属性浏览器 - 画笔设置

Edge浏览器默认全屏设置教程 对于使用虚拟机或远程连接的用户,建议在Edge的"系统"设置中启用"使用硬件加速",并关闭"自动调整缩放比例"功能,可显著提升全屏模式下的渲染性能。对于高分辨率屏幕用户,建议在"兼容性"选项卡勾选"替代高DPI缩放行为",选择"系统(增强)"选项,可避免全屏模式下界面元素模糊的问题。在"设置"→"系统"→"平板模式"中,将"当我登录时"设为"自动切换到平板模式",可使Edge在二合一设备上自动启用触控优化布局。右键点击Edge桌面图标→选择"属性"→进入"快捷方式"选项卡,这是修改程序启动参数的核心入口。 阅读详情

基本编辑界面

作为一个试图提供编辑功能的图形软件,RAD的做法是必须提供的。首当其冲的就是属性浏览器。
在uniGUI中,包装了Exj.grid.property.grid形成PropertyGrid,这在一般情况下是足够用了,美中不足的是,该控件还未实现分组与嵌套效果,刚上手uniGUI,这个暂且不管,留待后续有更好的解决方案。
基于uniGUI,实现界面组态效果比较轻松。在这里插入图片描述
这样看起来,感觉就是一个桌面软件。从编辑的角度,也还可以接受。而其他的页面功能,可以切换到另一种版式,这些想必是很容易支持与实现的。

Html5 Canvas

基于以前编程的认知,画图也就几个基本资源对象的处理:Pen、Brush、Font。不过要到网页上实现,HTML5 Canvas的原理就得搞明白。
这种基础知识,网上教学资源非常丰富,我粗搜了一下,最终选定mozilla网站的Canvas教程,算是把HTML5 Canvas入了个门。
边学的过程,边整理了个自用WORD文档,看起来更方便一些,有需要的可以在优快云下载。象征性地收一个下载积分,也方便我后续下载另的资源。
在这里插入图片描述

画笔

先实现画笔的属性设置。最终效果如下:
在这里插入图片描述
根据教程可知,可以通过一系列属性来设置线的样式。

  • lineWidth = value,设置线条宽度。
  • lineCap = type,设置线条末端样式。
  • lineJoin = type,设定线条与线条间接合处的样式。
  • miterLimit = value,限制当两条线相交时交接处最大长度;所谓交接处长度(斜接长度)是指线条交接处内角顶点到外角顶- 点的长度。
  • getLineDash(),返回一个包含当前虚线样式,长度为非负偶数的数组。
  • setLineDash(segments) 接受一个数组,来指定线段与间隙的交替,设置当前虚线样式。
  • lineDashOffset = value,设置虚线样式的起始偏移量。

