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monotonicity_Classes.m
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monotonicity_Classes.m
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function modularity_Classes()
addpath('/home/ros/Dropbox/code/SmoothSeries/Classification/20130227_xlwrite/20130227_xlwrite/poi_library/');
% addpath('./sumSeriesTest/Classification/20130227_xlwrite/20130227_xlwrite/');
% javaaddpath('./Yair/Utils/JExcelAPI/MXL.jar')
pathlib='./Classification/20130227_xlwrite/20130227_xlwrite/poi_library/';
% javaaddpath(fullfile(matlabroot,'work','./sumSeriesTest/Classification/20130227_xlwrite/20130227_xlwrite/poi_library/'))
% xlswrite([path nome],tab,foglio);
javaaddpath([pathlib 'poi-3.8-20120326.jar']);
javaaddpath([pathlib 'poi-ooxml-3.8-20120326.jar']);
javaaddpath([pathlib 'poi-ooxml-schemas-3.8-20120326.jar']);
javaaddpath([pathlib 'xmlbeans-2.3.0.jar']);
javaaddpath([pathlib 'dom4j-1.6.1.jar']);
javaaddpath([pathlib 'stax-api-1.0.1.jar']);
javaaddpath('/home/ros/Dropbox/code/SmoothSeries/Classification/jexcelapi/jxl.jar');
addpath('./util/distances');
addpath('./ESFtool');
addpath('./ESFtool/matchphase');
addpath('./fncsmooth');
addpath('./Classification/');
% smoothing=4; % all elements
addpath('./Classification/jexcelapi/');
jsmoothstr= {'R+','E+','Pr+','R-','E-','Pr-'};
addpath(genpath('./Classification/jexcelapi/'));
sigmabasePerc={1,5,10};%percent INTERVAL MINIMUM SIZE =3*sigmabase
xlsKTM={'one','five','ten'};
jsmooth={1,2,3,4,5,6};
longIntsize=size(sigmabasePerc,2);
longSmooth=size(jsmooth,2);
Entr=[];
%lighiting 11.
nomeDS='sonyaiborobotsurface';
% [namematrix1,data11]=importfile1(['./data/' nomeDS '.csv']); % import matrix dataset
% DSfull=data11';
for ids=30:30
% ecct=[0,0,10,11,27,28,24,22,23,15];
% if ids>=43
% if ids==50
% dsID=15;
% else
% a=mod(ids,10);
% dsID=ecct(a);
% end
% else
dsID=ids;
% end
nomefile=['_Random_', num2str(dsID)];
DSRaw=csvread(strcat(['./data/',nomeDS,'1d/'],nomeDS,'_Random_', num2str(dsID)));
% size(DSRaw)
labels=DSRaw(:,1);
[separationtwoClass1,sumsimInClasss1,sumTotalClass1,modularity1] =distanceClass(DSRaw','RAW',nomefile,nomeDS);
ids
%
% [datanolabels,labels]=randomRetireved(nomeDS,dsID);
for jids=1:longIntsize
intervalPercentage=(cell2mat((sigmabasePerc(1,jids))));
intervalPercentageSTRING=num2str(intervalPercentage);%(cell2mat((xlsKTM(1,jids))));
%global
DSFix=csvread(strcat(['./data/' nomeDS '/percentagewin_' num2str(intervalPercentage) '_Pr+/' ],'Global_percentage_', num2str(intervalPercentage),'_',num2str(ids)));%,[labels;datasetsmoothedPR]);
[separationtwoClass2,sumsimInClasss2,sumTotalClass2,modularity2] =distanceClass(DSFix,[intervalPercentageSTRING,'_FIXED'],nomefile,nomeDS);
smoothdistances=[];
for js=1:3
DSSmooth=[];
smoothdist=[];
sst=num2str(cell2mat((jsmoothstr(1,js))));
pathmatrix2=['./data/' nomeDS '/percentagewin_' num2str(intervalPercentage) '_' sst '/' ];
sstmin=num2str(cell2mat((jsmoothstr(1,js+3))));
pathmatrix3=['./data/' nomeDS '/percentagewin_' num2str(intervalPercentage) '_' sstmin '/' ];
DSSmoothPlus=csvread(strcat(pathmatrix2,nomeDS,'_', num2str(intervalPercentage), '_smth_',sst,'numRun_',num2str(dsID)));%, [labels;datasetsmoothed2]);
