Matlabcodeforcomputingandvisualization:ConfusionMatrix,Precision/Recall,ROC,Accuracy,F-Measureetc.forClassification.Matlab通过分类的label计算混淆矩阵ConfusionMatrix并且显示的函数只要一句代码就行了，方便。
[confusion_matrix]=compute_confusion_matrix(predict_label,num_in_class,name_class);

Visustinforsoftwaredevelopers.Visustinisanautomatedflowchartprogramforsoftwaredevelopersanddocumentwriters.Savedocumentationeffortswithautomaticcodevisualization.VisustinreverseengineersyoursourcecodetoflowchartsorUMLActivityDiagrams.Visustinreadstheifandelsestatements,loopsandjumpsandbuildsadiagram—fullyautomated.

可视化是数据描述的图形表示，旨在一目了然地揭示数据中的复杂信息。
可视化的典型如纽约地铁图和人脑图。
成功的可视化的美丽之处既在于其艺术设计，也在于其通过对细节的优雅展示，能够有效地产生对数据的洞察和新的理解。
　　在本书中，20多位可视化专家包括艺术家、设计师、评论家、科学家、分析师、统计学家等，展示了他们如何在各自的学科领域内开展项目。
他们共同展示了可视化所能实现的功能以及如何使用它来改变世界。
在《数据可视化之美》中，你将：　　·通过简单的可视化实践探索讲故事的重要性。
　　·了解颜色如何传达我们尚未充分意识到而大脑已经识别出的信息。
　　·发现我们购买的书籍和我们的交际圈如何揭示内心的自我。
　　·通过对民航交通的可视化探索识别航空旅行的混乱的一种方法。
　　·揭秘研究人员如何调查未知问题，包括从最初的草图到发表的论文。
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Moduledeveloper’sguidetoFreeCADsourcecodebyQingfengXiahttp://www.iesensor.com•2015-09-18version0.1forFreeCADversion0.16-dev•2016-09-18version0.2forFreeCADversion0.17-devLicenseofthisbookThisebookislicensedthesameasFreeCADdocumentlicenseCC-BY3.0http://creativecommons.org/licenses/by/3.0/Contents1FreeCADoverviewandarchitecture71.1IntroductiontoFreeCAD...............................................71.2Keyfeatures.......................................................71.3Softwarearchitecture..................................................71.3.1Keysoftwarelibraries.............................................71.3.2Mixedpythonandc++............................................81.3.3GPLcodewillnotbeincludedintoinstaller.................................81.4How3Dmodelarerenderred.............................................81.4.1Selectionof3Dvisualizationlibarary.....................................81.4.2Discussionof3DrenderinglibraryselectiononFreeCADForum.....................81.5RoadmapofFreeCAD.................................................91.5.1Keepupdatedwithmaincomponents:....................................91.5.2C++11.....................................................91.5.3Pyside2projectforQt5.x..........................................92OrganisationofFreeCADsourcecode112.1Build

2017年的新书呀，D34.x以及ES2017!!2017年的新书呀，D34.x以及ES2017!!

里面的网址现在找不到了，但是资源又删不了，请大家不要下载了。
一个日本人的3D曲面逼近相关的科研成果，包括原始数据、代码及其发表的文章。
内有详细列表及网址链接，有兴趣的可以下载看看。
附其中一篇文章的题目及摘要：SparseLow-degreeImplicitSurfaceswithApplicationstoHighQualityRendering,FeatureExtraction,andSmoothingAbstractWeproposeanewsurfacerepresentationdeliveringanaccurateapproximationtoasetofpointsscatteredoverasmoothsurfacebySparseLow-degreeIMplicits(SLIM).TheSLIMsurfacerepresentationconsistsofasparsemulti-scalesetofnonconformingsurfaceprimitiveswhichareblendedalongviewraysduringtherenderingphase.Thisnewrepresentationleadstoaninteractivereal-timevisualizationoflarge-sizemodelsanddeliversabetterrenderingqualitythanstandardsplattingtechniquesbasedonlinearprimitives.Further,SLIMallowsustoachieveafastandaccurateestimationofsurfacecurvatureandcurvaturederivativesand,therefore,isverysuitableformanynon-photorealisticrenderingtasks.Applicationstoray-tracingandsurfacesmoothingarealsoconsidered.

