From 866727fd2dca9e9eb2ccb808816fc57fe8885ded Mon Sep 17 00:00:00 2001 From: "Kemelli C. Estacio-Hiroms" Date: Mon, 25 Nov 2013 18:23:40 -0800 Subject: [PATCH 01/20] [queso/examples/modal] adding new example, modal, which was originally available at 'svn.ices.utexas.edu/repos/pecos/uq/appls/modal/'. The current version uses the new QUESO API. --- .../matlab/plot_modal_all_levels_1mode.m | 266 +++++++++++++++ .../matlab/plot_modal_all_levels_2modes.m | 321 ++++++++++++++++++ examples/modal/src/Makefile_modal_violeta | 44 +++ examples/modal/src/example_compute.C | 170 ++++++++++ examples/modal/src/example_compute.h | 39 +++ examples/modal/src/example_likelihood.C | 99 ++++++ examples/modal/src/example_likelihood.h | 51 +++ examples/modal/src/example_main.C | 60 ++++ .../modal/tests/test_2013_11_22/example.inp | 46 +++ 9 files changed, 1096 insertions(+) create mode 100644 examples/modal/matlab/plot_modal_all_levels_1mode.m create mode 100644 examples/modal/matlab/plot_modal_all_levels_2modes.m create mode 100644 examples/modal/src/Makefile_modal_violeta create mode 100644 examples/modal/src/example_compute.C create mode 100644 examples/modal/src/example_compute.h create mode 100644 examples/modal/src/example_likelihood.C create mode 100644 examples/modal/src/example_likelihood.h create mode 100644 examples/modal/src/example_main.C create mode 100644 examples/modal/tests/test_2013_11_22/example.inp diff --git a/examples/modal/matlab/plot_modal_all_levels_1mode.m b/examples/modal/matlab/plot_modal_all_levels_1mode.m new file mode 100644 index 000000000..5e7408108 --- /dev/null +++ b/examples/modal/matlab/plot_modal_all_levels_1mode.m @@ -0,0 +1,266 @@ +clear all; +cd ../tests/test_2013_11_22/outputData_1mode +rawChain_ml + +% Level 0 +fprintf(1,'Scatter plots and histograms of raw chains - Level 0 \n'); +plotmatrix(ip_ml_0_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 0 - 1 mode') +print -dpng modal_1_mode_level_0 +pause; +clf; + +% Level 1 +fprintf(1,'Scatter plots and histograms of raw chains - Level 1 \n'); +plotmatrix(ip_ml_1_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 1 - 1 mode') +print -dpng modal_1_mode_level_1 +pause; +clf; + +% Level 2 +fprintf(1,' Scatter plots and histograms of raw chains - Level 2 \n'); +plotmatrix(ip_ml_2_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 2 - 1 mode') +print -dpng modal_1_mode_level_2 +pause; +clf; + +% Level 3 +fprintf(1,' Scatter plots and histograms of raw chains - Level 3 \n'); +plotmatrix(ip_ml_3_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 3 - 1 mode') +print -dpng modal_1_mode_level_3 +pause; +clf; + +% Level 4 +fprintf(1,' Scatter plots and histograms of raw chains - Level 4 \n'); +plotmatrix(ip_ml_4_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 4 - 1 mode') +print -dpng modal_1_mode_level_4 +pause; +clf; + +% Level 5 +fprintf(1,' Scatter plots and histograms of raw chains - Level 5 \n'); +plotmatrix(ip_ml_5_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 5 - 1 mode') +print -dpng modal_1_mode_level_5 +pause; +clf; + +% Level 6 +fprintf(1,' Scatter plots and histograms of raw chains - Level 6 \n'); +plotmatrix(ip_ml_6_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 6 - 1 mode') +print -dpng modal_1_mode_level_6 +pause; +clf; + +% Level 7 +fprintf(1,' Scatter plots and histograms of raw chains - Level 7 \n'); +plotmatrix(ip_ml_7_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 7 - 1 mode') +print -dpng modal_1_mode_level_7 + + +% KDE estimation +% theta 1 +fprintf(1,' Plotting KDE - theta_1 \n'); +[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) +[f,xi] = ksdensity(ip_ml_5_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'-k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'--r','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','location','northeast'); +title('\theta_1 Kernel Density Estimation (raw chain)','fontname', 'Times', 'fontsize',20); +set(gca,'FontSize',16); +grid on; +axis([-1 5 0 .6]); +hold off; +print -dpng modal_1_mode_kde_theta1 +pause; +clf; + +%theta2 +fprintf(1,' Plotting KDE - theta_2 \n'); +[f,xi] = ksdensity(ip_ml_1_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-r','linewidth',3); +hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_2_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-g','linewidth',3) +[f,xi] = ksdensity(ip_ml_3_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-b','linewidth',3) +[f,xi] = ksdensity(ip_ml_4_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-c','linewidth',3) +[f,xi] = ksdensity(ip_ml_5_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'--r','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','location','northeast'); +title('\theta_2 Kernel Density Estimation (raw chain)','fontname', 'Times', 'fontsize',20); +set(gca,'FontSize',16);grid on; +axis([-1 4 0 1]); +hold off; +print -dpng modal_1_mode_kde_theta2 +pause; +clf; + + + +%theta 3 +fprintf(1,' Plotting KDE - theta_3 \n'); +[f,xi] = ksdensity(ip_ml_1_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-r','linewidth',3); +hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_2_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-g','linewidth',3) +[f,xi] = ksdensity(ip_ml_3_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-b','linewidth',3) +[f,xi] = ksdensity(ip_ml_4_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-c','linewidth',3) +[f,xi] = ksdensity(ip_ml_5_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'--r','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','location','northeast'); +title('\theta_3 Kernel Density Estimation (raw chain)','fontname', 'Times', 'fontsize',20); +set(gca,'FontSize',16);grid on; +axis([-0.2 .8 0 30]); +hold off; +print -dpng modal_1_mode_kde_theta3 +pause; +clf; + +% Target pdf +fprintf(1,' Plotting KDE - target PDF \n'); +%[f,xi] = ksdensity(ip_ml_0_rawLogTarget_unified,'function','pdf'); +%plot(xi,f,'-k','linewidth',3) +%hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_1_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-r','linewidth',3); +hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_2_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-g','linewidth',3) +[f,xi] = ksdensity(ip_ml_3_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-b','linewidth',3) +[f,xi] = ksdensity(ip_ml_4_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-c','linewidth',3) +[f,xi] = ksdensity(ip_ml_5_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'--k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'--r','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','location','northwest'); +title('Target PDF Kernel Density Estimation','fontname', 'Times', 'fontsize',20); +hold off; grid on;set(gca,'FontSize',16); +axis([0 15 0 0.6]); +print -dpng modal_1_mode_kde_target +pause; +clf; + +% AUTOCORRELATION PLOTS +nlags=20; +%Theta 1 +i=1; +[ACF0,lags,bounds]= autocorr(ip_ml_0_rawChain_unified(:,i), nlags, 0); +[ACF1,lags,bounds]= autocorr(ip_ml_1_rawChain_unified(:,i), nlags, 0); +[ACF2,lags,bounds]= autocorr(ip_ml_2_rawChain_unified(:,i), nlags, 0); +[ACF3,lags,bounds]= autocorr(ip_ml_3_rawChain_unified(:,i), nlags, 0); +[ACF4,lags,bounds]= autocorr(ip_ml_4_rawChain_unified(:,i), nlags, 0); +[ACF5,lags,bounds]= autocorr(ip_ml_5_rawChain_unified(:,i), nlags, 0); +[ACF6,lags,bounds]= autocorr(ip_ml_6_rawChain_unified(:,i), nlags, 0); +[ACF7,lags,bounds]= autocorr(ip_ml_7_rawChain_unified(:,i), nlags, 0); +plot(ACF0,lags,'-k', ACF1,lags,'-r',ACF2,lags,'-g',ACF3,lags,'-b',ACF4,lags,'-c',ACF5,lags,'-m',ACF6,lags,'--k',ACF7,lags,'--r','linewidth',3); +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','location','northeast'); +set(gca,'FontSize',16); +grid minor; +title('Autocorrelation - Parameter \theta_1','fontname', 'Times', 'fontsize',20); +print -dpng modal_1_mode_autocorrelation_theta1 +pause; +clf; + +% AUTOCORRELATION PLOTS +%Theta 2 +i=2; +[ACF0,lags,bounds]= autocorr(ip_ml_0_rawChain_unified(:,i), nlags, 0); +[ACF1,lags,bounds]= autocorr(ip_ml_1_rawChain_unified(:,i), nlags, 0); +[ACF2,lags,bounds]= autocorr(ip_ml_2_rawChain_unified(:,i), nlags, 0); +[ACF3,lags,bounds]= autocorr(ip_ml_3_rawChain_unified(:,i), nlags, 0); +[ACF4,lags,bounds]= autocorr(ip_ml_4_rawChain_unified(:,i), nlags, 0); +[ACF5,lags,bounds]= autocorr(ip_ml_5_rawChain_unified(:,i), nlags, 0); +[ACF6,lags,bounds]= autocorr(ip_ml_6_rawChain_unified(:,i), nlags, 0); +[ACF7,lags,bounds]= autocorr(ip_ml_7_rawChain_unified(:,i), nlags, 0); +plot(ACF0,lags,'-k', ACF1,lags,'-r',ACF2,lags,'-g',ACF3,lags,'-b',ACF4,lags,'-c',ACF5,lags,'-m',ACF6,lags,'--k',ACF7,lags,'--r','linewidth',3); +h=legend('Level 0','Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','location','northeast'); +set(gca,'FontSize',16); +grid minor; +title('Autocorrelation - Parameter \theta_2','fontname', 'Times', 'fontsize',20); +print -dpng modal_1_mode_autocorrelation_theta2 +pause; +clf; + + +%Theta 3 +i=3; +[ACF0,lags,bounds]= autocorr(ip_ml_0_rawChain_unified(:,i), nlags, 0); +[ACF1,lags,bounds]= autocorr(ip_ml_1_rawChain_unified(:,i), nlags, 0); +[ACF2,lags,bounds]= autocorr(ip_ml_2_rawChain_unified(:,i), nlags, 0); +[ACF3,lags,bounds]= autocorr(ip_ml_3_rawChain_unified(:,i), nlags, 0); +[ACF4,lags,bounds]= autocorr(ip_ml_4_rawChain_unified(:,i), nlags, 0); +[ACF5,lags,bounds]= autocorr(ip_ml_5_rawChain_unified(:,i), nlags, 0); +[ACF6,lags,bounds]= autocorr(ip_ml_6_rawChain_unified(:,i), nlags, 0); +[ACF7,lags,bounds]= autocorr(ip_ml_7_rawChain_unified(:,i), nlags, 0); +plot(ACF0,lags,'-k', ACF1,lags,'-r',ACF2,lags,'-g',ACF3,lags,'-b',ACF4,lags,'-c',ACF5,lags,'-m',ACF6,lags,'--k',ACF7,lags,'--r','linewidth',3); +h=legend('Level 0','Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','location','northeast'); +set(gca,'FontSize',16); +grid minor; +title('Autocorrelation - Parameter \theta_3','fontname', 'Times', 'fontsize',20); +print -dpng modal_1_mode_autocorrelation_theta3 +pause; +clf; + + + +cd ../matlab + diff --git a/examples/modal/matlab/plot_modal_all_levels_2modes.m b/examples/modal/matlab/plot_modal_all_levels_2modes.m new file mode 100644 index 000000000..06ff576be --- /dev/null +++ b/examples/modal/matlab/plot_modal_all_levels_2modes.m @@ -0,0 +1,321 @@ +clear all +cd ../tests/test_2013_11_22/outputData_2modes +rawChain_ml + +% Level 1 +fprintf(1,'Scatter plots and histograms of raw chains - Level 0 \n'); +plotmatrix(ip_ml_0_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 0 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_0 +pause; +clf; + +% Level 1 +fprintf(1,'Scatter plots and histograms of raw chains - Level 1 \n'); +plotmatrix(ip_ml_1_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 1 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_1 +pause; +clf; + +% Level 2 +fprintf(1,'Scatter plots and histograms of raw chains - Level 2 \n'); +plotmatrix(ip_ml_2_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 2 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_2 +pause; +clf; + +% Level 3 +fprintf(1,'Scatter plots and histograms of raw chains - Level 3 \n'); +plotmatrix(ip_ml_3_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 3 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_3 +pause; +clf; + +% Level 4 +fprintf(1,'Scatter plots and histograms of raw chains - Level 4 \n'); +plotmatrix(ip_ml_4_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 4 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_4 +pause; +clf; + +% Level 5 +fprintf(1,'Scatter plots and histograms of raw chains - Level 5 \n'); +plotmatrix(ip_ml_5_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 5 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_5 +pause; +clf; + +% Level 6 +fprintf(1,'Scatter plots and histograms of raw chains - Level 6 \n'); +plotmatrix(ip_ml_6_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 6 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_6 +pause; +clf; + +% Level 7 +fprintf(1,'Scatter plots and histograms of raw chains - Level 7 \n'); +plotmatrix(ip_ml_7_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 7 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_7 +pause; +clf; + +% Level 8 +fprintf(1,'Scatter plots and histograms of raw chains - Level 8 \n'); +plotmatrix(ip_ml_8_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 8 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_8 +pause; +clf; + +% Level 9 +fprintf(1,'Scatter plots and histograms of raw chains - Level 9 \n'); +plotmatrix(ip_ml_9_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 9 - 2 modes') +set(gca,'FontSize',16); +print -dpng modal_2_modes_level_9 +pause; +clf; + +% KDE estimation +% theta 1 +fprintf(1,' Plotting KDE - theta_1 \n'); +[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) +[f,xi] = ksdensity(ip_ml_5_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'-k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'--r','linewidth',3) +[f,xi] = ksdensity(ip_ml_8_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'--g','linewidth',3) +[f,xi] = ksdensity(ip_ml_9_rawChain_unified(:,1),'function','pdf'); +plot(xi,f,'--b','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','Level 8','Level 9','location','northeast'); +title('\theta_1 Kernel Density Estimation (raw chain)','fontname', 'Times', 'fontsize',20); +set(gca,'FontSize',16); +grid on; +axis([-1 4 0 6]); +hold off; +print -dpng modal_2_modes_kde_theta1 +pause; +clf; + +%theta 2 +fprintf(1,' Plotting KDE - theta_2 \n'); +% [f,xi] = ksdensity(ip_ml_0_rawChain_unified(:,2),'function','pdf'); +% plot(xi,f,'-ok','linewidth',3); +% hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_1_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-r','linewidth',3); +hold on;clear xi; clear f; +[f,xi] = ksdensity(ip_ml_2_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-g','linewidth',3) +[f,xi] = ksdensity(ip_ml_3_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-b','linewidth',3) +[f,xi] = ksdensity(ip_ml_4_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-c','linewidth',3) +[f,xi] = ksdensity(ip_ml_5_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'-k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'--r','linewidth',3) +[f,xi] = ksdensity(ip_ml_8_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'--g','linewidth',3) +[f,xi] = ksdensity(ip_ml_9_rawChain_unified(:,2),'function','pdf'); +plot(xi,f,'--b','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','Level 8','Level 9','location','northeast'); + +title('\theta_2 Kernel Density Estimation (raw chain)','fontname', 'Times', 'fontsize',20); +set(gca,'FontSize',16);grid on; +hold off; +print -dpng modal_2_modes_kde_theta2 +pause; +clf; + + + +%theta 3 +fprintf(1,' Plotting KDE - theta_3 \n'); +[f,xi] = ksdensity(ip_ml_1_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-r','linewidth',3); +hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_2_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-g','linewidth',3) +[f,xi] = ksdensity(ip_ml_3_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-b','linewidth',3) +[f,xi] = ksdensity(ip_ml_4_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-c','linewidth',3) +[f,xi] = ksdensity(ip_ml_5_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'-k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'--r','linewidth',3) +[f,xi] = ksdensity(ip_ml_8_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'--g','linewidth',3) +[f,xi] = ksdensity(ip_ml_9_rawChain_unified(:,3),'function','pdf'); +plot(xi,f,'--b','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','Level 8','Level 9','location','northeast'); +title('\theta_3 Kernel Density Estimation (raw chain)','fontname', 'Times', 'fontsize',20); +set(gca,'FontSize',16);grid on; +axis([-0.1 .6 0 22]); +hold off; +print -dpng modal_2_modes_kde_theta3 +pause; +clf; + +% Target pdf +fprintf(1,' Plotting KDE - target PDF \n'); +%[f,xi] = ksdensity(ip_ml_0_rawLogTarget_unified,'function','pdf'); +%plot(xi,f,'-k','linewidth',3) +%hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_1_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-r','linewidth',3); +hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_2_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-g','linewidth',3) +[f,xi] = ksdensity(ip_ml_3_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-b','linewidth',3) +[f,xi] = ksdensity(ip_ml_4_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-c','linewidth',3) +[f,xi] = ksdensity(ip_ml_5_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'-m','linewidth',3) +[f,xi] = ksdensity(ip_ml_6_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'--k','linewidth',3) +[f,xi] = ksdensity(ip_ml_7_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'--r','linewidth',3) +[f,xi] = ksdensity(ip_ml_8_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'--g','linewidth',3) +[f,xi] = ksdensity(ip_ml_9_rawLogTarget_unified,'function','pdf'); +plot(xi,f,'--b','linewidth',3) +h=legend('Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','Level 8','Level 9','location','northwest'); + + +title('Target PDF Kernel Density Estimation','fontname', 'Times', 'fontsize',20); +hold off; grid on; +axis([0 15 0 .45]); +print -dpng modal_2_modes_kde_target +pause; +clf; + +% AUTOCORRELATION PLOTS +%Theta 1 +nlags=20; +i=1; +[ACF0,lags,bounds]= autocorr(ip_ml_0_rawChain_unified(:,i), nlags, 0); +[ACF1,lags,bounds]= autocorr(ip_ml_1_rawChain_unified(:,i), nlags, 0); +[ACF2,lags,bounds]= autocorr(ip_ml_2_rawChain_unified(:,i), nlags, 0); +[ACF3,lags,bounds]= autocorr(ip_ml_3_rawChain_unified(:,i), nlags, 0); +[ACF4,lags,bounds]= autocorr(ip_ml_4_rawChain_unified(:,i), nlags, 0); +[ACF5,lags,bounds]= autocorr(ip_ml_5_rawChain_unified(:,i), nlags, 0); +[ACF6,lags,bounds]= autocorr(ip_ml_6_rawChain_unified(:,i), nlags, 0); +[ACF7,lags,bounds]= autocorr(ip_ml_7_rawChain_unified(:,i), nlags, 0); +[ACF8,lags,bounds]= autocorr(ip_ml_8_rawChain_unified(:,i), nlags, 0); +plot(ACF0,lags,'-k', ACF1,lags,'-r',ACF2,lags,'-g',ACF3,lags,'-b',ACF4,lags,'-c',ACF5,lags,'-m',ACF6,lags,'--k',ACF7,lags,'--r',ACF8,lags,'--g','linewidth',3); +h=legend('Level 0','Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','Level 8','location','northeast'); +set(gca,'FontSize',16); +grid minor; +title('Autocorrelation - Parameter \theta_1','fontname', 'Times', 'fontsize',20); +print -dpng modal_2_modes_autocorrelation_theta1 +pause; +clf; + +% AUTOCORRELATION PLOTS +%Theta 2 +i=2; +[ACF0,lags,bounds]= autocorr(ip_ml_0_rawChain_unified(:,i), nlags, 0); +[ACF1,lags,bounds]= autocorr(ip_ml_1_rawChain_unified(:,i), nlags, 0); +[ACF2,lags,bounds]= autocorr(ip_ml_2_rawChain_unified(:,i), nlags, 0); +[ACF3,lags,bounds]= autocorr(ip_ml_3_rawChain_unified(:,i), nlags, 0); +[ACF4,lags,bounds]= autocorr(ip_ml_4_rawChain_unified(:,i), nlags, 0); +[ACF5,lags,bounds]= autocorr(ip_ml_5_rawChain_unified(:,i), nlags, 0); +[ACF6,lags,bounds]= autocorr(ip_ml_6_rawChain_unified(:,i), nlags, 0); +[ACF7,lags,bounds]= autocorr(ip_ml_7_rawChain_unified(:,i), nlags, 0); +[ACF8,lags,bounds]= autocorr(ip_ml_8_rawChain_unified(:,i), nlags, 0); +plot(ACF0,lags,'-k', ACF1,lags,'-r',ACF2,lags,'-g',ACF3,lags,'-b',ACF4,lags,'-c',ACF5,lags,'-m',ACF6,lags,'--k',ACF7,lags,'--r',ACF8,lags,'--g','linewidth',3); +h=legend('Level 0','Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','Level 8','location','northeast'); +set(gca,'FontSize',16); +grid minor; +title('Autocorrelation - Parameter \theta_2','fontname', 'Times', 'fontsize',20); +print -dpng modal_2_modes_autocorrelation_theta2 +pause; +clf; + + +%Theta 3 +i=3; +[ACF0,lags,bounds]= autocorr(ip_ml_0_rawChain_unified(:,i), nlags, 0); +[ACF1,lags,bounds]= autocorr(ip_ml_1_rawChain_unified(:,i), nlags, 0); +[ACF2,lags,bounds]= autocorr(ip_ml_2_rawChain_unified(:,i), nlags, 0); +[ACF3,lags,bounds]= autocorr(ip_ml_3_rawChain_unified(:,i), nlags, 0); +[ACF4,lags,bounds]= autocorr(ip_ml_4_rawChain_unified(:,i), nlags, 0); +[ACF5,lags,bounds]= autocorr(ip_ml_5_rawChain_unified(:,i), nlags, 0); +[ACF6,lags,bounds]= autocorr(ip_ml_6_rawChain_unified(:,i), nlags, 0); +[ACF7,lags,bounds]= autocorr(ip_ml_7_rawChain_unified(:,i), nlags, 0); +[ACF8,lags,bounds]= autocorr(ip_ml_8_rawChain_unified(:,i), nlags, 0); +plot(ACF0,lags,'-k', ACF1,lags,'-r',ACF2,lags,'-g',ACF3,lags,'-b',ACF4,lags,'-c',ACF5,lags,'-m',ACF6,lags,'--k',ACF7,lags,'--r',ACF8,lags,'--g','linewidth',3); +h=legend('Level 0','Level 1','Level 2','Level 3','Level 4','Level 5','Level 6','Level 7','Level 8','location','northeast'); +set(gca,'FontSize',16); +grid minor; +title('Autocorrelation - Parameter \theta_3','fontname', 'Times', 'fontsize',20); +print -dpng modal_2_modes_autocorrelation_theta3 +pause; +clf; + +cd ../matlab + diff --git a/examples/modal/src/Makefile_modal_violeta b/examples/modal/src/Makefile_modal_violeta new file mode 100644 index 000000000..eb5af9e35 --- /dev/null +++ b/examples/modal/src/Makefile_modal_violeta @@ -0,0 +1,44 @@ +########################################################## +# Using installed QUESO 0.47.1, from tarball, in 'violeta' +########################################################## + +QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 +BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 +GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 +HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 + +INC_PATHS = \ + -I. \ + -I$(QUESO_DIR)/include \ + -I$(BOOST_DIR)/include \ + -I$(GSL_DIR)/include \ + -I$(HDF5_DIR)/include + +LIBS = \ + -L$(QUESO_DIR)/lib -lqueso \ + -L$(BOOST_DIR)/lib -lboost_program_options \ + -L$(GSL_DIR)/lib -lgsl \ + -L$(HDF5_DIR)/lib -lhdf5 + +CXX = mpic++ +CXXFLAGS += -g -Wall -c + +default: all + +.SUFFIXES: .o .C + +all: modal_example_gsl + +clean: + rm -f *~ + rm -f *.o + rm -f modal_gsl + +modal_example_gsl: example_main.o example_likelihood.o example_compute.o + $(CXX) example_main.o \ + example_likelihood.o \ + example_compute.o \ + -o modal_gsl $(LIBS) + +%.o: %.C + $(CXX) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/modal/src/example_compute.C b/examples/modal/src/example_compute.C new file mode 100644 index 000000000..f0e7a07e6 --- /dev/null +++ b/examples/modal/src/example_compute.C @@ -0,0 +1,170 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define APPLS_MODAL_USES_CONCATENATION + +void compute(const QUESO::FullEnvironment& env, unsigned int numModes) { + //////////////////////////////////////////////////////// + // Step 1 of 5: Instantiate the parameter space + //////////////////////////////////////////////////////// + + +#ifdef APPLS_MODAL_USES_CONCATENATION + QUESO::VectorSpace + paramSpaceA(env, "paramA_", 2, NULL); + QUESO::VectorSpace + paramSpaceB(env, "paramB_", 1, NULL); +#endif + QUESO::VectorSpace + paramSpace(env, "param_", 3, NULL); + + //////////////////////////////////////////////////////// + // Step 2 of 5: Instantiate the parameter domain + //////////////////////////////////////////////////////// +#ifdef APPLS_MODAL_USES_CONCATENATION + QUESO::GslVector paramMinsA(paramSpaceA.zeroVector()); + paramMinsA[0] = 0.; + paramMinsA[1] = 0.; + QUESO::GslVector paramMaxsA(paramSpaceA.zeroVector()); + paramMaxsA[0] = 3.; + paramMaxsA[1] = 3.; + QUESO::BoxSubset + + paramDomainA("paramA_",paramSpaceA,paramMinsA,paramMaxsA); + + QUESO::GslVector paramMinsB(paramSpaceB.zeroVector()); + paramMinsB[0] = 0.; + QUESO::GslVector paramMaxsB(paramSpaceB.zeroVector()); + paramMaxsB[0] = INFINITY; + QUESO::BoxSubset + paramDomainB("paramB_",paramSpaceB,paramMinsB,paramMaxsB); + + QUESO::ConcatenationSubset + paramDomain("",paramSpace,paramDomainA,paramDomainB); +#else + QUESO::GslVector paramMins(paramSpace.zeroVector()); + paramMins[0] = 0.; + paramMins[1] = 0.; + paramMins[2] = 0.; + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + paramMaxs[0] = 3.; + paramMaxs[1] = 3.; + paramMaxs[2] = .3; + QUESO::BoxSubset + paramDomain("param_",paramSpace,paramMins,paramMaxs); +#endif + + //////////////////////////////////////////////////////// + // Step 3 of 5: Instantiate the likelihood function object + //////////////////////////////////////////////////////// + likelihoodRoutine_DataType likelihoodRoutine_Data; + + likelihoodRoutine_Data.numModes = numModes; + QUESO::GenericScalarFunction + likelihoodFunctionObj("like_", + paramDomain, + likelihoodRoutine, + (void *) &likelihoodRoutine_Data, + true); // routine computes [-2.