下来的工作,就是设计一个人机交互界面,代码中根据各个参数实现预览效果即可。

object UniForm_GdiOption: TUniForm_GdiOption
  Left = 0
  Top = 0
  ClientHeight = 320
  ClientWidth = 631
  Caption = #30011#22270#36164#28304#35774#32622#31383#21475
  OnShow = UniFormShow
  OldCreateOrder = False
  MonitoredKeys.Keys = <>
  PixelsPerInch = 96
  TextHeight = 13
  object UniPageControl1: TUniPageControl
    Left = 0
    Top = 0
    Width = 631
    Height = 276
    Hint = ''
    ActivePage = UniTabSheet1
    Align = alClient
    TabOrder = 0
    ExplicitWidth = 635
    ExplicitHeight = 256
    object UniTabSheet1: TUniTabSheet
      Hint = ''
      Caption = #30011#31508
      ExplicitWidth = 627
      ExplicitHeight = 228
      object UniPanel2: TUniPanel
        Left = 0
        Top = 0
        Width = 623
        Height = 248
        Hint = ''
        Align = alClient
        TabOrder = 0
        BorderStyle = ubsNone
        Caption = ''
        ExplicitLeft = 3
        ExplicitTop = 3
        ExplicitWidth = 624
        ExplicitHeight = 408
        DesignSize = (
          623
          248)
        object UniLabel1: TUniLabel
          Left = 10
          Top = 13
          Width = 32
          Height = 19
          Hint = ''
          Caption = #32447#23485
          ParentFont = False
          Font.Height = -16
          TabOrder = 1
        end
        object UniSpinEdit_PenWidth: TUniSpinEdit
          Left = 52
          Top = 12
          Width = 120
          Hint = ''
          Value = 1
          MaxValue = 100
          MinValue = 1
          TabOrder = 2
          OnChange = UniSpinEdit_PenWidthChange
        end
        object UniPanel_PenColor: TUniPanel
          Left = 52
          Top = 48
          Width = 120
          Height = 25
          Hint = ''
          TabOrder = 3
          BorderStyle = ubsNone
          Caption = ''
          Color = 33023
        end
        object UniLabel3: TUniLabel
          Left = 10
          Top = 86
          Width = 32
          Height = 19
          Hint = ''
          Caption = #26411#31471
          ParentFont = False
          Font.Height = -16
          TabOrder = 4
        end
        object UniComboBox_PenCap: TUniComboBox
          Left = 52
          Top = 85
          Width = 122
          Height = 27
          Hint = ''
          Text = 'UniComboBox_PenCap'
          Items.Strings = (
            'butt'
            'round'
            'square')
          ItemIndex = 0
          ParentFont = False
          Font.Height = -16
          TabOrder = 5
          IconItems = <>
          OnChange = UniSpinEdit_PenWidthChange
        end
        object UniComboBox_PenJoin: TUniComboBox
          Left = 52
          Top = 121
          Width = 122
          Height = 27
          Hint = ''
          Text = 'UniComboBox1'
          Items.Strings = (
            'round'
            'bevel'
            'miter')
          ItemIndex = 2
          ParentFont = False
          Font.Height = -16
          TabOrder = 6
          IconItems = <>
          OnChange = UniSpinEdit_PenWidthChange
        end
        object UniLabel5: TUniLabel
          Left = 10
          Top = 160
          Width = 32
          Height = 19
          Hint = ''
          Caption = #32447#22411
          ParentFont = False
          Font.Height = -16
          TabOrder = 7
        end
        object UniComboBox_PenStyle: TUniComboBox
          Left = 52
          Top = 158
          Width = 122
          Height = 27
          Hint = ''
          Text = 'UniComboBox1'
          Items.Strings = (
            #23454#32447
            '1, 1'
            '4, 2')
          ItemIndex = 0
          ParentFont = False
          Font.Height = -16
          TabOrder = 8
          IconItems = <>
          OnChange = UniSpinEdit_PenWidthChange
        end
        object UniLabel6: TUniLabel
          Left = 10
          Top = 197
          Width = 32
          Height = 19
          Hint = ''
          Caption = #28176#21464
          ParentFont = False
          Font.Height = -16
          TabOrder = 9
        end
        object UniEdit_ColorStops: TUniEdit
          Left = 261
          Top = 195
          Width = 355
          Hint = ''
          Text = '0, '#39'#FF0000'#39'; 1, '#39'#FFFFFF'#39
          ParentFont = False
          Font.Height = -16
          Anchors = [akLeft, akTop, akRight]
          TabOrder = 10
          OnChange = UniSpinEdit_PenWidthChange
        end
        object UniCanvas_Pen: TUniCanvas
          Left = 192
          Top = 8
          Width = 425
          Height = 169
          Hint = ''
          AutoSubmit = False
          Editable = False
        end
        object UniComboBox_Gradient: TUniComboBox
          Left = 52
          Top = 195
          Width = 122
          Height = 27
          Hint = ''
          Text = 'UniComboBox1'
          Items.Strings = (
            #26080#28176#21464
            #32447#24615
            #38647#36798)
          ItemIndex = 0
          ParentFont = False
          Font.Height = -16
          TabOrder = 12
          IconItems = <>
          OnChange = UniSpinEdit_PenWidthChange
        end
        object UniLabel7: TUniLabel
          Left = 193
          Top = 197
          Width = 64
          Height = 19
          Hint = ''
          Caption = #20851#38190#39068#33394
          ParentFont = False
          Font.Height = -16
          TabOrder = 13
        end
        object UniLabel2: TUniLabel
          Left = 10
          Top = 49
          Width = 32
