diff --git a/.gitignore b/.gitignore index 8ed4b000c..8c85666c7 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,11 @@ *.gcda *.gcno *~ +*.log +*.trs +*.dirstamp +*.aux +queso-*tar.gz Makefile.in Makefile @@ -17,6 +22,13 @@ aminclude.am autom4te.cache build-aux .license.stamp +queso.pc +queso-deps.pc +m4/libtool.m4 +m4/ltoptions.m4 +m4/ltsugar.m4 +m4/ltversion.m4 +m4/lt~obsolete.m4 _configs.sed config.log @@ -27,9 +39,22 @@ config_queso.h.tmp.in config_queso.h.tmp.in~ configure +m4/libtool.m4 +m4/ltoptions.m4 +m4/ltsugar.m4 +m4/ltversion.m4 +m4/lt~obsolete.m4 + doxygen/txt_common/about_vpath.page doxygen/queso.dox docs +manual/users/figs +manual/users/make_all_failed +manual/users/users.bbl +manual/users/users.blg +manual/users/users.out +manual/users/users.toc +manual/users/QUESO_users_manual.pdf examples/exInfoTheory_gsl examples/exSimpleStatisticalInverseProblem_gsl @@ -45,6 +70,17 @@ examples/infinite_dim/parallel_inverse_options examples/inverse_problem examples/operator examples/parallel_inverse_problem +examples/gpmsa_scalar +examples/bimodal_gsl +examples/exSimpleStatisticalForwardProblem_gsl +examples/gp/scalar/outputData +examples/gpscalar +examples/gpscalar2 +examples/hysteretic_gsl +examples/outputData +examples/template_eg +examples/template_example/outputData +examples/template_example/template_example src/core/inc/queso.h src/libqueso.la @@ -94,3 +130,34 @@ test/test_infinite/inf_options test/test_operator test/test_uqexception test/test_SequenceOfVectorsErase +test/test_gpmsa_cobra +test/test_LlhdTargetOutput +test/test_StatisticalInverseProblem/both_input.txt +test/test_StatisticalInverseProblem/llhdout_input.txt +test/test_StatisticalInverseProblem/neither_input.txt +test/test_StatisticalInverseProblem/targetout_input.txt +test/test_StatisticalInverseProblem/test_LlhdTargetOutput.sh +test/test_outputBoth/ +test/test_outputLlhd/ +test/test_outputNeither/ +test/test_outputTarget/ +test/test_Regression/jeffreys_input.txt +test/test_Regression/test_jeffreys_samples.m +test/test_Regression/test_jeffreys_samples.diff.sh +test/test_jeffreys +test/test_gsloptimizer +test/test_seedwithmap +test/test_seedwithmap_fd +test/test_GammaVectorRVDomain +test/test_GaussianMean1DRegression +test/test_Regression/GaussianMean1DRegression_options +test/test_Regression/ctf_dat.txt +test/test_Regression/dakota_pstudy.dat +test/test_Regression/gpmsa_cobra_input.txt +test/test_Regression/test_cobra_samples_diff.sh +test/test_Regression/test_gpmsa_cobra_samples.m +test/test_Regression/adaptedcov_input.txt +test/test_Regression/test_jeffreys_samples_diff.sh +test/test_adaptedcov_output/ +test/test_gpmsa_cobra_output/ +test/test_logitadaptedcov diff --git a/AUTHORS b/AUTHORS index 327ce6555..c90f9a060 100644 --- a/AUTHORS +++ b/AUTHORS @@ -1,12 +1,12 @@ - Contributors to QUESO Development: Paul T. Bauman Sai Hung Cheung Nicholas Malaya Damon McDougall -Todd Oliver +Todd A. Oliver Ernesto E. Prudencio Karl W. Schulz Rhys Ulerich - +Eric Wright +Rebecca E. Morrison diff --git a/CHANGES b/CHANGES index 483267a7a..a102d7962 100644 --- a/CHANGES +++ b/CHANGES @@ -2,22 +2,29 @@ QUESO: Quantification of Uncertainty for Estimation, Simulation, and Optimization. ----------------------------------------------------- + +Version 0.51.0 + * Add canned likelihood for scalar GPMSA use-case a la Higdon et al + * Add a logit-transformed transition kernel for more efficient proposals + * Adding jeffreys distribution as an available prior distribution + * Adding likelihood value caching to ML sampler + * Adding Eric Wright to contributors list + +Version 0.50.0 + * Implemented MCMC sampling on function spaces + * Added function space MCMC unit tests + * Added serial and parallel function space MCMC examples + * Replace exit() calls with a macro exception handler + Version 0.47.1 * created two simple statistical examples, simpleStatisticalForwardProblem and simpleStatisticalInverseProblem * included