[separationtwoClass3,sumsimInClasss3,sumTotalClass3,modularity3] =distanceClass(DSSmoothPlus,[intervalPercentageSTRING,'_',sst],nomefile,nomeDS);
DSSmoothMinus=csvread(strcat(pathmatrix3,nomeDS,'_', num2str(intervalPercentage), '_smth_',sstmin,'numRun_',num2str(dsID)));%, [labels;datasetsmoothed2]);
[separationtwoClass4,sumsimInClasss4,sumTotalClass4,modularity4]=distanceClass(DSSmoothMinus,[intervalPercentageSTRING,'_',sstmin],nomefile,nomeDS);
numClasses=length(unique(labels));
WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[modularity1;modularity2;modularity3;modularity4], 'modularity',numClasses);
% save it
% WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[separationClass1;separationClass2;separationClass3;separationClass4], 'totalSeparationSim',numClasses);
% WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[sumsimInClasss1;sumsimInClasss2;sumsimInClasss3;sumsimInClasss4], 'totPSim',numClasses);
% WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[sumdistancesClasss1;sumdistancesClasss2;sumdistancesClasss3;sumdistancesClasss4], 'cohesion',numClasses);
% WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[sumTotalClass1;sumTotalClass2;sumTotalClass3;sumTotalClass4],'totPairwiseSimTOT',numClasses);
% %
% WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[separationtwoClass1;separationtwoClass2;separationtwoClass3;separationtwoClass4], 'totPSim2class',numClasses);
% % WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[separationClassMIN1;separationClassMIN2;separationClassMIN3;separationClassMIN4],'separationMIN',numClasses);
% % WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[separationClassMAX1;separationClassMAX2;separationClassMAX3;separationClassMAX4],'separationMAX',numClasses);
% % %%%UNION
% % WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[separationClassMeanUNION1;separationClassMeanUNION2;separationClassMeanUNION3;separationClassMeanUNION4], 'sepMeanUnion',numClasses);
% % WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[separationClassMinUNION1;separationClassMinUNION2;separationClassMinUNION3;separationClassMinUNION4], 'sepMinUnion',numClasses);
% % WriteAllEntropy(sst,ids,['./data/',nomeDS,'1d/'],nomeDS,intervalPercentage,[separationClassMaxUNION1;separationClassMaxUNION2;separationClassMaxUNION3;separationClassMaxUNION4], 'sepMaxUnion',numClasses);
end
% [rawdistances;fixdistances;smoothdistPlus;smoothdistMinus]
end
end
function [separationtwoClass2,sumsimInClasss,sumTotalClass,modularity]=distanceClass(DSR,sheet,nomefile,nomeDS)
DistancesMatrixAll=[];
accsum=0;
distancesClasss=[];
% separationClass=[];
matrixDist=[];
matrixC=[];
% quantityClss=arrayfun( @(x)sum(DSR(1,:)==x), unique(DSR(1,:)));
labels=DSR(1,:);
orderedlabels=unique(labels);
clear matrixDist;% matrice1 matrixC ;
numOfClasses=length(orderedlabels);
for iclsss=1:numOfClasses
alab=find(labels==orderedlabels(iclsss));
ci=DSR(2:end,alab);
% cilength=size(ci,1);
for jclsss=1:numOfClasses
alab=find(labels==orderedlabels(jclsss));
cj=DSR(2:end,alab);
matrixDist=[];
matrixD1=[];
matrixD=[];
% cj=classes{jclsss};
if jclsss==iclsss
matrixDist=ClassificationDTWGlobal(cj);
for mi=1:size(matrixDist,1)
rowD1=matrixDist(mi,:);