          Height = 19
          Hint = ''
          Caption = #39068#33394
          ParentFont = False
          Font.Height = -16
          TabOrder = 14
        end
        object UniLabel4: TUniLabel
          Left = 10
          Top = 123
          Width = 32
          Height = 19
          Hint = ''
          Caption = #36830#25509
          ParentFont = False
          Font.Height = -16
          TabOrder = 15
        end
      end
    end
    object UniTabSheet2: TUniTabSheet
      Hint = ''
      Caption = 'UniTabSheet2'
      ExplicitWidth = 627
      ExplicitHeight = 228
    end
    object UniTabSheet3: TUniTabSheet
      Hint = ''
      Caption = 'UniTabSheet3'
      ExplicitWidth = 627
      ExplicitHeight = 228
    end
  end
  object UniPanel1: TUniPanel
    Left = 0
    Top = 276
    Width = 631
    Height = 44
    Hint = ''
    Align = alBottom
    TabOrder = 1
    BorderStyle = ubsNone
    Caption = ''
    ExplicitTop = 256
    ExplicitWidth = 635
    object UniBitBtn1: TUniBitBtn
      Left = 432
      Top = 8
      Width = 75
      Height = 25
      Hint = ''
      Glyph.Data = {
        36030000424D3603000000000000360000002800000010000000100000000100
        18000000000000030000C40E0000C40E00000000000000000000FFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFAFBFDC0C9EDE5E9F8FFFFFFFF
        FFFFFFFFFFFFFFFFE5E9F8BEC8EDF9FAFDFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
        FCFCFE93A4E21538C2546DD2E4E8F7FFFFFFFFFFFFE4E8F7546DD21437C291A2
        E1FCFCFEFFFFFFFFFFFFFFFFFFFAFBFD94A5E2143AC3062EC20830C25671D3E5
        E9F8E5E9F85671D30830C2062EC2143AC393A4E2FAFBFDFFFFFFFFFFFFBFCAEE
        1942C90A36C60A36C60A36C60B37C5516FD3516FD30B37C50A36C60A36C60A36
        C61942C9BFCAEEFFFFFFFFFFFFE4E8F85677DE0F41D10E3FCA0E3FCA0E3FCA0F
        3FC90F3FC90E3FCA0E3FCA0E3FCA0F41D15677DDE4E8F8FFFFFFFFFFFFFFFFFF
        E2E7F8587CE1134BD61249CF1249CF1249CF1249CF1249CF1249CF134BD6597C
        E1E3E8F8FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFE3E8F8557BE11753D71753D417
        53D41753D41753D41753D7557CE1E4E9F8FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFE5E9F8587FDB1B5CD81B5DD91B5DD91B5DD91B5DD91B5CD8587FDBE5E9
        F7FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFE4E8F76088DE1F65DC2068DE2067DE20
        67DE2067DE2067DE2068DE1F65DC6088DEE4E8F7FFFFFFFFFFFFFFFFFFE5EAF8
        618BE0246EE02471E32471E32471E42572E72572E72471E42471E32471E3246D
        E0628CE0E5EAF8FFFFFFFFFFFFC2CEF02F74E4297CEA287AE7287AE8297BEC5E
        8FE85E8FE8297BEC287AE8287AE7297CEA2F75E5BFCDF0FFFFFFFFFFFFF9FAFD
        9AB4EC3280EC2D84EE2C82EF6597EAE5EAF8E5EAF86597EA2C82EF2D84EE3280
        ED95B1ECF8F9FDFFFFFFFFFFFFFFFFFFFBFBFD9AB6EC3685EB679AEAE3E8F8FF
        FFFFFFFFFFE3E8F8679AEA3686EC95B3ECF9FAFDFFFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFF9FAFDC2D1F1E6EBF9FFFFFFFFFFFFFFFFFFFFFFFFE6EBF9C0CFF1F8F9
        FDFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF}
      Caption = #21462#28040
      ModalResult = 2
      ParentFont = False
      Font.Height = -16
      TabOrder = 1
    end
    object UniBitBtn2: TUniBitBtn
      Left = 536
      Top = 8
      Width = 75
      Height = 25
      Hint = ''
      Glyph.Data = {
        06030000424D060300000000000036000000280000000F0000000F0000000100
        180000000000D0020000C40E0000C40E00000000000000000000FFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFFFFCEDFCEB4D0B4EDF3EDFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFFFFFFFFF000000FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF94BE941983191A
        8E1A3B8E3BF2F6F2FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF000000FFFFFFFFFFFF
        FFFFFFFFFFFFF3F4F3007200059A050BA10B0084008FBA8FFFFFFFFFFFFFFFFF
        FFFFFFFFFFFFFF000000FFFFFFFFFFFFFFFFFFFEFCFE3E8C3E008F0000980000
        9800009400057605FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF000000FFFFFFFFFFFF
        FFFFFF95BE95007E00009B00009200008F00019B010081009CBE9CFFFFFFFFFF
        FFFFFFFFFFFFFF000000FFFFFFFFFFFFD5E3D5007800009A000088003186312C
        832C009100009A00137B13F1F4F1FFFFFFFFFFFFFFFFFF000000FFFFFFFFFFFF
        3D903D0091000084004B944BF2F6F2D0E0D0037B030098000082008DBA8DFFFF
        FFFFFFFFFFFFFF000000FFFFFF89B7892495241F8D1F5C9C5CFFFFFFFFFFFFFF
        FFFF9FC39F007B00009800147E14FFFDFFFFFFFFFFFFFF000000D8E6D8198019
        1E871E9CC39CFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF619E61008B0000860092BC
        92FFFFFFFFFFFF0000004B934B066D06B7D3B7FFFFFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFFFFFFFFFFF207E2036A836298729FFFFFFFFFFFF000000A7CAA7F3F7F3
        FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFDFEADF2B8A2B2897
        2895BF95FFFFFF000000FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFF97C0971186113B903BFEFDFE000000FFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF6BA7
        6B007200B8D4B8000000FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFFFFFFFFFEFEFE3284323F8C3F000000FFFFFFFFFFFF
        FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
        FFDFEBDF3D8A3D000000}
      Caption = #30830#23450
      ModalResult = 1
      ParentFont = False
      Font.Height = -16
      TabOrder = 2
    end
  end
end
浏览器系列之 Cookie 和 SameSite 属性 2 月份发布的 Chrome 80 版本中默认屏蔽了第三方的 Cookie,在灰度期间,就导致了阿里系的很多应用都产生了问题,为此还专门成立了小组,推动各 BU 进行改造,目前阿里系基本已经改造完成。所有的前端团队估计都收到过通知,也着实加深了一把大家对于 Cookie 的理解,所以很可能就此出个面试题,而即便不是面试题,当问到 HTTP 相关内容的时候,不妨也扯到这件事情来,一能表明你对前端时事的跟进,二还能借此引申到前端安全方面的内容,为你的面试加分。 阅读详情

