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Conflicts: doxygen/queso.page Conflict was in the email address. Trivial fix.
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| # Process this file with autoconf to produce a configure script. | ||
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| AC_PREREQ(2.65) | ||
| AC_INIT([queso], [0.50.0], [[email protected]]) | ||
| AC_INIT([queso], [0.50.1], [[email protected]]) | ||
| AC_CONFIG_MACRO_DIR([m4]) | ||
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| AC_CONFIG_HEADERS(config_queso.h.tmp) | ||
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| @@ -1,9 +1,9 @@ | ||
| /*! \mainpage The QUESO Library | ||
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| <b>Version \version</b>, October 2009 | ||
| <b>Version \version</b>, May 2014 | ||
| <hr> | ||
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| <b>Quantification of Uncertainty for Estimation, Simulation and Optimization (QUES0) </b> | ||
| <b>Quantification of Uncertainty for Estimation, Simulation and Optimization (QUESO) </b> | ||
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| The QUESO library is a collection of C++ classes and algorithms to | ||
| support model validation and the prediction of quantities of interest | ||
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@@ -19,9 +19,11 @@ based/html format. | |
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| \section bugs Reporting Bugs | ||
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| Bugs in the code and errors or omissions in the documentation can be | ||
| reported to [email protected]. Requests and contributions are | ||
| welcome at the same e-mail address. | ||
| Bugs in the code and errors or omissions in the documentation can be reported | ||
| to [email protected] or as a | ||
| <a href="https://github.com/libqueso/queso/issues/new>GitHub ticket</a>. | ||
| Requests and contributions are also welcome using either of the two methods | ||
| mentioned above. | ||
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| All bug reports should include: | ||
| <ul> | ||
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@@ -38,12 +40,12 @@ All bug reports should include: | |
| bot Coverage</a> | ||
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| \section licence License | ||
| \copydoc License | ||
| See <a href="https://github.com/libqueso/queso/blob/master/LICENSE>here</a>. | ||
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| \section acknowledgements Acknowledgments | ||
| \copydoc Acknowledgments | ||
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| \section pecos-center More Information About PECOS | ||
| \copydoc About | ||
| See <a href="http://pecos.ices.utexas.edu/">here</a>. | ||
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| */ | ||
| */ | ||
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| clear all; | ||
| %cd ../test/test_2013_12_02/outputData | ||
| cd outputData | ||
| rawChain_ml | ||
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| % KDE estimation | ||
| fprintf(1,' Plotting KDE - theta <press any key>\n'); | ||
| % [f,xi] = ksdensity(ip_ml_0_rawChain_unified(:,1),'function','pdf'); | ||
| % plot(xi,f,'-m','linewidth',3); | ||
| % hold on; clear xi; clear f; | ||
| [f,xi] = ksdensity(ip_ml_1_rawChain_unified(:,1),'function','pdf'); | ||
| plot(xi,f,'-r','linewidth',3); | ||
| hold on; clear xi; clear f; | ||
| [f,xi] = ksdensity(ip_ml_2_rawChain_unified(:,1),'function','pdf'); | ||
| plot(xi,f,'-g','linewidth',3) | ||
| [f,xi] = ksdensity(ip_ml_3_rawChain_unified(:,1),'function','pdf'); | ||
| plot(xi,f,'-b','linewidth',3) | ||
| [f,xi] = ksdensity(ip_ml_4_rawChain_unified(:,1),'function','pdf'); | ||
| plot(xi,f,'-c','linewidth',3) | ||
| % h=legend('Level0','Level 1','Level 2','Level 3','Level 4','location','northeast'); | ||
