成果matlab六课教程

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3IntroductiontoMatlab3IntroductiontoMatlabWhatisaMatlabToolboxesarespecializedcollectionsofMatlabWhydoweneedthe4WhattypeoftoolboxesdoesMatlab4WhattypeoftoolboxesdoesMatlab▪▪ParallelComputing数学、统计与优SymbolicMathPartialDifferentialEquationStatisticsandMachineLearningCurveFittingOptimizationNeuralNetworkModel-BasedCalibrationControlSystemToolboxFuzzyLogicToolboxRobustControlModelPredictiveControlToolboxAerospaceToolboxRoboticsSystem▪▪▪▪▪▪▪5WhattypeoftoolboxesdoesMatlab5WhattypeoftoolboxesdoesMatlab信号处无线DSPSystemToolboxAudioSystemToolboxWaveletToolboxRFToolboxLTESystemToolboxWLANSystem▪▪▪▪▪▪▪▪▪▪图像处理与计算机视ImageProcessingToolboxVisionHDLToolboxMappingToolbox▪▪▪▪▪测试&DataAcquisitionToolboxImageAcquisitionToolboxOPCToolboxVehicleNetwork▪▪▪▪▪6Whattypeoftoolboxesdoes6WhattypeoftoolboxesdoesMatlab计算FinancialDatafeedToolboxDatabaseToolboxSpreadsheetLink(forMicrosoftExcel)FinancialInstrumentsToolbox▪▪▪▪▪▪▪计算生Bioinformatics代码MATLABHDLVisionHDLHDLFilterDesignHDLFixed-Point7Whattypeoftoolboxes7WhattypeoftoolboxesdoesMatlabMATLABMATLABCompilerSpreadsheetLink(forMicrosoftMATLABProductionDatabaseMATLABReportFromDeepLearning8WherecanI8WherecanIfindinformationregarding9ClusteringusingtheStatistics9ClusteringusingtheStatisticsandMachineLearningToolboxTheMatlabStatisticsandMachineLearningToolboxhasmanyStatisticsandDataimportandexport,descriptivestatistics,visualizationProbabilityDistributionsDatafrequencymodels,randomsamplegeneration,parameterestimationHypothesisTestst-test,F-test,chi-squaregoodness-of-fittest,andClusterUnsupervisedlearningtechniquestofindnaturalgroupingsandpatternsinAnalysisofvarianceandcovariance,multivariateANOVA,repeatedmeasuresANOVALinear,generalizedlinear,nonlinear,andnonparametrictechniquesforsupervisedSupervisedlearningalgorithmsforbinaryandmulticlassproblemsDimensionalityReductionPCA,factoranalysis,nonnegativematrixfactorization,sequentialfeatureselection,andmoreIndustrialStatisticsDesignofexperiments(DOE);survivalandreliabilityanalysis;statisticalprocesscontrolSpeedUpStatisticalComputationsParallelordistributedcomputationofstatistical▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪WhatisAWhatisAwaytoform'naturalgroupings'inyourItisaformofunsupervisedlearning–yougenerallydoNOThaveexamplesdemonstratinghowthedatashouldbegroupedWhydoweneedMarketsegmentation:Assistmarketerstoidentifydistinctsub-groupsofcustomersinorderWhydoweneedMarketsegmentation:Assistmarketerstoidentifydistinctsub-groupsofcustomersinordertodeveloptargetedmarketingprogramsGuiltbyassoito”:IdentifyinggroupsofgenesthatbehavesimilarlyunderasetofdifferentexperimentalClusterClusterClusterClustering Clustering K-means K-means