*ln(function)] + + //////////////////////////////////////////////////////// + // Step 4 of 5: Instantiate the inverse problem + //////////////////////////////////////////////////////// +#ifdef APPLS_MODAL_USES_CONCATENATION + QUESO::UniformVectorRV + priorRvA("priorA_", paramDomainA); + + QUESO::GslVector alpha(paramSpaceB.zeroVector()); + alpha[0] = 3.; + QUESO::GslVector beta(paramSpaceB.zeroVector()); + if (numModes == 1) { + beta[0] = 0.09709133373799; + } + else { + beta[0] = 0.08335837191688; + } + QUESO::InverseGammaVectorRV + priorRvB("priorB_", paramDomainB,alpha,beta); + + QUESO::ConcatenatedVectorRV + priorRv("prior_", priorRvA, priorRvB, paramDomain); +#else + QUESO::UniformVectorRV + priorRv("prior_", paramDomain); +#endif + + QUESO::GenericVectorRV + postRv("post_", paramSpace); + + QUESO::StatisticalInverseProblem + ip("", NULL, priorRv, likelihoodFunctionObj, postRv); + + //////////////////////////////////////////////////////// + // Step 5 of 5: Solve the inverse problem + //////////////////////////////////////////////////////// + ip.solveWithBayesMLSampling(); + + //////////////////////////////////////////////////////// + // Print some statistics + //////////////////////////////////////////////////////// + unsigned int numPosTotal = postRv.realizer().subPeriod(); + if (env.subDisplayFile()) { + *env.subDisplayFile() << "numPosTotal = " << numPosTotal + << std::endl; + } + + QUESO::GslVector auxVec(paramSpace.zeroVector()); + unsigned int numPosTheta1SmallerThan1dot5 = 0; + for (unsigned int i = 0; i < numPosTotal; ++i) { + postRv.realizer().realization(auxVec); + if (auxVec[0] < 1.5) numPosTheta1SmallerThan1dot5++; + } + + if (env.subDisplayFile()) { + *env.subDisplayFile() << "numPosTheta1SmallerThan1dot5 = " << numPosTheta1SmallerThan1dot5 + << ", ratio = " << ((double) numPosTheta1SmallerThan1dot5)/((double) numPosTotal) + << std::endl; + } + + return; +} diff --git a/examples/modal/src/example_compute.h b/examples/modal/src/example_compute.h new file mode 100644 index 000000000..6d70a89f3 --- /dev/null +++ b/examples/modal/src/example_compute.h @@ -0,0 +1,39 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ +#ifndef __EX_COMPUTE_H__ +#define __EX_COMPUTE_H__ + +#include + +void compute(const QUESO::FullEnvironment& env, unsigned int numModes); + +#endif diff --git a/examples/modal/src/example_likelihood.C b/examples/modal/src/example_likelihood.C new file mode 100644 index 000000000..68535773c --- /dev/null +++ b/examples/modal/src/example_likelihood.C @@ -0,0 +1,99 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#include + +double likelihoodRoutine( + const QUESO::GslVector& paramValues, + const QUESO::GslVector* paramDirection, + const void* functionDataPtr, + QUESO::GslVector* gradVector, + QUESO::GslMatrix* hessianMatrix, + QUESO::GslVector* hessianEffect) +{ + double theta1 = paramValues[0]; + double theta2 = paramValues[1]; + double sigmaSq = paramValues[2]; + + unsigned int numModes = ((likelihoodRoutine_DataType *) functionDataPtr)->numModes; + + double aux1 = theta1 + 2.*theta2; + double aux2 = sqrt(theta1*theta1+4.*theta2*theta2); + + double w1 = 10.*sqrt(10.*(aux1+aux2)); + double w2 = 10.*sqrt(10.*(aux1-aux2)); + + double sum1 = 0.; + double sum2 = 0.; + + double aux = (w1 - 72.0470); + sum1 += aux*aux; + aux = (w1 - 71.8995); + sum1 += aux*aux; + aux = (w1 - 72.2801); + sum1 += aux*aux; + aux = (w1 - 71.9421); + sum1 += aux*aux; + aux = (w1 - 72.3578); + sum1 += aux*aux; + + if (numModes == 1) { + // Ok, do nothing + } + else if (numModes == 2) { + aux = (w2 - 28.0292); + sum2 += aux*aux; + aux = (w2 - 27.3726); + sum2 += aux*aux; + aux = (w2 - 27.5388); + sum2 += aux*aux; + aux = (w2 - 27.0357); + sum2 += aux*aux; + aux = (w2 - 27.1588); + sum2 += aux*aux; + } + else { + UQ_FATAL_TEST_MACRO(true, + paramValues.env().fullRank(), + "example_likelihood()", + "invalid 'numModes'"); + } + + double result = -0.5*((double) numModes)*5.*log(2.*M_PI*sigmaSq) - 0.5*(sum1+sum2)/sigmaSq; + + +#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN + return result; +#else + return -2.*result; +#endif +} diff --git a/examples/modal/src/example_likelihood.h b/examples/modal/src/example_likelihood.h new file mode 100644 index 000000000..ba2b2e419 --- /dev/null +++ b/examples/modal/src/example_likelihood.h @@ -0,0 +1,51 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ +#ifndef __EX_LIKELIHOOD_H__ +#define __EX_LIKELIHOOD_H__ + +#include + +struct +likelihoodRoutine_DataType +{ + unsigned int numModes; +}; + +double likelihoodRoutine( + const QUESO::GslVector& paramValues, + const QUESO::GslVector* paramDirection, + const void* functionDataPtr, + QUESO::GslVector* gradVector, + QUESO::GslMatrix* hessianMatrix, + QUESO::GslVector* hessianEffect); + +#endif diff --git a/examples/modal/src/example_main.C b/examples/modal/src/example_main.C new file mode 100644 index 000000000..736826b61 --- /dev/null +++ b/examples/modal/src/example_main.C @@ -0,0 +1,60 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + * Usage: ./example_gsl example.inp <(int) numModes> + *-------------------------------------------------------------------------- */ + +#include + +int main(int argc, char* argv[]) +{ + // Initialize environment + MPI_Init(&argc,&argv); + + UQ_FATAL_TEST_MACRO(argc != 3, + QUESO::UQ_UNAVAILABLE_RANK, + "main()", + "after executable argv[0], input file must be specified in command line as argv[1], then numModes (1 or 2) must be specified as argv[2]"); + + QUESO::FullEnvironment* env = + new QUESO::FullEnvironment(MPI_COMM_WORLD,argv[1],"",NULL); + + // Compute + unsigned int numModes = (unsigned int) atoi(argv[2]); + + compute(*env,numModes); + + // Finalize environment + delete env; + MPI_Finalize(); + + std::cout << std::endl << "FIM!" << std::endl << std::endl; + + return 0; +} diff --git a/examples/modal/tests/test_2013_11_22/example.inp b/examples/modal/tests/test_2013_11_22/example.inp new file mode 100644 index 000000000..be2c0b706 --- /dev/null +++ b/examples/modal/tests/test_2013_11_22/example.inp @@ -0,0 +1,46 @@ +############################################### +# UQ Environment +############################################### +#env_help = anything +env_numSubEnvironments = 1 +env_subDisplayFileName = outputData/display +env_subDisplayAllowAll = 0 +env_subDisplayAllowedSet = 0 +env_displayVerbosity = 0 +env_syncVerbosity = 0 +env_seed = 0 + +############################################### +# Statistical inverse problem (ip) +############################################### +#ip_help = anything +ip_computeSolution = 1 +ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Multilevel algorithm +############################################### +#ip_ml_help = anything +ip_ml_dataOutputFileName = outputData/sipOutput_ml +ip_ml_dataOutputAllowedSet = 0 + +############################################### +# All levels, unless otherwise specified +############################################### +ip_ml_default_minEffectiveSizeRatio = 0.49 +ip_ml_default_maxEffectiveSizeRatio = 0.51 +ip_ml_default_rawChain_size = 10000 +ip_ml_default_rawChain_dataOutputFileName = outputData/rawChain_ml +ip_ml_default_scaleCovMatrix = 1 +ip_ml_default_dr_maxNumExtraStages = 2 +ip_ml_default_dr_listOfScalesForExtraStages = 10. 25. + +############################################### +# Last level (level 7 or 8 for 'example_gsl' executable) +############################################### +ip_ml_last_dataOutputFileName = outputData/sipOutput_ml +ip_ml_last_dataOutputAllowedSet = 0 1 +ip_ml_last_rawChain_size = 10000 +ip_ml_last_rawChain_computeStats = 1 +ip_ml_last_rawChain_dataOutputFileName = outputData/rawChain_ml From 5609ff010da44f1a5f01e39d9ababa2b1a9b16c1 Mon Sep 17 00:00:00 2001 From: "Kemelli C. Estacio-Hiroms" Date: Mon, 25 Nov 2013 18:29:13 -0800 Subject: [PATCH 02/20] [queso/manual/modal] adding new example, modal. The section presents a short description of this example, which is the first one using MULTILEVEL method, as well as some figs. --- .../modal_1_mode_autocorrelation_theta1.png | Bin 0 -> 38664 bytes .../modal_1_mode_autocorrelation_theta2.png | Bin 0 -> 39061 bytes .../modal_1_mode_autocorrelation_theta3.png | Bin 0 -> 36538 bytes .../users/rawfigs/modal_1_mode_kde_target.png | Bin 0 -> 22860 bytes .../users/rawfigs/modal_1_mode_kde_theta1.png | Bin 0 -> 24463 bytes .../users/rawfigs/modal_1_mode_kde_theta2.png | Bin 0 -> 23336 bytes .../users/rawfigs/modal_1_mode_kde_theta3.png | Bin 0 -> 18424 bytes manual/users/rawfigs/modal_1_mode_level_1.png | Bin 0 -> 33486 bytes manual/users/rawfigs/modal_1_mode_level_3.png | Bin 0 -> 28002 bytes manual/users/rawfigs/modal_1_mode_level_5.png | Bin 0 -> 21664 bytes manual/users/rawfigs/modal_1_mode_level_7.png | Bin 0 -> 17912 bytes .../modal_2_modes_autocorrelation_theta1.png | Bin 0 -> 41321 bytes .../modal_2_modes_autocorrelation_theta2.png | Bin 0 -> 41049 bytes .../modal_2_modes_autocorrelation_theta3.png | Bin 0 -> 39352 bytes .../rawfigs/modal_2_modes_kde_target.png | Bin 0 -> 31755 bytes .../rawfigs/modal_2_modes_kde_theta1.png | Bin 0 -> 24029 bytes .../rawfigs/modal_2_modes_kde_theta2.png | Bin 0 -> 20838 bytes .../rawfigs/modal_2_modes_kde_theta3.png | Bin 0 -> 26002 bytes .../users/rawfigs/modal_2_modes_level_1.png | Bin 0 -> 32370 bytes .../users/rawfigs/modal_2_modes_level_3.png | Bin 0 -> 26327 bytes .../users/rawfigs/modal_2_modes_level_6.png | Bin 0 -> 21092 bytes .../users/rawfigs/modal_2_modes_level_9.png | Bin 0 -> 13718 bytes manual/users/users.tex | 16 +- manual/users/users_5_examples.tex | 2 + manual/users/users_5c_example_gravity.tex | 4 +- manual/users/users_5d_example_tga.tex | 2 +- manual/users/users_5e_example_ml_modal.tex | 321 ++++++++++++++++++ 27 files changed, 334 insertions(+), 11 deletions(-) create mode 100644 manual/users/rawfigs/modal_1_mode_autocorrelation_theta1.png create mode 100644 manual/users/rawfigs/modal_1_mode_autocorrelation_theta2.png create mode 100644 manual/users/rawfigs/modal_1_mode_autocorrelation_theta3.png create mode 100644 manual/users/rawfigs/modal_1_mode_kde_target.png create mode 100644 manual/users/rawfigs/modal_1_mode_kde_theta1.png create mode 100644 manual/users/rawfigs/modal_1_mode_kde_theta2.png create mode 100644 manual/users/rawfigs/modal_1_mode_kde_theta3.png create mode 100644 manual/users/rawfigs/modal_1_mode_level_1.png create mode 100644 manual/users/rawfigs/modal_1_mode_level_3.png create mode 100644 manual/users/rawfigs/modal_1_mode_level_5.png create mode 100644 manual/users/rawfigs/modal_1_mode_level_7.png create mode 100644 manual/users/rawfigs/modal_2_modes_autocorrelation_theta1.png create mode 100644 manual/users/rawfigs/modal_2_modes_autocorrelation_theta2.png create mode 100644 manual/users/rawfigs/modal_2_modes_autocorrelation_theta3.png create mode 100644 manual/users/rawfigs/modal_2_modes_kde_target.png create mode 100644 manual/users/rawfigs/modal_2_modes_kde_theta1.png create mode 100644 manual/users/rawfigs/modal_2_modes_kde_theta2.png create mode 100644 manual/users/rawfigs/modal_2_modes_kde_theta3.png create mode 100644 manual/users/rawfigs/modal_2_modes_level_1.png create mode 100644 manual/users/rawfigs/modal_2_modes_level_3.png create mode 100644 manual/users/rawfigs/modal_2_modes_level_6.png create mode 100644 manual/users/rawfigs/modal_2_modes_level_9.png create mode 100644 manual/users/users_5e_example_ml_modal.tex diff --git a/manual/users/rawfigs/modal_1_mode_autocorrelation_theta1.png b/manual/users/rawfigs/modal_1_mode_autocorrelation_theta1.png new file mode 100644 index 0000000000000000000000000000000000000000..f6be03733f6e55e09f8998317bf94249602a86b1 GIT binary patch literal 38664 zcmc$`1yGdhA3wT?w1l+Mh@^lZu!Mksl$12G2rAOTQqmzM4JyqNN=i$2N(ll|A}p{A zNXN3|()V3F=lst3&)k_icjnG@bXIrXUEcSJ@Ap%W;g2 zAP{aa;T7N=8FI4{;DyLh<%tUjL`RAJgJYFB0$c?ma8=b%B$y$&38KKE>%#E?-ePxE 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znIt3~Py;3{=`!$+ZVM$?n11fJTX;%LBCjR*JEJI46duuXfVt%R&-?VD Zg7GiCk(J754A=#bnzHt_e5GFl{}1t2ab*Ai literal 0 HcmV?d00001 diff --git a/manual/users/users.tex b/manual/users/users.tex index 0668aae17..6986cee17 100644 --- a/manual/users/users.tex +++ b/manual/users/users.tex @@ -143,17 +143,17 @@ \numberwithin{figure}{section} \numberwithin{table}{section} - \include{users_0_cover} +\include{users_0_cover} \pagenumbering{arabic} -% \include{users_1_introduction} -% -% \include{users_2_installation} -% -% \include{users_3_classes} -% -% \include{users_4_remarks} +\include{users_1_introduction} + +\include{users_2_installation} + +\include{users_3_classes} + +\include{users_4_remarks} \include{users_5_examples} diff --git a/manual/users/users_5_examples.tex b/manual/users/users_5_examples.tex index 52c5f9a17..120a39d9c 100644 --- a/manual/users/users_5_examples.tex +++ b/manual/users/users_5_examples.tex @@ -25,3 +25,5 @@ \chapter{QUESO Examples}\label{chap:Queso-examples} \input{users_5c_example_gravity} \input{users_5d_example_tga} + +\input{users_5e_example_ml_modal} diff --git a/manual/users/users_5c_example_gravity.tex b/manual/users/users_5c_example_gravity.tex index 355337789..a36e8838c 100644 --- a/manual/users/users_5c_example_gravity.tex +++ b/manual/users/users_5c_example_gravity.tex @@ -35,7 +35,7 @@ \subsubsection{Experimental Data} -\begin{figure}[!h] +\begin{figure}[!ht] \centering \input{rawfigs/free_fall.latex} \vspace*{-8pt} @@ -165,7 +165,7 @@ \subsection{Statistical Forward Problem} $v_{0x} = v_{0} \cos(\alpha)$, $v_{0y} = v_{0} \sin(\alpha)$, and $v_0=\|\bv{v_0}\|^2$. % Figure \ref{fig:projectile} displays the projectile motion of an object in these conditions. -\begin{figure}[!h] +\begin{figure}[!ht] \centering \includegraphics[scale=1]{figs/projectile} \vspace*{-8pt} diff --git a/manual/users/users_5d_example_tga.tex b/manual/users/users_5d_example_tga.tex index fae56a99c..6ffd33952 100644 --- a/manual/users/users_5d_example_tga.tex +++ b/manual/users/users_5d_example_tga.tex @@ -34,7 +34,7 @@ \subsection{Thermogravimetric Experiments and a Simple Model} % A material sample with initial mass $m_0$ and at initial temperature $T_0$ is heated % with constant heating rate $\beta$ (K/min) until it fully decomposes. % Let $w(T)=m(T)/m_0$ denote the mass fraction. -\begin{figure}[!h] +\begin{figure}[!ht] \centering \includegraphics[scale=0.4]{figs/tga_experiment_data.png} \vspace*{-8pt} diff --git a/manual/users/users_5e_example_ml_modal.tex b/manual/users/users_5e_example_ml_modal.tex new file mode 100644 index 000000000..525d4f2dc --- /dev/null +++ b/manual/users/users_5e_example_ml_modal.tex @@ -0,0 +1,321 @@ +\section{\texttt{modal}}\label{sec:example_modal} + + +This example presents a combination of two statistical inverse problems in one. +It presents the capability of the Multilevel method in sampling from a target distribution that has either one or two models. The random variable of interest has three parameters, i.e., $\bv{\theta}=(\theta_1,\theta_2, \sigma^2) \in \mathbb{R}^3$, where the third parameter may be seen as variation. + +The example also it gives the user the opportunity to chose either one single type of prior distribution, uniform, for the three components of the random variable, or two different priors: a uniform and a beta distribution. + +Choosing between a one-mode or a two-mode target distribution is done at execution level, as presented in the following code line: + +\begin{lstlisting}[label={},caption={}] +$ cd $HOME/LIBRARIES/QUESO-0.50.0/ +$ cd examples/modal +$ rm outputData/* +$ ./modal_gsl example.inp +\end{lstlisting} +where \verb++ is either 1 or 2. +% +% +% This simple statistical inverse problem presents a combination of two, suppose a random variable of interest with three parameters $\bv{\theta}=(\theta_1,\theta_2, \sigma) \in \mathbb{R}^3$, namely $\bv{\theta}:[0,3]\times[0,3]\times[0,0.3] \rightarrow\mathbb{R}$. +% +% +% +% This simple statistical inverse problem, suppose a random variable of interest with three parameters $\bv{\theta}=(\theta_1,\theta_2, \sigma) \in \mathbb{R}^3$, namely $\bv{\theta}:[0,3]\times[0,3]\times[0,0.3] \rightarrow\mathbb{R}$. +% +% +% Suppose also that the distribution we aim to sample has either one or two modes. +% +\subsection{One-mode distribution} + +In this case, the target distribution is assumed to have only one mode. +Suppose also that the random variable $\bv{\theta}$ can either have a uniform distribution for all its components, i.e.: +$$ +% \pi_{\text{prior}}=\mathcal{U}([0,3],[0,3],[0,0.3]). +\pi_{\text{prior}}=\mathcal{U}([0,3]) \times \mathcal{U}([0,3]) \times \mathcal{U}([0,0.3]). +$$ +or, the prior distribution is defined as a combination of uniform prior for the $\theta_1$, with a beta prior for $\theta_2$: +$$ +% \pi_{\text{prior}}=\mathcal{U}([0,3],[0,3],[0,0.3]). +\pi_{\text{prior}}=\mathcal{U}([0,3]) \times \mathcal{U}([0,3]) \times \mathcal{B}(\alpha,\beta), \quad \text{with} \quad \alpha=3 \quad\text{and}\quad \beta=0.09709133373799. +$$ + +The likelihood function is defined as follows: +\begin{equation} +\begin{split} \small +%\pi_{\text{likelihood}}(\mathbf{d}|\boldsymbol{\theta}) = +\quad f(\D|\bv{\theta})= -\dfrac{5}{2} \log\left(2 \pi \sigma^2\right)-\dfrac{1}{2\sigma^2} &\Bigg[ + \left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.0470\right)^2 +\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-71.8995\right)^2 +\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.2801\right)^2 +\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-71.9421\right)^2 +\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.3578\right)^2 \Bigg]. +\end{split} +\end{equation} + +\subsubsection{Running the One-Mode Example} + +To run the executable provided considering a \underline{one-mode} distribution, enter the following commands: +\begin{lstlisting}[label={},caption={Running the example with a one-mode distribution.}] +$ cd $HOME/LIBRARIES/QUESO-0.50.0/ +$ cd examples/modal +$ rm outputData/* +$ ./modal_gsl example.inp 1 #one mode! +$ matlab + $ plot_modal_all_levels_1mode # inside matlab + $ exit # inside matlab +$ ls -l outputData/*.png +modal_1_mode_kde_target.png modal_1_mode_level_1.png modal_1_mode_level_5.png +modal_1_mode_kde_theta1.png modal_1_mode_level_2.png modal_1_mode_level_6.png +modal_1_mode_kde_theta2.png modal_1_mode_level_3.png modal_1_mode_level_7.png +modal_1_mode_kde_theta3.png modal_1_mode_level_4.png +\end{lstlisting} + +As a result, the user should have created several of PNG figures scatter plots of each one of the levels and the kernel density estimation of the parameters, for each level in the Multilevel method. The name of the figure files have been chosen to be informative, as shown in the Listing above. + + + +\subsection{Two-mode distribution} + +In this case, the target distribution is assumed to have two modes. +Suppose that $\bv{\theta}$ has a either uniform distribution for all its components, i.e.: +$$ +% \pi_{\text{prior}}=\mathcal{U}([0,3],[0,3],[0,0.3]). +\pi_{\text{prior}}=\mathcal{U}([0,3]) \times \mathcal{U}([0,3]) \times \mathcal{U}([0,0.3]). +$$ +or, the prior distribution is defined as a combination of uniform prior for the $\theta_1$, with a beta prior for $\theta_2$: +$$ +% \pi_{\text{prior}}=\mathcal{U}([0,3],[0,3],[0,0.3]). +\pi_{\text{prior}}=\mathcal{U}([0,3]) \times \mathcal{U}([0,3]) \times \mathcal{B}(\alpha,\beta), \quad \text{with} \quad \alpha=3 \quad\text{and}\quad \beta=0.08335837191688. +$$ + +The likelihood function is defined as follows: +\begin{equation} +\begin{split}\small +f(\D|\bv{\theta}, M_j)= -5 \log\left(2 \pi \sigma^2\right)- \dfrac{1}{2\sigma^2} &\Bigg[ + \left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.0470\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-71.8995\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.2801\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-71.9421\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.3578\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2-10 \sqrt{\theta_1^2+4 \theta_2^2}}-28.0292\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2-10 \sqrt{\theta_1^2+4 \theta_2^2}}-27.3726\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2-10 \sqrt{\theta_1^2+4 \theta_2^2}}-27.5388\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2-10 \sqrt{\theta_1^2+4 \theta_2^2}}-27.0357\right)^2+\\ +&+\left(10 \sqrt{10 \theta_1+20 \theta_2-10 \sqrt{\theta_1^2+4 \theta_2^2}}-27.1588\right)^2 \Bigg]. +\end{split} +\end{equation} + + + + +\subsubsection{Running the Two-Mode Example} + +To run the executable provided considering a \underline{one-mode} distribution, enter the following commands: +\begin{lstlisting}[label={},caption={Running the example with a two-mode distribution.}] +$ cd $HOME/LIBRARIES/QUESO-0.50.0/ +$ cd examples/modal +$ rm outputData/* +$ ./modal_gsl example.inp 2 # two modes! +$ matlab + $ plot_modal_all_levels_2modes # inside matlab + $ exit # inside matlab +$ ls -l outputData/*.png +modal_2_modes_kde_target.png modal_2_modes_level_1.png modal_2_modes_level_5.png +modal_2_modes_kde_theta1.png modal_2_modes_level_2.png modal_2_modes_level_6.png +modal_2_modes_kde_theta2.png modal_2_modes_level_3.png modal_2_modes_level_7.png +modal_2_modes_kde_theta3.png modal_2_modes_level_4.png modal_2_modes_level_8.png +\end{lstlisting} + +As a result, the user should have created several of PNG figures scatter plots of each one of the levels and the kernel density estimation of the parameters, for each level in the Multilevel method. The name of the figure files have been chosen to be informative, as shown in the Listing above. + + + +\subsection{Example Code}\label{sec:modal-code} + +The source code for the example is composed of 5 files: +\texttt{example\_main.C} (Listing \ref{code:modal-main-c}), \linebreak +\texttt{example\_likelihood.h} and \texttt{example\_likelihood.C} (Listings \ref{fig-like-modal-h} and \ref{fig-like-modal-c}), +\texttt{example\_compute.h} and \texttt{example\_compute.C} (Listings \ref{code:modal-compute-h} and \ref{code:modal-compute-c}). + + +\lstinputlisting[caption=File \texttt{example\_main.C.}, label={code:modal-main-c}, linerange={33-1000}]{../../examples/modal/src/example_main.C} + +\lstinputlisting[caption=File \texttt{example\_likelihood.h}., label={fig-like-modal-h}, linerange={32-1000}]{../../examples/modal/src/example_likelihood.h} + +\lstinputlisting[caption=File \texttt{example\_likelihood.C}., label={fig-like-modal-c}, linerange={33-1000}]{../../examples/modal/src/example_likelihood.C} + +\lstinputlisting[caption=File \texttt{example\_compute.h.}, label={code:modal-compute-h}, linerange={32-1000}]{../../examples/modal/src/example_compute.h} + + +Note that in line 12 of Listings \ref{code:modal-compute-c} the \verb+#define+ directive creates the macro + \linebreak +\verb+APPLS_MODAL_USES_CONCATENATION+. Such macro, together with the directives \verb+#ifdef+, \verb+#else+, and \verb+#endif+, tells the compiler that the application will use concatenated priors, by controlling compilation of portions of file \texttt{example\_compute.C}. Commenting line 12 of Listings \ref{code:modal-compute-c} will make the application to use uniform priors only: + +\lstinputlisting[caption={File \texttt{example\_compute.C}.