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Account for NaN in graphs during identification if ML is used

time-shift
Johannes Pfeifer 2015-06-05 16:40:01 +02:00
parent d09bf195c9
commit 01cb238a22
1 changed files with 44 additions and 24 deletions
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68
matlab/plot_identification.m
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@ -56,14 +56,22 @@ if SampleSize == 1,
subplot(211)
mmm = (idehess.ide_strength_J);
[ss, is] = sort(mmm);
bar(log([idehess.ide_strength_J(:,is)' idehess.ide_strength_J_prior(:,is)']))
if ~all(isnan(idehess.ide_strength_J_prior))
bar(log([idehess.ide_strength_J(:,is)' idehess.ide_strength_J_prior(:,is)']))
else
bar(log([idehess.ide_strength_J(:,is)' ]))
end
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
legend('relative to param value','relative to prior std','Location','Best')
if ~all(isnan(idehess.ide_strength_J_prior))
legend('relative to param value','relative to prior std','Location','Best')
else
legend('relative to param value','Location','Best')
end
if idehess.flag_score,
title('Identification strength with asymptotic Information matrix (log-scale)')
else
@ -71,14 +79,23 @@ if SampleSize == 1,
end
subplot(212)
bar(log([idehess.deltaM(is) idehess.deltaM_prior(is)]))
if ~all(isnan(idehess.deltaM_prior))
bar(log([idehess.deltaM(is) idehess.deltaM_prior(is)]))
else
bar(log([idehess.deltaM(is)]))
end
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
legend('relative to param value','relative to prior std','Location','Best')
if ~all(isnan(idehess.deltaM_prior))
legend('relative to param value','relative to prior std','Location','Best')
else
legend('relative to param value','Location','Best')
end
if idehess.flag_score,
title('Sensitivity component with asymptotic Information matrix (log-scale)')
else
@ -91,27 +108,30 @@ if SampleSize == 1,
skipline()
disp('Press ENTER to plot advanced diagnostics'), pause(5),
end
hh = dyn_figure(options_,'Name',[tittxt, ' - Sensitivity plot']);
subplot(211)
mmm = (siJnorm)'./max(siJnorm);
mmm1 = (siHnorm)'./max(siHnorm);
mmm=[mmm mmm1];
mmm1 = (siLREnorm)'./max(siLREnorm);
offset=length(siHnorm)-length(siLREnorm);
mmm1 = [NaN(offset,1); mmm1];
mmm=[mmm mmm1];
bar(log(mmm(is,:).*100))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
if all(isnan([siJnorm';siHnorm';siLREnorm']))
fprintf('\nIDENTIFICATION: Skipping sensitivity plot, because standard deviation of parameters is NaN, possibly due to the use of ML.\n')
else
hh = dyn_figure(options_,'Name',[tittxt, ' - Sensitivity plot']);
subplot(211)
mmm = (siJnorm)'./max(siJnorm);
mmm1 = (siHnorm)'./max(siHnorm);
mmm=[mmm mmm1];
mmm1 = (siLREnorm)'./max(siLREnorm);
offset=length(siHnorm)-length(siLREnorm);
mmm1 = [NaN(offset,1); mmm1];
mmm=[mmm mmm1];
bar(log(mmm(is,:).*100))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
legend('Moments','Model','LRE model','Location','Best')
title('Sensitivity bars using derivatives (log-scale)')
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1 ],options_);
end
legend('Moments','Model','LRE model','Location','Best')
title('Sensitivity bars using derivatives (log-scale)')
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1 ],options_);
% identificaton patterns
for j=1:size(idemoments.cosnJ,2),
pax=NaN(nparam,nparam);