实现稳定启动与故障保护的关键。本文以德州仪器UCD9081评估模块为例，深入剖析其硬件跳线配置与图形用户界面操作，涵盖I2C地址设置、电压监控分压计算、时序条件与故障响应策略等工程实践要点，帮助工程师快速掌握这一电源时
VLA-JEPA：具身智能中潜空间世界模型的工程落地实践
世界模型是具身智能实现环境理解与因果推演的核心基础，其本质是在状态空间中建模‘动作→结果’的动力学规律。传统基于像素或文本的世界模型因高维、非平稳、弱语义等缺陷，在真实机器人场景中鲁棒性差、部署成本高。JEPA（联合嵌入预测架构）通过构建物理约束的潜空间，将视觉-语言-动作（VLA）信号统一编码为低维、可微、可解释的状态表征，显著提升模型对抖动、遮挡、光照突变等干扰的适应能力，并支持实时闭环控制。该范式已在UR5e、Franka等多构型机械臂及Jetson边缘平台完成实测验证，成为VLA系统从实验室走向产线
深度测评：Accio Work——目前普通用户最友好的多 Agent 协同工具
摘要：Accio Work是阿里国际站推出的多Agent协同工具，通过借鉴古代"三省六部制"构建高效协作框架。该工具支持主流大模型，可配置专业Agent角色，实现40分钟内完成复杂产品PRD等高效协作。其亮点在于直观的UI设计和流程驱动机制，能强制各Agent从专业视角深度思考。虽然超长对话稳定性有待优化，但作为目前最用户友好的多Agent工具，显著降低了构建复杂AI团队的门槛，成为提升个人和小微企业生产力的利器。
完整的 Python 数据分析案例：新能源汽车续航里程预测
随着新能源汽车的普及，续航里程成为消费者关注的重要指标。准确预测新能源汽车的续航里程，有助于车企优化电池管理系统、提升车辆性能，也能帮助消费者合理规划出行。本案例将基于新能源汽车的各项参数，如电池容量、行驶速度、环境温度等，构建续航里程预测模型。通过以上改进，可以提高数据处理的准确性、模型的性能和可视化的效果。
GStreamer中h264parse元素
h264parse是 GStreamer 中处理 H.264 视频流的关键元素，主要负责解析和准备 H.264 流以便后续处理。
Windows 10 本地硬盘变成了可弹出磁盘
今天新装了系统, 电脑是一个SSD+HHD, 没想到装好之后右下角显示可安全删除设备, 两个硬盘在那里. 这还得了. 查了一下午, 大致知道了是什么问题. Standard SATA AHCI controller使用高性能的硬盘模式就会把硬盘看作是一个可热插拔的存储器, 所以自然就会显示可弹出. 如果你的BIOS支持关闭HotSwap或者HotPlug, 那么直接关闭就可以了. 或者你直接在BIOS里把磁盘模式改为IDE, 不过这样就发挥不好你SSD的高性能了. 还有一种解决方法, 就是手动让系统把这两个
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...
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
最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版
Part-E1-SDIO-Specification-Ver4.00-Final-120220.pdf：最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版，包括SDIO 接口的外设，SDIO 接口官方协议等。
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")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
3561
扫一扫