相关推荐

UniGUI的Canvas使用

Procedure TMainForm.UniImage1MouseUp(Sender: TObject;Button:TMouseButton; Shift:TShiftState;X,Y:Integer); begin UniImage1.Picture.Bitmap.Canvas.Pen.Width := 1; UniImage1.Picture.Bitmap.Transparent := True; if Button=mbLeft then begin if x0=’...

大龙软件工作室 526

uniGUI之事项合集

uniGUI

zheng的博客 3784

unigui_Canvas图片轮播_左推移

unigui_Canvas图片轮播_左推移 unigui_Canvas图片轮播_左推移 简单构造

利用uniGUI中的TUniPageControl实现多页面

远行效果: 实现代码: procedure TfrmMain.OpenForm(Caption,FormClassName:string);var i:integer; sheet:TUniTabSheet;begin for i := 0 to pgeMain.PageCount-1 do begin if pgeMain.Pages[i].Caption=C...

weixin_33836874的博客 652

UniGUI集合说明--追月无名.pdf

unigui开发集合

unigui_css引用路径

unigui_css引用路径

unigui多页签UI框架

procedure TMainForm.openForm(Caption, FormClassName: string);var i: integer; sheet: TUniTabSheet;begin for i := 0 to page.PageCount - 1 do begin if page.Pages[i].Caption = Caption then be...

weixin_30924087的博客 1316

unigui_canvasjs统计图.rar

unigui_canvasjs统计图.rar

UniGUI的 TUniPageControl控件动态拖动tabsheet的实现方法

实现可以用鼠标动态拖动tabsheet,共三个步骤: 1.在ServerModule中,打开Custom Files属性,输入以下两行代码引用js文件。 files/BoxReorderer.js files/TabReorderer.js 2.找到UniGUI安装目录下的目录:FMSoft\Framework\uniGUI\ext-4.2.5.1763\examples\ux 中的B

记性不好,笔记而已 2765

JLink 添加国产芯片手把手教程(雅特力 + 华大)