Datavisualizationisoneofthemostimportantpartofdatascience.Manybooksandcoursespresentacatalogueofgraphicsbuttheydon'tteachyouwhichchartstouseaccordingtothetypeofthedata.Inthisbook,westartbypresentingthekeygraphicsystemsandpackagesavailableinR,includingRbasegraphs,latticeandggplot2plottingsystems.Next,weprovidemorethan200practicalexamplestocreategreatgraphicsfortherightdatausingeithertheggplot2packageandextensionsorthetraditionalRgraphics.Withthisbook,you'lllearn:-Howtoquicklycreatebeautifulgraphicsusingggplot2packages-Howtoproperlycustomizeandannotatetheplots-Typeofgraphicsforvisualizingcategoricalandcontinuousvariables-Howtoaddautomaticallyp-valuestoboxplots,barplotsandalternatives-Howtoaddmarginaldensityplotsandcorrelationcoefficientstoscatterplots-Keymethodsforanalyzingandvisualizingmultivariatedata-Rfunctionsandpackagesforplottingtimeseriesdata-Howtocombinemultipleplotsononepagetocreateproduction-qualityfigures.

Chapter1providesin-depthinformationabouthowneuralnetworksfunction,wheretogetdatafrom,andhowtopreprocessthatdatatomakeiteasiertoconsume.Chapter2isaboutgettingstuckandwhattodoaboutit.Neuralnetsarenotoriouslyhardtodebugandthetipsandtricksinthischapteronhowtomakethembehavewillcomeinhandywhengoingthroughthemoreproject-orientedrecipesintherestofthebook.Ifyouareimpatient,youcanskipthischapterandgobacktoitlaterwhenyoudogetstuck.Chapters3through15aregroupedaroundmedia,startingwithtextrocessing,followedbyimageprocessing,andfinallymusicprocessinginChapter15.Eachchapterdescribesoneprojectsplitintovariousrecipes.Typicallyachapterwillstartwithadataacquisitionrecipe,followedbyafewrecipesthatbuildtowardthegoalofthechapterandarecipeondatavisualization.Chapter16isaboutusingmodelsinproduction.Runningexperimentsinnotebooksisgreat,butultimatelywewanttoshareourresultswithactualusersandgetourmodelsrunonrealserversormobiledevices.Thischaptergoesthroughtheoptions.

Author:WesMcKinneyISBN-10:1491957662Year:2017Pages:544Language:EnglishFileformat:PDFGetcompleteinstructionsformanipulating,processing,cleaning,andcrunchingdatasetsinPython.UpdatedforPython3.6,thesecondeditionofthishands-onguideispackedwithpracticalcasestudiesthatshowyouhowtosolveabroadsetofdataanalysisproblemseffectively.You’lllearnthelatestversionsofpandas,NumPy,IPython,andJupyterintheprocess.WrittenbyWesMcKinney,thecreatorofthePythonpandasproject,thisbookisapractical,modernintroductiontodatasciencetoolsinPython.It’sidealforanalystsnewtoPythonandforPythonprogrammersnewtodatascienceandscientificcomputing.DatafilesandrelatedmaterialareavailableonGitHub.UsetheIPythonshellandJupyternotebookforexploratorycomputingLearnbasicandadvancedfeaturesinNumPy(NumericalPython)GetstartedwithdataanalysistoolsinthepandaslibraryUseflexibletoolstoload,clean,transform,merge,andreshapedataCreateinformativevisualizationswithmatplotlibApplythepandasgroupbyfacilitytoslice,dice,andsummarizedatasetsAnalyzeandmanipulateregularandirregulartimeseriesdataLearnhowtosolvereal-worlddataanalysisproblemswiththorough,detailedexamples

Matlabcodeforcomputingandvisualization:ConfusionMatrix,Precision/Recall,ROC,Accuracy,F-Measureetc.forClassification

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。