description of the new examples on QUESO user's manual, including how to run and to process the generated data * updated QUESO user's manual to include description of validationCycle example * updated links for how to obtain QUESO and contact information (github/git) - + Version 0.47.0 * removed example statisticalInverseProblem as it has a bug - it fails to recreate the problem described in the Normal test from Laine's MCMC tool: http://helios.fmi.fi/~lainema/mcmc/ex/normalex.html. -Version 0.50.0 (In progress) - - * Implemented MCMC sampling on function spaces - * Added function space MCMC unit tests - * Added serial and parallel function space MCMC examples - * Replace exit() calls with a macro exception handler - Version 0.47.1 (23 Sep 2013) * Update manual and fix broken Makefile @@ -97,7 +104,7 @@ https://svn.ices.utexas.edu/repos/pecos/uq/queso/releases/0.44.0 * added uqMpiCommClass * added uqMapClass * added uqDistArrayClass - * configure options modified to allow for optional use of Trilinos, HDF5 and GLPK; + * configure options modified to allow for optional use of Trilinos, HDF5 and GLPK; with the change, the minimum dependencies for QUESO are Boost, GSL, and GRVY * improved uqScalarSequenceClass and uqSequenceOfVectorsClass @@ -118,7 +125,7 @@ https://svn.ices.utexas.edu/repos/pecos/uq/queso/releases/0.42 * fixed output runtime failure when running multiple chains in parallel * fixed race condition in output directory creation * inclusion of automatic licensing header support - * added minimum version requirement for autoconf + * added minimum version requirement for autoconf * added configuration support for the GNU Linear Programming Toolkit (GLPK) * added interface to HDF5 for some I/O * added quadrature classes @@ -140,7 +147,7 @@ https://svn.ices.utexas.edu/repos/pecos/uq/queso/releases/0.41 * user likelihood routines should now return ln(value), instead of -2*ln(value) * inclusion of inverse Gamma random variable (in addition to standard uniform and Gaussian options) - * uniform pdf now returns (1/volume of domain), instead of just 1. If volume is infinite or zero, + * uniform pdf now returns (1/volume of domain), instead of just 1. If volume is infinite or zero, then the uniform pdf returns 1. * random variables can be now be formed by concatenation of existing random variable definitions (uniform, Gaussian, inverse Gamma) diff --git a/Makefile.am b/Makefile.am index 4b29dd442..ec9036893 100644 --- a/Makefile.am +++ b/Makefile.am @@ -14,6 +14,9 @@ include $(top_srcdir)/doxygen/aminclude.am AUX_DIST = build-aux/install-sh \ build-aux/missing +pkgconfigdir = $(libdir)/pkgconfig +pkgconfig_DATA = queso.pc queso-deps.pc + if CODE_COVERAGE_ENABLED lcov_dir=$(top_builddir)/docs/html/lcov @@ -124,6 +127,14 @@ DISTCLEANFILES += test/t01_valid_cycle/outputData/file_val_fp_qoi2.m DISTCLEANFILES += test/t01_valid_cycle/outputData/file_val_ip_raw2.m DISTCLEANFILES += test/t01_valid_cycle/outputData/tgaValOutput_sub0.m DISTCLEANFILES += test/t01_valid_cycle/outputData/tgaCalOutput_sub0.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_cal_ip_raw2_loglikelihood.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_cal_ip_raw2_loglikelihood_sub0.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_cal_ip_raw2_logtarget.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_cal_ip_raw2_logtarget_sub0.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_val_ip_raw2_loglikelihood.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_val_ip_raw2_loglikelihood_sub0.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_val_ip_raw2_logtarget.m +DISTCLEANFILES += test/t01_valid_cycle/outputData/file_val_ip_raw2_logtarget_sub0.m DISTCLEANFILES += test/t04_bimodal/outputData/display_sub0.txt DISTCLEANFILES += test/t04_bimodal/outputData/sipOutput_ml_sub0.m DISTCLEANFILES += test/t04_bimodal/outputData/filtChain_ml.m @@ -143,6 +154,14 @@ DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_filt_chain_sub0.m DISTCLEANFILES += test/t02_sip_sfp/outputData/fp_q_seq_sub0.m DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_filt_chain.m DISTCLEANFILES += test/t02_sip_sfp/outputData/fp_p_seq_sub0.