复杂网络与我们的生活息息相关，它常常包括三类特征参数：度分布、聚类系数、平均路径长度，该文档是关于聚类系数计算的简单程序，很有用。

小秋SLAM笔记

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

*Packedwithmorethanfortypercentnewandupdatedmaterial,thiseditionshowsbusinessmanagers,marketinganalysts,anddataminingspecialistshowtoharnessfundamentaldataminingmethodsandtechniquestosolvecommontypesofbusinessproblems　　*Eachchaptercoversanewdataminingtechnique,andthenshowsreadershowtoapplythetechniqueforimprovedmarketing,sales,andcustomersupport　　*Theauthorsbuildontheirreputationforconcise,clear,andpracticalexplanationsofcomplexconcepts,makingthisbooktheperfectintroductiontodatamining　　*Moreadvancedchapterscoversuchtopicsashowtopreparedataforanalysisandhowtocreatethenecessaryinfrastructurefordatamining　　*Coverscoredataminingtechniques,includingdecisiontrees,neuralnetworks,collaborativefiltering,associationrules,linkanalysis,clustering,andsurvivalanalysis

DeriveusefulinsightsfromyourdatausingPython.Learnthetechniquesrelatedtonaturallanguageprocessingandtextanalytics,andgaintheskillstoknowwhichtechniqueisbestsuitedtosolveaparticularproblem.TextAnalyticswithPythonteachesyoubothbasicandadvancedconcepts,includingtextandlanguagesyntax,structure,semantics.Youwillfocusonalgorithmsandtechniques,suchastextclassification,clustering,topicmodeling,andtextsummarization.Astructuredandcomprehensiveapproachisfollowedinthisbooksothatreaderswithlittleornoexperiencedonotfindthemselvesoverwhelmed.YouwillstartwiththebasicsofnaturallanguageandPythonandmoveontoadvancedanalyticalandmachinelearningconcepts.Youwilllookateachtechniqueandalgorithmwithbothabird'seyeviewtounderstandhowitcanbeusedaswellaswithamicroscopicviewtounderstandthemathematicalconceptsandtoimplementthemtosolveyourownproblems.ThisbookProvidescompletecoverageofthemajorconceptsandtechniquesofnaturallanguageprocessing(NLP)andtextanalyticsIncludespracticalreal-worldexamplesoftechniquesforimplementation,suchasbuildingatextclassificationsystemtocategorizenewsarticles,analyzingapporgamereviewsusingtopicmodelingandtextsummarization,andclusteringpopularmoviesynopsesandanalyzingthesentimentofmoviereviewsShowsimplementationsbasedonPythonandseveralpopularopensourcelibrariesinNLPandtextanalytics,suchasthenaturallanguagetoolkit(nltk),gensim,scikit-learn,spaCyandPatternWhatyouwilllearnNaturalLanguageconceptsAnalyzingTextsyntaxandstructureTextClassificationTextClusteringandSimilarityanalysisTextSummarizationSemanticandSentimentanalysisReadershipThebookisforITprofessionals,analysts,developers,linguisticexperts,datascientists,andanyonewithakeeninterestinlinguistics,analytics,andgeneratinginsightsfrom

种种繁杂收集的天生模子代码，如BA,WS,NW收集等等(allkindsofmodelsforcomplexnetworkssuchasBA,WS,NWetc)繁杂收集中底子收集模子的matlab实现\Aver_Path_Length.m繁杂收集中底子收集模子的matlab实现\BA_net.m繁杂收集中底子收集模子的matlab实现\Clustering_Coefficient.m繁杂收集中底子收集模子的matlab实现\Degree_Distribution.m繁杂收集中底子收集模子的matlab实现\NN_coupled_net.m繁杂收集中底子收集模子的matlab实现\NW_net.m繁杂收集中底子收集模子的matlab实现\randomgraph.m繁杂收集中底子收集模子的matlab实现\suijitu.m繁杂收集中底子收集模子的matlab实现\WS_net.m繁杂收集中底子收集模子的matlab实现