}, label={code:modal-compute-c}, linerange={33-1000},numbers=left]{../../examples/modal/src/example_compute.C} + + + + +\subsection{Input File}\label{sec:modal-input-file} + + +QUESO reads an input file for solving statistical problems, which provides options for the Multilevel or MCMC method. In this example, the Multilevel method is chosen to sample from the distribution. Many variables are common to both MCMC and Multilevel method, especially because the Multilevel method also has the option of delaying the rejection of a candidate. The names of the variables have been designed to be informative in this case as well: +\begin{description}\vspace{-8pt} +\item[ \texttt{env}:] refers to QUESO environment; \vspace{-8pt} +\item[ \texttt{ip}:] refers to inverse problem;\vspace{-8pt} +\item[ \texttt{ml}:] refers to Multilevel;\vspace{-8pt} +\item[ \texttt{dr}:] refers to delayed rejection;\vspace{-8pt} +\item[ \texttt{rawChain}:] refers to the raw, entire chain; \vspace{-8pt} +\item[ \texttt{filteredChain}:] refers to a filtered chain (related to a specified \texttt{lag});\vspace{-8pt} +\item[ \texttt{last}:] refers to instructions specific for the last level of the Multilevel algorithm. +\end{description} + +The user may select options for a specific level by naming its number, i.e., in case the user wants to write the raw chain of the level 3 in a separate file, say \verb+'rawChain_ml_level3.m'+, he/she may include the line: +\begin{lstlisting} +ip_ml_3_rawChain_dataOutputFileName = outputData/rawChain_ml_level3 +\end{lstlisting} +in the input file. + + +The options used for solving this example are displayed in Listing \ref{code:modal-input-file}. + +\lstinputlisting[caption={Options for QUESO library used in application code (Listings \ref{code:modal-main-c}-\ref{code:modal-compute-c}})., +label={code:modal-input-file},]{../../examples/modal/tests/test_2013_11_22/example.inp} + + + +\subsection{Create your own Makefile}\label{sec:modal-makefile} + +Makefiles are special format files that together with the make utility will help one to compile and automatically build and manage projects (programs). +Listing \ref{code:modal_makefile} presents a Makefile, named `\texttt{Makefile\_modal\_example\_margarida}', that may be used to compile the code and create the executable \verb+modal_gsl+. Naturally, it must be adapted to the user's settings, i.e., it has to have the correct paths for the user's libraries that have actually been used to compile and install QUESO (see Sections \ref{sec:Pre_Queso}--\ref{sec:install_Queso_make}). + +\lstinputlisting[caption={Makefile for the application code in Listings \ref{code:modal-main-c}-\ref{code:modal-compute-c}}, label={code:modal_makefile},language={bash}]{../../examples/modal/src//Makefile_modal_violeta} + +Thus, to compile, build and execute the code, the user just needs to run the following commands in the same directory where the files are: +\begin{lstlisting} +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/modal/ +$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ + $HOME/LIBRARIES/gsl-1.15/lib/:\ + $HOME/LIBRARIES/boost-1.53.0/lib/:\ + $HOME/LIBRARIES/hdf5-1.8.10/lib:\ + $HOME/LIBRARIES/QUESO-0.50.0/lib +$ make -f Makefile_modal_violeta +$ ./modal_gsl example.inp +\end{lstlisting} + +The `\verb+export+' instruction above is only necessary if the user has not saved it in his/her \verb+.bashrc+ file. + + +\subsection{Data Post-Processing and Visualization}\label{sec:modal-results} + + + +According to the specifications of the input file in Listing~\ref{code:modal-input-file}, both a folder named \verb+outputData+ and a the following files should be generated: +\begin{verbatim} +rawChain_ml.m +display_sub0.txt +\end{verbatim} + + +The sequence of Matlab commands is identical to the ones presented in Sections +\ref{sec:sip-results}, \ref{sec:sfp-results}, \ref{sec:gravity-results} and \ref{sec:tga-results}; +therefore, are omitted here. The reader is invited to explore the Matlab files +\texttt{plot\_modal\_all\_levels\_1mode.m} and/or \texttt{plot\_modal\_all\_levels\_2modes.m} +%\texttt{validationCycle/tests/test\_2012\_11\_15/tga\_cycle\_plot.m} +for details of how the figures have been generated. + + +\subsubsection{Scatter Plots} + +The code presented in Listing \ref{matlab:modal_scatter} uses Matlab function \verb+plotmatrixr+ to generate Figures \ref{fig:modal_scatter_1mode} and \ref{fig:modal_scatter_2modes} +which presents the scatter plots and histograms of the parameters $\theta_1$ and $\theta_2$, based on the generated raw chains. + + +\begin{lstlisting}[label=matlab:modal_scatter,caption={Matlab code for the scatter plots depicted in Figures \ref{fig:modal_scatter_1mode} and \ref{fig:modal_scatter_2modes}.}] +fprintf(1,'Scatter plots and histograms of raw chains - Level 1 \n'); +plotmatrix(ip_ml_1_rawChain_unified, '+b') +set(gca,'fontsize',20); +xlabel('\theta_1 \theta_2 \theta_3','fontsize',16); +ylabel('\theta_3 \theta_2 \theta_1','fontsize',16); +title('Scatter plots and histograms, Level 1 - 1 mode') +\end{lstlisting} + +\begin{figure}[htpb] +\centering +%\subfloat[$\theta_1$]{\includegraphics[scale=0.35]{figs/simple_ip_autocorrelation_raw_filt_theta1.png}} +%\subfloat[$\theta_2$]{\includegraphics[scale=0.35]{figs/simple_ip_autocorrelation_raw_filt_theta2.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_level_1.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_level_3.png}}\\ +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_level_5.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_level_7.png}} +\vspace{-10pt} +\caption{Scatter plots for $\theta_1$, $\theta_2$ and $\theta_3=\sigma^2$, levels 1, 3, 5 and 7 (last). One mode distribution.} +\label{fig:modal_scatter_1mode} +\end{figure} + +\begin{figure}[htpb] +\centering +%\subfloat[$\theta_1$]{\includegraphics[scale=0.35]{figs/simple_ip_autocorrelation_raw_filt_theta1.png}} +%\subfloat[$\theta_2$]{\includegraphics[scale=0.35]{figs/simple_ip_autocorrelation_raw_filt_theta2.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_level_1.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_level_3.png}}\\ +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_level_6.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_level_9.png}} +\vspace{-10pt} +\caption{Scatter plots for $\theta_1$, $\theta_2$ and $\theta_3=\sigma^2$, levels 1, 3, 6 and 9 (last). Two-mode distribution.} +\label{fig:modal_scatter_2modes} +\end{figure} + +\subsubsection{KDE Plots} + +Figures \ref{fig:modal_kde_1mode} and \ref{fig:modal_kde_2modes} present the KDE plots of the parameters $\theta_1$, $\theta_2$, $\theta_3$ and target PDF in both cases: one-mode and two-modes distribution. + + + +\begin{figure}[hptb] +\centering +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_kde_theta1.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_kde_theta2.png}}\\ +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_kde_theta3.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_1_mode_kde_target.png}} +\vspace{-10pt} +\caption{KDE plots for $\theta_1$, $\theta_2$, $\theta_3=\sigma^2$, and the target PDF. One mode distribution.} +\label{fig:modal_kde_1mode} +\end{figure} + +\begin{figure}[hptb] +\centering +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_kde_theta1.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_kde_theta2.png}}\\ +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_kde_theta3.png}} +\subfloat{\includegraphics[scale=0.3]{figs/modal_2_modes_kde_target.png}} +\vspace{-10pt} +\caption{KDE plots for $\theta_1$, $\theta_2$, $\theta_3=\sigma^2$, and the target PDF. Two-mode distribution.} +\label{fig:modal_kde_2modes} +\end{figure} + + +\subsubsection{Autocorrelation Plots} + +Figures \ref{fig:modal_autocorr_1mode} and \ref{fig:modal_autocorr_2modes} present the autocorrelation of the parameters $\theta_1$, $\theta_2$ and $\theta_3$ in both cases: one-mode and two-modes distribution. + +\begin{figure}[htpb] +\centering +\subfloat{\includegraphics[scale=0.25]{figs/modal_1_mode_autocorrelation_theta1.png}} +\subfloat{\includegraphics[scale=0.25]{figs/modal_1_mode_autocorrelation_theta2.png}} +\subfloat{\includegraphics[scale=0.25]{figs/modal_1_mode_autocorrelation_theta3.png}} +\vspace{-10pt} +\caption{Autocorrelation plots for $\theta_1$, $\theta_2$ and $\theta_3=\sigma^2$. One-mode distribution.} +\label{fig:modal_autocorr_1mode} +\end{figure} + +\begin{figure}[htpb] +\centering +\subfloat{\includegraphics[scale=0.25]{figs/modal_2_modes_autocorrelation_theta1.png}} +\subfloat{\includegraphics[scale=0.25]{figs/modal_2_modes_autocorrelation_theta2.png}} +\subfloat{\includegraphics[scale=0.25]{figs/modal_2_modes_autocorrelation_theta3.png}} +\vspace{-10pt} +\caption{Autocorrelation plots for $\theta_1$, $\theta_2$ and $\theta_3=\sigma^2$. Two-mode distribution.} +\label{fig:modal_autocorr_2modes} +\end{figure} \ No newline at end of file From 898413577f2c91da59b811e8b3bc05cfbd3731e3 Mon Sep 17 00:00:00 2001 From: "Kemelli C. Estacio-Hiroms" Date: Tue, 3 Dec 2013 16:41:54 -0800 Subject: [PATCH 03/20] [queso/examples/bimodal] Adding new example 'bimodal' to QUESO examples. This example uses the Multilevel algorithm to sample from a bimodal distribution and reproduces the example presented in Prudencio&Chegung 2012, Section 4.1 A 1D Problem. --- examples/Makefile.am | 33 +++- examples/bimodal/matlab/plot_all.m | 77 ++++++++ examples/bimodal/matlab/plot_likelihood.m | 26 +++ .../matlab/plot_likelihood_normalized_taus.m | 118 ++++++++++++ .../plot_likelihood_unnormalized_taus.m | 42 +++++ examples/bimodal/src/Makefile_bimodal_violeta | 47 +++++ examples/bimodal/src/bimodal_compute.C | 172 ++++++++++++++++++ examples/bimodal/src/bimodal_compute.h | 32 ++++ examples/bimodal/src/bimodal_likelihood.C | 92 ++++++++++ examples/bimodal/src/bimodal_likelihood.h | 45 +++++ examples/bimodal/src/bimodal_main.C | 40 ++++ .../tests/test_2013_12_02/bimodal_1chain.inp | 76 ++++++++ 12 files changed, 799 insertions(+), 1 deletion(-) create mode 100644 examples/bimodal/matlab/plot_all.m create mode 100644 examples/bimodal/matlab/plot_likelihood.m create mode 100644 examples/bimodal/matlab/plot_likelihood_normalized_taus.m create mode 100644 examples/bimodal/matlab/plot_likelihood_unnormalized_taus.m create mode 100644 examples/bimodal/src/Makefile_bimodal_violeta create mode 100644 examples/bimodal/src/bimodal_compute.C create mode 100644 examples/bimodal/src/bimodal_compute.h create mode 100644 examples/bimodal/src/bimodal_likelihood.C create mode 100644 examples/bimodal/src/bimodal_likelihood.h create mode 100644 examples/bimodal/src/bimodal_main.C create mode 100644 examples/bimodal/tests/test_2013_12_02/bimodal_1chain.inp diff --git a/examples/Makefile.am b/examples/Makefile.am index 2e03f1091..13691ca36 100644 --- a/examples/Makefile.am +++ b/examples/Makefile.am @@ -296,7 +296,38 @@ dist_gravity_gsl_DATA = dist_gravity_gsl_DATA += $(gravity_gsl_DATA) dist_gravity_gsl_DATA += ${gravity_gsl_HEADERS} dist_gravity_gsl_DATA += ${gravity_gsl_SOURCES} - + +#################### +# bimodal # +#################### +bimodal_gsldir = $(prefix)/examples/bimodal + +bimodal_gsl_DATA = $(top_srcdir)/examples/bimodal/tests/test_2013_12_02/bimodal_1chain.inp +bimodal_gsl_DATA += $(top_srcdir)/examples/bimodal/matlab/plot_all.m +bimodal_gsl_DATA += $(top_srcdir)/examples/bimodal/matlab/plot_likelihood.m +bimodal_gsl_DATA += $(top_srcdir)/examples/bimodal/matlab/plot_likelihood_normalized_taus.m +bimodal_gsl_DATA += $(top_srcdir)/examples/bimodal/matlab/plot_likelihood_unnormalized_taus.m +bimodal_gsl_DATA += $(top_srcdir)/examples/bimodal/src/Makefile_bimodal_violeta + +bimodal_gsl_PROGRAMS = bimodal_gsl + +bimodal_gsl_SOURCES = $(top_srcdir)/examples/bimodal/src/bimodal_main.C +bimodal_gsl_SOURCES += $(top_srcdir)/examples/bimodal/src/bimodal_compute.C +bimodal_gsl_SOURCES += $(top_srcdir)/examples/bimodal/src/bimodal_likelihood.C + +bimodal_gsl_HEADERS = $(top_srcdir)/examples/bimodal/src/bimodal_compute.h +bimodal_gsl_HEADERS += $(top_srcdir)/examples/bimodal/src/bimodal_likelihood.h + +bimodal_gsl_LDADD = $(top_builddir)/src/libqueso.la + +bimodal_gsl_CPPFLAGS = -I$(top_srcdir)/examples/bimodal/src $(QUESO_CPPFLAGS) + +dist_bimodal_gsl_DATA = +dist_bimodal_gsl_DATA += $(bimodal_gsl_DATA) +dist_bimodal_gsl_DATA += ${bimodal_gsl_HEADERS} +dist_bimodal_gsl_DATA += ${bimodal_gsl_SOURCES} + +#################### if CODE_COVERAGE_ENABLED CLEANFILES = *.gcda *.gcno endif diff --git a/examples/bimodal/matlab/plot_all.m b/examples/bimodal/matlab/plot_all.m new file mode 100644 index 000000000..f1d2f8497 --- /dev/null +++ b/examples/bimodal/matlab/plot_all.m @@ -0,0 +1,77 @@ +clear all; +%cd ../test/test_2013_12_02/outputData +cd outputData +rawChain_ml + + +% KDE estimation +fprintf(1,' Plotting KDE - theta \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; + +% CDF estimation +fprintf(1,' Plotting CDF - theta \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; + +% AUTOCORRELATION PLOTS +fprintf(1,' Plotting autocorrelation - theta \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; + +%cd ../../../matlab +cd .. diff --git a/examples/bimodal/matlab/plot_likelihood.m b/examples/bimodal/matlab/plot_likelihood.m new file mode 100644 index 000000000..e53d4a34e --- /dev/null +++ b/examples/bimodal/matlab/plot_likelihood.m @@ -0,0 +1,26 @@ +%cd ../test/test_2013_12_02/outputData +cd outputData + +x=0.:.1:150.; + +v1=1; +v2=25; + +y1=(x-10).*(x-10)/(2*v1); +y2=(x-100).*(x-100)/(2*v2); + +f1=(exp(-y1))/(sqrt(v1)*sqrt(2*pi)); +f2=(exp(-y2))/(sqrt(v2)*sqrt(2*pi)); + +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); + +print -dpng bimodal_likelihood.png + +%cd ../../../matlab +cd .. diff --git a/examples/bimodal/matlab/plot_likelihood_normalized_taus.m b/examples/bimodal/matlab/plot_likelihood_normalized_taus.m new file mode 100644 index 000000000..b617fc473 --- /dev/null +++ b/examples/bimodal/matlab/plot_likelihood_normalized_taus.m @@ -0,0 +1,118 @@ +%cd ../test/test_2013_12_02/outputData +cd outputData + +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; + +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)); + +% Level 1------------------------------------------------------------------ +z=((z1+ z2)/2).^tau1; +intZ = 0.; +for i=1:(n-1) +intZ = intZ + z(i)*deltaX; +end +intZ=intZ; + +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); + +x1=x; +f1=z; +clear z; clear intZ; + +% Level 2------------------------------------------------------------------ +z=((z1+ z2)/2).^tau2; +intZ = 0.; +for i=1:(n-1) +intZ = intZ + z(i)*deltaX; +end +intZ=intZ; + +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); + +x2=x; +f2=z; +clear z; clear intZ; + +% Level 3------------------------------------------------------------------ +z=((z1+ z2)/2).^tau3; +intZ = 0.; +for i=1:(n-1) +intZ = intZ + z(i)*deltaX; +end +intZ=intZ; + +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); + +x3=x; +f3=z; +clear z; clear intZ; + +% Level 4------------------------------------------------------------------ +z=((z1+ z2)/2).^tau4; +intZ = 0.; +for i=1:(n-1) +intZ = intZ + z(i)*deltaX; +end +intZ=intZ; + +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); + +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); + +print -dpng bimodal_likelihood_taus_normalized.png + +%cd ../../../matlab +cd .. diff --git a/examples/bimodal/matlab/plot_likelihood_unnormalized_taus.m b/examples/bimodal/matlab/plot_likelihood_unnormalized_taus.m new file mode 100644 index 000000000..848beb63b --- /dev/null +++ b/examples/bimodal/matlab/plot_likelihood_unnormalized_taus.m @@ -0,0 +1,42 @@ +%cd ../test/test_2013_12_02/outputData +cd outputData + +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; + + +x=-100.:.1:300.; +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)); +y=(z1+z2)/2.; + +plot(x,y.^tau1,'r-','linewidth',2); +hold +plot(x,y.^tau2,'g-','linewidth',2); +plot(x,y.^tau3,'b-','linewidth',2); +plot(x,y.^tau4,'c-','linewidth',2); + +a=axis; +axis([-100 300 a(3) 1]); +ylabel('f^{\tau}(\theta)','fontsize',16); +xlabel('\theta','fontsize',16); + +title('Intermediary likelihood functions','fontname', 'Times', 'fontsize',20); +grid minor +set(gca,'fontsize',16); + +legend(['\tau = ', num2str(tau1)],... + ['\tau = ', num2str(tau2)],... + ['\tau = ', num2str(tau3)],... + ['\tau = ', num2str(tau4)],... + 'location','northeast'); + +print -dpng bimodal_likelihood_taus.png +hold off; + +%cd ../../../matlab +cd .. diff --git a/examples/bimodal/src/Makefile_bimodal_violeta b/examples/bimodal/src/Makefile_bimodal_violeta new file mode 100644 index 000000000..ccd15b2f3 --- /dev/null +++ b/examples/bimodal/src/Makefile_bimodal_violeta @@ -0,0 +1,47 @@ +############################################################## +# Using installed QUESO 0.50.0, external release, in 'violeta' +############################################################## + +QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 +BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 +GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 +HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 + +INC_PATHS = \ + -I. \ + -I$(QUESO_DIR)/include \ + -I$(BOOST_DIR)/include \ + -I$(GSL_DIR)/include \ + -I$(HDF5_DIR)/include + +LIBS = \ + -L$(QUESO_DIR)/lib -lqueso \ + -L$(BOOST_DIR)/lib -lboost_program_options \ + -L$(GSL_DIR)/lib -lgsl \ + -L$(HDF5_DIR)/lib -lhdf5 + +CXX = mpic++ +#CXX = mpicxx +#CXX = /opt/local/bin/openmpicxx + +CXXFLAGS += -g -Wall -c + +default: all + +.SUFFIXES: .o .C + +all: bimodal_bimodal_gsl + +clean: + rm -f *~ + rm -f *.o + rm -f bimodal_gsl + +bimodal_bimodal_gsl: bimodal_main.o bimodal_likelihood.o bimodal_compute.o + $(CXX) bimodal_main.o \ + bimodal_likelihood.o \ + bimodal_compute.o \ + -o bimodal_gsl $(LIBS) + +%.o: %.C + $(CXX) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/bimodal/src/bimodal_compute.C b/examples/bimodal/src/bimodal_compute.C new file mode 100644 index 000000000..e9678ae34 --- /dev/null +++ b/examples/bimodal/src/bimodal_compute.C @@ -0,0 +1,172 @@ +//-----------------------------------------------------------------------bl- +//-------------------------------------------------------------------------- +// +// QUESO - a library to support the Quantification of Uncertainty +// for Estimation, Simulation and Optimization +// +// Copyright (C) 2008,2009,2010,2011,2012,2013 The PECOS Development Team +// +// This library is free software; you can redistribute it and/or +// modify it under the terms of the Version 2.1 GNU Lesser General +// Public License as published by the Free Software Foundation. +// +// This library is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// Lesser General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License along with this library; if not, write to the Free Software +// Foundation, Inc. 51 Franklin Street, Fifth Floor, +// Boston, MA 02110-1301 USA +// +//-----------------------------------------------------------------------el- + +#include +#include +#include +#include +#include +#include +#include +#include + +void compute(const QUESO::FullEnvironment& env) { + //------------------------------------------------------ + // Step 1 of 5: Instantiate the parameter space + //------------------------------------------------------ + QUESO::VectorSpace + paramSpace(env, "param_", 1, NULL); + + //------------------------------------------------------ + // Step 2 of 5: Instantiate the parameter domain + //------------------------------------------------------ + QUESO::GslVector paramMins(paramSpace.zeroVector()); + paramMins.cwSet(-250.); + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + paramMaxs.cwSet( 250.); + QUESO::BoxSubset + paramDomain("param_",paramSpace,paramMins,paramMaxs); + + //------------------------------------------------------ + // Step 3 of 5: Instantiate the likelihood function object + //------------------------------------------------------ + QUESO::GslVector meanVector(paramSpace.zeroVector()); + meanVector[0] = 10.; + QUESO::GslMatrix* covMatrix = paramSpace.newMatrix(); + (*covMatrix)(0,0) = 1.; + likelihoodRoutine_DataType likelihoodRoutine_Data; + likelihoodRoutine_Data.meanVector = &meanVector; + likelihoodRoutine_Data.covMatrix = covMatrix; + QUESO::GenericScalarFunction + likelihoodFunctionObj("like_", + paramDomain, + likelihoodRoutine, + (void *) &likelihoodRoutine_Data, + true); // routine computes [-2.*ln(function)] + + //------------------------------------------------------ + // Step 4 of 5: Instantiate the inverse problem + //------------------------------------------------------ + QUESO::UniformVectorRV + priorRv("prior_", paramDomain); + QUESO::GenericVectorRV + postRv("post_", paramSpace); + QUESO::StatisticalInverseProblem + ip("", NULL, priorRv, likelihoodFunctionObj, postRv); + + //------------------------------------------------------ + // Step 5 of 5: Solve the inverse problem + //------------------------------------------------------ +#if 0 + uqGslVector paramInitials(paramSpace.zeroVector()); + paramInitials[0] = 45.; + uqGslMatrix* proposalCovMatrix = paramSpace.newMatrix(); + (*proposalCovMatrix)(0,0) = 1600.; + ip.solveWithBayesMetropolisHastings(NULL, paramInitials, proposalCovMatrix); + delete proposalCovMatrix; +#else + ip.solveWithBayesMLSampling(); +#endif + + //------------------------------------------------------ + // Print some statistics in the file 'display_sub0.txt' + // They will be in the last part of the file. + //------------------------------------------------------ + unsigned int numPosTotal = postRv.realizer().subPeriod(); + if (env.subDisplayFile()) { + *env.subDisplayFile() << "numPosTotal = " << numPosTotal + << std::endl; + } + + QUESO::GslVector auxVec(paramSpace.zeroVector()); + unsigned int numPosSmallerThan40 = 0; + for (unsigned int i = 0; i < numPosTotal; ++i) { + postRv.realizer().realization(auxVec); + if (auxVec[0] < 40.) numPosSmallerThan40++; + } + if (env.subDisplayFile()) { + *env.subDisplayFile() << "numPosSmallerThan40 = " << numPosSmallerThan40 + << ", ratio = " << ((double) numPosSmallerThan40)/((double) numPosTotal) + << std::endl; + } + + QUESO::ScalarSequence seq1 (env,numPosSmallerThan40,""); + QUESO::ScalarSequence seq2 (env,numPosTotal - numPosSmallerThan40,""); + QUESO::ScalarSequence seqAll(env,numPosTotal,""); + unsigned int i1 = 0; + unsigned int i2 = 0; + for (unsigned int i = 0; i < numPosTotal; ++i) { + postRv.realizer().realization(auxVec); + if (auxVec[0] < 40.) seq1[i1++] = auxVec[0]; + else seq2[i2++] = auxVec[0]; + seqAll[i] = auxVec[0]; + } + + double mean1 = seq1.subMeanExtra(0,seq1.subSequenceSize()); + if (env.subDisplayFile()) { + *env.subDisplayFile() << "seq1.size() = " << seq1.subSequenceSize() + << "\n seq1.mean() = " << mean1 + << "\n seq1.std() = " << sqrt(seq1.subSampleVarianceExtra(0,seq1.subSequenceSize(),mean1)) + << std::endl; + } + + double mean2 = seq2.subMeanExtra(0,seq2.subSequenceSize()); + if (env.subDisplayFile()) { + *env.subDisplayFile() << "seq2.size() = " << seq2.subSequenceSize() + << "\n seq2.mean() = " << mean2 + << "\n seq2.std() = " << sqrt(seq2.subSampleVarianceExtra(0,seq2.subSequenceSize(),mean2)) + << std::endl; + } + + double meanAll = seqAll.subMeanExtra(0,seqAll.subSequenceSize()); + if (env.subDisplayFile()) { + *env.subDisplayFile() << "seqAll.size() = " << seqAll.subSequenceSize() + << "\n seqAll.mean() = " << meanAll + << "\n seqAll.std() = " << sqrt(seqAll.subSampleVarianceExtra(0,seqAll.subSequenceSize(),meanAll)) + << std::endl; + } + + //------------------------------------------------------ + // Test if likelihood is normalized + //------------------------------------------------------ + unsigned int numGridPoints = 1000001; + double xMin = paramDomain.minValues()[0]; + double xMax = paramDomain.maxValues()[0]; + double intervalSize = (xMax-xMin)/((double) numGridPoints - 1); + double integral = 0.; + for (unsigned int i = 0; i < numGridPoints; ++i) { + auxVec[0] = xMin + i*intervalSize; + integral += likelihoodFunctionObj.actualValue(auxVec,NULL,NULL,NULL,NULL); + } + integral *= intervalSize; + if (env.subDisplayFile()) { + *env.subDisplayFile() << "integral = " << integral + << std::endl; + } + + // Return + delete covMatrix; + + return; +} diff --git a/examples/bimodal/src/bimodal_compute.h b/examples/bimodal/src/bimodal_compute.h new file mode 100644 index 000000000..fcd66a87b --- /dev/null +++ b/examples/bimodal/src/bimodal_compute.h @@ -0,0 +1,32 @@ +//-----------------------------------------------------------------------bl- +//-------------------------------------------------------------------------- +// +// QUESO - a library to support the Quantification of Uncertainty +// for Estimation, Simulation and Optimization +// +// Copyright (C) 2008,2009,2010,2011,2012,2013 The PECOS Development Team +// +// This library is free software; you can redistribute it and/or +// modify it under the terms of the Version 2.1 GNU Lesser General +// Public License as published by the Free Software Foundation. +// +// This library is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// Lesser General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License along with this library; if not, write to the Free Software +// Foundation, Inc. 51 Franklin Street, Fifth Floor, +// Boston, MA 02110-1301 USA +// +//-----------------------------------------------------------------------el- + +#ifndef EX_COMPUTE_H +#define EX_COMPUTE_H + +#include + +void compute(const QUESO::FullEnvironment& env); + +#endif diff --git a/examples/bimodal/src/bimodal_likelihood.C b/examples/bimodal/src/bimodal_likelihood.C new file mode 100644 index 000000000..234d4fc2f --- /dev/null +++ b/examples/bimodal/src/bimodal_likelihood.C @@ -0,0 +1,92 @@ +//-----------------------------------------------------------------------bl- +//-------------------------------------------------------------------------- +// +// QUESO - a library to support the Quantification of Uncertainty +// for Estimation, Simulation and Optimization +// +// Copyright (C) 2008,2009,2010,2011,2012,2013 The PECOS Development Team +// +// This library is free software; you can redistribute it and/or +// modify it under the terms of the Version 2.1 GNU Lesser General +// Public License as published by the Free Software Foundation. +// +// This library is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// Lesser General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License along with this library; if not, write to the Free Software +// Foundation, Inc. 51 Franklin Street, Fifth Floor, +// Boston, MA 02110-1301 USA +// +//-----------------------------------------------------------------------el- + +#include +#include + +static unsigned int likelihoodCounter = 0; + +double likelihoodRoutine( + const QUESO::GslVector& paramValues, + const QUESO::GslVector* paramDirection, + const void* functionDataPtr, + QUESO::GslVector* gradVector, + QUESO::GslMatrix* hessianMatrix, + QUESO::GslVector* hessianEffect) +{ + likelihoodCounter++; + + if (paramDirection || + functionDataPtr || + gradVector || + hessianMatrix || + hessianEffect) {}; // just to remove compiler warning + + double returnValue = 0.; + double x = paramValues[0]; + double mean1 = 10.; + double sigma1 = 1.; + double y1 = (x-mean1)*(x-mean1)/(2.