拷贝厂商算法文件夹 + 配置 JLinkDevices.xml。雅特力直接复制官方配置,华大按需添加芯片节点,操作一次,永久生效,以后调试 AT32、HC32 再也不用愁找不到型号。这套方法适配全版本 JLink,也是国产 MCU 替代项目的必备技能,看完照着做,新手也能轻松搞定。百度网盘资料分享:通过网盘分享的文件:jink驱动(包括新增芯片型号)链接: https://pan.baidu.com/s/14SLgrPfyof1lglD6VQguAw?pwd=cfuq 提取码: cfuq。

weixin_48867130的博客 713

Cloudflare 5秒盾破解实战:Python补环境框架下的13次请求全解析

本文深入解析Cloudflare 5秒盾(5-second challenge)的工作原理,并提供基于Python环境的自动化应对策略。通过13次请求流程分解、动态脚本处理技术和环境指纹模拟,帮助开发者有效绕过这一复杂的人机验证机制,提升自动化工作效率。

weixin_30772105的博客 177

Chrome浏览器的跨域设置

Chrome浏览器的跨域设置

weixin_53841730的博客 4万+

【具身算法】VLA算法-Diffusion Policy

《扩散策略:多模态机器人控制的新方法》摘要 本文提出了一种基于扩散模型的机器人控制策略(DiffusionPolicy),用于解决视觉运动控制中的多模态行为、高维动作空间和训练稳定性问题。该方法利用去噪扩散概率模型(DDPM)生成动作序列,通过迭代优化处理复杂动作分布,显著提升了性能。实验表明,在15项机器人操作任务中,扩散策略平均性能提升46.9%,有效解决了传统显式/隐式策略的局限性。其核心优势在于:1)优雅建模多模态动作分布;2)适应高维动作空间;3)训练过程稳定。未来工作将探索与强化学习的结合及计算

puzhinan7781的博客 1391

Selenium打开Chrome浏览器和设置浏览器属性

Selenium打开Chrome浏览器和设置浏览器属性 代码如下(替换成自己的配置就可以直接使用): public static WebDriver getWebDriver(){ /** * 设置指定键对值的系统属性 * * 参数: * 第一个参数:系统属性的名称 * 第二个参数:系统属性的值 * * 返回: * 系统属性以前的值,如果没有以前的值...

mry6的博客 1930

使用 Pfam 和 InterProScan 进行蛋白质家族和功能域的分析

本文介绍了使用Pfam和InterProScan进行蛋白质家族与功能域分析的方法。主要内容包括:1) Pfam数据库的本地化使用,通过hmmscan工具比对Pfam-A.hmm数据库,并提供了批量处理Python脚本;2) InterProScan的安装配置与命令行参数详解,支持多数据库整合分析;3) 补充介绍了基于curl的Uniprot在线查询方法。文章提供了从软件安装到实战分析的完整流程,适用于本地化、批量化蛋白质功能注释需求,特别是无网络环境或大规模数据分析场景,适合不同水平的生物信息学用户。

Protein Designer的博客 1918

一文读懂 STAR-CCM+:多物理场仿真王者,从入门到精通全攻略(附实战路线)

STAR-CCM+是西门子旗下的多物理场仿真平台,集流体、传热、结构、电磁等耦合求解于一体。其核心优势包括:全自动网格处理技术(支持十亿级网格)、多物理场无缝耦合、CPU/GPU并行加速计算、流程自动化与设计探索等。广泛应用于汽车(空气动力学优化、电池热管理)、航空航天(气动设计、发动机仿真)、能源(风电叶片流固耦合)、电子(芯片散热)等行业。相比ANSYS Fluent和OpenFOAM,STAR-CCM+在多物理场耦合和复杂几何处理方面更具优势,适合工业级高精度仿真需求。学习路径建议从基础操作入手,逐步

源代码杀手的博客 340

设置placeholder属性样式的多种写法

如何设置placeholder的样式 &amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;我们经常用到placeholder属性是在input标签里面,placeholder属性主要作用是让输入框有个提示的显示。 &amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;am

weixin_44484756的博客 2万+

手把手教你学simulink(26.7)--控制算法场景示例:基于模型预测控制(MPC)的智能温室环境控制系统

首先,我们需要将温室环境的动力学模型转换为状态空间形式。假设我们有四个状态变量:温度 TT、湿度 HH、光照强度 II 和二氧化碳浓度 CCO2CCO2​,以及四个控制输入:加热器功率 u1u1​、加湿器加湿量 u2u2​、通风扇风速 u3u3​ 和补光灯光照强度 u4u4​。状态空间模型可以表示为:x(t)=[T(t),H(t),I(t),CCO2(t)]Tx(t)=[T(t),H(t),I(t),CCO2​(t)]T 是状态向量。