K-meansThek-meansalgorithmK-meansThek-meansalgorithmpartitionsthedataintokexclusiveFeatureFeatureK-meansThek-meansalgorithmpartitionsK-meansThek-meansalgorithmpartitionsthedataintokexclusiveK=FeatureFeatureK-meansThek-meansalgorithmpartitionsK-meansThek-meansalgorithmpartitionsthedataintokmutuallyexclusiveK=FeatureFeatureHowdoesitK-meansFormalminimizetotalintra-clusterKd(xjK-meansFormalminimizetotalintra-clusterKd(xj,iSiistheithcluster(i=1,2,...,i1xjµiistheithcentroidofallthepointsinclusterdisadistanceOptimalSuboptimalK-meansIfweknewK-meansIfweknewtheclusterassignmentofeachwecouldeasilycomputethecentroidsIfweknewthecentroidpositionswecouldassigneachpointtoaButwedon’tknowneitherofK-meansAlgorithmChooseK-meansAlgorithmChoosethenumberofclusters,RandomlychooseinitialpositionsofKAssigneachofthepointstothe“nat(dependsondistanceK=K-meansAlgorithmK-meansAlgorithmChoosethenumberofclusters-RandomlychooseinitialpositionsofKAssigneachofthepointstothe“astn”(dependsondistanceK=K-meansAlgorithmChooseK-meansAlgorithmChoosethenumberofclusters-RandomlychooseinitialpositionsofKAssigneachofthepointstothe“artend”ondistancemeasure)CalculatetheintraclusterRe-computecentroidIfsolutionconverges→K=K-meansAlgorithmChooseK-meansAlgorithmChoosethenumberofclusters-RandomlychooseinitialpositionsofKAssigneachofthepointstothe“restetrod”ondistancemeasure)CalculatetheintraclusterRe-computecentroidIfsolutionconverges→K=K-meansAlgorithmChoosethenumberK-meansAlgorithmChoosethenumberofclusters-RandomlychooseinitialpositionsofKcentroidsAssigneachofthepointstothe“restetrod”ondistancemeasure)CalculatetheintraclusterRe-computecentroidpositionsIfsolutionconverges→Stop!K=▪▪▪▪▪▪K-meansAlgorithmChooseK-meansAlgorithmChoosethenumberofclusters-RandomlychooseinitialpositionsofKAssigneachofthepointstothe“restetrod”ondistancemeasure)CalculatetheintraclusterRe-computecentroidIfsolutionconverges→K=K-meansAlgorithmChooseK-meansAlgorithmChoosethenumberofclusters-RandomlychooseinitialpositionsofKAssigneachofthepointstothe“artend”ondistancemeasure)CalculatetheintraclusterRe-computecentroidIfsolution(theintraclustervarianceddnchange)→K=K-means:otherthingsweneedtoHowshouldK-means:otherthingsweneedtoHowshouldwechooseWhattypeofdistancemeasurescanweuse,andhowtochoosebetweenthem?((x2–x1)2+–y1)2)Sumofabsolute|x2–x1|+|y2–1–tAndK-means:otherthingsweneedK-means:otherthingsweneedtoDoesthealgorithmconvergencetoanoptimalCanyouthinkofstrategiesforsolvingBeforewelearnhowtoBeforewelearnhowtodoK-meansinMatlablet’slookatsomerealdata…Inthe1920's,botanistscollectedmeasurementsonsepalsepalpetalpetalof150iris,50fromeachofthreespecies(setosa,versicolor,ThemeasurementsbecameknownasFisher'sirisFisher’sIrisload4Fisher’sIrisload4SampleSampleSampleSample>>1>>'versicolor''virginica','virginica','setosa','setosa','setosa','setosa',FeatureFeatureFeatureFeatureExploringcorrelationsintheExploringcorrelationsintheFisher’sIrisparam_names={'sepallength','sepalwidth','petallength','petaltext([.05.30.55.80],[-0.1,-0.1,-0.1,-0.1],param_names,text([-0.12,-0.12,-0.12,-0.12],[0.800.550.300.05],'FontSize',12,ThepetallengthandwidtharehighlyVisualizingFisher’sIris%76543218276545SepalSepalVisualizingFisher’sIris%76543218276545SepalSepalPetal4K-meansusingDoingK-meansinMatlabis=K-meansusingDoingK-meansinMatlabis=BydefaultkmeansusessquaredEuclidiandistanceTheKTheclusterbelongstoK-meansusingDisplayingthealgorithm=K-meansusingDisplayingthealgorithm=210sumof123441112=K-meansusingClusteringptsymbK-meansusingClusteringptsymb=%Plotclusterpointsfori=1:2clust=(cidx2==i);holdNoticethatclusteringisdonebutvisualization%Plotclustercentroidholdxlabel('SepalLength');ylabel('SepalWidth');zlabel('Petalgridtitle('IrisdataclusteredwithK-meanswhereK=K-meansusingClusteringCluster765Cluster43218276544SepalSepalPetal K-meansusingClusteringCluster765Cluster43218276544SepalSepalPetal K-meansusingClustering765becausetheupperclusterisspreadout,thesethreepointsareclosertothecentroidofthelowerclusteruppercluster43218276544SepalSepalPetal K-meansusingClustering765becausetheupperclusterisspreadout,thesethreepointsareclosertothecentroidofthelowerclusteruppercluster43218276544SepalSepalPetal K-meansusingIncreasingthenumberofK-meansusingIncreasingthenumberof=11112430sumdistances=123455K-meansusingClusteringK-meansusingClusteringfori=clust=(cidx3==i);holdonholdxlabel('SepalLength');ylabel('SepalWidth');zlabel('PetalgridK-meansusingK-meansusingK-meansusingAvoidinglocalminimausingareplicates=4K-meansusingAvoidinglocalminimausingareplicates=458=====K-meansusing76543218276544SepalSepalPetal K-meansusing76543218276544SepalSepalPetal K-meansusing=7Wecanusethecosfunctionasadistancemeasurebetween6543218276544SepalSepalPetal K-meansusing=7Wecanusethecosfunctionasadistancemeasurebetween6543218276544SepalSepalPetal K-meansusingWhichdistancemeasureisK-meansusingWhichdistancemeasureismoreeWeknowthelabelofeachsample.Wecancompareclustersdiscoveredbykmeanstotheactualflowertypes.Note:usuallyinunsupervisedlearningwedoNOTknowthelabelsoftheK-meansusing%TestingtheclusteringaccuracyK-meansusing%Testingtheclusteringaccuracyfori=clust=find(cidx_cos==i);holdonxlabel('Sepalylabel('SepalWidth');gridonmiss=find(cidx_cos===holdK-meansusingCosinebaseddistance:576543218276544SepalPetal K-meansusingCosinebaseddistance:576543218276544SepalPetal K-meansusingEuclideanbaseddistance:1476543218276544SepalSepalPetal K-meansusingEuclideanbaseddistance:1476543218276544SepalSepalPetal HowtochooseWeneedaquantitativemethodtoHowtochooseWeneedaquantitativemethodtoassessthequalityofaThesilhouettevalueofapointisameasureofhowsimilarapointispointsinitsownclustercomparedtopointsinother-Formaldefinition:s(i)max(a(i),istheaveragedistanceofthepointitotheotherpointsinitsownclusterd(i,C)istheaveragedistanceofthepointitotheotherpointsintheclusteristheminimald(i,C)overallclustersotherthanHowtochooseSilhouettevaluesrangesfromHowtochooseSilhouettevaluesrangesfrom-1to→~=objectiswell→~objectisontheborderbetween2→~=-ObjectisclassifiedThesilhouettecoefficientistheaveragesilhouettevalueoverItisaquantitativemeasurethatcanassessthequalityofHowtochooseToHowtochooseTodemonstratetheutilityofthesilhouettecoefficientwecantestitonsyntheticdataforwhichweknowthenumberofx1=randn(1,100);y1=randn(1,scatter(x1,y1,25,[100],holdx2=randn(1,100)+3;y2=randn(1,scatter(x2,y2,25,[010],+x3=randn(1,100)+8;y3=randn(1,100);scatter(x3,y3,25,[001],'filled');holdHowtochooseTodemonstratetheutilityofthesilhouettecoefficientwecantestitonsyntheticdataforwhichweknowthenumberofWeknowthatKis65432100HowtochooseTodemonstratetheutilityofthesilhouettecoefficientwecantestitonsyntheticdataforwhichweknowthenumberofWeknowthatKis654321002468HowtochooseWerunthek-meansalgorithmfordifferentx=[x1,x2,y=[y1,data=[x',K====K=K=6665554443332221110000505 05HowtochooseWerunthek-meansalgorithmfordifferentx=[x1,x2,y=[y1,data=[x',K====K=K=6665554443332221110000505 05Howtochoose>>[silh2,h]=>>1arepoorly201Howtochoose>>[silh2,h]=>>1arepoorly201Howtochoose>>[silh3,h]>>12301SilhouetteHowtochoose>>[silh3,h]>>12301SilhouetteHowtochoose>>[silh4,h]>>12340SilhouetteHowtochoose>>[silh4,h]>>12340Silhouette1HowtochooseOptimalSilhouettevalueisachievedwhenK=32345K6HowtochooseOptimalSilhouettevalueisachievedwhenK=32345K678MeansilhouetteK-means investigateK-means investigategroupinginyourdata,simultaneouslyoveravarietyofscalesAlgorithm1)DeterminethedistancebetweenAlgorithm1)DeterminethedistancebetweeneachpairofdifferentTypesofdistances(Euclidean,correlation,1234512345Algorithm1)DeterminethedistancebetweeneachpairAlgorithm1)Determinethedistancebetweeneachpairof2)IterativelygrouppointsintoabinaryhierarchicaltreeConnecttheclosestpairofpointsandre-computedistance9876Thedistanceatwhichthepairofpointswere34521Algorithm1)DeterminethedistancebetweeneachpairofAlgorithm1)Determinethedistancebetweeneachpairof2)Iterativelygrouppointsintoabinaryhierarchicaltree3)Cutthehierarchicaltreeinto34521Hierarchicalclustering,otherthingsweHierarchicalclustering,otherthingswetoTypesofSinglelinkageiorDistancebetweengroupsisdefinedasthebetweentheclosestpairofpointsfromeachHierarchicalclustering,otherthingsweHierarchicalclustering,otherthingswetoTypesofCompletelinkageibDistancebetweengroupsisdefinedasthedistancebetweenthemostdistantpairofpointsfromtwoHierarchicalclustering,otherHierarchicalclustering,otherthingsweneedtoconsiderTypesofAveragelinkageclustering:Thedistancebetweentwoclustersisdefinedastheaverageofdistancesbetweenallpairsofpoints(ofoppositeHierarchicalclustering,otherthingswetoHierarchicalclustering,otherthingswetoWheretocuttheCuttingatanarbitraryHierarchicalclustering,otherthingsweneedtoconsiderWheretocuttheHierarchicalclustering,otherthingsweneedtoconsiderWheretocutthe▪▪CuttingatanarbitraryCuttingatinconsistencyComparetheheightofeachlinkinthetreewiththeheightsoflinksbelowit:IfapproximatelyequalThislinkexhibitsahighlevelofconsistency.Therearenodistinctdivisionsbetweentheobjectsjoinedatthislevelofthehierarchy.▪IfheightsdifferThislinkissaidtobeinconsistentinrespecttothelinksbelowit.Thisindicatestheborderofanaturaldivisioninadataset.Forformaldefinitionsseetoolbox▪▪HierarchicalclusteringusingLoadtheIris>>HierarchicalclusteringusingLoadtheIris>>load1)Computethedistancesbetweeneach>>euc