*sigma1*sigma1); + double z1 = (1./sigma1/sqrt(2*M_PI))*exp(-y1); + + double mean2 = 100.; + double sigma2 = 5.; + double y2 = (x-mean2)*(x-mean2)/(2.*sigma2*sigma2); + double z2 = (1./sigma2/sqrt(2*M_PI))*exp(-y2); + + double resultValue = -2*log((z1+2.*z2)/3.); + + if (resultValue == INFINITY) { + //std::cerr << "WARNING In likelihoodRoutine" + // << ", fullRank " << paramValues.env().fullRank() + // << ", subEnvironment " << paramValues.env().subId() + // << ", subRank " << paramValues.env().subRank() + // << ", inter0Rank " << paramValues.env().inter0Rank() + // << ": x = " << x + // << ", z1 = " << z1 + // << ", z2 = " << z2 + // << ", resultValue = " << resultValue + // << std::endl; + resultValue = 1040.; + } + +#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN + returnValue = -.5*resultValue; +#else + returnValue = resultValue; +#endif + + + if (paramValues.env().exceptionalCircumstance()) { + if ((paramValues.env().subDisplayFile() ) && + (paramValues.env().displayVerbosity() >= 0)) { // detailed output debug + *paramValues.env().subDisplayFile() << "Leaving likelihood function" + << ": paramValues = " << paramValues + << ", returnValue = " << returnValue + << std::endl; + } + } + + return returnValue; +} diff --git a/examples/bimodal/src/bimodal_likelihood.h b/examples/bimodal/src/bimodal_likelihood.h new file mode 100644 index 000000000..4bd14f483 --- /dev/null +++ b/examples/bimodal/src/bimodal_likelihood.h @@ -0,0 +1,45 @@ +//-----------------------------------------------------------------------bl- +//-------------------------------------------------------------------------- +// +// QUESO - a library to support the Quantification of Uncertainty +// for Estimation, Simulation and Optimization +// +// Copyright (C) 2008,2009,2010,2011,2012,2013 The PECOS Development Team +// +// This library is free software; you can redistribute it and/or +// modify it under the terms of the Version 2.1 GNU Lesser General +// Public License as published by the Free Software Foundation. +// +// This library is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// Lesser General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License along with this library; if not, write to the Free Software +// Foundation, Inc. 51 Franklin Street, Fifth Floor, +// Boston, MA 02110-1301 USA +// +//-----------------------------------------------------------------------el- + +#ifndef EX_LIKELIHOOD_H +#define EX_LIKELIHOOD_H + +#include + +struct +likelihoodRoutine_DataType +{ + const QUESO::GslVector* meanVector; + const QUESO::GslMatrix* covMatrix; +}; + +double likelihoodRoutine( + const QUESO::GslVector& paramValues, + const QUESO::GslVector* paramDirection, + const void* functionDataPtr, + QUESO::GslVector* gradVector, + QUESO::GslMatrix* hessianMatrix, + QUESO::GslVector* hessianEffect); + +#endif diff --git a/examples/bimodal/src/bimodal_main.C b/examples/bimodal/src/bimodal_main.C new file mode 100644 index 000000000..042be6817 --- /dev/null +++ b/examples/bimodal/src/bimodal_main.C @@ -0,0 +1,40 @@ +//-----------------------------------------------------------------------bl- +//-------------------------------------------------------------------------- +// +// QUESO - a library to support the Quantification of Uncertainty +// for Estimation, Simulation and Optimization +// +// Copyright (C) 2008,2009,2010,2011,2012,2013 The PECOS Development Team +// +// This library is free software; you can redistribute it and/or +// modify it under the terms of the Version 2.1 GNU Lesser General +// Public License as published by the Free Software Foundation. +// +// This library is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// Lesser General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License along with this library; if not, write to the Free Software +// Foundation, Inc. 51 Franklin Street, Fifth Floor, +// Boston, MA 02110-1301 USA +// +//-----------------------------------------------------------------------el- + +#include + +int main(int argc, char* argv[]) +{ + // Initialize environment + MPI_Init(&argc,&argv); + QUESO::FullEnvironment* env = new QUESO::FullEnvironment(MPI_COMM_WORLD,argv[1],"",NULL); + + // Compute + compute(*env); + + // Finalize environment + delete env; + MPI_Finalize(); + return 0; +} diff --git a/examples/bimodal/tests/test_2013_12_02/bimodal_1chain.inp b/examples/bimodal/tests/test_2013_12_02/bimodal_1chain.inp new file mode 100644 index 000000000..36aa3e846 --- /dev/null +++ b/examples/bimodal/tests/test_2013_12_02/bimodal_1chain.inp @@ -0,0 +1,76 @@ +############################################### +# UQ Environment +############################################### +env_numSubEnvironments = 1 +env_subDisplayFileName = outputData/display +env_subDisplayAllowAll = 0 +env_subDisplayAllowedSet = 0 1 +env_displayVerbosity = 2 +env_syncVerbosity = 0 +env_seed = -1 + +############################################### +# Statistical inverse problem (ip) +############################################### +ip_computeSolution = 1 +ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Multilevel algorithm +############################################### +ip_ml_dataOutputFileName = outputData/sipOutput_ml +ip_ml_dataOutputAllowedSet = 0 1 + +############################################### +# All levels, unless otherwise specified +############################################### +#ip_ml_default_loadBalance = 0 +ip_ml_default_rawChain_size = 10000 +ip_ml_default_rawChain_dataOutputFileName = outputData/rawChain_ml #data from all levels will be written +ip_ml_default_minEffectiveSizeRatio = 0.49 +ip_ml_default_maxEffectiveSizeRatio = 0.51 +ip_ml_default_minRejectionRate = 0.24 +ip_ml_default_maxRejectionRate = 0.4 +ip_ml_default_putOutOfBoundsInChain = 0 + +############################################### +# Level 3 +############################################### +#ip_ml_3_dr_maxNumExtraStages = 1 +#ip_ml_3_dr_listOfScalesForExtraStages = 3.333 + +############################################### +# Last level (level 4 for 'bimodal_gsl' executable) +############################################### +ip_ml_last_dataOutputFileName = outputData/sipOutput_ml +ip_ml_last_dataOutputAllowedSet = 0 1 + +#ip_ml_last_loadBalance = 0 +ip_ml_last_rawChain_size = 10000 +ip_ml_last_rawChain_dataOutputFileName = outputData/rawChain_ml +ip_ml_last_rawChain_dataOutputAllowedSet = 0 +ip_ml_last_rawChain_computeStats = 1 +ip_ml_last_rawChain_stats_kde_compute = 1 +ip_ml_last_rawChain_stats_kde_numEvalPositions = 200 +ip_ml_last_rawChain_stats_covMatrix_compute = 1 +ip_ml_last_rawChain_stats_corrMatrix_compute = 1 + +ip_ml_last_filteredChain_generate = 1 +ip_ml_last_filteredChain_lag = 2 +ip_ml_last_filteredChain_dataOutputFileName = outputData/filtChain_ml +ip_ml_last_filteredChain_dataOutputAllowedSet = 0 +ip_ml_last_filteredChain_computeStats = 1 +ip_ml_last_filteredChain_stats_kde_compute = 1 +ip_ml_last_filteredChain_stats_kde_numEvalPositions = 200 +ip_ml_last_filteredChain_stats_covMatrix_compute = 1 +ip_ml_last_filteredChain_stats_corrMatrix_compute = 1 + +ip_ml_last_dr_maxNumExtraStages = 1 +ip_ml_last_dr_listOfScalesForExtraStages = 5. + +ip_ml_last_minEffectiveSizeRatio = 0.49 +ip_ml_last_maxEffectiveSizeRatio = 0.51 +ip_ml_last_minRejectionRate = 0.24 +ip_ml_last_maxRejectionRate = 0.4 +ip_ml_last_putOutOfBoundsInChain = 0 From 9969bb5cc64d84cbdda13af8d3f7dd76b8bfbf00 Mon Sep 17 00:00:00 2001 From: "Kemelli C. Estacio-Hiroms" Date: Tue, 3 Dec 2013 16:56:37 -0800 Subject: [PATCH 04/20] =?UTF-8?q?[queso/manual]:=20Including=20a=20new=20s?= =?UTF-8?q?ection=20'bimodal'=20at=20the=20user=C5=9B=20manual,=20which=20?= =?UTF-8?q?presents=20an=20example=20of=20the=20Multilevel=20algorithm=20b?= =?UTF-8?q?eing=20used=20to=20sample=20from=20a=20bimodal=20distribution.?= =?UTF-8?q?=20This=20example=20replicates=20the=201D=20problem=20(section?= =?UTF-8?q?=204.1)=20of=20the=20Multilevel=20paper=20(Prudencio&Cheung=202?= =?UTF-8?q?012).?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- manual/users/Makefile_old | 69 ------- manual/users/McCheese.tex | 56 ------ .../bimodal_autocorrelation_rawchain.png | Bin 0 -> 31722 bytes manual/users/rawfigs/bimodal_cdf_rawchain.png | Bin 0 -> 27910 bytes manual/users/rawfigs/bimodal_kde_rawchain.png | Bin 0 -> 26128 bytes manual/users/rawfigs/bimodal_likelihood.png | Bin 0 -> 21598 bytes .../users/rawfigs/bimodal_likelihood_taus.png | Bin 0 -> 35160 bytes .../bimodal_likelihood_taus_normalized.png | Bin 0 -> 26707 bytes manual/users/users_2_installation.tex | 28 +-- manual/users/users_5_examples.tex | 2 + manual/users/users_5a_example_sip.tex | 6 +- manual/users/users_5b_example_sfp.tex | 6 +- manual/users/users_5c_example_gravity.tex | 8 +- manual/users/users_5d_example_tga.tex | 2 +- manual/users/users_5f_example_ml_bimodal.tex | 185 ++++++++++++++++++ 15 files changed, 212 insertions(+), 150 deletions(-) delete mode 100644 manual/users/Makefile_old delete mode 100644 manual/users/McCheese.tex create mode 100644 manual/users/rawfigs/bimodal_autocorrelation_rawchain.png create mode 100644 manual/users/rawfigs/bimodal_cdf_rawchain.png create mode 100644 manual/users/rawfigs/bimodal_kde_rawchain.png create mode 100644 manual/users/rawfigs/bimodal_likelihood.png create mode 100644 manual/users/rawfigs/bimodal_likelihood_taus.png create mode 100644 manual/users/rawfigs/bimodal_likelihood_taus_normalized.png create mode 100644 manual/users/users_5f_example_ml_bimodal.tex diff --git a/manual/users/Makefile_old b/manual/users/Makefile_old deleted file mode 100644 index bcaddeca0..000000000 --- a/manual/users/Makefile_old +++ /dev/null @@ -1,69 +0,0 @@ -all: all_texs - -cheese: tab_mac_cheese - -clean: clean_texs clean_exec - -clean_texs: - rm -f *.aux - rm -f *.log - rm -f *.dvi - rm -f *.toc - rm -f *.blg - rm -f *.bbl - rm -f users*.ps - rm -f *.pdf - -clean_exec: - rm -f *~ - -OBJS_convert = figs/uqEnvironment.eps \ - figs/uqEnvironmentOptions.eps \ - figs/uqVectorSet.eps \ - figs/uqVector.eps \ - figs/uqMatrix.eps \ - figs/uqScalarFunction.eps \ - figs/uqVectorFunction.eps \ - figs/uqScalarSequence.eps \ - figs/uqVectorSequence.eps \ - figs/uqSip.eps \ - figs/uqSipOptions.eps \ - figs/uqSfp.eps \ - figs/uqSfpOptions.eps \ - figs/uqMetropolisHastingsSG.eps \ - figs/uqMetropolisHastingsSGOptions.eps \ - figs/uqMonteCarloSG.eps \ - figs/uqMonteCarloSGOptions.eps \ - figs/uqSequenceStatisticalOptions.eps \ - figs/uqVectorRandomVariable.eps \ - figs/paper_plot1.eps \ - figs/paper_plot2.eps \ - figs/paper_plot3.eps \ - figs/paper_plot4.eps \ - figs/paper_plot5.eps \ - figs/queso_paper1_03.eps \ - figs/queso_paper1_09.eps \ - figs/queso_paper1_10.eps - -all_texs: $(OBJS_convert) - latex users.tex - latex users.tex - bibtex users - latex users.tex - latex users.tex - dvips -t letter users.dvi -o users.ps - ps2pdf users.ps users_0_41_0.pdf - -tab_mac_cheese: - latex McCheese - latex McCheese - dvips -t letter McCheese.dvi -o McCheese.ps - ps2pdf McCheese.ps McCheese.pdf - -.SUFFIXES: .eps .png .dia - -%.eps: %.png - convert $< $(basename $<).eps - -%.eps: %.dia - dia -t eps $< diff --git a/manual/users/McCheese.tex b/manual/users/McCheese.tex deleted file mode 100644 index f5a8d9350..000000000 --- a/manual/users/McCheese.tex +++ /dev/null @@ -1,56 +0,0 @@ -\documentclass[12pt,letterpaper,twoside,onecolumn,portrait,leqno]{book} -\bibliographystyle{plain} -\usepackage{amssymb,amsfonts,amsmath,amscd,amsthm} -%\usepackage[draft]{graphicx} -\usepackage{graphicx} -\usepackage[dvips]{epsfig} -\usepackage{rotating} -\usepackage{color} - -\setlength{\textwidth}{6.6in} -\setlength{\topmargin}{-0.25in} -\setlength{\textheight}{8.60in} -\setlength{\oddsidemargin}{0.2in} -\setlength{\evensidemargin}{-0.3in} - -\begin{document} - -\setlength{\unitlength}{1.0in} -\setlength{\parindent}{0cm} -\setlength{\parskip}{2ex} -\thispagestyle{empty} -\pagenumbering{roman} -\numberwithin{equation}{section} -\numberwithin{figure}{section} -\numberwithin{table}{section} - -\centerline{\bf Users of QUESO on a Mac (McCheese)} - -$~$\\ - -\begin{table}[!h] -\begin{center} -\begin{tabular}{|l|c|c|c|c|c|c|c|c|} -\hline -\multicolumn{1}{|c|}{User} & Ok? & O.S. & gcc & mpicc & boost & gsl & hpct & queso \\ -\hline -\hline -Paul Bauman & Yes & X 10.5.8 & 4.4.1 & 1.3.3 & 1.42.0 & 0.26.0 & trunk & 0.42.0 \\ - & & & self & openmpi & & & & \\ - & & & compiled & & & & & \\ -\hline -Kenji Miki & No & X 10.6.2 & 4.3.4 & 4.3.4 & 1.42.0 & 1.13 & 0.26.0 & 0.42.0 \\ -\hline -Todd Oliver & Yes & X 10.5.8 & 4.3.4 & 4.3.4 & 1.40.0 & 1.13 & trunk & 0.42.0 \\ -\hline -Ernesto Prudencio & Yes & X 10.5.8 & 4.3.4 & 4.0.1 & 1.42.0 & 1.13 & 0.27 & 0.42.0 \\ -\hline -Karl Schulz & ? & & & & & & & \\ -\hline -Rochan Upadhyay & No & X 10.6.2 & 4.3.4 & 4.3.4 & 1.42.0 & 1.13 & 0.26.0 & 0.42.0 \\ -\hline -\end{tabular} -\end{center} -\end{table} - -\end{document} diff --git a/manual/users/rawfigs/bimodal_autocorrelation_rawchain.png b/manual/users/rawfigs/bimodal_autocorrelation_rawchain.png new file mode 100644 index 0000000000000000000000000000000000000000..545e80b03ed766eaea638c6d8cff565d2a99ddb2 GIT binary patch literal 31722 zcmb4rc|6oz__mNG*+r4HLWL0`yDX74Qj{$UStt8W9wf<9_G}4B%90rCOd(<{*_V-N z8he(pFN1l{4E6N<-rpbZ`$wNX)r{}?o^#*Vecji6ouaPkYtkR#JVHiBMt@aH-H42g 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The code bellow presents a sample output: \begin{lstlisting}[{basicstyle=\footnotesize\ttfamily}] -kemelli@margarida:~/LIBRARIES/QUESO-0.47.1/bin$ ./queso_version +kemelli@margarida:~/LIBRARIES/QUESO-0.50.0/bin$ ./queso_version --------------------------------------------------------------- QUESO Library: Version = 0.47.1 (47.1) @@ -381,13 +381,13 @@ \section{The Installed Directory Structure} \label{sc-installed-dir-structure} GLPK DIR = HDF5 DIR = --------------------------------------------------------------- -kemelli@margarida:~/LIBRARIES/QUESO-0.47.1/bin$ +kemelli@margarida:~/LIBRARIES/QUESO-0.50.0/bin$ \end{lstlisting} - \item \verb+lib+: contains the static and dynamic versions of the library. The full to path to this directory, e.g., \verb+$HOME/LIBRARIES/QUESO-0.47.1/lib+ should be added to the user's \verb+LD_LIBRARY_PATH+ environmental variable in order to use QUESO library with his/her application code: + \item \verb+lib+: contains the static and dynamic versions of the library. The full to path to this directory, e.g., \verb+$HOME/LIBRARIES/QUESO-0.50.0/lib+ should be added to the user's \verb+LD_LIBRARY_PATH+ environmental variable in order to use QUESO library with his/her application code: \begin{lstlisting}[{basicstyle=\footnotesize\ttfamily}] $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ - $HOME/LIBRARIES/QUESO-0.47.1/lib + $HOME/LIBRARIES/QUESO-0.50.0/lib \end{lstlisting} @@ -410,13 +410,13 @@ \section{Create your Application with the Installed QUESO} \label{sc-use-queso} your \verb+.bashrc+ (supposing you have a BASH-shell): \begin{lstlisting}[{basicstyle=\footnotesize\ttfamily}] $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ - $HOME/LIBRARIES/QUESO-0.47.1/lib + $HOME/LIBRARIES/QUESO-0.50.0/lib \end{lstlisting} % or into your \verb+.cshrc+ file (supposing you have a C-shell): % \begin{lstlisting}[label={},caption={}] % setenv LD_LIBRARY_PATH \$LD_LIBRARY_PATH: -% $HOME/LIBRARIES/QUESO-0.47.1/lib +% $HOME/LIBRARIES/QUESO-0.50.0/lib % \end{lstlisting} @@ -426,7 +426,7 @@ \section{Create your Application with the Installed QUESO} \label{sc-use-queso} library through a Makefile such as the one displayed as follows: \begin{lstlisting}[basicstyle={\footnotesize\ttfamily},deletekeywords={export,rm}] -QUESO_DIR = $HOME/LIBRARIES/QUESO-0.47.1/ +QUESO_DIR = $HOME/LIBRARIES/QUESO-0.50.0/ BOOST_DIR = $HOME/LIBRARIES/boost-1.53.0/ GSL_DIR = $HOME/LIBRARIES/gsl-1.15/ GRVY_DIR = $HOME/LIBRARIES/grvy-0.32.0 diff --git a/manual/users/users_5_examples.tex b/manual/users/users_5_examples.tex index 120a39d9c..4b09aec72 100644 --- a/manual/users/users_5_examples.tex +++ b/manual/users/users_5_examples.tex @@ -27,3 +27,5 @@ \chapter{QUESO Examples}\label{chap:Queso-examples} \input{users_5d_example_tga} \input{users_5e_example_ml_modal} + +\input{users_5f_example_ml_bimodal} \ No newline at end of file diff --git a/manual/users/users_5a_example_sip.tex b/manual/users/users_5a_example_sip.tex index 1deaad747..a5d495862 100644 --- a/manual/users/users_5a_example_sip.tex +++ b/manual/users/users_5a_example_sip.tex @@ -98,7 +98,7 @@ \subsection{Running the Example}\label{sec:sip-run} To run the executable provided (available after QUESO installation), enter the following commands: \begin{lstlisting}[label={},caption={}] -$ cd $HOME/LIBRARIES/QUESO-0.47.1/ +$ cd $HOME/LIBRARIES/QUESO-0.50.0/ $ cd examples/simpleStatisticalInverseProblem $ rm outputData/* $ ./exSimpleStatisticalInverseProblem_gsl example.inp @@ -178,12 +178,12 @@ \subsection{Create your own Makefile}\label{sec:sip-makefile} Thus, to compile, build and execute the code, the user just needs to run the following commands in the same directory where the files are: \begin{lstlisting} -$ cd $HOME/LIBRARIES/QUESO-0.47.1/examples/simpleStatisticalInverseProblem/ +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/simpleStatisticalInverseProblem/ $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ $HOME/LIBRARIES/gsl-1.15/lib/:\ $HOME/LIBRARIES/boost-1.53.0/lib/:\ $HOME/LIBRARIES/hdf5-1.8.10/lib:\ - $HOME/LIBRARIES/QUESO-0.47.1/lib + $HOME/LIBRARIES/QUESO-0.50.0/lib $ make -f Makefile_example_margarida $ ./simple_sip_example example.inp \end{lstlisting} diff --git a/manual/users/users_5b_example_sfp.tex b/manual/users/users_5b_example_sfp.tex index 85c817946..382cd42de 100644 --- a/manual/users/users_5b_example_sfp.tex +++ b/manual/users/users_5b_example_sfp.tex @@ -50,7 +50,7 @@ \subsection{Running the Example}\label{sec:sfp-run} To run the executable provided (available after QUESO installation), enter the following commands: \begin{lstlisting}[label={},caption={}] -$ cd $HOME/LIBRARIES/QUESO-0.47.1/ +$ cd $HOME/LIBRARIES/QUESO-0.50.0/ $ cd examples/simpleStatisticalForwardProblem $ rm outputData/* $ ./exSimpleStatisticalForwardProblem_gsl example.inp @@ -117,12 +117,12 @@ \subsection{Create your own Makefile}\label{sec:sfp-makefile} Thus, to compile, build and execute the code, the user just needs to run the following commands in the same directory where the files are: \begin{lstlisting} -$ cd HOME/LIBRARIES/QUESO-0.47.1/examples/simpleStatisticalForwardProblem +$ cd HOME/LIBRARIES/QUESO-0.50.0/examples/simpleStatisticalForwardProblem $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ $HOME/LIBRARIES/gsl-1.15/lib/:\ $HOME/LIBRARIES/boost-1.53.0/lib/:\ $HOME/LIBRARIES/hdf5-1.8.10/lib:\ - $HOME/LIBRARIES/QUESO-0.47.1/lib + $HOME/LIBRARIES/QUESO-0.50.0/lib $ make -f Makefile_sfp_example_margarida $ ./simple_sfp_example simple_sfp_example.inp \end{lstlisting} diff --git a/manual/users/users_5c_example_gravity.tex b/manual/users/users_5c_example_gravity.tex index a36e8838c..18eeabdb0 100644 --- a/manual/users/users_5c_example_gravity.tex +++ b/manual/users/users_5c_example_gravity.tex @@ -198,14 +198,14 @@ \subsection{Running the Example}\label{sec:gravity-run} To run the executable provided (available after QUESO installation), enter the following commands: \begin{lstlisting}[label={},caption={}] -$ cd $HOME/LIBRARIES/QUESO-0.47.1/examples/gravity +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/gravity $ rm outputData/* $ ./gravity_gsl gravity_inv_fwd.inp \end{lstlisting} The console output of the program is: \begin{lstlisting}[caption={Console output of program \texttt{gravity\_gsl}}, label={code:console_output},language={bash}] -kemelli@violeta:~/LIBRARIES/QUESO-0.47.1/examples/gravity$ ./gravity_gsl gravity_inv_fwd.inp +kemelli@violeta:~/LIBRARIES/QUESO-0.50.0/examples/gravity$ ./gravity_gsl gravity_inv_fwd.inp --------------------------------------------------------------------- QUESO Library: Version = 0.47.1 (47.1) @@ -251,7 +251,7 @@ \subsection{Running the Example}\label{sec:gravity-run} Ending run at Mon Apr 29 17:27:33 2013 Total run time = 1 seconds -kemelli@violeta:~/LIBRARIES/QUESO-0.47.1/examples/gravity$ +kemelli@violeta:~/LIBRARIES/QUESO-0.50.0/examples/gravity$ \end{lstlisting} @@ -334,7 +334,7 @@ \subsection{Running the Gravity Example with Several Processors} \begin{lstlisting}[caption={}, label={},language={bash}] -cd $HOME/LIBRARIES/QUESO-0.47.1/examples/gravity/ +cd $HOME/LIBRARIES/QUESO-0.50.0/examples/gravity/ mpirun -np 8 ./gravity_gsl gravity_inv_fwd.inp \end{lstlisting} diff --git a/manual/users/users_5d_example_tga.tex b/manual/users/users_5d_example_tga.tex index 6ffd33952..a6b5344bd 100644 --- a/manual/users/users_5d_example_tga.tex +++ b/manual/users/users_5d_example_tga.tex @@ -232,7 +232,7 @@ \subsection{Running the Example}\label{sec:tga-run} To run the executable provided (available after QUESO installation), and generate figures for the chains, PDFs, CDFs, etc., enter the following commands: \begin{lstlisting}[label={},caption={}] -$ cd $HOME/LIBRARIES/QUESO-0.47.1/examples/validationCycle +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/validationCycle $ rm outputData/* $ ./exTgaValidationCycle_gsl tagCycle.inp $ matlab diff --git a/manual/users/users_5f_example_ml_bimodal.tex b/manual/users/users_5f_example_ml_bimodal.tex new file mode 100644 index 000000000..eb779732c --- /dev/null +++ b/manual/users/users_5f_example_ml_bimodal.tex @@ -0,0 +1,185 @@ +\section{\texttt{bimodal}}\label{sec:bimodal_bimodal} + + +This example replicates the problem in ``Section 4.1 A 1D Problem'' of~\cite{CheungPrudencio2012}: it presents how to use QUESO and the multivevel method for sampling from a posterior PDF composed of the sum of two Gaussian distributions. + +Let's define $D=[-250,250]$ and the three distributions $\pi_\prior: D \rightarrow \mathbb{R}_+ $, $f_1: \mathbb{R} \rightarrow \mathbb{R}_+ $ and $f_2: \mathbb{R} \rightarrow \mathbb{R}_+$ by: +\begin{equation} +\begin{split}\label{eq:bimodal_functions} +\pi_\prior &= \dfrac{1}{|D|} = \dfrac{1}{500}, \quad \forall \, \theta \in D \\ +f_1(\theta) &= \dfrac{1}{(2\pi)^{1/2} \sqrt{|V_1|}} \exp \left(-\dfrac{1}{2}(\theta - \mu_1)^T \, V_1^{-1} \, (\theta - \mu_1) \right), \quad \forall \, \theta \in \mathbb{R} \\ +f_2(\theta) &= \dfrac{1}{(2\pi)^{1/2} \sqrt{|V_2|}} \exp \left(-\dfrac{1}{2}(\theta - \mu_2)^T \, V_2^{-1} \, (\theta - \mu_2) \right), \quad \forall \, \theta \in \mathbb{R}, +\end{split} +\end{equation} +where +\begin{equation*} +\mu_1 = 10, \quad V_1 = 1^2, \quad \mu_2 = 100, \quad V_2= 5^2. +\end{equation*} + +In this example, we want to sample the posterior PDF given by: +\begin{equation} +\pi_\post(\theta) \propto \left[\dfrac{1}{2} f_1(\theta) + \dfrac{1}{2} f_2(\theta) \right] \cdot \pi_\prior = f(\theta) \cdot \pi_\prior +\end{equation} +where $f(\theta)= \dfrac{1}{2} f_1(\theta) + \dfrac{1}{2} f_2(\theta)$ is the likelihood function, which is depicted in Figure \ref{fig:bimodal:likelihood}. + +\begin{figure}[htpb] +\centering +\includegraphics[scale=0.3]{figs/bimodal_likelihood.png} +\vspace{-10pt} +\caption{Likelihood function given by $f=f_1/2+ f_2/2$, where $f_1$ and $f_2$ are defined in Equation \eqref{eq:bimodal_functions}.