MHD0815的博客 400

属性浏览器 - 画刷设置

画刷设置画笔与画刷效果无填充纯色填充线性渐变雷达渐变 QQ: 282397369 画笔与画刷 在TUniCanvas中,已定义画笔画刷 TUniCanvasPen* FPen; TUniCanvasBrush* FBrush; 再看一下,这两个类还有点关系: TUniCanvasPenTUniPenTUniBrushColorOpacityWidthTUniCanvasBrush 其实也很简...

drgraph的博客 373

01-诊断开发流程概述

传送门 ==>> AutoSAR入门和实战系列总目录。

1786

S7-200 SMART PLC 数字量阀门控制功能块(FB)汇总

S7-200 SMART PLC功能块的各种写法可以查看专栏的其它文章,链接地址如下:S7-200 SMART PLC 子程序功能块(阀门控制)_RXXW_Dor的博客-优快云博客_plc控制阀门开关程序由于S7-200 SMARTPLC的子程序种种功能、容量限制,有时想实现多次调用的子程序可能并不是太容易,这篇博客还是给出一些推荐写法,供大家参看。

RXXW_Dor的博客 4001

js获取、设置元素属性值

文章目录setAttributegetAttributegetComputedStylecurrentStyle`元素.style.属性名` 网页css和样式结构 #box{ width:100px; height:100px; } <div id="box" style="background:red;"></div> setAttribute 元素...

最初的梦想 1万+

pscad IEEE标准系统搭建说明

pscad IEEE标准系统搭建说明,包含典型的IEEE 9节点、14节点、30节点、39节点和118节点系统。内含标准测试系统的原始数据和潮流结果。

谷歌浏览器跨域设置

[转] Chrome - 浏览器跨域访问设置(附:新老版本两种设置方法) 【From】http://www.hangge.com/blog/cache/detail_1703.html 在进行前后分离的webapp开发,或者H5移动App开发时,我们会使用PC端浏览器进行开发调试。但默认情况下Ajax请求无法跨域访问,请求时会报如下错误: XMLHttpRequ...

qq_42310995的博客 1万+

lwip-2.0.3

lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。

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")

selenium设置:浏览器不显示图片

想解决的问题: 跑selenium脚本时,会运行浏览器,浏览器会显示图片,而出于效率或者其他方面的考虑,我们并不希望显示图片。那这里怎么做呢?本文会向大家介绍方法,并且触类旁通,通过本文的办法,你会知道,怎么自定义浏览器的其他属性。 一、chrome driver是否设置浏览器属性 chrome driver是否支持对浏览器属性的定义?答案当然是YES 通过chrome drive

lizeyang的专栏 1万+

Probability and Statistics - 4th edition

Probability and Statistics - 4th edition

不能装载文档控件。请在检查浏览器的选项中检查浏览器的安全设置_网络安全科普 | 如何正确设置浏览器!...

李夏是一名医院的护士,一天晚上加班赶制文档,由于要向患者宣讲健康科普知识,需要获取大量网上信息,然而在制作中却发现浏览器的网页打不开了。第二天原计划向患者宣讲的材料未能完整汇总,宣传效果也打了折扣。 在日常的工作和生活中,浏览器是我们不可或缺的工具。掌握浏览器的设置、使用方法十分必要,否则就会像李夏一样,使工作变得被动。浏览器的设置包括安全、隐私、连接等,设置相关功能可以...

weixin_39775577的博客 1万+

谷歌浏览器如何设置internet选项

歌浏览器设置internet选项具体操作方法如下: 1、首先进入谷歌浏览器主页,点击浏览器工具栏上的Chrome菜单(三横那个按钮) 2、点击菜单,然后打开设置。 3、把网页拉到最下面,找到底部的“显示高级设置...” 4、在增加的高级设置的页面中,找到“网络”下面的内容,点击“更改代理服务器设置” 5、internet属性的修改页面就弹出来了,我们就可以修改其中的内容了。 ...

程序员 4万+
上一篇: 富客户端管理系统 - 原理示教
下一篇: 属性浏览器 - 画刷设置
DrGraph
博客等级 码龄14年 228粉丝 77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