%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Native samples %%%%%%%%%%%%%%%%%%%%%% >> Y = pdist(X,'spearman'); squareform(Y) Z = linkage(Y,'average'); dendrogram(Z, 'Labels',{'CVBR';'TVBR';'FhG';'CT';'Nero';'anchor'}) Coph = cophenet(Z,Y) trineqtest(Y) ans = 0 0.1120 0.1207 0.1432 0.1471 0.2439 0.1120 0 0.1111 0.1688 0.1382 0.2130 0.1207 0.1111 0 0.0388 0.0344 0.1047 0.1432 0.1688 0.0388 0 0.0592 0.1511 0.1471 0.1382 0.0344 0.0592 0 0.0799 0.2439 0.2130 0.1047 0.1511 0.0799 0 Coph = 0.7970 Triangle inequality holds for 110 cases from 120 possible. >> XX = [X(3,:); X(4,:); X(5,:); X(6,:)]; Y = pdist(XX,'spearman'); AvrgD = mean(Y) MaxD = max(Y) trineqtest(Y) AvrgD = 0.0780 MaxD = 0.1511 Triangle inequality holds for 20 cases from 24 possible. % ----------------------------------------------------------------- >> Y = pdist(X,@distancecorr); squareform(Y) Z = elinkage(Y); % e-link dendrogram(Z, 'Labels',{'CVBR';'TVBR';'FhG';'CT';'Nero';'anchor'}) Coph = cophenet(Z,Y) trineqtest(Y) ans = 0 0.1370 0.1426 0.1644 0.1755 0.2786 0.1370 0 0.1394 0.2012 0.1686 0.2467 0.1426 0.1394 0 0.0462 0.0452 0.1289 0.1644 0.2012 0.0462 0 0.0751 0.1784 0.1755 0.1686 0.0452 0.0751 0 0.0990 0.2786 0.2467 0.1289 0.1784 0.0990 0 Coph = 0.7910 Triangle inequality holds for 110 cases from 120 possible. >> XX = [X(3,:); X(4,:); X(5,:); X(6,:)]; Y = pdist(XX,@distancecorr); AvrgD = mean(Y) MaxD = max(Y) trineqtest(Y) AvrgD = 0.0955 MaxD = 0.1784 Triangle inequality holds for 20 cases from 24 possible. % FhG CT Nero anchor df = [-28.3934 -26.8074 -27.8717 -24.0195] sq = [4.2530 4.0390 3.6980 1.5450] sqa = sepsy3(df, sq, df) ((sqa-sq) ./ sq) * 100 RMSE = sqrt(mean((sqa-sq).^2)) % 4.2530 4.0390 3.6980 1.5450 % 4.3546 3.4044 4.0420 1.7341 % 2.3878 -15.7128 9.3022 12.2391 % 0.3766 df1 = -24:-0.1:-29; sq1 = sepsy3(df, sq, df1); plot(df1,sq1, 'Marker','none', 'LineStyle','-') hold on xlabel('Difference Level (dB)') ylabel('Quality score') xlim([-31,-22]) ylim([1,5]) % FhG CT Nero anchor CVBR TVBR df = [-28.3934 -26.8074 -27.8717 -24.0195 -23.6646 -22.8196]; sq = [4.2530 4.0390 3.6980 1.5450 4.3910 4.3420]; plot(df,sq, 'Marker','o', 'MarkerFaceColor',[0 0 1], 'LineStyle','none') hold off FhG CT Nero anchor RMSEr True QS 4.2530 4.0390 3.6980 1.5450 Computed QS 4.3546 3.4044 4.0420 1.7341 Error +2.3878% -15.7128% +9.3022% +12.2391% 0.3766 %%%%%%%%%%%%%%%%%%%%%%%%%% The random mix %%%%%%%%%%%%%%%%%%%%%%% >> Y = pdist(X,'spearman'); squareform(Y) Z = linkage(Y,'average'); dendrogram(Z, 'Labels',{'CVBR';'TVBR';'FhG';'CT';'Nero';'anchor'}) Coph = cophenet(Z,Y) trineqtest(Y) ans = 0 0.1103 0.1779 0.2134 0.1672 0.2445 0.1103 0 0.1312 0.1712 0.1086 0.1958 0.1779 0.1312 0 0.0385 0.0455 0.1161 0.2134 0.1712 0.0385 0 0.0601 0.1513 0.1672 0.1086 0.0455 0.0601 0 0.1013 0.2445 0.1958 0.1161 0.1513 0.1013 0 Coph = 0.8486 Triangle inequality holds for 116 cases from 120 possible. >> XX = [X(3,:); X(4,:); X(5,:); X(6,:)]; Y = pdist(XX,'spearman'); AvrgD = mean(Y) MaxD = max(Y) trineqtest(Y) AvrgD = 0.0855 MaxD = 0.1513 Triangle inequality holds for 24 cases from 24 possible. % ----------------------------------------------------------------- >> Y = pdist(X,@distancecorr); squareform(Y) Z = elinkage(Y); % e-link dendrogram(Z, 'Labels',{'CVBR';'TVBR';'FhG';'CT';'Nero';'anchor'}) Coph = cophenet(Z,Y) trineqtest(Y) ans = 0 0.1319 0.2086 0.2442 0.1967 0.2775 0.1319 0 0.1581 0.2014 0.1320 0.2269 0.2086 0.1581 0 0.0490 0.0564 0.1337 0.2442 0.2014 0.0490 0 0.0734 0.1637 0.1967 0.1320 0.0564 0.0734 0 0.1186 0.2775 0.2269 0.1337 0.1637 0.1186 0 Coph = 0.8510 Triangle inequality holds for 120 cases from 120 possible. >> XX = [X(3,:); X(4,:); X(5,:); X(6,:)]; Y = pdist(XX,@distancecorr); AvrgD = mean(Y) MaxD = max(Y) trineqtest(Y) AvrgD = 0.0991 MaxD = 0.1637 Triangle inequality holds for 24 cases from 24 possible. % FhG CT Nero anchor df = [-30.0467 -28.4818 -29.5052 -26.1084] sq = [4.2530 4.0390 3.6980 1.5450] sqa = sepsy3(df, sq, df) ((sqa-sq) ./ sq) * 100 RMSE = sqrt(mean((sqa-sq).^2)) % 4.2530 4.0390 3.6980 1.5450 % 4.3858 3.3481 4.0267 1.7743 % 3.1228 -17.1051 8.8897 14.8428 % 0.4048 df1 = -26:-0.1:-30.5; sq1 = sepsy3(df, sq, df1); plot(df1,sq1, 'Marker','none', 'LineStyle','-') hold on xlabel('Difference Level (dB)') ylabel('Quality score') xlim([-31,-22]) ylim([1,5]) % FhG CT Nero anchor CVBR TVBR df = [-30.0467 -28.4818 -29.5052 -26.1084 -24.7685 -23.4721]; sq = [4.2530 4.0390 3.6980 1.5450 4.3910 4.3420]; plot(df,sq, 'Marker','o', 'MarkerFaceColor',[0 0 1], 'LineStyle','none') hold off FhG CT Nero anchor RMSEr True QS 4.2530 4.0390 3.6980 1.5450 Computed QS 4.3858 3.3481 4.0267 1.7743 Error +3.1228% -17.1051% +8.8897% +14.8428% 0.4048 Native samples The random mix Average / Max Spearman's correlation distance 0.0780 / 0.1511 0.0855 / 0.1513 Average / Max Distance correlation distance 0.0955 / 0.1784 0.0991 / 0.1637