} +\label{fig:bimodal:likelihood} +\end{figure} + + +\subsection{Running the Example}\label{sec:bimodal-run} + +To run the executable provided (available after QUESO installation), and generate figures for the chains, PDFs, CDFs, etc., enter the following commands: +\begin{lstlisting}[label={},caption={}] +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/bimodal +$ rm outputData/* +$ ./bimodal_gsl bimodal_1chain.inp +$ matlab + $ plot_all.m # inside matlab + $ plot_likelihood_normalized_taus.m # inside matlab + $ plot_likelihood_unnormalized_taus.m # inside matlab + $ exit # inside matlab +$ ls -l outputData/*.png +bimodal_autocorrelation_rawchain.png bimodal_likelihood.png +bimodal_cdf_rawchain.png bimodal_likelihood_taus_normalized.png +bimodal_kde_rawchain.png bimodal_likelihood_taus.png +\end{lstlisting} + + +As a result, the user should have created several of PNG figures containing marginal posterior PDF, cumulative density distribution and autocorrelation. The name of the figure files have been chosen to be informative, as shown in the Listing above. + + + + +\subsection{Example Code}\label{sec:bimodal-code} + +The source code for the example is composed of 5 files: +\texttt{bimodal\_main.C} (Listing \ref{code:bimodal-main-c}), \linebreak +\texttt{bimodal\_likelihood.h} and \texttt{bimodal\_likelihood.C} (Listings \ref{fig-like-bimodal-h} and \ref{fig-like-bimodal-c}), +\texttt{bimodal\_compute.h} and \texttt{bimodal\_compute.C} (Listings \ref{code:bimodal-compute-h} and \ref{code:bimodal-compute-c}). + + +\lstinputlisting[caption=File \texttt{bimodal\_main.C.}, label={code:bimodal-main-c}, linerange={25-1000}]{../../examples/bimodal/src/bimodal_main.C} + +\lstinputlisting[caption=File \texttt{bimodal\_likelihood.h}., label={fig-like-bimodal-h}, linerange={25-1000}]{../../examples/bimodal/src/bimodal_likelihood.h} + +\lstinputlisting[caption=File \texttt{bimodal\_likelihood.C}., label={fig-like-bimodal-c}, linerange={25-1000}]{../../examples/bimodal/src/bimodal_likelihood.C} + +\lstinputlisting[caption=File \texttt{bimodal\_compute.h.}, label={code:bimodal-compute-h}, linerange={25-1000}]{../../examples/bimodal/src/bimodal_compute.h} + +Note that in line 57 of Listings \ref{code:bimodal-compute-c} the `\verb+#if 0+' directive tells the compiler that the application will not use DRAM algorithm, but rather the Multilevel solver (line 65). Naturally, the user may chose to use the DRAM algorithm by changing the directive in line 57 to `\verb+#if 1+'. + +\lstinputlisting[caption={File \texttt{bimodal\_compute.C}.}, label={code:bimodal-compute-c}, linerange={25-1000},numbers=left]{../../examples/bimodal/src/bimodal_compute.C} + + + + +\subsection{Input File}\label{sec:bimodal-input-file} + + +QUESO reads an input file for solving statistical problems, which provides options for the Multilevel or MCMC method. In this example, the Multilevel method is chosen to sample from the distribution. Many variables are common to both MCMC and Multilevel method, especially because the Multilevel method also has the option of delaying the rejection of a candidate. The names of the variables have been designed to be informative in this case as well: +\begin{description}\vspace{-8pt} +\item[ \texttt{env}:] refers to QUESO environment; \vspace{-8pt} +\item[ \texttt{ip}:] refers to inverse problem;\vspace{-8pt} +\item[ \texttt{ml}:] refers to Multilevel;\vspace{-8pt} +\item[ \texttt{dr}:] refers to delayed rejection;\vspace{-8pt} +\item[ \texttt{rawChain}:] refers to the raw, entire chain; \vspace{-8pt} +\item[ \texttt{filteredChain}:] refers to a filtered chain (related to a specified \texttt{lag});\vspace{-8pt} +\item[ \texttt{last}:] refers to instructions specific for the last level of the Multilevel algorithm. +\end{description} + +The user may select options for a specific level by naming its number, i.e., in case the user wants to define a different number of extra stages together with the scales for each stage (in the DRAM part of the ML algorithm) for the level 3, he/she may include the following instructions: +\begin{lstlisting} +ip_ml_3_dr_maxNumExtraStages = 1 +ip_ml_3_dr_listOfScalesForExtraStages = 3.333 +\end{lstlisting} +in the input file. + + +The options used for solving this example are displayed in Listing \ref{code:bimodal-input-file}. + +\lstinputlisting[caption={Options for QUESO library used in application code (Listings \ref{code:bimodal-main-c}-\ref{code:bimodal-compute-c}})., +label={code:bimodal-input-file},]{../../examples/bimodal/test/test_2013_12_02/bimodal_1chain.inp} + + + +\subsection{Create your own Makefile}\label{sec:bimodal-makefile} + +Similarly to the other examples presented in this user's manual and also available with QUESO distribution, a user-created makefile is available: `\texttt{Makefile\_bimodal\_violeta}'. When adapted to the user's settings, namely paths for QUESO required libraries, it may be used to compile the code and create the executable \verb+bimodal_gsl+. + +Thus, to compile, build and execute the code, the user just needs to run the following commands in the same directory where the files are: +\begin{lstlisting} +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/bimodal/ +$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ + $HOME/LIBRARIES/gsl-1.15/lib/:\ + $HOME/LIBRARIES/boost-1.53.0/lib/:\ + $HOME/LIBRARIES/hdf5-1.8.10/lib:\ + $HOME/LIBRARIES/QUESO-0.50.0/lib +$ make -f Makefile_bimodal_violeta +$ ./bimodal_gsl example.inp +\end{lstlisting} + +Again, the `\verb+export+' instruction above is only necessary if the user has not saved it in his/her \verb+.bashrc+ file. + + +\subsection{Data Post-Processing and Visualization}\label{sec:bimodal-results} + + + +According to the specifications of the input file in Listing~\ref{code:bimodal-input-file}, both a folder named \verb+outputData+ and a the following files should be generated: +\begin{verbatim} +rawChain_ml.m +display_sub0.txt +\end{verbatim} + + +The sequence of Matlab commands is identical to the ones presented in Sections +\ref{sec:sip-results}, \ref{sec:sfp-results}, \ref{sec:gravity-results} and \ref{sec:tga-results}; +therefore, are omitted here. The reader is invited to explore the Matlab files +\texttt{plot\_likelihood\_normalized\_taus.m}, +\texttt{plot\_likelihood\_unnormalized\_taus.m} and/or \texttt{plot\_all.m}, for details of how the figures have been generated. + + + +\subsubsection{KDE and CDF Plots} + +Figure \ref{fig:bimodal_kde} presents the KDE and CDF plots of the parameter $\theta$. + + + +\begin{figure}[hptb] +\centering +\subfloat[KDE]{\includegraphics[scale=0.3]{figs/bimodal_kde_rawchain.png}} +\subfloat[CDF]{\includegraphics[scale=0.3]{figs/bimodal_cdf_rawchain.png}} +\vspace{-8pt} +\caption{KDE and CDF plots of parameter $\theta$, for all fours levels.} +\label{fig:bimodal_kde} +\end{figure} + +\subsubsection{Autocorrelation Plots} + +Figure \ref{fig:bimodal_autocorr} presents the autocorrelation of the parameter $\theta$, in each one of the intermediate levels. + +\begin{figure}[htpb] +\centering +\includegraphics[scale=0.25]{figs/bimodal_autocorrelation_rawchain} +\vspace{-10pt} +\caption{Autocorrelation plots for $\theta$, all four levels.} +\label{fig:bimodal_autocorr} +\end{figure} + + + +\subsubsection{Intermediary Likelihood Plots} +\begin{figure}[htpb] +\centering +\subfloat[normalized]{\includegraphics[scale=0.3]{figs/bimodal_likelihood_taus_normalized.png}} +\subfloat[unnormalized]{\includegraphics[scale=0.3]{figs/bimodal_likelihood_taus.png}} +\vspace{-10pt} +\caption{Intermediary likelihood functions $f(\theta)^\tau$, where $\tau_i$ is the exponent computed at the $i$-th level of Multilevel algorithm. In this simple problem, only four levels are needed, i.e. $i=1\ldots 4$. The cyan-colored curve (exponent $\tau = 1$) is the same curve as in Figure \ref{fig:bimodal:likelihood}.} +\label{fig:bimodal:likelihood_taus} +\end{figure} From 52a36dc2f2deee6785365a8493a7b7181c7637b6 Mon Sep 17 00:00:00 2001 From: "Kemelli C. Estacio-Hiroms" Date: Wed, 4 Dec 2013 10:24:06 -0800 Subject: [PATCH 05/20] [queso/manual] correcting a path on users_5f_example_ml_bimodal.tex. --- manual/users/users_5f_example_ml_bimodal.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manual/users/users_5f_example_ml_bimodal.tex b/manual/users/users_5f_example_ml_bimodal.tex index eb779732c..dd2fa3d32 100644 --- a/manual/users/users_5f_example_ml_bimodal.tex +++ b/manual/users/users_5f_example_ml_bimodal.tex @@ -103,7 +103,7 @@ \subsection{Input File}\label{sec:bimodal-input-file} The options used for solving this example are displayed in Listing \ref{code:bimodal-input-file}. \lstinputlisting[caption={Options for QUESO library used in application code (Listings \ref{code:bimodal-main-c}-\ref{code:bimodal-compute-c}})., -label={code:bimodal-input-file},]{../../examples/bimodal/test/test_2013_12_02/bimodal_1chain.inp} +label={code:bimodal-input-file},]{../../examples/bimodal/tests/test_2013_12_02/bimodal_1chain.inp} From d07b04d6b5508033713a17ed39e539487e502cc8 Mon Sep 17 00:00:00 2001 From: "Kemelli C. Estacio-Hiroms" Date: Thu, 12 Dec 2013 16:17:07 -0800 Subject: [PATCH 06/20] [queso/manual] adding another example that uses the multilevel method: hysteretic. 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zIS@mK&4+#{oZvr)^Xi|KGWxOGh5U9(_Q$O=tEP8`{68y@HG6LwaSrbIUm}NgT28fI zzdd`hWoj1i7J*A2we5_rPT;fXY`c;2o{frs6fN?l@1Wl7Nv=U8~_ z_SX|lM_qokzrNNSX_X~ooed1-DS{L7cQ|p_7er6pV>`)IrrO5ao%eB!v(1K0z*b&iW4wbj4DS7nHBYnX6cq0 To1G>BgN(t`)z4*}Q$iB}jH=*K literal 0 HcmV?d00001 diff --git a/manual/users/users_1_introduction.tex b/manual/users/users_1_introduction.tex index 7e42a88cc..9ffd2bc0b 100644 --- a/manual/users/users_1_introduction.tex +++ b/manual/users/users_1_introduction.tex @@ -484,7 +484,7 @@ \section{Algorithms for solving Statistical Inverse Problems} versions of the same method have been implemented as improvements of its predecessors~\cite{BeckAu2002,ChingChen2007,CheungPrudencio2012}. QUESO hosts the novel Adaptive Multilevel Stochastic Simulation Algorithm -(Throughout)~\cite{CheungPrudencio2012}, which is described in Section +(AMSSA)~\cite{CheungPrudencio2012}, which is described in Section \ref{sec:ML:intro}. For details about the method, please refer to \cite{CheungPrudencio2012}. diff --git a/manual/users/users_5_examples.tex b/manual/users/users_5_examples.tex index 4b09aec72..aa3da1ff1 100644 --- a/manual/users/users_5_examples.tex +++ b/manual/users/users_5_examples.tex @@ -3,12 +3,14 @@ \chapter{QUESO Examples}\label{chap:Queso-examples} This chapter assumes that the user has successfully installed QUESO and its dependencies. % -It presents a variety of examples of how to use QUESO in order to develop applications that solves a statistical inverse problem (Section \ref{sec:example_sip}), a statistical forward problem (Section \ref{sec:example_sfp}) or a combination of both, where the solution of the former serves as input to the later (Sections \ref{sec:example_gravity} and \ref{sec:example_tga}). +It presents a variety of examples of how to use QUESO in order to develop applications. + +There are examples that solve a statistical inverse problem (Sections \ref{sec:example_sip}, \ref{sec:example_modal}, \ref{sec:example_bimodal} and \ref{sec:example_hysteretic}), a statistical forward problem (Section \ref{sec:example_sfp}) or a combination of both, where the solution of the former serves as input to the later (Sections \ref{sec:example_gravity} and \ref{sec:example_tga}). Three of the first four examples (Sections \ref{sec:example_sip}, \ref{sec:example_gravity} --\ref{sec:example_tga}) use the DRAM algorithm for solving the SIP, and the last three examples use the Multilevel algorithm. Each section presents: \begin{itemize} \item the mathematical models for the SIP and/or the SFP; \vspace*{-6pt} \item the application codes that translate the mathematical language into C++ using the QUESO classes and algorithms;\vspace*{-6pt} - \item the input file that contains a list of options for the Markov chain Monte Carlo algorithm (in case of SIPs) and the Monte Carlo algorithm (in case of SFPs) which will be used by QUESO classes and algorithms\footnote{At this moment, all the SIP examples are solved with MCMC (DRAM) algorithm; in a future version, examples using the Multilevel algorithm will be included.};\vspace*{-6pt} + \item the input file that contains a list of options for either the Markov chain Monte Carlo algorithm or the Multilevel algorithm (in case of SIPs) and the Monte Carlo algorithm (in case of SFPs) which will be used by QUESO classes and algorithms;\vspace*{-6pt} \item examples of Makefiles which may be used to link the code with QUESO library;\vspace*{-6pt} \item how to plot figures using Matlab/GNU Octave and the output data generated by each application. \end{itemize} @@ -18,6 +20,10 @@ \chapter{QUESO Examples}\label{chap:Queso-examples} All the examples presented in this chapter may be found under the directory \texttt{examples} in both \Queso{} installation and build directories and are compatible with QUESO \QUESOversion{}. +\paragraph*{Note:} Even though the Multilevel method is a methodology very useful for stochastic system model class comparison (model updating, model selection, model validation)~\cite{Cheung_2009A}, such tasks are not discussed in this manual. Thus the explicit dependency of the statistical variables on the predictive model in the set $M_j$ as presented in Section \ref{sec:ML:intro} are omitted herein. + + + \input{users_5a_example_sip} \input{users_5b_example_sfp} @@ -28,4 +34,6 @@ \chapter{QUESO Examples}\label{chap:Queso-examples} \input{users_5e_example_ml_modal} -\input{users_5f_example_ml_bimodal} \ No newline at end of file +\input{users_5f_example_ml_bimodal} + +\input{users_5g_example_ml_hysteretic} \ No newline at end of file diff --git a/manual/users/users_5c_example_gravity.tex b/manual/users/users_5c_example_gravity.tex index 18eeabdb0..1f84b60b5 100644 --- a/manual/users/users_5c_example_gravity.tex +++ b/manual/users/users_5c_example_gravity.tex @@ -574,7 +574,7 @@ \subsubsection{Statistical Forward Problem} \paragraph{Chain Plots} \ It is quite simple to plot, using Matlab, the chain of positions generated by the Monte Carlo algorithm implemented within QUESO and called during the solution of the statistical forward problem. -%The sequence of Matlab commands presented in Listing \ref{matlab:chain_qoi} generates the graphic depicted in Figure~\ref{fig:sfp_gravity_chain}. +The sequence of Matlab commands presented bellow generates the graphic depicted in Figure~\ref{fig:sfp_gravity_chain}. \begin{lstlisting}[label=matlab:chain_qoi,caption={Matlab code for the chain plot.}] % inside Matlab @@ -585,19 +585,19 @@ \subsubsection{Statistical Forward Problem} >> title('MC Chain Positions','fontsize',20); \end{lstlisting} -\begin{figure}[p] -\centering -\includegraphics[scale=0.35]{figs/sfp_gravity_chain_pos.png} -\vspace*{-10pt} -\caption{MC chain positions for the QoI.} -\label{fig:sfp_gravity_chain} -\end{figure} +% \begin{figure}[p] +% \centering +% \includegraphics[scale=0.35]{figs/sfp_gravity_chain_pos.png} +% \vspace*{-10pt} +% \caption{MC chain positions for the QoI.} +% \label{fig:sfp_gravity_chain} +% \end{figure} \paragraph{Histogram Plots} \ In order to plot a histogram of the QoI, you may use the pre-defined Matlab function \verb+hist+. -%The Matlab code presented in Listing \ref{matlab:hist_qoi} below shows how to create the Figure~\ref{fig:sfp_gravity_hist}. -% +The Matlab code presented in below shows how to create the Figure~\ref{fig:sfp_gravity_hist}. + \begin{lstlisting}[label=matlab:hist_qoi,caption={Matlab code for the QoI histogram plot.}] >> sfp_gravity_qoi_seq.m >> nbins=100; @@ -608,17 +608,18 @@ \subsubsection{Statistical Forward Problem} >> grid on; \end{lstlisting} -\begin{figure}[p] +\begin{figure}[h] \centering -\includegraphics[scale=0.35]{figs/sfp_gravity_hist.png} -\vspace{-10pt} -\caption{Histogram of QoI $=d$.} -\label{fig:sfp_gravity_hist} +\subfloat[Chain positions]{\includegraphics[scale=0.35]{figs/sfp_gravity_chain_pos.png}\label{fig:sfp_gravity_chain}} +\subfloat[Hystogram]{\includegraphics[scale=0.35]{figs/sfp_gravity_hist.png}\label{fig:sfp_gravity_hist}} +\vspace{-6pt} +\caption{MC chain positions and histogram of QoI $=d$.} \end{figure} \paragraph{KDE Plots} \ -Matlab function \verb+ksdensity+ (Kernel smoothing density estimate) together with the option \verb+'pdf'+ may be used for plotting the KDE of the he QoI. +Matlab function \verb+ksdensity+ (Kernel smoothing density estimate) together with the option \verb+'pdf'+ may be used for plotting the KDE of the he QoI, displayed in Figure \ref{fig:sfp_gravity_kde}. + \begin{lstlisting}[label=matlab:kde_qoi,caption={Matlab code for the QoI KDE plot.}] % inside Matlab >> sfp_gravity_qoi_seq.m @@ -630,19 +631,19 @@ \subsubsection{Statistical Forward Problem} >> grid on; \end{lstlisting} -\begin{figure}[p] -\centering -\includegraphics[scale=0.35]{figs/sfp_gravity_kde.png} -\vspace*{-10pt} -\caption{Kernel Density Estimation.} -\label{fig:sfp_gravity_kde} -\end{figure} +% \begin{figure}[htp] +% \centering +% \includegraphics[scale=0.35]{figs/sfp_gravity_kde.png} +% \vspace*{-10pt} +% \caption{Kernel Density Estimation.} +% \label{fig:sfp_gravity_kde} +% \end{figure} -\newpage +% \newpage \paragraph{CDF Plots} \ -Matlab function \verb+ksdensity+ (Kernel smoothing density estimate) with \verb+'cdf'+ option may also be used for plotting the Cumulative Distribution Function of the QoI. +Matlab function \verb+ksdensity+ (Kernel smoothing density estimate) with \verb+'cdf'+ option may also be used for plotting the Cumulative Distribution Function of the QoI, displayed in Figure~\ref{fig:sfp_gravity_cdf}. \begin{lstlisting}[label=matlab:cdf_qoi,caption={Matlab code for the QoI CDF plot.}] % inside Matlab @@ -654,19 +655,25 @@ \subsubsection{Statistical Forward Problem} >> ylabel('CDF','fontsize',20); >> grid on; \end{lstlisting} +% +% \begin{figure}[p] +% \centering +% \includegraphics[scale=0.35]{figs/sfp_gravity_cdf.png} +% \vspace*{-10pt} +% \caption{Cumulative Distribution Function.} +% \label{fig:sfp_gravity_cdf} +% \end{figure} -\begin{figure}[p] +\begin{figure}[htp] \centering -\includegraphics[scale=0.35]{figs/sfp_gravity_cdf.png} -\vspace*{-10pt} -\caption{Cumulative Distribution Function.} -\label{fig:sfp_gravity_cdf} +\subfloat[KDE]{\includegraphics[scale=0.35]{figs/sfp_gravity_kde.png}\label{fig:sfp_gravity_kde}} +\subfloat[CDF]{\includegraphics[scale=0.35]{figs/sfp_gravity_cdf.png}\label{fig:sfp_gravity_cdf}} +\vspace*{-6pt} +\caption{Kernel Density Estimation and Cumulative Distribution Function of QoI.} \end{figure} - \paragraph{Autocorrelation Plots} \ -The code presented in Listing \ref{matlab:autocorr_qoi} uses Matlab function \verb+autocorr+ to generate Figure \ref{fig:sfp_gravity_autocorrelation}, -which presents the autocorrelation of the QoI $d$. +The code presented in Listing \ref{matlab:autocorr_qoi} uses Matlab function \verb+autocorr+ to generate Figure~\ref{fig:sfp_gravity_autocorrelation}, which presents the autocorrelation of the QoI $d$. \begin{lstlisting}[label=matlab:autocorr_qoi,caption={Matlab code for the QoI autocorrelation plot.}] % inside Matlab @@ -680,7 +687,7 @@ \subsubsection{Statistical Forward Problem} >> grid on; \end{lstlisting} -\begin{figure}[p] +\begin{figure}[htp] \centering \includegraphics[scale=0.35]{figs/sfp_gravity_autocorrelation.png} \vspace*{-10pt} diff --git a/manual/users/users_5d_example_tga.tex b/manual/users/users_5d_example_tga.tex index a6b5344bd..3332a3be0 100644 --- a/manual/users/users_5d_example_tga.tex +++ b/manual/users/users_5d_example_tga.tex @@ -38,7 +38,7 @@ \subsection{Thermogravimetric Experiments and a Simple Model} \centering \includegraphics[scale=0.4]{figs/tga_experiment_data.png} \vspace*{-8pt} -\caption{Mass fraction decay over temperature given different heating rates. Data from J. A. Conesa, R. F. Marcilla and J. A. Caballero, ``Thermogravimetric studies on the thermal decomposition of polyethylenes'', {\it J. Anal. Appl. Pyrolysis}, 36(1):1-15, 1996} +\caption{Mass fraction decay over temperature given different heating rates. Data from J. A. Conesa, R. F. Marcilla and J. A. Caballero, ``Thermogravimetric studies on the thermal decomposition of polyethylene'', {\it J. Anal. Appl. Pyrolysis}, 36(1):1-15, 1996} \label{fig:tga-exp-data} \end{figure} diff --git a/manual/users/users_5e_example_ml_modal.tex b/manual/users/users_5e_example_ml_modal.tex index 525d4f2dc..c5a78c965 100644 --- a/manual/users/users_5e_example_ml_modal.tex +++ b/manual/users/users_5e_example_ml_modal.tex @@ -2,7 +2,7 @@ \section{\texttt{modal}}\label{sec:example_modal} This example presents a combination of two statistical inverse problems in one. -It presents the capability of the Multilevel method in sampling from a target distribution that has either one or two models. The random variable of interest has three parameters, i.e., $\bv{\theta}=(\theta_1,\theta_2, \sigma^2) \in \mathbb{R}^3$, where the third parameter may be seen as variation. +It presents the capability of the Multilevel method in sampling from a target distribution that has either one or two modes (distinct peaks). The random variable of interest has three parameters, i.e., $\bv{\theta}=(\theta_1,\theta_2, \sigma^2) \in \mathbb{R}^3$, where the third parameter may be seen as variation. The example also it gives the user the opportunity to chose either one single type of prior distribution, uniform, for the three components of the random variable, or two different priors: a uniform and a beta distribution. @@ -29,12 +29,12 @@ \section{\texttt{modal}}\label{sec:example_modal} \subsection{One-mode distribution} In this case, the target distribution is assumed to have only one mode. -Suppose also that the random variable $\bv{\theta}$ can either have a uniform distribution for all its components, i.e.: +Suppose also that the random variable $\bv{\theta}$ can either have a uniform prior distribution for all its components, i.e.: $$ % \pi_{\text{prior}}=\mathcal{U}([0,3],[0,3],[0,0.3]). \pi_{\text{prior}}=\mathcal{U}([0,3]) \times \mathcal{U}([0,3]) \times \mathcal{U}([0,0.3]). $$ -or, the prior distribution is defined as a combination of uniform prior for the $\theta_1$, with a beta prior for $\theta_2$: +or, the prior distribution is defined as a combination of uniform prior for $\theta_1$ and $\theta_2$, with a beta prior for $\sigma^2$: $$ % \pi_{\text{prior}}=\mathcal{U}([0,3],[0,3],[0,0.3]). \pi_{\text{prior}}=\mathcal{U}([0,3]) \times \mathcal{U}([0,3]) \times \mathcal{B}(\alpha,\beta), \quad \text{with} \quad \alpha=3 \quad\text{and}\quad \beta=0.09709133373799. @@ -92,7 +92,7 @@ \subsection{Two-mode distribution} The likelihood function is defined as follows: \begin{equation} \begin{split}\small -f(\D|\bv{\theta}, M_j)= -5 \log\left(2 \pi \sigma^2\right)- \dfrac{1}{2\sigma^2} &\Bigg[ +f(\D|\bv{\theta})= -5 \log\left(2 \pi \sigma^2\right)- \dfrac{1}{2\sigma^2} &\Bigg[ \left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.0470\right)^2+\\ &+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-71.8995\right)^2+\\ &+\left(10 \sqrt{10 \theta_1+20 \theta_2+10 \sqrt{\theta_1^2+4 \theta_2^2}}-72.2801\right)^2+\\ @@ -111,7 +111,7 @@ \subsection{Two-mode distribution} \subsubsection{Running the Two-Mode Example} -To run the executable provided considering a \underline{one-mode} distribution, enter the following commands: +To run the executable provided considering a \underline{two-modes} distribution, enter the following commands: \begin{lstlisting}[label={},caption={Running the example with a two-mode distribution.