ThedatasetiscollectedbyYonseiUniversity.Wedeployedourmobilitymonitoringsystem,namedLifeMap,tocollectmobilitydataovertwomonthsinSeoul,Korea.LifeMapusedlearningschemeproposedinfollowingpaper.Pleasereferthispaperwhenyouuseourdataset.*YohanChon,ElmurodTalipov,HyojeongShin,andHojungCha.2011.Mobilityprediction-basedsmartphoneenergyoptimizationforeverydaylocationmonitoring.InProceedingsofthe9thACMConferenceonEmbeddedNetworkedSensorSystems(SenSys'11).ACM,NewYork,NY,USA,82-95.Visitourhomepageformoreinformation(http://lifemap.yonsei.ac.kr).

TheWikiTextlanguagemodelingdatasetisacollectionofover100milliontokensextractedfromthesetofverifiedGoodandFeaturedarticlesonWikipedia.

Stanfordlarge-scale3DIndoorSpacesDataset(S3DIS)

mnist.pkl.gz数据文件直接下载拷贝到keras的dataset下方便许多

WeizmannDataset动作图片集，包括bend，jack，jump，pjump，run，side,skip,walk,wave-onehand,wave-twohand。
一共提取9300张，适合机器学习的开发人使用

Thispracticalguideprovidesnearly200self-containedrecipestohelpyousolvemachinelearningchallengesyoumayencounterinyourdailywork.Ifyou’recomfortablewithPythonanditslibraries,includingpandasandscikit-learn,you’llbeabletoaddressspecificproblemssuchasloadingdata,handlingtextornumericaldata,modelselection,anddimensionalityreductionandmanyothertopics.Eachrecipeincludescodethatyoucancopyandpasteintoatoydatasettoensurethatitactuallyworks.Fromthere,youcaninsert,combine,oradaptthecodetohelpconstructyourapplication.Recipesalsoincludeadiscussionthatexplainsthesolutionandprovidesmeaningfulcontext.Thiscookbooktakesyoubeyondtheoryandconceptsbyprovidingthenutsandboltsyouneedtoconstructworkingmachinelearningapplications.You’llfindrecipesfor:Vectors,matrices,andarraysHandlingnumericalandcategoricaldata,text,images,anddatesandtimesDimensionalityreductionusingfeatureextractionorfeatureselectionModelevaluationandselectionLinearandlogicalregression,treesandforests,andk-nearestneighborsSupportvectormachines(SVM),naïveBayes,clustering,andneuralnetworksSavingandloadingtrainedmodels

defGMM_algorithm(iterMax,gmm,dataset):'''高斯混合聚类算法:paramiterMax:最大迭代次数:paramgmm:保存gmm模型的数据:return:簇划分结果'''step=0m=len(dataset)flagMat=np.mat(np.zeros((m,1)))#保存每个样本的簇标记lateProbMat=np.mat(np.zeros((m,3)))#保存后验概率whilestep3):k+=1print(k)mark=['or','ob','og','ok','^r','+r','sr','dr','＜r','pr']#画出所有样例点属于同一分类的绘制同样的颜色foriinrange(numSamples):

DavidJ.HandDepartmentofMathematics,ImperialCollegeLondon,London,UKDataminingisthediscoveryofinteresting,unexpectedorvaluablestructuresinlargedatasets.Assuch,ithastworatherdifferentaspects.Oneoftheseconcernslarge-scale,‘global’structures,andtheaimistomodeltheshapes,orfeaturesoftheshapes,ofdistributions.

车牌识别数据集，在原基础上做了部分调整及增加了部分数据

VB.net中DataSet和JSON形式的数据相互转换的代码，可以拿来直接说用

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。