rowD1(mi)=[];
% rowDist=matrixDist(mi,:);
% rowDist(mi)=[];
normal=max(rowD1);
rowD=rowD1./normal;
% matrixC=cat(1,matrixC,rowD);
matrixD1=cat(1,matrixD1,rowD);
end
matrixD=(1-matrixD1);
%%computes within same class
sumsimInClasss(iclsss)=sum(sum(matrixD));
clear rowD1 rowD;
else
% matrixDist=pdist2(ci,cj);
matrixDist=ClassificationDTWDoubleMTX(ci,cj);
for mi=1:size(matrixDist,1)
rowD1=matrixDist(mi,:);
% rowDist=matrixDist(mi,:);
% rowDist(mi)=[];
normal=max(rowD1);
rowD=rowD1./normal;
% matrixC=cat(1,matrixC,rowD);
matrixD=cat(1,matrixD,rowD);
end
matrixD=(1-matrixD);
end
%%compute the separation
separationtwoClass(iclsss,jclsss)=(sum(sum(matrixD)));
%stores it
DistancesMatrixAll{iclsss,jclsss}=matrixDist;
% separationClass(iclsss,jclsss)=(sum(sum(matrice1)))*(1/(cilength*(cjlength)));
end
separationtwoClass2(iclsss)=(sum(sum(separationtwoClass)));
% separationClassMINavg(iclsss)=(min(min(separationClassMIN)));
% separationClassMAXavg(iclsss)=(max(max(separationClassMAX)));
end
% DistancesMatrixAll
xMTX=[];
xMTX2=[];
for iclsss=1:numOfClasses
for jclsss=1:numOfClasses
xMTX=cat(2,xMTX,DistancesMatrixAll{iclsss,jclsss});
end
xMTX2=cat(1,xMTX2,xMTX);
xMTX=[];
end
% xlwrite(strcat(['./data/',nomeDS,'1d/'],'DTW_',nomeDS,nomefile,'.xls'),xMTX2,sheet,[1,1]);
csvwrite(strcat(['./data/' nomeDS '/'],'DTW_',nomeDS,nomefile,sheet),xMTX2);
sumTotalClass=(sum(sum(xMTX2)));
for icl=1:numOfClasses
modularity(icl)=((sumsimInClasss(icl))/(sumTotalClass))-((separationtwoClass2(icl))/(sumTotalClass))^2;
end
clear matrixDist matrixD;
%
% for iclsss=1:numOfClasses
% matrixDist=[];
% % cjM=[];
% %
% % ci=classes{iclsss};
% %
% % cj=classes;
% % cj(iclsss)=[];
% % cjM=[cj{1},cj{2},cj{3},cj{4},cj{5}];
% for icS=1:numOfClasses
% if icS~=iclsss
% matrixDist=cat(2,matrixDist,DistancesMatrixAll{iclsss,icS});
% end
% end
% % DistancesMatrixAll
% % size(matrixDist)
% % mmmmmm
% separationClassMeanUNION(iclsss)=(mean(mean(matrixDist)));
% separationClassMinUNION(iclsss)=(min(min(matrixDist)));
% separationClassMaxUNION(iclsss)=(max(max(matrixDist)));
% % separationClass(iclsss,jclsss)=(sum(sum(matrice1)))*(1/(cilength*(cjlength)));
% end
function WriteAllEntropy(whatSmooth,randomRun,path,nomeds,percentage,taball,nomesheet,numClasses)
n=numClasses;
switch whatSmooth
case 'R+'
colum=1;
nume=randomRun;
case 'E+'
colum=1;
nume=50+6+randomRun;
case 'Pr+'
colum=1;
nume=50*2+12+randomRun;
end
sheet=[ num2str(percentage) 'percentage_' nomesheet];%[num2str(n),'_',num2str(jsmooth),num2str(percentage) num2str(numwindowsusr)];[num2str(n),'_',num2str(jsmooth),num2str(numwindowsusr)];
nome=['modularity','Study_'];
% vvvvvvvvvvvvv
% xlwrite(strcat([path nome],'_',nomeds,'.xls'),[quantityClss,nums,sizeS],sheet,[nume,50]);
% xlwrite(strcat([path nome],'_',nomeds,'.xls'),measures,sheet,[nume,(colum+3)]);
xlwrite(strcat([path nome],'_',nomeds,'.xls'),taball(1,:),sheet,[nume,1]);
% xlwrite(strcat([path nome],'_',nomeds,'.xls'),num2str(randomRun),sheet,[nume,0]);
for itj=1:3
columnR=((itj)*n)+(n/2)*(itj);
xlwrite(strcat([path nome],'_',nomeds,'.xls'),taball(itj+1,:),sheet,[nume,columnR]);
% xlwrite(strcat([path nome],'_',nomeds,'.xls'),strcat(num2str(randomRun)),sheet,[nume,0]);
end