}] $ cd $HOME/LIBRARIES/QUESO-0.50.0/ $ cd examples/modal @@ -228,7 +228,7 @@ \subsection{Data Post-Processing and Visualization}\label{sec:modal-results} \subsubsection{Scatter Plots} -The code presented in Listing \ref{matlab:modal_scatter} uses Matlab function \verb+plotmatrixr+ to generate Figures \ref{fig:modal_scatter_1mode} and \ref{fig:modal_scatter_2modes} +The code presented in Listing \ref{matlab:modal_scatter} uses Matlab function \verb+plotmatrix+ to generate Figures \ref{fig:modal_scatter_1mode} and \ref{fig:modal_scatter_2modes} which presents the scatter plots and histograms of the parameters $\theta_1$ and $\theta_2$, based on the generated raw chains. diff --git a/manual/users/users_5f_example_ml_bimodal.tex b/manual/users/users_5f_example_ml_bimodal.tex index dd2fa3d32..5a7556606 100644 --- a/manual/users/users_5f_example_ml_bimodal.tex +++ b/manual/users/users_5f_example_ml_bimodal.tex @@ -1,7 +1,7 @@ -\section{\texttt{bimodal}}\label{sec:bimodal_bimodal} +\section{\texttt{bimodal}}\label{sec:example_bimodal} -This example replicates the problem in ``Section 4.1 A 1D Problem'' of~\cite{CheungPrudencio2012}: it presents how to use QUESO and the multivevel method for sampling from a posterior PDF composed of the sum of two Gaussian distributions. +This example replicates the problem in ``Section 4.1 A 1D Problem'' of~\cite{CheungPrudencio2012}: it presents how to use QUESO and the Multilevel method for sampling from a posterior PDF composed of the sum of two Gaussian distributions. Let's define $D=[-250,250]$ and the three distributions $\pi_\prior: D \rightarrow \mathbb{R}_+ $, $f_1: \mathbb{R} \rightarrow \mathbb{R}_+ $ and $f_2: \mathbb{R} \rightarrow \mathbb{R}_+$ by: \begin{equation} @@ -24,7 +24,7 @@ \section{\texttt{bimodal}}\label{sec:bimodal_bimodal} \begin{figure}[htpb] \centering -\includegraphics[scale=0.3]{figs/bimodal_likelihood.png} +\includegraphics[scale=0.35]{figs/bimodal_likelihood.png} \vspace{-10pt} \caption{Likelihood function given by $f=f_1/2+ f_2/2$, where $f_1$ and $f_2$ are defined in Equation \eqref{eq:bimodal_functions}.} \label{fig:bimodal:likelihood} @@ -166,7 +166,7 @@ \subsubsection{Autocorrelation Plots} \begin{figure}[htpb] \centering -\includegraphics[scale=0.25]{figs/bimodal_autocorrelation_rawchain} +\includegraphics[scale=0.3]{figs/bimodal_autocorrelation_rawchain} \vspace{-10pt} \caption{Autocorrelation plots for $\theta$, all four levels.} \label{fig:bimodal_autocorr} diff --git a/manual/users/users_5g_example_ml_hysteretic.tex b/manual/users/users_5g_example_ml_hysteretic.tex new file mode 100644 index 000000000..0edc8dad8 --- /dev/null +++ b/manual/users/users_5g_example_ml_hysteretic.tex @@ -0,0 +1,269 @@ +\section{\texttt{hysteretic}}\label{sec:example_hysteretic} + +This example replicates the problem in ``Section 4.3 A Hysteretic Model Class +'' of~\cite{CheungPrudencio2012}, and which is also discussed in \cite{Cheung_2009A}. +In this example we consider the nonlinear seismic response of a four-story building. +This response is modeled with +an inelastic shear building model with some linear viscous damping and hysteretic bilinear interstory restoring forces~\cite{Cheung_2009A}. More +specifically, let $t\geq0 $ denote time, let $a_g(t)$ be a given total acceleration at the base (Fig. 13), and for the i-th floor [degree of freedom (dof)], $1 \leq i \leq N_o \equiv 4$, let us denote: +\begin{equation} +\begin{split} +m_i &= \text{lumped mass},\\ +q_i(t) &= \text{horizontal displacement},\\ +F_i(t) &= \text{hysteretic restoring force} +\end{split} +\end{equation} + +The hysteretic restoring force is illustrated in Figure \ref{fig:hyst_rest_force} and the horizontal base (ground) acceleration (input data) used in~\cite{CheungPrudencio2012} is illustrated in \ref{hig:hist_base_acceleration}. +\begin{figure}[h!] +\centerline{ +\input{rawfigs/hyst_restoring_force.latex}\\ +} +\caption{Illustration of the hysteretic restoring force [see Eq. \eqref{eq:hyst:motion}] used in our hysteretic test problem. The terms $r_i$, $k_i$, and $u_i$ denote model parameters.} +\label{fig:hyst_rest_force} +\end{figure} +% +\begin{figure}[hptb] +\centering +\includegraphics[scale=0.6]{figs/hysteretic_eg_cpp_1.png} +\vspace{-8pt} +\caption{Horizontal base acceleration (input data) used in the hysteretic test problem~\cite{CheungPrudencio2012}.} +\label{hig:hist_base_acceleration} +\end{figure} + +We also define the mass matrix $\bv{M}$ and the stiffness matrix $\bv{K}$: +\begin{equation*} +\bv{M}= +\begin{bmatrix} + m_1 & 0 & 0 & 0 \\ + 0 & m_2 & 0 & 0 \\ + 0 & 0 & m_3 & 0 \\ + 0 & 0 & 0 & m_4 \\ +\end{bmatrix} +% +\quad \text{and} \quad +% +\bv{K}= +\begin{bmatrix} + k_1 + k_2 & -k_2 & 0 & 0 \\ + -k_2 & k_2+k_3 & -k_3 & 0 \\ + 0 & -k_3 & k_3+k_4 & -k4 \\ + 0 & 0 & -k_4 & k_4 \\ +\end{bmatrix} +\end{equation*} +and the Rayleigh damping matrix +\begin{equation*} +\bv{C} = \rho \bv{M} + \gamma \bv{K} +\end{equation*} +for given positive scalar parameters $\rho$ and $\gamma$. The response $\bv{q}(t) \equiv [q_1(t),q_2(t),q_3(t),q_4(t)]$ is modeled as satisfying the equation of motion: +\begin{equation}\label{eq:hyst:motion} +\bv{M} \ddot{\bv{q}}(t) + \bv{C} \dot{\bv{q}}(t) + \bv{F}(t) = -\bv{M}\cdot +\begin{bmatrix} +1 \\ +\vdots \\ +1 +\end{bmatrix}_{4\times1} \cdot a_g(t), +\end{equation} +where $\bv{F}(t) \equiv [F_1(t), F_2(t), F_3(t), F_4(t)]$. In this model, the hysteretic restoring force $\bv{F}(t)$ depends on the whole +time history $[\bv{q}(t),\dot{\bv{q}}(t)]$ of responses from the initial instant until time~$t$. + + +The (noisy) measured data $y = (y_1, y_2, y_3, y_4)$ available for model calibration consists of 4 s of accelerometer data at each floor (refer to Fig. \ref{hig:hist_acceleration_4_floors}), with a sample interval $\Delta t = 0.01$ s. The simulated dynamic data was obtained by adding Gaussian white noise to the output simulation of the hysteretic model with the following input values: +\begin{align*} +m_1 & = m_2 = m_3 = m_4 = 2\times 10^4 \,kg,\\ +k_1 &= 2.2 \times 10^7 \,Nm^{-1},\\ +k_2 &= 2.0 \times 10^7 \,Nm^{-1},\\ +k_3 &= 1.7 \times 10^7 \,Nm^{-1},\\ +k_4 &= 1.45 \times 10^7 \,Nm^{-1},\\ +r_1 & = r_2 = r_3 = r_4 = 0.1,\\ +u_1 & = u_2 = 8 \times 10^{-3} \,m, \\ +u_3 & = u_4 = 7 \times 10^{-3} \,m, \\ +\rho &= 7.959 \times 10^{-1}, \\ +\gamma &= 2.5 \times 10^{-3},\\ +\sigma^2 &= 0.6^2, +\end{align*} +where for $i=1,2,3,4$, $k_i$ is the initial stiffness, $r_i$ is the post-yield stiffness reduction factor, and $u_i$ is yield displacement. + + +\begin{figure}[hptb] +\centering +\includegraphics[scale=0.6]{figs/hysteretic_eg_cpp_4.png} +\vspace{-8pt} +\caption{Horizontal acceleration of each of the four floors (measured data aimed for calibration) used in our hysteretic test problem.} +\label{hig:hist_acceleration_4_floors} +\end{figure} + +According to Cheung and Prudencio~\cite{CheungPrudencio2012}, these input values were chosen deliberately so that the excitation $a_g$ did not cause some of the upper floors to enter the nonlinear regime; that is, so that our test inverse problem did not become globally identifiable. + +In this section, 400 time-steps are used, as the data is available at instants +$$t_n = (n - 1) \times \Delta t, \quad 1 \leq n \leq N_T \equiv 401, \quad \Delta t = 0.01$$ +however, Cheung and Prudencio used only 250 time steps~\cite{CheungPrudencio2012}. + +An additive noise is assumed to be present in the measurements; i.e., +\begin{equation*} +y_i(n)=q_i(n)+\varepsilon_i(n), \quad 1 \leq i \leq N_o, \quad 1 \leq n \leq N_T \equiv 401, +\end{equation*} +where $q_i(n; \theta_2 ,...,\theta_{15})$ denotes the output at time $t_n= n\Delta t \, (\Delta t=0.01s)$ at the $i$-th observed degree of freedom predicted by the proposed structural model, and $y_i(n)$ denotes the corresponding +measured output. + + +They considered a total of 15 unknown parameters $\bv{\theta} = (\theta_1 , . . . , \theta_{15})$ and modeled the variables $\varepsilon_i$ as independently and identically distributed Gaussian variables with mean zero and some unknown prediction-error variance $\sigma^2$. The variance $\sigma^2$ is assumed to be the same for all $N_o = 4$ floors. The first component $\theta_1$ is equal to the prediction error variance $\sigma^2$ and the other 14 parameters are related to the four triples $(k_i, r_i, u_i),\, 1 \leq i \leq N_o$ (see Fig. \ref{fig:hyst_rest_force}), to $\rho$, and to $\gamma$. The likelihood function is given by: +\begin{equation}\label{eq:hyst:like} + f(\bv{y}|\bv{\theta}) = \dfrac{1}{(2 \pi \sigma^2)^{N_oN_T/2}}\exp \left( -\dfrac{1}{2\sigma^2} \displaystyle \sum_{i=1}^{N_o} \sum_{n=1}^{N_T} [y_i(t_n) - {q}_i(t_n; \theta_2,\ldots,\theta_{15})]^2 \right). +\end{equation} +An inverse gamma prior was used for $\theta_1=\sigma^2$, and a 14-dimensional Gaussian prior was used for $\theta_2 , ..., \theta_{15}$ with zero mean and diagonal covariance matrix equal to a scaled identity matrix. + + +\subsection{Running the Example}\label{sec:hysteretic-run} + +To run the executable provided (available after QUESO installation), and generate figures for the chains, KDEs, CDFs, autocorrelation and scatter plots, enter the following commands: +\begin{lstlisting}[label={},caption={}] +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/example +$ rm outputData/* +$ ./example_gsl example_1chain.inp +$ matlab + $ plot_all.m # inside matlab +$ ls -l outputData/*.png +hysteretic_autocorr_thetas.png hysteretic_kde_theta8.png +hysteretic_cdf_thetas.png hysteretic_kde_theta9.png +hysteretic_kde_theta1.png hysteretic_kde_theta10.png +hysteretic_kde_theta2.png hysteretic_kde_theta11.png +hysteretic_kde_theta3.png hysteretic_kde_theta12.png +hysteretic_kde_theta4.png hysteretic_kde_theta13.png +hysteretic_kde_theta5.png hysteretic_kde_theta14.png +hysteretic_kde_theta6.png hysteretic_kde_theta15.png +hysteretic_kde_theta7.png hysteretic_scatter_thetas.png +\end{lstlisting} + + +As a result, the user should have created several of PNG figures containing kernel density estimate of the 15 parameters, cumulative density distribution, autocorrelation and scatter plots. The name of the figure files have been chosen to be informative, as shown in the Listing above. + +Additional figures may be generated if the user allows the procedure \texttt{debug\_hyst(} be called by the compiler in Line 11 of file \texttt{example\_main.C}; in that case, call the function \texttt{cpp\_gen.m} inside Matlab/Octave. + + +\subsection{Example Code}\label{sec:hysteretic-code} + +The source code for the example is composed of 5 files: +\texttt{example\_main.C} (Listing \ref{code:hysteretic-main-c}), \linebreak +\texttt{example\_likelihood.h} and \texttt{example\_likelihood.C} (Listings \ref{fig-like-example-h} and \ref{fig-like-example-c}), +\texttt{example\_compute.h} and \texttt{example\_compute.C} (Listings \ref{code:hysteretic-compute-h} and \ref{code:hysteretic-compute-c}), and finally \texttt{example\_hyst.h} and \texttt{example\_hyst.C}, which contain the Hysteretic model properly said. + + +Note that in line 11 of Listings \ref{code:hysteretic-main-c} the `\verb+#if 1+' directive tells the compiler that the application will call \texttt{compute()}, which internally uses QUESO and the Multilevel algorithm. +On the contrary, the user may calculate the hysteretic force without uncertainty by changing the directive to `\verb+#if 0+', which can assist the analysis of the resulting data. + +\lstinputlisting[caption=File \texttt{example\_main.C.}, label={code:hysteretic-main-c}, linerange={33-1000},numbers=left]{../../examples/hysteretic/src/example_main.C} + +\lstinputlisting[caption=File \texttt{example\_likelihood.h}., label={fig-like-example-h}, linerange={33-1000}]{../../examples/hysteretic/src/example_likelihood.h} + +\lstinputlisting[caption=File \texttt{example\_likelihood.C}., label={fig-like-example-c}, linerange={33-1000}]{../../examples/hysteretic/src/example_likelihood.C} + +\lstinputlisting[caption=File \texttt{example\_compute.h.}, label={code:hysteretic-compute-h}, linerange={33-1000}]{../../examples/hysteretic/src/example_compute.h} + +\lstinputlisting[caption={File \texttt{example\_compute.C}.}, label={code:hysteretic-compute-c}, linerange={33-1000}]{../../examples/hysteretic/src/example_compute.C} + + + +\subsection{Input File}\label{sec:hysteretic-input-file} + + +The options used for solving this example are displayed in Listing \ref{code:hysteretic-input-file}. + +\lstinputlisting[caption={Options for QUESO library used in application code (Listings \ref{code:hysteretic-main-c}-\ref{code:hysteretic-compute-c}})., +label={code:hysteretic-input-file},]{../../examples/hysteretic/tests/test_2013_12_11/example.inp} + +\subsection{Create your own Makefile}\label{sec:hysteretic-makefile} + +Similarly to the other examples presented in this user's manual and also available with QUESO distribution, a user-created makefile is available: `\texttt{Makefile\_hysteretic\_violeta}' which may personalized to each user's computer settings and used to compile the code and create the executable \verb+hysteretic_gsl+. + +Thus to compile, build and execute the code, commands similar to the following should be entered: +\begin{lstlisting} +$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/hysteretic/ +$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ + $HOME/LIBRARIES/gsl-1.15/lib/:\ + $HOME/LIBRARIES/boost-1.53.0/lib/:\ + $HOME/LIBRARIES/hdf5-1.8.10/lib:\ + $HOME/LIBRARIES/QUESO-0.50.0/lib +$ make -f Makefile_hysteretic_violeta +$ ./hysteretic_gsl example.inp +\end{lstlisting} + +Again, the `\verb+export+' instruction above is only necessary if the user has not saved the path for the libraries used during QUESO installation in his/her \verb+.bashrc+ file. + + + +\subsection{Data Post-Processing and Visualization}\label{sec:hysteretic-results} + + + +According to the specifications of the input file in Listing~\ref{code:hysteretic-input-file}, both a folder named \verb+outputData+ and a the following files should be generated: +\begin{verbatim} +rawChain_ml.m +display_sub0.txt +\end{verbatim} + +Note that in this hysteretic problem a total of 13 levels are required for the Multilevel method (e.g. see the contents of file \texttt{rawChain\_ml.m}). + +The sequence of Matlab commands is identical to the ones presented in Sections +\ref{sec:sip-results}, \ref{sec:sfp-results}, \ref{sec:gravity-results} and \ref{sec:tga-results}; +therefore, are omitted here. The reader is invited to explore the Matlab files +\texttt{plot\_all.m} and/or \texttt{cpp\_gen.m}, for details of how the figures have been generated. + + + +\subsubsection{KDE Plots} + +Figure \ref{fig:hysteretic_kde} presents the KDE plots of each parameter $\theta_i,\, i=1,\ldots,15$. +The Multilevel method also provides data about the logarithm of the likelihood function as well as of the target PDF. +Figure \ref{fig:hysteretic_kde_like} presents the KDE plots of both the likelihood function and of its logarithm. + + + +\begin{figure}[hptb] +\centering +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta1.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta2.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta3.png}}\\ +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta4.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta5.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta6.png}}\\ +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta7.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta8.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta9.png}}\\ +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta10.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta11.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta12.png}}\\ +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta13.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta14.png}} +\subfloat{\includegraphics[scale=0.25]{figs/hysteretic_kde_theta15.png}} +\caption{KDE plots of parameter $\bv{\theta}$ at the last level.} +\label{fig:hysteretic_kde} +\end{figure} + + +\begin{figure}[hptb] +\centering +\subfloat[$\log( f(\bv{y}|\bv{\theta}) )$]{\includegraphics[scale=0.4]{figs/hysteretic_kde_log_likelihood.png}} +\subfloat[$f(\bv{y}|\bv{\theta})$]{\includegraphics[scale=0.4]{figs/hysteretic_kde_likelihood.png}} +\vspace{-8pt} +\caption{KDE plots of the likelihood function, given by Eq. \eqref{eq:hyst:like}, and of its logarithm, at the last level.} +\label{fig:hysteretic_kde_like} +\end{figure} + + +\subsubsection{Autocorrelation and CDF Plots} + +Figure \ref{fig:hysteretic_cdf} combines the CDF of all parameters $\theta_i,\, i=1,\ldots,15$ into a single plot. +Figure~\ref{fig:hysteretic_autocorr} presents their autocorrelations. + +\begin{figure}[hptb] +\centering +\hspace{-10pt} +\subfloat[CDF]{ + \includegraphics[scale=0.45]{figs/hysteretic_cdf_thetas.png}\label{fig:hysteretic_cdf}\hspace{-20pt}} +\subfloat[Autocorrelation]{ + \includegraphics[scale=0.45]{figs/hysteretic_autocorr_thetas.png}\label{fig:hysteretic_autocorr}} +\vspace{-8pt} +\caption{CDF and autocorrelation plots of parameter $\bv{\theta}$ at the last level.} +\end{figure} + From 6d98d55b4362a8daf274a582f04a45bfd43087db Mon Sep 17 00:00:00 2001 From: "Kemelli C. Estacio-Hiroms" Date: Fri, 13 Dec 2013 17:40:28 -0800 Subject: [PATCH 07/20] [queso/examples/hysteretic] Adding new example 'hysteretic' to Queso examples. It partially reproduces the Section 4.3 'A Hysteretic Model Class' of the Multilevel paper (Prudencio&Cheung 2012). --- examples/Makefile.am | 35 ++ examples/hysteretic/matlab/cpp_gen.m | 72 ++++ examples/hysteretic/matlab/plot_all.m | 127 ++++++ .../src/Makefile_hysteretic_violeta | 45 ++ examples/hysteretic/src/example_compute.C | 349 +++++++++++++++ examples/hysteretic/src/example_compute.h | 41 ++ examples/hysteretic/src/example_hyst.C | 393 +++++++++++++++++ examples/hysteretic/src/example_hyst.h | 55 +++ examples/hysteretic/src/example_likelihood.C | 138 ++++++ examples/hysteretic/src/example_likelihood.h | 53 +++ examples/hysteretic/src/example_main.C | 54 +++ .../hysteretic/tests/test_2013_12_11/an.txt | 401 ++++++++++++++++++ .../tests/test_2013_12_11/example.inp | 57 +++ .../test_2013_12_11/measured_data1_1.txt | 401 ++++++++++++++++++ .../test_2013_12_11/measured_data1_2.txt | 401 ++++++++++++++++++ .../test_2013_12_11/measured_data1_3.txt | 401 ++++++++++++++++++ .../test_2013_12_11/measured_data1_4.txt | 401 ++++++++++++++++++ 17 files changed, 3424 insertions(+) create mode 100644 examples/hysteretic/matlab/cpp_gen.m create mode 100644 examples/hysteretic/matlab/plot_all.m create mode 100644 examples/hysteretic/src/Makefile_hysteretic_violeta create mode 100644 examples/hysteretic/src/example_compute.C create mode 100644 examples/hysteretic/src/example_compute.h create mode 100644 examples/hysteretic/src/example_hyst.C create mode 100644 examples/hysteretic/src/example_hyst.h create mode 100644 examples/hysteretic/src/example_likelihood.C create mode 100644 examples/hysteretic/src/example_likelihood.h create mode 100644 examples/hysteretic/src/example_main.C create mode 100644 examples/hysteretic/tests/test_2013_12_11/an.txt create mode 100644 examples/hysteretic/tests/test_2013_12_11/example.inp create mode 100644 examples/hysteretic/tests/test_2013_12_11/measured_data1_1.txt create mode 100644 examples/hysteretic/tests/test_2013_12_11/measured_data1_2.txt create mode 100644 examples/hysteretic/tests/test_2013_12_11/measured_data1_3.txt create mode 100644 examples/hysteretic/tests/test_2013_12_11/measured_data1_4.txt diff --git a/examples/Makefile.am b/examples/Makefile.am index 13691ca36..e81eff4df 100644 --- a/examples/Makefile.am +++ b/examples/Makefile.am @@ -327,6 +327,41 @@ dist_bimodal_gsl_DATA += $(bimodal_gsl_DATA) dist_bimodal_gsl_DATA += ${bimodal_gsl_HEADERS} dist_bimodal_gsl_DATA += ${bimodal_gsl_SOURCES} +#################### +# hysteretic # +#################### +hysteretic_gsldir = $(prefix)/examples/hysteretic + +hysteretic_gsl_DATA = $(top_srcdir)/examples/hysteretic/tests/test_2013_12_11/example.inp +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/tests/test_2013_12_11/an.txt +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/tests/test_2013_12_11/measured_data1_1.txt +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/tests/test_2013_12_11/measured_data1_2.txt +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/tests/test_2013_12_11/measured_data1_3.txt +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/tests/test_2013_12_11/measured_data1_4.txt +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/matlab/plot_all.m +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/matlab/cpp_gen.m +hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/src/Makefile_hysteretic_violeta + +hysteretic_gsl_PROGRAMS = hysteretic_gsl + +hysteretic_gsl_SOURCES = $(top_srcdir)/examples/hysteretic/src/example_main.C +hysteretic_gsl_SOURCES += $(top_srcdir)/examples/hysteretic/src/example_compute.C +hysteretic_gsl_SOURCES += $(top_srcdir)/examples/hysteretic/src/example_likelihood.C +hysteretic_gsl_SOURCES += $(top_srcdir)/examples/hysteretic/src/example_hyst.C + +hysteretic_gsl_HEADERS = $(top_srcdir)/examples/hysteretic/src/example_compute.h +hysteretic_gsl_HEADERS += $(top_srcdir)/examples/hysteretic/src/example_likelihood.h +hysteretic_gsl_HEADERS += $(top_srcdir)/examples/hysteretic/src/example_hyst.h + +hysteretic_gsl_LDADD = $(top_builddir)/src/libqueso.la + +hysteretic_gsl_CPPFLAGS = -I$(top_srcdir)/examples/hysteretic/src $(QUESO_CPPFLAGS) + +dist_hysteretic_gsl_DATA = +dist_hysteretic_gsl_DATA += $(hysteretic_gsl_DATA) +dist_hysteretic_gsl_DATA += ${hysteretic_gsl_HEADERS} +dist_hysteretic_gsl_DATA += ${hysteretic_gsl_SOURCES} + #################### if CODE_COVERAGE_ENABLED CLEANFILES = *.gcda *.gcno diff --git a/examples/hysteretic/matlab/cpp_gen.m b/examples/hysteretic/matlab/cpp_gen.m new file mode 100644 index 000000000..90e8d271d --- /dev/null +++ b/examples/hysteretic/matlab/cpp_gen.m @@ -0,0 +1,72 @@ +clear all; +close all; +cd outputData + +cpp_output +figure(1); plot(t_cpp,a_cpp,'linewidth',2);xlabel('Time (s)','fontsize',16); ylabel('Ground acceleration (a_g)','fontsize',16); grid minor; +set(gca,'fontsize',16); +for j=1:4 + figure(2);subplot(4,1,4-j+1);plot(t_cpp,u_cpp(j,:),'linewidth',2);%legend([num2str(j),'-th floor'],'location','EastOutside'); + if j==1 + title([num2str(j),'st floor'],'fontsize',16); + else if j==2 + title([num2str(j),'nd floor'],'fontsize',16); + else if j==3 + title([num2str(j),'rd floor'],'fontsize',16); + else + title([num2str(j),'th floor'],'fontsize',16); + end + end + end + set(gca,'fontsize',16); + if j==1 + xlabel('Time (s)','fontsize',16); + end + if j==2 + ylabel('Displacement (m)','fontsize',16); + end + figure(3);subplot(4,1,4-j+1);plot(t_cpp,ud_cpp(j,:),'linewidth',2);legend(strcat('dof ',num2str(j)),'location','southeast'); + set(gca,'fontsize',16); + if j==1 + xlabel('Time (s)','fontsize',16); + end + if j==2 + ylabel('Velocity (m/s)','fontsize',16); + end + figure(4);subplot(4,1,4-j+1);plot(t_cpp,udd_cpp(j,:),'linewidth',2); + %legend([num2str(j),'-th floor'],'location','EastOutside'); + if j==1 + title([num2str(j),'st floor'],'fontsize',16); + else if j==2 + title([num2str(j),'nd floor'],'fontsize',16); + else if j==3 + title([num2str(j),'rd floor'],'fontsize',16); + else + title([num2str(j),'th floor'],'fontsize',16); + end + end + end + grid minor; + set(gca,'fontsize',16); + if j==1 + xlabel('Time (s)','fontsize',16); + end + if j==2 + ylabel('Acceleration (m/s^2)','fontsize',16); + end + figure(5);subplot(4,1,4-j+1);plot(t_cpp,ru_cpp(j,:),'linewidth',2);legend(strcat('dof ',num2str(j-1),'-',num2str(j))); + set(gca,'fontsize',16); + if j==1 + xlabel('Time (s)','fontsize',16); + end + if j==2 + ylabel('Relative displacement(m)','fontsize',16); + end + figure(6+j-1);plot(ru_cpp(j,:),resfor_cpp(j,:),'linewidth',2); xlabel('Relative displacement (m)','fontsize',16); ylabel('Restoring force (N)','fontsize',16); grid on; + set(gca,'fontsize',16); + legend(strcat('floor ',num2str(j)),'location','southeast'); +end +for j=1:9 +figure(j);print(gcf,'-dpng',strcat('hysteretic_eg_cpp_',num2str(j))) +end +cd .. diff --git a/examples/hysteretic/matlab/plot_all.m b/examples/hysteretic/matlab/plot_all.m new file mode 100644 index 000000000..549a11b7f --- /dev/null +++ b/examples/hysteretic/matlab/plot_all.m @@ -0,0 +1,127 @@ +clear all; +close all; +cd outputData +rawChain_ml; + +fprintf(1,' Plotting KDE - theta_i, i=1..15 \n'); +for j=1:15 +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,j),'function','pdf'); +plot(xi,f,'-b','linewidth',3); +clear xi; clear f; +title(['\theta_{',num2str(j),'}: Kernel Density Estimation (raw chain)'],'fontsize',16); +set(gca,'FontSize',16); +ylabel('KDE','fontsize',16); +grid minor; +print(gcf,'-dpng',strcat('hysteretic_kde_theta',num2str(j))) +end + + +fprintf(1,' Plotting CDF - theta_i, i=1..15 \n'); +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,1),'function','cdf'); +plot(xi,f,'-r','linewidth',3); hold on; clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,2),'function','cdf'); +plot(xi,f,'-g','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,3),'function','cdf'); +plot(xi,f,'-b','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,4),'function','cdf'); +plot(xi,f,'-c','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,5),'function','cdf'); +plot(xi,f,'-m','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,6),'function','cdf'); +plot(xi,f,'-k','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,7),'function','cdf'); +plot(xi,f,'--r','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,8),'function','cdf'); +plot(xi,f,'--g','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,9),'function','cdf'); +plot(xi,f,'--b','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,10),'function','cdf'); +plot(xi,f,'--c','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,11),'function','cdf'); +plot(xi,f,'--m','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,12),'function','cdf'); +plot(xi,f,'--k','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,13),'function','cdf'); +plot(xi,f,'-.r','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,14),'function','cdf'); +plot(xi,f,'-.g','linewidth',3); clear xi; clear f; +[f,xi] = ksdensity(ip_ml_13_rawChain_unified(:,15),'function','cdf'); +plot(xi,f,'-.b','linewidth',3); clear xi; clear f; +title('\theta_i: Cumulative Distribution Function (raw chain)','fontsize',16); +h=legend('\theta_1','\theta_2','\theta_3','\theta_4','\theta_5',... + '\theta_6','\theta_7','\theta_8','\theta_9','\theta_{10}',... + '\theta_{11}','\theta_{12}','\theta_{13}','\theta_{14}',... + '\theta_{15}','location','EastOutside'); +grid minor; +hold off; +axis([-1.5 1.5 0 1]); +print -dpng hysteretic_cdf_thetas + + +fprintf(1,' Plotting autocorrelation - theta_i, i=1..15 \n'); +nlags=20; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,1), nlags,0); +plot(ACF1, lags1,'-r','linewidth',3); hold on; clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,2), nlags,0); +plot(ACF1, lags1,'-g','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,3), nlags,0); +plot(ACF1, lags1,'-b','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,4), nlags,0); +plot(ACF1, lags1,'-c','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,5), nlags,0); +plot(ACF1, lags1,'-m','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,6), nlags,0); +plot(ACF1, lags1,'-k','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,7), nlags,0); +plot(ACF1, lags1,'--r','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,8), nlags,0); +plot(ACF1, lags1,'--g','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,9), nlags,0); +plot(ACF1, lags1,'--b','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,10), nlags,0); +plot(ACF1, lags1,'--c','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,11), nlags,0); +plot(ACF1, lags1,'--m','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,12), nlags,0); +plot(ACF1, lags1,'--k','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,13), nlags,0); +plot(ACF1, lags1,'-.r','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,14), nlags,0); +plot(ACF1, lags1,'-.g','linewidth',3); clear ACF1; clear lags1; +[ACF1,lags1,bounds] = autocorr(ip_ml_13_rawChain_unified(:,15), nlags,0); +plot(ACF1, lags1,'-.b','linewidth',3); clear ACF1; clear lags1; +title('\theta_i: Autocorrelation (raw chain)','fontsize',16); +h=legend('\theta_1','\theta_2','\theta_3','\theta_4','\theta_5',... + '\theta_6','\theta_7','\theta_8','\theta_9','\theta_{10}',... + '\theta_{11}','\theta_{12}','\theta_{13}','\theta_{14}',... + '\theta_{15}','location','EastOutside'); +grid minor; +hold off; +axis([0.3 1 0 20]); +print -dpng hysteretic_autocorr_thetas +clf; + +% scatter plots - parameters +% plotmatrix(ip_ml_13_rawChain_unified) +gplotmatrix(ip_ml_13_rawChain_unified) +print -dpng hysteretic_scatter_thetas + + +% Likelihood plots +fprintf(1,' Plotting KDE - log(likelihood) and likelihood \n'); +[f,xi] = ksdensity(exp(ip_ml_13_rawLogLikelihood_unified),'function','pdf'); +plot(xi,f,'-k','linewidth',3); clear xi; clear f; +title('KDE of Likelihood (f({\bfy}|{\bf\theta})) - last level','fontsize',16); +grid minor; +set(gca,'FontSize',16); +print -dpng hysteretic_kde_likelihood + +[f,xi] = ksdensity(ip_ml_13_rawLogLikelihood_unified,'function','pdf'); +plot(xi,f,'-k','linewidth',3); clear xi; clear f; +title('KDE of the Logarithm of the Likelihood (log(f({\bfy}|{\bf\theta}))) - last level','fontsize',16); +grid minor; +set(gca,'FontSize',16); +print -dpng hysteretic_kde_log_likelihood + + +cd .. diff --git a/examples/hysteretic/src/Makefile_hysteretic_violeta b/examples/hysteretic/src/Makefile_hysteretic_violeta new file mode 100644 index 000000000..4bddee60b --- /dev/null +++ b/examples/hysteretic/src/Makefile_hysteretic_violeta @@ -0,0 +1,45 @@ +########################################################## +# Using installed QUESO 0.50.0, from external release, in 'violeta' +########################################################## + +QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 +BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 +GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 +HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 + +INC_PATHS = \ + -I. \ + -I$(QUESO_DIR)/include \ + -I$(BOOST_DIR)/include \ + -I$(GSL_DIR)/include \ + -I$(HDF5_DIR)/include + +LIBS = \ + -L$(QUESO_DIR)/lib -lqueso \ + -L$(BOOST_DIR)/lib -lboost_program_options \ + -L$(GSL_DIR)/lib -lgsl \ + -L$(HDF5_DIR)/lib -lhdf5 + +CXX = mpic++ +CXXFLAGS += -g -Wall -c + +default: all + +.SUFFIXES: .o .C + +all: hyst_example_gsl + +clean: + rm -f *~ + rm -f *.o + rm -f example_gsl + +hyst_example_gsl: example_main.o example_likelihood.o example_compute.o example_hyst.o + $(CXX) example_main.o \ + example_likelihood.o \ + example_compute.o \ + example_hyst.o \ + -o hysteretic_gsl $(LIBS) + +%.o: %.C + $(CXX) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/hysteretic/src/example_compute.C b/examples/hysteretic/src/example_compute.C new file mode 100644 index 000000000..ed3552d95 --- /dev/null +++ b/examples/hysteretic/src/example_compute.C @@ -0,0 +1,349 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +void compute(const QUESO::FullEnvironment& env) { + + struct timeval timevalNow; + gettimeofday(&timevalNow, NULL); + std::cout << std::endl << "Beginning run of 'Hysteretic' example at " + << ctime(&timevalNow.tv_sec); + + //------------------------------------------------------ + // Step 1 of 5: Instantiate the parameter space + //------------------------------------------------------ + QUESO::VectorSpace + paramSpaceA(env, "paramA_", 1, NULL); + QUESO::VectorSpace + paramSpaceB(env, "paramB_", 14, NULL); + QUESO::VectorSpace + paramSpace (env, "param_", 15, NULL); + + //------------------------------------------------------ + // Step 2 of 5: Instantiate the parameter domain + //------------------------------------------------------ + QUESO::GslVector paramMinsA(paramSpaceA.zeroVector()); + paramMinsA.cwSet(0); + QUESO::GslVector paramMaxsA(paramSpaceA.zeroVector()); + paramMaxsA.cwSet(5); + QUESO::BoxSubset + paramDomainA("paramA_",paramSpaceA,paramMinsA,paramMaxsA); + + QUESO::GslVector paramMinsB(paramSpaceB.zeroVector()); + paramMinsB.cwSet(-INFINITY); + QUESO::GslVector paramMaxsB(paramSpaceB.zeroVector()); + paramMaxsB.cwSet( INFINITY); + QUESO::BoxSubset + paramDomainB("paramB_",paramSpaceB,paramMinsB,paramMaxsB); + + QUESO::ConcatenationSubset + paramDomain("",paramSpace,paramDomainA,paramDomainB); + + //------------------------------------------------------ + // Step 3 of 5: Instantiate the likelihood function object + //------------------------------------------------------ + std::cout << "\tInstantiating the Likelihood; calling internally the hysteretic model" + << std::endl; + + likelihoodRoutine_DataType likelihoodRoutine_Data; + likelihoodRoutine_Data.floor.resize(4,NULL); + unsigned int numTimeSteps = 401; + for (unsigned int i = 0; i < 4; ++i) { + likelihoodRoutine_Data.floor[i] = new std::vector(numTimeSteps,0.); + } + likelihoodRoutine_Data.accel.resize(numTimeSteps,0.); + FILE *inp; + inp = fopen("an.txt","r"); + unsigned int numObservations = 0; + double tmpA; + while (fscanf(inp,"%lf",&tmpA) != EOF) { + likelihoodRoutine_Data.accel[numObservations] = tmpA; + numObservations++; + } + + numObservations=0; + FILE *inp1_1; + inp1_1=fopen("measured_data1_1.txt","r"); + while (fscanf(inp1_1,"%lf",&tmpA) != EOF) { + (*likelihoodRoutine_Data.floor[0])[numObservations]=tmpA; + numObservations++; + } + + numObservations=0; + FILE *inp1_2; + inp1_2=fopen("measured_data1_2.txt","r"); + while (fscanf(inp1_2,"%lf",&tmpA) != EOF) { + (*likelihoodRoutine_Data.floor[1])[numObservations]=tmpA; + numObservations++; + } + + numObservations=0; + FILE *inp1_3; + inp1_3=fopen("measured_data1_3.txt","r"); + while (fscanf(inp1_3,"%lf",&tmpA) != EOF) { + (*likelihoodRoutine_Data.floor[2])[numObservations]=tmpA; + numObservations++; + } + + numObservations=0; + FILE *inp1_4; + inp1_4=fopen("measured_data1_4.txt","r"); + while (fscanf(inp1_4,"%lf",&tmpA) != EOF) { + (*likelihoodRoutine_Data.floor[3])[numObservations]=tmpA; + numObservations++; + } + + QUESO::GenericScalarFunction + likelihoodFunctionObj("like_", + paramDomain, + likelihoodRoutine, + (void *) &likelihoodRoutine_Data, + true); // routine computes [ln(function)] + + //------------------------------------------------------ + // Step 4 of 5: Instantiate the inverse problem + //------------------------------------------------------ + std::cout << "\tInstantiating the SIP" << std::endl; + + QUESO::UniformVectorRV + priorRvA("priorA_", paramDomainA); + + QUESO::GslVector meanVec(paramSpaceB.zeroVector()); + QUESO::GslVector diagVec(paramSpaceB.zeroVector()); + + diagVec.cwSet(0.6*0.6); + + QUESO::GslMatrix covMatrix(diagVec); + + QUESO::GaussianVectorRV + priorRvB("priorB_", paramDomainB,meanVec,covMatrix); + + QUESO::ConcatenatedVectorRV + priorRv("prior_", priorRvA, priorRvB, paramDomain); + + QUESO::GenericVectorRV + postRv("post_", paramSpace); + + QUESO::StatisticalInverseProblem + ip("", NULL, priorRv, likelihoodFunctionObj, postRv); + + //------------------------------------------------------ + // Step 5 of 5: Solve the inverse problem + //------------------------------------------------------ + std::cout << "\tSolving the SIP with Multilevel method" << std::endl; + + ip.solveWithBayesMLSampling(); + + gettimeofday(&timevalNow, NULL); + std::cout << "Ending run of 'Hysteretic' example at " + << ctime(&timevalNow.tv_sec) << std::endl; + return; +} + +//------------------------------------------------------ +//------------------------------------------------------ +//------------------------------------------------------ +void debug_hyst(const QUESO::FullEnvironment& env) { + unsigned int numFloors = 4; + unsigned int numTimeSteps = 401; + + std::vector accel(numTimeSteps,0.); + FILE *inp; + inp = fopen("an.txt","r"); + unsigned int numObservations = 0; + double tmpA; + while (fscanf(inp,"%lf",&tmpA) != EOF) { + UQ_FATAL_TEST_MACRO((numObservations >= accel.size()), + env.fullRank(), + "debug_hyst()", + "input file has too many lines"); + accel[numObservations] = tmpA; + numObservations++; + } + UQ_FATAL_TEST_MACRO((numObservations != accel.size()), + env.fullRank(), + "debug_hyst()", + "input file has a smaller number of observations than expected"); + + QUESO::VectorSpace floorSpace(env, "floor_", numFloors, NULL); + + QUESO::GslVector kVec(floorSpace.zeroVector()); + kVec[0] = 2.20e+7; + kVec[1] = 2.00e+7; + kVec[2] = 1.70e+7; + kVec[3] = 1.45e+7; + + QUESO::GslVector rVec(floorSpace.zeroVector()); + rVec[0] = 0.1; + rVec[1] = 0.1; + rVec[2] = 0.1; + rVec[3] = 0.1; + + QUESO::GslVector uVec(floorSpace.zeroVector()); + uVec[0] = 0.008; + uVec[1] = 0.008; + uVec[2] = 0.007; + uVec[3] = 0.007; + + double rho = 7.959e-1 ;//0.1976; + double gamma = 2.500e-3 ; //0.0038; + + std::vector t(numTimeSteps,0.); + QUESO::SequenceOfVectors u (floorSpace,numTimeSteps,""); // absolute displacement + QUESO::SequenceOfVectors ud (floorSpace,numTimeSteps,""); // velocity + QUESO::SequenceOfVectors udd (floorSpace,numTimeSteps,""); // acceleration + QUESO::SequenceOfVectors resfor(floorSpace,numTimeSteps,""); // restoring force + QUESO::SequenceOfVectors ru (floorSpace,numTimeSteps,""); // relative displacement + + u.setPositionValues (0,floorSpace.zeroVector()); + ud.setPositionValues (0,floorSpace.zeroVector()); + udd.setPositionValues (0,floorSpace.zeroVector()); + resfor.setPositionValues(0,floorSpace.zeroVector()); + ru.setPositionValues (0,floorSpace.zeroVector()); + + QUESO::GslVector massVec(floorSpace.zeroVector()); + massVec.cwSet(2.0e+4); + + hystereticModel(env, + massVec, + kVec, + rVec, + uVec, + rho, + gamma, + accel, + t, // output + u, + ud, + udd, + resfor, + ru); + + std::set auxSet; + auxSet.insert(0); + + // Writing some data to the file 'outputData/cpp_output.m' + std::ofstream myFile; + myFile.open ("outputData/cpp_output.m"); + + // Write 't_cpp' + myFile << "t_cpp = zeros(" << 1 << "," << numTimeSteps << ");\n" + << "t_cpp = ["; + for (unsigned int j = 0; j < numTimeSteps; ++j) { + myFile << t[j] << " "; + } + myFile << "];" << std::endl; + + // Write 'a_cpp' + myFile << "a_cpp = zeros(" << 1 << "," << numTimeSteps << ");\n" + << "a_cpp = ["; + for (unsigned int j = 0; j < numTimeSteps; ++j) { + myFile << accel[j] << " "; + } + myFile << "];" << std::endl; + + QUESO::GslVector auxVec(floorSpace.zeroVector()); + + // Write 'u_cpp' + myFile << "u_cpp = zeros(" << numFloors << "," << numTimeSteps << ");\n" + << "u_cpp = ["; + for (unsigned int i = 0; i < numFloors; ++i) { + for (unsigned int j = 0; j < numTimeSteps; ++j) { + u.getPositionValues(j,auxVec); + myFile << auxVec[i] << " "; + } + myFile << std::endl; + } + myFile << "];" << std::endl; + + // Write 'ud_cpp' + myFile << "ud_cpp = zeros(" << numFloors << "," << numTimeSteps << ");\n" + << "ud_cpp = ["; + for (unsigned int i = 0; i < numFloors; ++i) { + for (unsigned int j = 0; j < numTimeSteps; ++j) { + ud.getPositionValues(j,auxVec); + myFile << auxVec[i] << " "; + } + myFile << std::endl; + } + myFile << "];" << std::endl; + + // Write 'udd_cpp' + myFile << "udd_cpp = zeros(" << numFloors << "," << numTimeSteps << ");\n" + << "udd_cpp = ["; + for (unsigned int i = 0; i < numFloors; ++i) { + for (unsigned int j = 0; j < numTimeSteps; ++j) { + udd.getPositionValues(j,auxVec); + myFile << auxVec[i] << " "; + } + myFile << std::endl; + } + myFile << "];" << std::endl; + + // Write 'resfor_cpp' + myFile << "resfor_cpp = zeros(" << numFloors << "," << numTimeSteps << ");\n" + << "resfor_cpp = ["; + for (unsigned int i = 0; i < numFloors; ++i) { + for (unsigned int j = 0; j < numTimeSteps; ++j) { + resfor.getPositionValues(j,auxVec); + myFile << auxVec[i] << " "; + } + myFile << std::endl; + } + myFile << "];" << std::endl; + + // Write 'ru_cpp' + myFile << "ru_cpp = zeros(" << numFloors << "," << numTimeSteps << ");\n" + << "ru_cpp = ["; + for (unsigned int i = 0; i < numFloors; ++i) { + for (unsigned int j = 0; j < numTimeSteps; ++j) { + ru.getPositionValues(j,auxVec); + myFile << auxVec[i] << " "; + } + myFile << std::endl; + } + myFile << "];" << std::endl; + + myFile.close(); + + return; +} diff --git a/examples/hysteretic/src/example_compute.h b/examples/hysteretic/src/example_compute.h new file mode 100644 index 000000000..70af41b33 --- /dev/null +++ b/examples/hysteretic/src/example_compute.h @@ -0,0 +1,41 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008-2013 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#ifndef __EX_COMPUTE_H__ +#define __EX_COMPUTE_H__ + +#include + +void compute (const QUESO::FullEnvironment& env); +void debug_hyst(const QUESO::FullEnvironment& env); + +#endif diff --git a/examples/hysteretic/src/example_hyst.C b/examples/hysteretic/src/example_hyst.C new file mode 100644 index 000000000..29211c5d7 --- /dev/null +++ b/examples/hysteretic/src/example_hyst.C @@ -0,0 +1,393 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#include + +struct cellStruct { + double xu; + double yu; + double k; + double r; + double inter_y; +}; + +//------------------------------------------------------ +// Calculates the restoring force, based on +// function [fs,rigidez,newparam] = restoringforce(u,uold,v,vold,fsold,param) +// Elastic plastic hysteresis model +//------------------------------------------------------ +void restoringForce( + double u, + double uold, + double v, + double vold, + double fsold, + cellStruct* inpParam, + cellStruct* fsCell, // output + double* fsDouble, // output + double* rigidez, // output + cellStruct* newParam) // output +{ + if (fsCell != NULL) { + // Initialize hysteresis loop + fsCell->xu = 0; + fsCell->yu = 0; + fsCell->k = u; + fsCell->r = uold; + fsCell->inter_y = v; + return; + } + + double xu = inpParam->xu; + double yu = inpParam->yu; + double k = inpParam->k; + double r = inpParam->r; + double inter_y = inpParam->inter_y; + + double k_r = k+r; + + *fsDouble = yu + k_r*(u-xu); + + *rigidez = k_r; + + if (v>0.) { + double fyp = r*u + inter_y; + if (*fsDouble > fyp) { + *fsDouble = fyp; + xu = (inter_y + k_r*xu -yu)/k; // 1 + yu = r*xu + inter_y; + *rigidez = r; + } + if (vold<0.) { //% && (v>0) + if (fabs(fsold-r*uold+inter_y) < 1.e-3) { + xu = uold; + yu = fsold; + } + else { + xu = (-inter_y + k_r*u - *fsDouble)/k; // 4 + yu = r*xu - inter_y; + } + *fsDouble = yu + k_r*(u-xu); + } + } + else { //% (v<0) + double fyn = r*u - inter_y; + if (*fsDouble < fyn) { + *fsDouble = fyn; + xu = (-inter_y + k_r*xu - yu)/k; // 3 + yu = r*xu - inter_y; + *rigidez = r; + } + if (vold>0.) { //% && (v<0) + if (fabs(fsold-r*uold-inter_y) < 1.e-3) { + xu = uold; + yu = fsold; + } + else { + xu = (inter_y + k_r*u - *fsDouble)/k; // 2 + yu = r*xu + inter_y; + } + *fsDouble = yu + k_r*(u-xu); + } + } + + newParam->xu = xu; + newParam->yu = yu; + newParam->k = k; + newParam->r = r; + newParam->inter_y = inter_y; + + return; +} + +//------------------------------------------------------ +// Assembles: stiffness matrix K or dumping matrix C +//------------------------------------------------------ +void ckmatrix(const QUESO::GslVector& vec, QUESO::GslMatrix& mat) +{ + UQ_FATAL_TEST_MACRO(vec.sizeLocal() != mat.numRowsLocal(), + vec.env().fullRank(), + "ckmatrix()", + "incompatible sizes"); + + // Assemble C or K matrix + unsigned int ndof = vec.sizeLocal(); + //C = spdiags([[-c(2:end) 0]; c+[c(2:end) 0]; -c]',-1:1,ndof,ndof); + + if (ndof == 4) { + mat(0,0) = vec[0]+vec[1]; mat(0,1) = -vec[1]; mat(0,2) = 0.; mat(0,3) = 0.; + mat(1,0) = -vec[1]; mat(1,1) = vec[1]+vec[2]; mat(1,2) = -vec[2]; mat(1,3) = 0.; + mat(2,0) = 0.; mat(2,1) = -vec[2]; mat(2,2) = vec[2]+vec[3]; mat(2,3) = -vec[3]; + mat(3,0) = 0.; mat(3,1) = 0.; mat(3,2) = -vec[3]; mat(3,3) = vec[3]; + } + else { + UQ_FATAL_TEST_MACRO(true, + vec.env().fullRank(), + "ckmatrix()", + "ndof != 4"); + } + + return; +} + +//------------------------------------------------------ +// The hysteretic model. +// function [u,ud,udd,ru,resfor,a]=hysteretic_model_example3(thi,a); +// a is the total acceleration at the base of the building +//------------------------------------------------------ +void hystereticModel( + const QUESO::BaseEnvironment& env, + const QUESO::GslVector& massInputVec, + const QUESO::GslVector& kInputVec, + const QUESO::GslVector& rInputVec, + const QUESO::GslVector& uInputVec, + double rho, + double gamma, + const std::vector& a, + std::vector& t, + QUESO::SequenceOfVectors& u, + QUESO::SequenceOfVectors& ud, + QUESO::SequenceOfVectors& udd, + QUESO::SequenceOfVectors& resfor, + QUESO::SequenceOfVectors& ru) +{ + bool useLinear = false; // non linear model + unsigned int ndof = massInputVec.sizeLocal(); + unsigned int nsteps = a.size(); + double dt = 0.01; // number of integration steps + + QUESO::VectorSpace dofSpace(env, "dof_", ndof, NULL); + + // Time interval, 0.01=10%*5thperiod + QUESO::GslVector Mvec(massInputVec); + QUESO::GslVector R (kInputVec*rInputVec); + QUESO::GslVector Kd (kInputVec-R); // stiffness (N/m) + QUESO::GslVector S_K (uInputVec); // s/k + QUESO::GslVector S (S_K*Kd); + + // Initial conditions + QUESO::GslVector u0 (dofSpace.zeroVector()); + QUESO::GslVector ud0(dofSpace.zeroVector()); + + // Restoring force + std::vector param(4); + if (useLinear == false) { + for (unsigned int w = 0; w < ndof; ++w) { + restoringForce(Kd[w],R[w],S[w],0.,0.,NULL, + ¶m[w], NULL, NULL, NULL); + } + } + + QUESO::SequenceOfVectors p(dofSpace,nsteps,""); // momentum + for (unsigned int i = 0; i < nsteps; ++i) { + p.setPositionValues(i,-a[i]*Mvec); + } + + QUESO::GslMatrix M(Mvec); + + //if (useLinear == false) { + QUESO::GslVector kTdiag(dofSpace.zeroVector()); + //} + //else { + QUESO::GslMatrix kT(dofSpace.zeroVector()); + ckmatrix(Kd+R,kT); + //} + QUESO::GslMatrix tmp_mat(dofSpace.zeroVector()); + ckmatrix(kInputVec,tmp_mat); + QUESO::GslMatrix C(rho*M + gamma*tmp_mat); + + double gammaHere = 0.5; + double beta = 0.25; // Average acceleration method + + double bdt = beta*dt; + double g_b = gammaHere/beta; + double g_2b = g_b/2.; + double g_bdt = g_b/dt; + double dt2 = dt*dt; + double bdt2 = beta*dt2; + double one_bdt2 = 1./bdt2; + double one_2b = 1./(2.