updates from cx

src/MATLAB/+NCD/imNCDM.m

 % NCDM
 % X is a 1-D cell array of images
 % eg
-% im = rand(100,100); ncd=ncd.imNCDM({im,im})
+% im = rand(100,100); ncd=NCD.imNCDM({im,im})
 %
 function [ncd,GX]= imNCDM(X)
 ncd=NaN;

src/MATLAB/apps/nist digits/fixedCardinality/getKfixed.m

+function [kOD,mx,sd,clusterConfusion]=getKfixed(xDigits,kMax,NUMBER_OF_SAMPLES,clusterConfusion)
+
+dx=[];
+idx=[];
+idxsp=[];
+kTrue=length(unique([xDigits.idxTrue]));
+dDigits=MNISTdistance(xDigits);
+
+for k=1:kMax
+    
+    idxsp=Cluster.SpectralCluster(dDigits,k);
+    if k==kTrue
+        clusterConfusion=updateConfusion(xDigits,clusterConfusion,kTrue,kMax,idxsp);
+    end
+      
+    dx(k)=deltaK(xDigits,idxsp);
+
+end
+
+% mx=mean(dx,2,'omitnan');
+% mx=min(dx,[],2);
+
+% sx=sort(dx,2);
+% sx=sx(:,1:100);
+mx=mean(dx,2,'omitnan');
+sd=std(dx,0,2,'omitnan');
+% account for simulation error, as in gap statistic
+sd=sd.*sqrt(1+1/NUMBER_OF_SAMPLES); 
+kOD=find(mx(1:end-1)-sd(1:end-1)>mx(2:end),1,'first');
+
+if ~isempty(kOD)
+    kOD=kOD+1;
+%     fprintf(1,'sd :: kOD=%d\n',kOD);
+else    
+    deltaMx=diff(mx)';
+    kOD=find(deltaMx>0,1,'first');
+    if isempty(kOD)
+        od=max(deltaMx(deltaMx<0));
+        kOD=find(deltaMx==od,1,'first');
+%         fprintf(1,'max(deltaMX<0) :: kOD=%d\n',kOD);
+    else
+%         fprintf(1,'deltaMX>0 :: kOD=%d\n',kOD);
+    end
+
+end 
+4; 

src/MATLAB/apps/nist digits/fixedCardinality/goNISTkFixed.m

+
+% outfolder='./results/';
+% if ~exist(outfolder,'dir')
+%     mkdir(outfolder);
+%     mkdir(fullfile(outfolder,'true'));
+%     mkdir(fullfile(outfolder,'false'))
+% end
+path(path,'..');
+tblResults=table();
+maxDigits=[0,1,2,3,8,9];
+CARDINALITY=10;
+clusterConfusion={};
+rgResults=[]; % maxDigit,trial,iteration
+kMax=12;
+NUMBER_OF_TRIALS=100;
+NUMBER_OF_SAMPLES=1000;
+p=startParallel();
+
+% outname=['getK5k' datestr(now,'mm-dd-yyyy') '.mat']
+NDIGITS=length(maxDigits);
+kConfusion=zeros(10,kMax);
+for nTrial=1:NUMBER_OF_TRIALS    
+    for d=1:NDIGITS
+        target=[0:maxDigits(d)];
+        K=length(target);        
+        
+        xDigits=getNISTdigits(target,CARDINALITY);
+        [kpred,mx,sd,clusterConfusion]=getK(xDigits,kMax,NUMBER_OF_SAMPLES,clusterConfusion);
+        rgResults(d,nTrial)=kpred;
+        kConfusion(K,kpred)=kConfusion(K,kpred)+1;
+%         drawResults(kpred,K,mx,sd,outfolder,nTrial);
+        5;
+    end
+%     if 0==mod(nTrial,20)
+%         clusterConfusion;
+%         kConfusion
+%         mean(rgResults,2)        
+%     end
+    5;
+end
+  toc()  
+  save(outname);
+    % idxKPred=tblResults.kPred==K;
+    % idxKGap=tblResults.kGap==K;
+    % idxKSP=tblResults.kSP==K;
+    % accPredGapSP=[length(find(idxKPred))/length(idxKPred),length(find(idxKGap))/length(idxKGap)...
+    %     length(find(idxKSP))/length(idxKSP)]
+    % sciPred=bootci(20000,@mean,idxKPred)
+    % sciGap=bootci(20000,@mean,idxKGap)
+    % sciSP=bootci(20000,@mean,idxKSP)
+    
+    
+    
+    
+    

src/MATLAB/apps/nist digits/kTrials results 7 1 2021.txt

+mod5
+[0:1] 0.86
+
+mod4
+[0:1] 0.99
+[0:2] 0.94  [3.155;3.222] 
+[0:8] 0.97
+[0:9] 0.96

src/MATLAB/apps/nist digits/postProcessNistK.m

+load 'getK5k10-15-2021.mat'
+
+truncResults=[];
+for i=1:100
+    rg2=rgResults(:,(i-1)*50+1:i*50);
+    truncResults(:,i)=mean(abs(rg2),2,'omitnan');
+end
+
+bootci(20000,@mean,truncResults')
+% length(find(tr2(2,:)==2))
+% length(find(tr2(3,:)==3))
+% length(find(tr2(4,:)==4))
+% length(find(tr2(5,:)==9))
+% length(find(tr2(6,:)==10))
\ No newline at end of file