| h=legend('Level 1','Level 2','Level 3','Level 4','location','northeast'); | ||
| title('\theta Kernel Density Estimation (raw chain)','fontname', 'Times', 'fontsize',20); | ||
| set(gca,'FontSize',16); | ||
| grid minor; | ||
| a=axis; | ||
| axis([-50 200 a(3) a(4)]); | ||
| hold off; | ||
| print -dpng bimodal_kde_rawchain | ||
| pause; | ||
| clf; | ||
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| % CDF estimation | ||
| fprintf(1,' Plotting CDF - theta <press any key>\n'); | ||
| [f,xi] = ksdensity(ip_ml_1_rawChain_unified(:,1),'function','cdf'); | ||
| plot(xi,f,'-r','linewidth',3); | ||
| hold on; clear xi; clear f; | ||
| [f,xi] = ksdensity(ip_ml_2_rawChain_unified(:,1),'function','cdf'); | ||
| plot(xi,f,'-g','linewidth',3) | ||
| clear xi; clear f; | ||
| [f,xi] = ksdensity(ip_ml_3_rawChain_unified(:,1),'function','cdf'); | ||
| plot(xi,f,'-b','linewidth',3) | ||
| clear xi; clear f; | ||
| [f,xi] = ksdensity(ip_ml_4_rawChain_unified(:,1),'function','cdf'); | ||
| plot(xi,f,'-c','linewidth',3) | ||
| clear xi; clear f; | ||
| h=legend('Level 1','Level 2','Level 3','Level 4','location','southeast'); | ||
| title('\theta Cumulative Distribution Function (raw chain)','fontname', 'Times', 'fontsize',20);set(gca,'FontSize',16); | ||
| grid minor; | ||
| a=axis; | ||
| axis([-50 200 a(3) a(4)]); | ||
| hold off; | ||
| print -dpng bimodal_cdf_rawchain | ||
| pause; | ||
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| % AUTOCORRELATION PLOTS | ||
| fprintf(1,' Plotting autocorrelation - theta <press any key>\n'); | ||
| nlags=20; | ||
| i=1; | ||
| [ACF0,lags0,bounds]= autocorr(ip_ml_0_rawChain_unified(:,i), nlags, 0); | ||
| [ACF1,lags1,bounds]= autocorr(ip_ml_1_rawChain_unified(:,i), nlags, 0); | ||
| [ACF2,lags2,bounds]= autocorr(ip_ml_2_rawChain_unified(:,i), nlags, 0); | ||
| [ACF3,lags3,bounds]= autocorr(ip_ml_3_rawChain_unified(:,i), nlags, 0); | ||
| [ACF4,lags4,bounds]= autocorr(ip_ml_4_rawChain_unified(:,i), nlags, 0); | ||
| plot(ACF1,lags1,'-r',ACF2,lags2,'-g',ACF3,lags3,'-b',ACF4,lags4,'-c','linewidth',3); | ||
| h=legend('Level 1','Level 2','Level 3','Level 4','location','northeast'); | ||
| set(gca,'FontSize',16); | ||
| grid minor; | ||
| ylabel('Autocorrelation', 'fontsize',16); | ||
| xlabel('Lag','fontname', 'Times', 'fontsize',16); | ||
| title('Autocorrelation - Parameter \theta','fontname', 'Times','fontsize',20); | ||
| print -dpng bimodal_autocorrelation_rawchain | ||
| pause; | ||
| clf; | ||
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| %cd ../../../matlab | ||
| cd .. |
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| %cd ../test/test_2013_12_02/outputData | ||
| cd outputData | ||
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| x=0.:.1:150.; | ||
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| v1=1; | ||
| v2=25; | ||
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| y1=(x-10).*(x-10)/(2*v1); | ||
| y2=(x-100).*(x-100)/(2*v2); | ||
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| f1=(exp(-y1))/(sqrt(v1)*sqrt(2*pi)); | ||
| f2=(exp(-y2))/(sqrt(v2)*sqrt(2*pi)); | ||
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| f=f1/2+f2/2; | ||
| plot(x,f,'k-','linewidth',2); | ||
| ylabel('f(\theta)','fontsize',16); | ||
| xlabel('\theta','fontsize',16); | ||
| grid minor | ||
| set(gca,'fontsize',16); | ||
| title('Bimodal likelihood function','fontsize',20); | ||
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| print -dpng bimodal_likelihood.png | ||
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| %cd ../../../matlab | ||
| cd .. |
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examples/bimodal/matlab/plot_likelihood_normalized_taus.m
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| %cd ../test/test_2013_12_02/outputData | ||
| cd outputData | ||