数据挖掘算法算法目录18大DM算法包名 目录名 算法名AssociationAnalysis DataMining_Apriori Apriori-关联规则挖掘算法AssociationAnalysis DataMining_FPTree FPTree-频繁模式树算法BaggingAndBoosting DataMining_AdaBoost AdaBoost-装袋提升算法Classification DataMining_CART CART-分类回归树算法Classification DataMining_ID3 ID3-决策树分类算法Classification DataMining_KNN KNN-k最近邻算法工具类Classification DataMining_NaiveBayes NaiveBayes-朴素贝叶斯算法Clustering DataMining_BIRCH BIRCH-层次聚类算法Clustering DataMining_KMeans KMeans-K均值算法GraphMining DataMining_GSpan GSpan-频繁子图挖掘算法IntegratedMining DataMining_CBA CBA-基于关联规则的分类算法LinkMining DataMining_HITS HITS-链接分析算法LinkMining DataMining_PageRank PageRank-网页重要性/排名算法RoughSets DataMining_RoughSets RoughSets-粗糙集属性约简算法SequentialPatterns DataMining_GSP GSP-序列模式分析算法SequentialPatterns DataMining_PrefixSpan PrefixSpan-序列模式分析算法StatisticalLearning DataMining_EM EM-期望最大化算法StatisticalLearning DataMining_SVM SVM-支持向量机算法其他经典DM算法包名 目录名 算法名Others DataMining_ACO ACO-蚁群算法Others DataMining_BayesNetwork BayesNetwork-贝叶斯网络算法Others DataMining_CABDDCC CABDDCC-基于连通图的分裂聚类算法Others DataMining_Chameleon Chameleon-两阶段合并聚类算法Others DataMining_DBSCAN DBSCAN-基于密度的聚类算法Others DataMining_GA GA-遗传算法Others DataMining_GA_Maze GA_Maze-遗传算法在走迷宫游戏中的应用算法Others DataMining_KDTree KDTree-k维空间关键数据检索算法工具类Others DataMining_MSApriori MSApriori-基于多支持度的Apriori算法Others DataMining_RandomForest RandomForest-随机森林算法Others DataMining_TAN TAN-树型朴素贝叶斯算法Others DataMining_Viterbi Viterbi-维特比算法18大经典DM算法18大数据挖掘的经典算法以及代码实现，涉及到了决策分类，聚类，链接挖掘，关联挖掘，模式挖掘等等方面,后面都是相应算法的博文链接，希望能够协助大家学。
目前追加了其他的一些经典的DM算法，在others的包中涉及聚类，分类，图算法，搜索算等等，没有具体分类。
C4.5C4.5算法与ID3算法一样，都是数学分类算法，C4.5算法是ID3算法的一个改进。
ID3算法采用信息增益进行决策判断，而C4.5采用的是增益率。
详细介绍链接CARTCART算法的全称是分类回归树算法，他是一个二元分类，采用的是类似于熵的基尼指数作为分类决策，形成决策树后之后还要进行剪枝，我自己在实现整个算法的时候采用的是代价复杂度算法，详细介绍链接KNNK最近邻算法。
给定一些已经训练好的数据，输入一个新的测试数据点，计算包含于此测试数据点的最近的点的分类情况，哪个分类的类型占多数，则此测试点的分类与此相同，所以在这里,有的时候可以复制不同的分类点不同的权重。
近的点的权重大点，远的点自然就小点。
详细介绍链接NaiveBayes朴素贝叶斯算法。
朴素贝叶斯算法是贝叶斯算法里面一种比较简单的分类算法，用到了一个比较重要的贝叶斯定理，用一句简单的话概括就是条件概率的相互转换推导

AG'sNewsTopicClassificationDatasetVersion3,Updated09/09/2015ORIGINAGisacollectionofmorethan1millionnewsarticles.Newsarticleshavebeengatheredfrommorethan2000newssourcesbyComeToMyHeadinmorethan1yearofactivity.ComeToMyHeadisanacademicnewssearchenginewhichhasbeenrunningsinceJuly,2004.Thedatasetisprovidedbytheacademiccomunityforresearchpurposesindatamining(clustering,classification,etc),informationretrieval(ranking,search,etc),xml,datacompression,datastreaming,andanyothernon-commercialactivity.Formoreinformation,pleaserefertothelinkhttp://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html.TheAG'snewstopicclassificationdatasetisconstructedbyXiangZhang(xiang.zhang@nyu.edu)fromthedatasetabove.Itisusedasatextclassificationbenchmarkinthefollowingpaper:XiangZhang,JunboZhao,YannLeCun.Character-levelConvolutionalNetworksforTextClassification.AdvancesinNeuralInformationProcessingSystems28(NIPS2015).DESCRIPTIONTheAG'snewstopicclassificationdatasetisconstructedbychoosing4largestclassesfromtheoriginalcorpus.Eachclasscontains30,000trainingsamplesand1,900testingsamples.Thetotalnumberoftrainingsamplesis120,000andtesting7,600.Thefileclasses.txtcontainsalistofclassescorrespondingtoeachlabel.Thefilestrain.csvandtest.csvcontainallthetrainingsamplesascomma-sparatedvalues.Thereare3columnsinthem,correspondingtoclassindex(1to4),titleanddescription.Thetitleanddescriptionareescapedusingdoublequotes("),andanyinternaldoublequoteisescapedby2doublequotes("").Newlinesareescapedbyabackslashfollowedwithan"n"character,thatis"\n".

Focusingonimplementationratherthantheory,'StatisticalComputingwithR'servesasavaluabletutorial,providingexamplesthatillustrateprogra妹妹ingconceptsinthecontextofpracticalcomputationalproblems.ThisbookpresentsanoverviewofcomputationalstatisticswithanintroductiontotheRcomputingenvironment.Reviewingbasicconceptsinprobabilityandclassicalstatisticalinference,thetextdemonstrateseveryalgorithmthroughfullyimplementedexamplescodedinR.ChapterscovertopicssuchasMonteCarlomethods,clustering,bootstrap,nonparametricregression,densityestimation,andgoodness-of-fit.Manyexercisesareincludedforthestudentswhileasolutionsmanualisincludedfortheinstructor.

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。