 LDA | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| analyzeParms | |
| batch | |
| bestByTestTrial | |
| ccakirby | formulation in Johnson and Wichern but using SVD |
| collectPartitions | %% [X,Y,Xval,Yval] = collectTrainValidation(features,CV,oneTrialF,command) |
| confusionMatrix | [cf,allcf] = confusionMatrix(cv) |
| getfields | [values,names] = getfields(s) |
| lagize | lagged = lagize(datacellofwindows,nlags,delta) |
| makeMSFModes | %% features = makeMSFModes(trials,CV) |
| makeMSFProj | %% features = makeMSFProj(trials,CV) |
| makeRawEEGFeatures | %% featuresOrCV = makeRawEEGFeatures(trials,CV,command) |
| makeSVDModes | %% features = makeSVDModes(trials,CV) |
| makeSVDProj | %% features = makeSVDModes(trials,CV) |
| matrixOfAllResults | mat = matrixOfAllResults(cellarray) |
| matupdate | function go |
| mnf | function [phi,psi] = mnf(X) |
| mnf2 | function [psi,phi] = mnf(X) |
| msfize | |
| multiPlotParms | multiPlotParms(CV.msf,otherfields,names,x,multicurve,multiplot) |
| printConfusionMatrix | %% printConfusionMatrix(confusionMatrix(CV.msf)); |
| runAll | |
| runAll1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| runAll2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| runAll3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| runCV | %% This documentation needs to be updated. |
| runCompetitionSubject1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| runMsfLda | %% Making data |
| runRawLda | %% Making data |
| runSvdLda | %% Making data as |
| runSvdprojLda | %% Making data |
| selectModes | |
| selectResults | submat = selectResults(CV.msf,struct('TestTrial',1,'nlags',3,'winSize',125)) |
| selectSamples | |
| selectSamplesRows | |
| sensitivity | [accuracyByParm,names,allvalues] = sensitivity(cv) |
| showClasses | %% classes = showClasses(CV.msf,nbins) |
| showClassifiers | %% wts = showClassifiers(CV.msf) |
| showDiscriminantFuncs | %% funcs = showDiscriminantFuncs(cv,bwInvert) |
| showTestRep | %% rep = showTestRep(CV.msf) |
| sorteig | function [U,D] = sorteig(M) |
| summarizeTestResults | |
| svdize | |
| windowize | |
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