*beta); + + QUESO::SequenceOfVectors fs(dofSpace,nsteps,""); + + u.setPositionValues (0,u0 ); + ud.setPositionValues(0,ud0); + + QUESO::GslVector auxResforPosition(dofSpace.zeroVector()); + QUESO::GslVector auxFsPosition (dofSpace.zeroVector()); + + // Restoring Force (again) + if (useLinear == false) { + for (unsigned int w = 0; w < ndof; ++w) { + restoringForce(u0[w],0.,ud0[w],0.,0.,¶m[w], + NULL, &auxResforPosition[w], &kTdiag[w], ¶m[w]); + } + resfor.setPositionValues(0,auxResforPosition); + auxResforPosition.matlabDiff(0,auxResforPosition[0],auxFsPosition); + fs.setPositionValues(0,auxResforPosition); + ckmatrix(kTdiag,kT); + } + else { + //fs(:,1) = kT*u0; + fs.setPositionValues(0,kT*u0); + } + + //udd(:,1) = M\(p(:,1) - C*ud0 - fs(:,1)); + QUESO::GslVector auxPPosition (dofSpace.zeroVector()); + QUESO::GslVector auxUddPosition(dofSpace.zeroVector()); + p.getPositionValues (0,auxPPosition); + fs.getPositionValues(0,auxFsPosition); + + //auxUddPosition = M.invertMultiply(auxPPosition - C*ud0 - auxFsPosition); + //using the fact that inv(M) is diagonal + QUESO::GslVector invMvec(dofSpace.zeroVector()); + for (unsigned int ii=0; ii<4; ++ii){ + invMvec[ii]=1./Mvec[ii]; + } + QUESO::GslMatrix invM (invMvec); + auxUddPosition=invM*(auxPPosition-C*ud0-auxFsPosition); + + udd.setPositionValues(0,auxUddPosition); + + QUESO::GslMatrix A ((1./bdt)*M + g_b*C); + QUESO::GslMatrix B ((1./(2.*beta))*M + dt*(g_2b-1.)*C); + QUESO::GslMatrix kgT(kT + g_bdt*C + one_bdt2*M); + + QUESO::GslVector fsj (dofSpace.zeroVector()); + + QUESO::GslVector ui (dofSpace.zeroVector()); + QUESO::GslVector udi (dofSpace.zeroVector()); + QUESO::GslVector uddi (dofSpace.zeroVector()); + QUESO::GslVector dpi (dofSpace.zeroVector()); + QUESO::GslVector dpgi (dofSpace.zeroVector()); + QUESO::GslVector auxVec1(dofSpace.zeroVector()); + QUESO::GslVector auxVec2(dofSpace.zeroVector()); + QUESO::GslVector resfor0(dofSpace.zeroVector()); + QUESO::GslVector fs0 (dofSpace.zeroVector()); + + for (unsigned int i = 0; i < (nsteps-1); ++i) { + u.getPositionValues (i,ui); + ud.getPositionValues (i,udi); + udd.getPositionValues(i,uddi); + p.getPositionValues (i,auxVec1); + p.getPositionValues (i+1,auxVec2); + dpi = auxVec2 - auxVec1; + dpgi = dpi + A*udi + B*uddi; + + // Solve for duj from kgi and dpgi using Newton-Raphson + // BEGIN NEWTON RAPHSON + resfor.getPositionValues(i,resfor0); + fs.getPositionValues (i,fs0); + QUESO::GslVector dR(dpgi); + unsigned int j = 0; + QUESO::GslVector duj(dofSpace.zeroVector()); + + QUESO::GslVector ru0(dofSpace.zeroVector()); + QUESO::GslVector rv0(dofSpace.zeroVector()); + ui.matlabDiff (1,ui[0], ru0); //ru0 = [ui(1); diff(ui) ]; + udi.matlabDiff(1,udi[0],rv0); //rv0 = [udi(1); diff(udi)]; + + unsigned int aOutput = 1; + QUESO::GslVector ruj(dofSpace.zeroVector()); + QUESO::GslVector rvj(dofSpace.zeroVector()); + QUESO::GslVector uj (dofSpace.zeroVector()); + QUESO::GslVector vj (dofSpace.zeroVector()); + + while (true) { + j = j + 1; + if (j > 10000) { + // error('Newton Raphson is not converging'); + aOutput=0; + break; + } + if (aOutput == 0) { + break; + } + QUESO::GslVector dujold(duj); + //duj = kgT.invertMultiply(dR); + //replaced with exact solutions of linear eqs + double c3=(dR[1]-kgT(1,0)*dR[0]/kgT(0,0))/kgT(1,2); + double m3=(kgT(1,0)*kgT(0,1)/kgT(0,0)-kgT(1,1))/kgT(1,2); + double c4=(dR[2]-kgT(2,2)*c3)/kgT(2,3); + double m4=-(kgT(2,1)+kgT(2,2)*m3)/kgT(2,3); + duj[1]=(dR[3]-kgT(3,2)*c3-kgT(3,3)*c4)/(kgT(3,2)*m3+kgT(3,3)*m4); + duj[0]=dR[0]/kgT(0,0)-kgT(0,1)/kgT(0,0)*duj[1]; + duj[2]=c3+m3*duj[1];duj[3]=c4+m4*duj[1]; + + QUESO::GslVector dudj(g_bdt*duj - g_b*udi + dt*(1.-g_2b)*uddi); + uj = ui + duj; + vj = udi + dudj; + + if (useLinear == false) { + uj.matlabDiff(1,uj[0],ruj); //ruj = [uj(1); diff(uj)]; + vj.matlabDiff(1,vj[0],rvj); //rvj = [vj(1); diff(vj)]; + + for (unsigned int w = 0; w < ndof; ++w) { + restoringForce(ruj[w],ru0[w],rvj[w],rv0[w],resfor0[w],¶m[w], + NULL, &auxResforPosition[w], &kTdiag[w], ¶m[w]); + } + + resfor.setPositionValues(i+1,auxResforPosition); + auxResforPosition.matlabDiff(0,-auxResforPosition[ndof-1],fsj); //fsj = [-diff(resfor(:,i+1)); resfor(ndof,i+1)]; + fsj *= -1.; + + ckmatrix(kTdiag,kT); + kgT = kT + g_bdt*C + one_bdt2*M; + } + else { + fsj = kT*uj; + } + QUESO::GslVector df(fsj - fs0); + df -= kT*duj; + dR = dpgi - df; + + if (((duj-dujold).norm2() < 1.e-12) || + (df.norm2() < 1.e-12)) { + break; + } + } // end while + + fs.setPositionValues(i+1,fsj); + if (useLinear == false) { + ru.setPositionValues(i+1,ruj); + } + // END NEWTON RAPHSON + + t[i+1] = t[i] + dt; + QUESO::GslVector duddi((1./bdt2)*duj - (1./bdt)*udi - one_2b*uddi); + u.setPositionValues (i+1,uj); + ud.setPositionValues (i+1,vj); + udd.setPositionValues(i+1,uddi+duddi); + } // end for + + return; +} diff --git a/examples/hysteretic/src/example_hyst.h b/examples/hysteretic/src/example_hyst.h new file mode 100644 index 000000000..97a157f4f --- /dev/null +++ b/examples/hysteretic/src/example_hyst.h @@ -0,0 +1,55 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#ifndef __EX_HYST_MODEL_H__ +#define __EX_HYST_MODEL_H__ + +#include +#include + +void hystereticModel( + const QUESO::BaseEnvironment& env, + const QUESO::GslVector& massInputVec, + const QUESO::GslVector& kInputVec, + const QUESO::GslVector& rInputVec, + const QUESO::GslVector& uInputVec, + double rho, + double gamma, + const std::vector& a, + std::vector& t, + QUESO::SequenceOfVectors& u, + QUESO::SequenceOfVectors& ud, + QUESO::SequenceOfVectors& udd, + QUESO::SequenceOfVectors& resfor, + QUESO::SequenceOfVectors& ru); + +#endif diff --git a/examples/hysteretic/src/example_likelihood.C b/examples/hysteretic/src/example_likelihood.C new file mode 100644 index 000000000..f0c475e92 --- /dev/null +++ b/examples/hysteretic/src/example_likelihood.C @@ -0,0 +1,138 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#include +#include + +double likelihoodRoutine( + const QUESO::GslVector& paramValues, + const QUESO::GslVector* paramDirection, + const void* functionDataPtr, + QUESO::GslVector* gradVector, + QUESO::GslMatrix* hessianMatrix, + QUESO::GslVector* hessianEffect) +{ + const QUESO::BaseEnvironment& env = paramValues.env(); + UQ_FATAL_TEST_MACRO(paramValues.sizeLocal() != 15, + env.fullRank(), + "example_likelihood()", + "invalid parameter size"); + + const std::vector* >& + floor = ((likelihoodRoutine_DataType *) functionDataPtr)->floor; + const std::vector& + accel = ((likelihoodRoutine_DataType *) functionDataPtr)->accel; + + unsigned int numFloors = floor.size(); + unsigned int numTimeSteps = accel.size(); + + UQ_FATAL_TEST_MACRO((numFloors != 4), + env.fullRank(), + "example_likelihood()", + "invalid 'numFloors'"); + + UQ_FATAL_TEST_MACRO((numTimeSteps != 401), + env.fullRank(), + "example_likelihood()", + "invalid 'numTimeSteps'"); + + for (unsigned int i = 0; i < numFloors; ++i) { + UQ_FATAL_TEST_MACRO(floor[i]->size() != numTimeSteps, + env.fullRank(), + "example_likelihood()", + "invalid number of steps"); + } + + QUESO::VectorSpace floorSpace(env, "floor_", numFloors, NULL); + + double sigmaSq = paramValues[0]; + + QUESO::GslVector kVec(floorSpace.zeroVector()); + kVec[0] = 2.20e+7 * exp(paramValues[1]); + kVec[1] = 2.00e+7 * exp(paramValues[2]); + kVec[2] = 1.70e+7 * exp(paramValues[3]); + kVec[3] = 1.45e+7 * exp(paramValues[4]); + + QUESO::GslVector rVec(floorSpace.zeroVector()); + rVec[0] = 1.0e-1 * exp(paramValues[5]); + rVec[1] = 1.0e-1 * exp(paramValues[6]); + rVec[2] = 1.0e-1 * exp(paramValues[7]); + rVec[3] = 1.0e-1 * exp(paramValues[8]); + + QUESO::GslVector uVec(floorSpace.zeroVector()); + uVec[0] = 8.0e-3 * exp(paramValues[9]); + uVec[1] = 8.0e-3 * exp(paramValues[10]); + uVec[2] = 7.0e-3 * exp(paramValues[11]); + uVec[3] = 7.0e-3 * exp(paramValues[12]); + + double rho = 7.959e-1 * exp(paramValues[13]); + + double gamma = 2.500e-3 * exp(paramValues[14]); + + std::vector t(numTimeSteps,0.); + QUESO::SequenceOfVectors u (floorSpace,numTimeSteps,""); // absolute displacement + QUESO::SequenceOfVectors ud (floorSpace,numTimeSteps,""); // velocity + QUESO::SequenceOfVectors udd (floorSpace,numTimeSteps,""); // acceleration + QUESO::SequenceOfVectors resfor(floorSpace,numTimeSteps,""); // restoring force + QUESO::SequenceOfVectors ru (floorSpace,numTimeSteps,""); // relative displacement + + QUESO::GslVector massVec(floorSpace.zeroVector()); + massVec.cwSet(2.0e+4); + + hystereticModel(env, + massVec, + kVec, + rVec, + uVec, + rho, + gamma, + accel, + t, // output + u, + ud, + udd, + resfor, + ru); + + QUESO::GslVector auxVec(floorSpace.zeroVector()); + + double sum = 0.; + for (unsigned int i = 0; i < numFloors; ++i) { + for (unsigned int j = 0; j < numTimeSteps; ++j) { + udd.getPositionValues(j,auxVec); + sum += ((*floor[i])[j]-auxVec[i]-accel[j])*((*floor[i])[j]-auxVec[i]-accel[j]); + } + } + + double result = -0.5*((double) numFloors)*((double) numTimeSteps)*log(2.*M_PI*sigmaSq) - 0.5*sum/sigmaSq; + return result; +} diff --git a/examples/hysteretic/src/example_likelihood.h b/examples/hysteretic/src/example_likelihood.h new file mode 100644 index 000000000..c54affd3a --- /dev/null +++ b/examples/hysteretic/src/example_likelihood.h @@ -0,0 +1,53 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#ifndef __EX_LIKELIHOOD_H__ +#define __EX_LIKELIHOOD_H__ + +#include + +struct +likelihoodRoutine_DataType +{ + std::vector* > floor; + std::vector accel; +}; + +double likelihoodRoutine( + const QUESO::GslVector& paramValues, + const QUESO::GslVector* paramDirection, + const void* functionDataPtr, + QUESO::GslVector* gradVector, + QUESO::GslMatrix* hessianMatrix, + QUESO::GslVector* hessianEffect); + +#endif diff --git a/examples/hysteretic/src/example_main.C b/examples/hysteretic/src/example_main.C new file mode 100644 index 000000000..e7ce54231 --- /dev/null +++ b/examples/hysteretic/src/example_main.C @@ -0,0 +1,54 @@ +/*-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- + * + * Copyright (C) 2008-2013 The PECOS Development Team + * + * Please see http://pecos.ices.utexas.edu for more information. + * + * This file is part of the QUESO Library (Quantification of Uncertainty + * for Estimation, Simulation and Optimization). + * + * QUESO is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * QUESO is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with QUESO. If not, see . + * + *-------------------------------------------------------------------------- + * + * $Id$ + * + * Brief description of this file: + * + *-------------------------------------------------------------------------- + *-------------------------------------------------------------------------- */ + +#include + +int main(int argc, char* argv[]) +{ + // Initialize environment + MPI_Init(&argc,&argv); + QUESO::FullEnvironment* env = + new QUESO::FullEnvironment(MPI_COMM_WORLD,argv[1],"",NULL); + + // Compute +#if 1 + compute(*env); +#else + debug_hyst(*env); +#endif + + // Finalize environment + delete env; + MPI_Finalize(); + + return 0; +} diff --git a/examples/hysteretic/tests/test_2013_12_11/an.txt b/examples/hysteretic/tests/test_2013_12_11/an.txt new file mode 100644 index 000000000..78e67b521 --- /dev/null +++ b/examples/hysteretic/tests/test_2013_12_11/an.txt @@ -0,0 +1,401 @@ + -1.649272592509852 + -2.317018017753228 + 1.190280837001081 + 0.704296447081033 + 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+ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Multilevel algorithm +############################################### +#ip_ml_help = anything +ip_ml_dataOutputFileName = outputData/sipOutput_ml +ip_ml_dataOutputAllowedSet = 0 1 + +############################################### +# All levels, unless otherwise specified +############################################### +ip_ml_default_minEffectiveSizeRatio = 0.49 +ip_ml_default_maxEffectiveSizeRatio = 0.51 +ip_ml_default_rawChain_size = 1000 +#ip_ml_default_rawChain_dataOutputFileName = outputData/rawChain_ml +#ip_ml_0_rawChain_dataOutputFileName = outputData/rawChain_ml + +#ip_ml_default_scaleCovMatrix = 0 +#ip_ml_default_dr_maxNumExtraStages = 2 +#ip_ml_default_dr_listOfScalesForExtraStages = 10. 25. + +############################################### +# Last level (level 4 for 'example_gsl' executable) +############################################### +ip_ml_last_dataOutputFileName = outputData/sipOutput_ml +ip_ml_last_dataOutputAllowedSet = 0 1 +ip_ml_last_rawChain_size = 5000 +ip_ml_last_rawChain_computeStats = 1 +ip_ml_last_rawChain_dataOutputFileName = outputData/rawChain_ml + +#ip_ml_last_scaleCovMatrix = 0 +#ip_ml_last_dr_maxNumExtraStages = 2 +#ip_ml_last_dr_listOfScalesForExtraStages = 10. 25. + +ip_ml_last_rawChain_stats_kde_compute = 1 +ip_ml_last_rawChain_stats_kde_numEvalPositions = 250 +ip_ml_last_rawChain_stats_covMatrix_compute = 1 +ip_ml_last_rawChain_stats_corrMatrix_compute = 1 diff --git a/examples/hysteretic/tests/test_2013_12_11/measured_data1_1.txt b/examples/hysteretic/tests/test_2013_12_11/measured_data1_1.txt new file mode 100644 index 000000000..7ab68099c --- /dev/null +++ b/examples/hysteretic/tests/test_2013_12_11/measured_data1_1.txt @@ -0,0 +1,401 @@ + 0.705577705098143 + 0.190874466938426 + 0.690439318352932 + 0.127439984873230 + -0.186989347636732 + 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4fe5a65add1f30252b7d906ab8c8339ec451da9f Mon Sep 17 00:00:00 2001 From: Damon McDougall Date: Mon, 27 Jan 2014 16:19:27 -0600 Subject: [PATCH 08/20] Fix m4 badness --- m4/common/queso_new.m4 | 13 +++---------- 1 file changed, 3 insertions(+), 10 deletions(-) diff --git a/m4/common/queso_new.m4 b/m4/common/queso_new.m4 index eaf02ac7a..40874dcf1 100644 --- a/m4/common/queso_new.m4 +++ b/m4/common/queso_new.m4 @@ -82,7 +82,7 @@ HAVE_QUESO=0 AC_LANG_PUSH([C++]) - AC_CHECK_HEADER([uqDefines.h], [found_header=yes], [found_header=no]) + AC_CHECK_HEADER([queso/queso.h], [found_header=yes], [found_header=no]) #----------------------- # Minimum version check @@ -115,7 +115,7 @@ HAVE_QUESO=0 version_succeeded=no AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[ - @%:@include + @%:@include ]], [[ #if QUESO_MAJOR_VERSION > $MAJOR_VER /* Sweet nibblets */ @@ -136,14 +136,7 @@ HAVE_QUESO=0 fi dnl end test if header if available - dnl PB: Replaced the previous CHECK_HEADER test with COMPILE_IFELSE test becaue - dnl PB: uqDefines.h depends on MPI and the user will always get a CPP warning - dnl PB: since the c-preprocessor test will not use MPI. - dnl PB: However, I'm still not satisfied because no message is emitted and - dnl PB: if somehow the library was present, but not the includes, the user - dnl PB: will be a little lost. - -##### AC_COMPILE_IFELSE([#include "uqDefines.h"],[found_header=yes],[found_header=no]) +##### AC_COMPILE_IFELSE([#include "queso/queso.h"],[found_header=yes],[found_header=no]) ##### ##### ac_QUESO_BOOST_PROGRAM_OPTIONS_LDFLAGS_COMPILER='' ##### From ac67f5ba2051d0dc98870f9605b71d73b3c4d186 Mon Sep 17 00:00:00 2001 From: Damon McDougall Date: Wed, 12 Feb 2014 11:57:49 -0600 Subject: [PATCH 09/20] Make HDF dependency in hdf_new.m4 optional --- m4/common/hdf5_new.m4 | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/m4/common/hdf5_new.m4 b/m4/common/hdf5_new.m4 index fbee3f38f..394cec38b 100644 --- a/m4/common/hdf5_new.m4 +++ b/m4/common/hdf5_new.m4 @@ -51,7 +51,7 @@ is_package_required=ifelse([$2], ,no, $2 ) HAVE_HDF5=0 -### if test "${with_hdf5}" != no ; then +if test "${with_hdf5}" != no ; then if test -d "${HDF5_PREFIX}/lib" ; then @@ -182,7 +182,7 @@ HAVE_HDF5=0 AC_SUBST(HAVE_HDF5) -### fi +fi AM_CONDITIONAL(HDF5_ENABLED,test x$HAVE_HDF5 = x1) From 6bb9bbe5982bceb99d01ca90396aaf3436619597 Mon Sep 17 00:00:00 2001 From: Damon McDougall Date: Sat, 22 Feb 2014 12:00:54 -0600 Subject: [PATCH 10/20] Adding a skeletal template example inverse problem It's O(100) lines. The other examples are a little big. --- .../template_example/template_example.cpp | 107 ++++++++++++++++++ .../template_example_input.txt | 48 ++++++++ 2 files changed, 155 insertions(+) create mode 100644 examples/template_example/template_example.cpp create mode 100644 examples/template_example/template_example_input.txt diff --git a/examples/template_example/template_example.cpp b/examples/template_example/template_example.cpp new file mode 100644 index 000000000..01ee6f29a --- /dev/null +++ b/examples/template_example/template_example.cpp @@ -0,0 +1,107 @@ +#include +#include +#include +#include +#include +#include +#include + +template +class Likelihood : public QUESO::BaseScalarFunction +{ +public: + + Likelihood(const char * prefix, const QUESO::VectorSet & domain) + { + // Setup here + } + + virtual ~Likelihood() + { + // Deconstruct here + } + + virtual double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const + { + // 1) Run the forward code at the point domainVector + // domainVector[0] is the first element of the parameter vector + // damainVector[1] is the second element of the parameter vector + // and so on + // + // 2) Compare to data, y + // Usually we compute something like + // || MyModel(domainVector) - y ||^2 / (sigma * sigma) + // + // 3) Return below + +#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN + // return -0.5 * misfit; +#else + // return misfit; +#endif + } + + virtual double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const + { + // return exp(this->lnValue(...)) + } + +private: + // Maybe store the observed data, y, here. +}; + +int main(int argc, char ** argv) { + MPI_Init(&argc, &argv); + + // Step 0 of 5: Set up environment + QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL); + + // Step 1 of 5: Instantiate the parameter space + QUESO::VectorSpace paramSpace(env, + "param_", 1, NULL); + + // Step 2 of 5: Set up the prior + QUESO::GslVector paramMins(paramSpace.zeroVector()); + paramMins.cwSet(min_val); + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + paramMaxs.cwSet(max_val); + + QUESO::BoxSubset paramDomain("param_", + paramSpace, paramMins, paramMaxs); + + // Uniform prior here. Could be a different prior. + QUESO::UniformVectorRV priorRv("prior_", + paramDomain); + + // Step 3 of 5: Set up the likelihood using the class above + Likelihood lhood("llhd_", paramDomain); + + // Step 4 of 5: Instantiate the inverse problem + QUESO::GenericVectorRV + postRv("post_", paramSpace); + + QUESO::StatisticalInverseProblem + ip("", NULL, priorRv, lhood, postRv); + + // Step 5 of 5: Solve the inverse problem + QUESO::GslVector paramInitials(paramSpace.zeroVector()); + + // Initial condition of the chain + paramInitials[0] = 0.0; + paramInitials[1] = 0.0; + + QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector()); + + for (unsigned int i = 0; i < 2; i++) { + // Might need to tweak this + proposalCovMatrix(i, i) = 0.1; + } + + ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix); + + MPI_Finalize(); + + return 0; +} diff --git a/examples/template_example/template_example_input.txt b/examples/template_example/template_example_input.txt new file mode 100644 index 000000000..fe0a01a6c --- /dev/null +++ b/examples/template_example/template_example_input.txt @@ -0,0 +1,48 @@ +############################################### +# UQ Environment +############################################### +#env_help = anything +env_numSubEnvironments = 1 +env_subDisplayFileName = outputData/display +env_subDisplayAllowAll = 0 +env_subDisplayAllowedSet = 0 +env_displayVerbosity = 2 +env_syncVerbosity = 0 +env_seed = 0 + +############################################### +# Statistical inverse problem (ip) +############################################### +#ip_help = anything +ip_computeSolution = 1 +ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +############################################### +#ip_mh_help = anything +ip_mh_dataOutputFileName = outputData/sipOutput +ip_mh_dataOutputAllowedSet = 0 1 + +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_size = 10000 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 50000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputFileName = outputData/ip_raw_chain +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_rawChain_computeStats = 1 + +ip_mh_displayCandidates = 0 +ip_mh_putOutOfBoundsInChain = 1 +ip_mh_tk_useLocalHessian = 0 +ip_mh_tk_useNewtonComponent = 1 +ip_mh_dr_maxNumExtraStages = 1 +ip_mh_dr_listOfScalesForExtraStages = 5. +ip_mh_am_initialNonAdaptInterval = 0 +ip_mh_am_adaptInterval = 100 +ip_mh_am_eta = 1.92 +ip_mh_am_epsilon = 1.e-5 + +ip_mh_filteredChain_generate = 0 From 95b144d48028fab1fadf70f4c9b2754fa266c574 Mon Sep 17 00:00:00 2001 From: Damon McDougall Date: Fri, 18 Apr 2014 10:27:15 -0500 Subject: [PATCH 11/20] Switch off putOutOfBoundsInChain for template eg --- examples/template_example/template_example_input.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/template_example/template_example_input.txt b/examples/template_example/template_example_input.txt index fe0a01a6c..543539455 100644 --- a/examples/template_example/template_example_input.txt +++ b/examples/template_example/template_example_input.txt @@ -35,7 +35,7 @@ ip_mh_rawChain_dataOutputAllowedSet = 0 1 ip_mh_rawChain_computeStats = 1 ip_mh_displayCandidates = 0 -ip_mh_putOutOfBoundsInChain = 1 +ip_mh_putOutOfBoundsInChain = 0 ip_mh_tk_useLocalHessian = 0 ip_mh_tk_useNewtonComponent = 1 ip_mh_dr_maxNumExtraStages = 1 From 20a487db9d0da425f97a98cec4d5816a59bbddf9 Mon Sep 17 00:00:00 2001 From: Nicholas Malaya Date: Mon, 21 Apr 2014 13:40:43 -0500 Subject: [PATCH 12/20] update queso.page with damons email Karl's email is gone, Damon is maintainer. --- doxygen/queso.page | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doxygen/queso.page b/doxygen/queso.page index d46f59e67..52d733139 100644 --- a/doxygen/queso.page +++ b/doxygen/queso.page @@ -20,7 +20,7 @@ based/html format. \section bugs Reporting Bugs Bugs in the code and errors or omissions in the documentation can be -reported to karl@ices.utexas.edu. Requests and contributions are +reported to damon@ices.utexas.edu. Requests and contributions are welcome at the same e-mail address. All bug reports should include: From 646e3c4d8ba7a291cfb7b7e9c62793ae522ea162 Mon Sep 17 00:00:00 2001 From: "Roy H. Stogner" Date: Tue, 22 Apr 2014 17:05:57 -0500 Subject: [PATCH 13/20] warning/error fixes for icpc 13.1 I've seen functions with undefined return values lead to runtime corruption in the past, although intel only gives us a warning about it. Trying to use aggregate initialization for a copy, on the other hand, gives me a compile-time error. --- src/core/src/InfiniteDimensionalMCMCSampler.C | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/core/src/InfiniteDimensionalMCMCSampler.C b/src/core/src/InfiniteDimensionalMCMCSampler.C index d7a793693..320fa9779 100644 --- a/src/core/src/InfiniteDimensionalMCMCSampler.C +++ b/src/core/src/InfiniteDimensionalMCMCSampler.C @@ -270,6 +270,9 @@ hid_t InfiniteDimensionalMCMCSampler::_create_scalar_dataset(const std::string & return dset; } + + hid_t dummy; + return dummy; } void InfiniteDimensionalMCMCSampler::_append_scalar_dataset(hid_t dset, double data) @@ -284,7 +287,7 @@ void InfiniteDimensionalMCMCSampler::_append_scalar_dataset(hid_t dset, double d // Extend the dataset // Set dims to be the *new* dimension of the extended dataset - dims[0] = { this->_iteration / this->m_ov->m_save_freq }; + dims[0] = this->_iteration / this->m_ov->m_save_freq; err = H5Dset_extent(dset, dims); // Select hyperslab on file dataset From 24eaa78f62df7a9c691d7ef11dff180263075df7 Mon Sep 17 00:00:00 2001 From: Damon McDougall Date: Tue, 6 May 2014 14:13:00 -0500 Subject: [PATCH 14/20] Fix old algorithm2e command and update manual --- QUESO_users_manual.pdf | Bin 1408388 -> 3601100 bytes manual/users/users_1_introduction.tex | 16 ++++++++-------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/QUESO_users_manual.pdf b/QUESO_users_manual.pdf index fd6cea5058636840797016e3df30515e6228d4b8..20e659e4262d57e769ebeaef3d5ac1f853471c9f 100644 GIT binary patch literal 3601100 zcma&NcT`hR(=U7ogc7<`r7DVaP>^1wDZPWzLX!?bsse^YlwK61ccdsCR5}C;O{Ite zQiDjBqS9OLj_>>2?_2BMweIU5uEW`L_MVwN^PAZ-JC}?!v_!9nT|-mR@?Dpe^)|kH=OW{UAo`#|ZV_HLohvT{ zFELYnNk#PE(7t$)iZR8AeeOwe^zR$LGoSuU%DJIt;`WrWkKNbtsk@E|sR&=@6P?oM z7hllWYZ|BGX*F*@(Gkou5PbVqC>QxnmMNo5B=;O5bF7^S(2>p8duZmvaQEm>0bQeOvKosSPWiT@b{inmS)P6Tv6?gv2-2 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