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| tau1 = 0.00585938; %in display_sub0.txt, look for first occurence of prevExponent after 'generateSequence(), level 2, step 3' | ||
| tau2 = 0.0524597; %in display_sub0.txt, look for first occurence of prevExponent after 'generateSequence(), level 3, step 3' | ||
| tau3 = 0.348566; %in display_sub0.txt, look for first occurence of prevExponent after 'generateSequence(), level 4, step 3' | ||
| tau4 = 1; | ||
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| x=-50.:.1:200.; | ||
| [m,n]=size(x); | ||
| deltaX = (max(x)-min(x))/(n-1); | ||
| y1=(x-10).*(x-10)/(2*1); | ||
| y2=(x-100).*(x-100)/(2*25); | ||
| z1=(exp(-y1))/(1*sqrt(2*pi)); | ||
| z2=(exp(-y2))/(5*sqrt(2*pi)); | ||
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| % Level 1------------------------------------------------------------------ | ||
| z=((z1+ z2)/2).^tau1; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
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| fprintf(1,'Level 1, area before normalization = %4.3f,',intZ); | ||
| z = z/intZ; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
| fprintf(1,'\t area after normalization = %4.3f\n',intZ); | ||
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| x1=x; | ||
| f1=z; | ||
| clear z; clear intZ; | ||
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| % Level 2------------------------------------------------------------------ | ||
| z=((z1+ z2)/2).^tau2; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
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| fprintf(1,'Level 2, area before normalization = %4.3f,',intZ); | ||
| z = z/intZ; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
| fprintf(1,'\t area after normalization = %4.3f\n',intZ); | ||
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| x2=x; | ||
| f2=z; | ||
| clear z; clear intZ; | ||
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| % Level 3------------------------------------------------------------------ | ||
| z=((z1+ z2)/2).^tau3; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
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| fprintf(1,'Level 3, area before normalization = %4.3f,',intZ); | ||
| z = z/intZ; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
| fprintf(1,'\t area after normalization = %4.3f\n',intZ); | ||
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| x3=x; | ||
| f3=z; | ||
| clear z; clear intZ; | ||
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| % Level 4------------------------------------------------------------------ | ||
| z=((z1+ z2)/2).^tau4; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
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| fprintf(1,'Level 4, area before normalization = %4.3f,',intZ); | ||
| z = z/intZ; | ||
| intZ = 0.; | ||
| for i=1:(n-1) | ||
| intZ = intZ + z(i)*deltaX; | ||
| end | ||
| intZ=intZ; | ||
| fprintf(1,'\t area after normalization = %4.3f\n',intZ); | ||
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| x4=x; | ||
| f4=z; | ||
| % ------------------------------------------------------------------------- | ||
| plot(x1,f1, '--r',x2,f2, '--g',x3,f3,'--b',x4,f4,'--c','linewidth',3) | ||
| set(gca,'fontsize',20); | ||
| a=axis; | ||
| axis([-50 200 a(3) .15]); | ||
| legend(['\tau = ', num2str(tau1)],... | ||
| ['\tau = ', num2str(tau2)],... | ||
| ['\tau = ', num2str(tau3)],... | ||
| ['\tau = ', num2str(tau4)],... | ||
| 'location','northeast'); | ||
| ylabel('f^{\tau}(\theta)','fontsize',16); | ||
| xlabel('\theta','fontsize',16); | ||
| title('Intermediary normalized likelihood functions','fontname', 'Times', 'fontsize',20); | ||
| grid minor | ||
| set(gca,'fontsize',16); | ||
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| print -dpng bimodal_likelihood_taus_normalized.png | ||
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| %cd ../../../matlab | ||
| cd .. |
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