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_filt_chain_loglikelihood.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_filt_chain_loglikelihood_sub0.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_filt_chain_logtarget.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_filt_chain_logtarget_sub0.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_raw_chain_loglikelihood.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_raw_chain_loglikelihood_sub0.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_raw_chain_logtarget.m +DISTCLEANFILES += test/t02_sip_sfp/outputData/ip_raw_chain_logtarget_sub0.m DISTCLEANFILES += test/outputData/* DISTCLEANFILES += aminclude.am DISTCLEANFILES += config_queso.h diff --git a/README.md b/README.md index 150a9edb2..b8e9916ce 100644 --- a/README.md +++ b/README.md @@ -83,3 +83,21 @@ feel free to ask questions on one of the mailing lists: list for questions regarding usage and reporting bugs - [queso-dev](https://groups.google.com/forum/#!forum/queso-dev) mailing list for discussion regarding development of `QUESO` + +Citing QUESO +------- +Please add the following citation to any paper, technical report or +article describing the use of the `QUESO` library: + +```bibtex +@inproceedings{Prudencio2012, + author = {Prudencio, Ernesto E and Schulz, Karl W}, + booktitle = {Euro-Par 2011: Parallel Processing Workshops}, + pages = {398--407}, + publisher = {Springer}, + title = {{The parallel C++ statistical library ‘QUESO’: Quantification of + Uncertainty for Estimation, Simulation and Optimization}}, + url = {http://dx.doi.org/10.1007/978-3-642-29737-3\_44}, + year = {2012} +} +``` diff --git a/configure.ac b/configure.ac index 2fc452905..6f39f8bb0 100644 --- a/configure.ac +++ b/configure.ac @@ -2,7 +2,12 @@ # Process this file with autoconf to produce a configure script. AC_PREREQ(2.65) -AC_INIT([queso], [0.50.1], [queso-users@googlegroups.com]) +AC_INIT([queso], [0.51.0], [queso-users@googlegroups.com]) +PACKAGE_DESCRIPTION="The parallel C++ statistical library QUESO: Quantification of uncertainty for estimation, simulation and optimization" +AC_SUBST([PACKAGE_DESCRIPTION]) +PACKAGE_URL="https://github.com/libqueso/queso" +AC_SUBST([PACKAGE_URL]) + AC_CONFIG_MACRO_DIR([m4]) AC_CONFIG_HEADERS(config_queso.h.tmp) @@ -11,7 +16,7 @@ AX_PREFIX_CONFIG_H(config_queso.h,QUESO,config_queso.h.tmp) AC_CONFIG_AUX_DIR([build-aux]) AC_CANONICAL_TARGET() -AM_INIT_AUTOMAKE +AM_INIT_AUTOMAKE([color-tests subdir-objects]) # Release versioning @@ -171,11 +176,12 @@ AX_SUMMARIZE_ENV # Generate makefiles AC_CONFIG_FILES([ + queso.pc + queso-deps.pc Makefile inc/queso/Makefile src/Makefile src/contrib/ANN/Makefile - src/contrib/ANN/lib/Makefile src/contrib/ANN/test/Makefile src/core/inc/queso.h examples/Makefile @@ -190,11 +196,41 @@ AC_CONFIG_FILES([ test/gsl_tests/input test/test_Environment/copy_env test/test_infinite/inf_options + test/test_Regression/GaussianMean1DRegression_options + test/test_Regression/gpmsa_cobra_input.txt + test/test_Regression/test_cobra_samples_diff.sh + test/test_Regression/test_gpmsa_cobra_samples.m + test/test_Regression/ctf_dat.txt + test/test_Regression/dakota_pstudy.dat + test/test_StatisticalInverseProblem/test_LlhdTargetOutput.sh + test/test_StatisticalInverseProblem/both_input.txt + test/test_StatisticalInverseProblem/llhdout_input.txt + test/test_StatisticalInverseProblem/neither_input.txt + test/test_StatisticalInverseProblem/targetout_input.txt + test/test_Regression/jeffreys_input.txt + test/test_Regression/test_jeffreys_samples_diff.sh + test/test_Regression/test_jeffreys_samples.m + test/test_Regression/adaptedcov_input.txt doxygen/Makefile doxygen/queso.dox doxygen/txt_common/about_vpath.page +], +[ + chmod +x test/test_Regression/test_cobra_samples_diff.sh + chmod +x test/test_StatisticalInverseProblem/test_LlhdTargetOutput.sh + chmod +x test/test_Regression/test_jeffreys_samples_diff.sh ]) +dnl ---------------------------------------------- +dnl Collect files for licence header stamping here +dnl ---------------------------------------------- +_AC_SRCDIRS(.) + +# We have to do this by subdirectory because otherwise distcheck breaks as we +# start picking up files in the directories that it uses +QUESO_STAMPED_FILES=$(find $ac_abs_top_srcdir/{src,examples,test} -name "*.h" -or -name "*.C" | tr "\n" " ") +AC_SUBST(QUESO_STAMPED_FILES) + AC_OUTPUT() #--------------- diff --git a/examples/Makefile.am b/examples/Makefile.am index e81eff4df..02e900a50 100644 --- a/examples/Makefile.am +++ b/examples/Makefile.am @@ -36,7 +36,7 @@ if LIBMESH_ENABLED operatordir = $(prefix)/examples/infinite_dim/operator operator_DATA = $(top_srcdir)/examples/infinite_dim/operator/mesh.e operator_PROGRAMS = operator -operator_SOURCES = $(top_srcdir)/examples/infinite_dim/operator/operator.C +operator_SOURCES = infinite_dim/operator/operator.C operator_LDADD = $(top_builddir)/src/libqueso.la operator_CPPFLAGS = $(QUESO_CPPFLAGS) dist_operator_DATA = $(operator_SOURCES) $(operator_DATA) @@ -44,7 +44,7 @@ dist_operator_DATA = $(operator_SOURCES) $(operator_DATA) gaussian_random_fielddir = $(prefix)/examples/infinite_dim/gaussian_fields gaussian_random_field_DATA = $(top_srcdir)/examples/infinite_dim/gaussian_fields/mesh.e gaussian_random_field_PROGRAMS = gaussian_random_field -gaussian_random_field_SOURCES = $(top_srcdir)/examples/infinite_dim/gaussian_fields/gaussian_random_field.C +gaussian_random_field_SOURCES = infinite_dim/gaussian_fields/gaussian_random_field.C gaussian_random_field_LDADD = $(top_builddir)/src/libqueso.la gaussian_random_field_CPPFLAGS = $(QUESO_CPPFLAGS) dist_gaussian_random_field_DATA = $(gaussian_random_field_SOURCES) $(gaussian_random_field_DATA) @@ -52,7 +52,7 @@ dist_gaussian_random_field_DATA = $(gaussian_random_field_SOURCES) $(gaussian_ra gaussian_random_field_genmeshdir = $(prefix)/examples/infinite_dim/gaussian_fields gaussian_random_field_genmesh_DATA = $(top_srcdir)/examples/infinite_dim/gaussian_fields/mesh.e gaussian_random_field_genmesh_PROGRAMS = gaussian_random_field_genmesh -gaussian_random_field_genmesh_SOURCES = $(top_srcdir)/examples/infinite_dim/gaussian_fields/gaussian_random_field_genmesh.C +gaussian_random_field_genmesh_SOURCES = infinite_dim/gaussian_fields/gaussian_random_field_genmesh.C gaussian_random_field_genmesh_LDADD = $(top_builddir)/src/libqueso.la gaussian_random_field_genmesh_CPPFLAGS = $(QUESO_CPPFLAGS) dist_gaussian_random_field_genmesh_DATA = $(gaussian_random_field_genmesh_SOURCES) $(gaussian_random_field_genmesh_DATA) @@ -60,7 +60,7 @@ dist_gaussian_random_field_genmesh_DATA = $(gaussian_random_field_genmesh_SOURCE invprobdir = $(prefix)/examples/infinite_dim invprob_DATA = $(top_srcdir)/examples/infinite_dim/inverse_options.in invprob_PROGRAMS = inverse_problem -inverse_problem_SOURCES = $(top_srcdir)/examples/infinite_dim/inverse_problem.C +inverse_problem_SOURCES = infinite_dim/inverse_problem.C inverse_problem_LDADD = $(top_builddir)/src/libqueso.la inverse_problem_CPPFLAGS = $(QUESO_CPPFLAGS) dist_invprob_DATA = $(inverse_problem_SOURCES) $(invprob_DATA) @@ -69,7 +69,7 @@ dist_invprob_DATA = $(inverse_problem_SOURCES) $(invprob_DATA) parinvprobdir = $(prefix)/examples/infinite_dim parinvprob_DATA = $(top_srcdir)/examples/infinite_dim/parallel_inverse_options.in parinvprob_PROGRAMS = parallel_inverse_problem -parallel_inverse_problem_SOURCES = $(top_srcdir)/examples/infinite_dim/parallel_inverse_problem.C +parallel_inverse_problem_SOURCES = infinite_dim/parallel_inverse_problem.C parallel_inverse_problem_LDADD = $(top_builddir)/src/libqueso.la parallel_inverse_problem_CPPFLAGS = $(QUESO_CPPFLAGS) dist_parinvprob_DATA = $(parallel_inverse_problem_SOURCES) $(parinvprob_DATA) @@ -83,16 +83,15 @@ exSimpleStatisticalInverseProblem_gsldir = $(prefix)/examples/simpleStatisticalI exSimpleStatisticalInverseProblem_gsl_DATA = $(top_srcdir)/examples/simpleStatisticalInverseProblem/tests/test_2013_08_26/example.inp exSimpleStatisticalInverseProblem_gsl_DATA += $(top_srcdir)/examples/simpleStatisticalInverseProblem/tests/test_2013_08_26/simple_ip_plots.m exSimpleStatisticalInverseProblem_gsl_DATA += $(top_srcdir)/examples/simpleStatisticalInverseProblem/tests/test_2013_08_26/histyy.m -exSimpleStatisticalInverseProblem_gsl_DATA += $(top_srcdir)/examples/simpleStatisticalInverseProblem/src/Makefile_sip_example_margarida exSimpleStatisticalInverseProblem_gsl_PROGRAMS = exSimpleStatisticalInverseProblem_gsl -exSimpleStatisticalInverseProblem_gsl_SOURCES = $(top_srcdir)/examples/simpleStatisticalInverseProblem/src/example_main.C -exSimpleStatisticalInverseProblem_gsl_SOURCES += $(top_srcdir)/examples/simpleStatisticalInverseProblem/src/example_likelihood.C -exSimpleStatisticalInverseProblem_gsl_SOURCES += $(top_srcdir)/examples/simpleStatisticalInverseProblem/src/example_compute.C +exSimpleStatisticalInverseProblem_gsl_SOURCES = simpleStatisticalInverseProblem/src/example_main.C +exSimpleStatisticalInverseProblem_gsl_SOURCES += simpleStatisticalInverseProblem/src/example_likelihood.C +exSimpleStatisticalInverseProblem_gsl_SOURCES += simpleStatisticalInverseProblem/src/example_compute.C -exSimpleStatisticalInverseProblem_gsl_HEADERS = $(top_srcdir)/examples/simpleStatisticalInverseProblem/src/example_likelihood.h -exSimpleStatisticalInverseProblem_gsl_HEADERS += $(top_srcdir)/examples/simpleStatisticalInverseProblem/src/example_compute.h +exSimpleStatisticalInverseProblem_gsl_HEADERS = simpleStatisticalInverseProblem/src/example_likelihood.h +exSimpleStatisticalInverseProblem_gsl_HEADERS += simpleStatisticalInverseProblem/src/example_compute.h exSimpleStatisticalInverseProblem_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -111,16 +110,15 @@ exSimpleStatisticalForwardProblem_gsldir = $(prefix)/examples/simpleStatisticalF exSimpleStatisticalForwardProblem_gsl_DATA = $(top_srcdir)/examples/simpleStatisticalForwardProblem/tests/test_2013_08_27/simple_sfp_example.inp exSimpleStatisticalForwardProblem_gsl_DATA += $(top_srcdir)/examples/simpleStatisticalForwardProblem/tests/test_2013_08_27/simple_fp_plots.m -exSimpleStatisticalForwardProblem_gsl_DATA += $(top_srcdir)/examples/simpleStatisticalForwardProblem/src/Makefile_sfp_example_margarida exSimpleStatisticalForwardProblem_gsl_PROGRAMS = exSimpleStatisticalForwardProblem_gsl -exSimpleStatisticalForwardProblem_gsl_SOURCES = $(top_srcdir)/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_main.C -exSimpleStatisticalForwardProblem_gsl_SOURCES += $(top_srcdir)/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.C -exSimpleStatisticalForwardProblem_gsl_SOURCES += $(top_srcdir)/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.C +exSimpleStatisticalForwardProblem_gsl_SOURCES = simpleStatisticalForwardProblem/src/simple_sfp_example_main.C +exSimpleStatisticalForwardProblem_gsl_SOURCES += simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.C +exSimpleStatisticalForwardProblem_gsl_SOURCES += simpleStatisticalForwardProblem/src/simple_sfp_example_compute.C -exSimpleStatisticalForwardProblem_gsl_HEADERS = $(top_srcdir)/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.h -exSimpleStatisticalForwardProblem_gsl_HEADERS += $(top_srcdir)/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.h +exSimpleStatisticalForwardProblem_gsl_HEADERS = simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.h +exSimpleStatisticalForwardProblem_gsl_HEADERS += simpleStatisticalForwardProblem/src/simple_sfp_example_compute.h exSimpleStatisticalForwardProblem_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -141,10 +139,10 @@ dist_exSimpleStatisticalForwardProblem_gsl_DATA += ${exSimpleStatisticalForwardP # # exStatisticalForwardProblem_gsl_PROGRAMS = exStatisticalForwardProblem_gsl # -# exStatisticalForwardProblem_gsl_SOURCES = $(top_srcdir)/examples/statisticalForwardProblem/src/exStatisticalForwardProblem_gsl.C +# exStatisticalForwardProblem_gsl_SOURCES = statisticalForwardProblem/src/exStatisticalForwardProblem_gsl.C # -# exStatisticalForwardProblem_gsl_HEADERS = $(top_srcdir)/examples/statisticalForwardProblem/src/exStatisticalForwardProblem_appl.h -# exStatisticalForwardProblem_gsl_HEADERS += $(top_srcdir)/examples/statisticalForwardProblem/src/exStatisticalForwardProblem_qoi.h +# exStatisticalForwardProblem_gsl_HEADERS = statisticalForwardProblem/src/exStatisticalForwardProblem_appl.h +# exStatisticalForwardProblem_gsl_HEADERS += statisticalForwardProblem/src/exStatisticalForwardProblem_qoi.h # # exStatisticalForwardProblem_gsl_LDADD = $(top_builddir)/src/libqueso.la # @@ -166,10 +164,10 @@ dist_exSimpleStatisticalForwardProblem_gsl_DATA += ${exSimpleStatisticalForwardP # # exStatisticalInverseProblem_gsl_PROGRAMS = exStatisticalInverseProblem_gsl # -# exStatisticalInverseProblem_gsl_SOURCES = $(top_srcdir)/examples/statisticalInverseProblem/src/exStatisticalInverseProblem_gsl.C +# exStatisticalInverseProblem_gsl_SOURCES = statisticalInverseProblem/src/exStatisticalInverseProblem_gsl.C # -# exStatisticalInverseProblem_gsl_HEADERS = $(top_srcdir)/examples/statisticalInverseProblem/src/exStatisticalInverseProblem_appl.h -# exStatisticalInverseProblem_gsl_HEADERS += $(top_srcdir)/examples/statisticalInverseProblem/src/exStatisticalInverseProblem_likelihood.h +# exStatisticalInverseProblem_gsl_HEADERS = statisticalInverseProblem/src/exStatisticalInverseProblem_appl.h +# exStatisticalInverseProblem_gsl_HEADERS += statisticalInverseProblem/src/exStatisticalInverseProblem_likelihood.h # # exStatisticalInverseProblem_gsl_LDADD = $(top_builddir)/src/libqueso.la # @@ -194,11 +192,11 @@ exTgaValidationCycle_gsl_DATA += $(top_srcdir)/examples/validationCycle/tests/te exTgaValidationCycle_gsl_PROGRAMS = exTgaValidationCycle_gsl -exTgaValidationCycle_gsl_SOURCES = $(top_srcdir)/examples/validationCycle/src/exTgaValidationCycle_gsl.C +exTgaValidationCycle_gsl_SOURCES = validationCycle/src/exTgaValidationCycle_gsl.C -exTgaValidationCycle_gsl_HEADERS = $(top_srcdir)/examples/validationCycle/src/exTgaValidationCycle_appl.h -exTgaValidationCycle_gsl_HEADERS += $(top_srcdir)/examples/validationCycle/src/exTgaValidationCycle_likelihood.h -exTgaValidationCycle_gsl_HEADERS += $(top_srcdir)/examples/validationCycle/src/exTgaValidationCycle_qoi.h +exTgaValidationCycle_gsl_HEADERS = validationCycle/src/exTgaValidationCycle_appl.h +exTgaValidationCycle_gsl_HEADERS += validationCycle/src/exTgaValidationCycle_likelihood.h +exTgaValidationCycle_gsl_HEADERS += validationCycle/src/exTgaValidationCycle_qoi.h exTgaValidationCycle_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -223,16 +221,16 @@ tga2_gsl_DATA += $(top_srcdir)/examples/validationCycle2/tests/test_2012_11_15/t tga2_gsl_PROGRAMS = tga2_gsl -tga2_gsl_SOURCES = $(top_srcdir)/examples/validationCycle2/src/tga2_gsl.C -tga2_gsl_SOURCES += $(top_srcdir)/examples/validationCycle2/src/tga2_appl.C -tga2_gsl_SOURCES += $(top_srcdir)/examples/validationCycle2/src/tga2_likelihood.C -tga2_gsl_SOURCES += $(top_srcdir)/examples/validationCycle2/src/tga2_qoi.C -tga2_gsl_SOURCES += $(top_srcdir)/examples/validationCycle2/src/tga2_func.C +tga2_gsl_SOURCES = validationCycle2/src/tga2_gsl.C +tga2_gsl_SOURCES += validationCycle2/src/tga2_appl.C +tga2_gsl_SOURCES += validationCycle2/src/tga2_likelihood.C +tga2_gsl_SOURCES += validationCycle2/src/tga2_qoi.C +tga2_gsl_SOURCES += validationCycle2/src/tga2_func.C -tga2_gsl_HEADERS = $(top_srcdir)/examples/validationCycle2/src/tga2_appl.h -tga2_gsl_HEADERS += $(top_srcdir)/examples/validationCycle2/src/tga2_likelihood.h -tga2_gsl_HEADERS += $(top_srcdir)/examples/validationCycle2/src/tga2_qoi.h -tga2_gsl_HEADERS += $(top_srcdir)/examples/validationCycle2/src/tga2_func.h +tga2_gsl_HEADERS = validationCycle2/src/tga2_appl.h +tga2_gsl_HEADERS += validationCycle2/src/tga2_likelihood.h +tga2_gsl_HEADERS += validationCycle2/src/tga2_qoi.h +tga2_gsl_HEADERS += validationCycle2/src/tga2_func.h tga2_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -252,9 +250,9 @@ exInfoTheory_gsl_DATA = $(top_srcdir)/examples/infoTheoryProblem/tests/test_2011 exInfoTheory_gsl_PROGRAMS = exInfoTheory_gsl -exInfoTheory_gsl_SOURCES = $(top_srcdir)/examples/infoTheoryProblem/src/exInfoTheory_gsl.C +exInfoTheory_gsl_SOURCES = infoTheoryProblem/src/exInfoTheory_gsl.C -exInfoTheory_gsl_HEADERS = $(top_srcdir)/examples/infoTheoryProblem/src/exInfoTheory_appl.h +exInfoTheory_gsl_HEADERS = infoTheoryProblem/src/exInfoTheory_appl.h exInfoTheory_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -275,18 +273,17 @@ gravity_gsl_DATA = $(top_srcdir)/examples/gravity/tests/test_2013_01_22/gravity_ gravity_gsl_DATA += $(top_srcdir)/examples/gravity/tests/test_2013_01_22/gravity_plots_ip.m gravity_gsl_DATA += $(top_srcdir)/examples/gravity/tests/test_2013_01_22/gravity_plots_fp.m gravity_gsl_DATA += $(top_srcdir)/examples/gravity/tests/test_2013_01_22/autocorr_local.m -gravity_gsl_DATA += $(top_srcdir)/examples/gravity/src/Makefile_example_violeta gravity_gsl_PROGRAMS = gravity_gsl -gravity_gsl_SOURCES = $(top_srcdir)/examples/gravity/src/gravity_main.C -gravity_gsl_SOURCES += $(top_srcdir)/examples/gravity/src/gravity_compute.C -gravity_gsl_SOURCES += $(top_srcdir)/examples/gravity/src/gravity_likelihood.C -gravity_gsl_SOURCES += $(top_srcdir)/examples/gravity/src/gravity_qoi.C +gravity_gsl_SOURCES = gravity/src/gravity_main.C +gravity_gsl_SOURCES += gravity/src/gravity_compute.C +gravity_gsl_SOURCES += gravity/src/gravity_likelihood.C +gravity_gsl_SOURCES += gravity/src/gravity_qoi.C -gravity_gsl_HEADERS = $(top_srcdir)/examples/gravity/src/gravity_compute.h -gravity_gsl_HEADERS += $(top_srcdir)/examples/gravity/src/gravity_likelihood.h -gravity_gsl_HEADERS += $(top_srcdir)/examples/gravity/src/gravity_qoi.h +gravity_gsl_HEADERS = gravity/src/gravity_compute.h +gravity_gsl_HEADERS += gravity/src/gravity_likelihood.h +gravity_gsl_HEADERS += gravity/src/gravity_qoi.h gravity_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -307,16 +304,15 @@ 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_SOURCES = bimodal/src/bimodal_main.C +bimodal_gsl_SOURCES += bimodal/src/bimodal_compute.C +bimodal_gsl_SOURCES += 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_HEADERS = bimodal/src/bimodal_compute.h +bimodal_gsl_HEADERS += bimodal/src/bimodal_likelihood.h bimodal_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -340,18 +336,17 @@ hysteretic_gsl_DATA += $(top_srcdir)/examples/hysteretic/tests/test_2013_12_11/m 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_SOURCES = hysteretic/src/example_main.C +hysteretic_gsl_SOURCES += hysteretic/src/example_compute.C +hysteretic_gsl_SOURCES += hysteretic/src/example_likelihood.C +hysteretic_gsl_SOURCES += 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_HEADERS = hysteretic/src/example_compute.h +hysteretic_gsl_HEADERS += hysteretic/src/example_likelihood.h +hysteretic_gsl_HEADERS += hysteretic/src/example_hyst.h hysteretic_gsl_LDADD = $(top_builddir)/src/libqueso.la @@ -362,7 +357,19 @@ dist_hysteretic_gsl_DATA += $(hysteretic_gsl_DATA) dist_hysteretic_gsl_DATA += ${hysteretic_gsl_HEADERS} dist_hysteretic_gsl_DATA += ${hysteretic_gsl_SOURCES} -#################### +################## +# GPMSA examples # +################## +gpmsa_scalardir = $(prefix)/examples/gp/scalar + +gpmsa_scalar_PROGRAMS = gpmsa_scalar +gpmsa_scalar_SOURCES = gp/scalar/gpmsa_scalar.C +gpmsa_scalar_LDADD = $(top_builddir)/src/libqueso.la +gpmsa_scalar_CPPFLAGS = -I$(top_srcdir)/examples/gp/scalar $(QUESO_CPPFLAGS) + +dist_gpmsa_scalar_DATA = +dist_gpmsa_scalar_DATA += ${gpmsa_scalar_SOURCES} + if CODE_COVERAGE_ENABLED CLEANFILES = *.gcda *.gcno endif diff --git a/examples/bimodal/src/Makefile_bimodal_violeta b/examples/bimodal/src/Makefile_bimodal_violeta deleted file mode 100644 index ccd15b2f3..000000000 --- a/examples/bimodal/src/Makefile_bimodal_violeta +++ /dev/null @@ -1,47 +0,0 @@ -############################################################## -# Using installed QUESO 0.50.0, external release, in 'violeta' -############################################################## - -QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 -BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 -GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 -HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 - -INC_PATHS = \ - -I. \ - -I$(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_likelihood.C b/examples/bimodal/src/bimodal_likelihood.C index 234d4fc2f..ff10fd490 100644 --- a/examples/bimodal/src/bimodal_likelihood.C +++ b/examples/bimodal/src/bimodal_likelihood.C @@ -71,11 +71,7 @@ double likelihoodRoutine( resultValue = 1040.; } -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN returnValue = -.5*resultValue; -#else - returnValue = resultValue; -#endif if (paramValues.env().exceptionalCircumstance()) { diff --git a/examples/brian_williams_verif1/src/Makefile_prudenci_mac b/examples/brian_williams_verif1/src/Makefile_prudenci_mac deleted file mode 100644 index 28ff36bc6..000000000 --- a/examples/brian_williams_verif1/src/Makefile_prudenci_mac +++ /dev/null @@ -1,50 +0,0 @@ -HOME_DIR = /Users/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/i_queso_0_45_trunk/ -BOOST_DIR = $(HOME_DIR)/Installations/i_boost_1_51_0/ -GSL_DIR = $(HOME_DIR)/Installations/i_gsl_1_15/ -GRVY_DIR = $(HOME_DIR)/Installations/i_grvy_0_32_0/ -HDF5_DIR = $(HOME_DIR)/Installations/i_hdf5_1_8_9/ -UQBT = gsl - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 \ - -L/opt/local/lib \ - -llapack -lblas - -CXX = mpic++ -CXXFLAGS += -O2 -Wall -c - -default: all - -.SUFFIXES: .o .C - -all: codeVerif1 - -clean: - rm -f *~ - rm -f *.o - rm -f verif1_$(UQBT) - -codeVerif1: verif1_$(UQBT).o - $(CXX) verif1_$(UQBT).o -o verif1_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/brian_williams_verif2/src/Makefile_prudenci_mac b/examples/brian_williams_verif2/src/Makefile_prudenci_mac deleted file mode 100644 index ff8edd63e..000000000 --- a/examples/brian_williams_verif2/src/Makefile_prudenci_mac +++ /dev/null @@ -1,50 +0,0 @@ -HOME_DIR = /Users/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/i_queso_0_45_trunk/ -BOOST_DIR = $(HOME_DIR)/Installations/i_boost_1_51_0/ -GSL_DIR = $(HOME_DIR)/Installations/i_gsl_1_15/ -GRVY_DIR = $(HOME_DIR)/Installations/i_grvy_0_32_0/ -HDF5_DIR = $(HOME_DIR)/Installations/i_hdf5_1_8_9/ -UQBT = gsl - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 \ - -L/opt/local/lib \ - -llapack -lblas - -CXX = mpic++ -CXXFLAGS += -O2 -Wall -c - -default: all - -.SUFFIXES: .o .C - -all: codeVerif2 - -clean: - rm -f *~ - rm -f *.o - rm -f verif2_$(UQBT) - -codeVerif2: verif2_$(UQBT).o - $(CXX) verif2_$(UQBT).o -o verif2_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/brian_williams_verif3/src/Makefile_prudenci_mac b/examples/brian_williams_verif3/src/Makefile_prudenci_mac deleted file mode 100644 index ce24ef1a2..000000000 --- a/examples/brian_williams_verif3/src/Makefile_prudenci_mac +++ /dev/null @@ -1,50 +0,0 @@ -HOME_DIR = /Users/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/i_queso_0_45_trunk/ -BOOST_DIR = $(HOME_DIR)/Installations/i_boost_1_51_0/ -GSL_DIR = $(HOME_DIR)/Installations/i_gsl_1_15/ -GRVY_DIR = $(HOME_DIR)/Installations/i_grvy_0_32_0/ -HDF5_DIR = $(HOME_DIR)/Installations/i_hdf5_1_8_9/ -UQBT = gsl - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 \ - -L/opt/local/lib \ - -llapack -lblas - -CXX = mpic++ -CXXFLAGS += -O2 -Wall -c - -default: all - -.SUFFIXES: .o .C - -all: codeVerif3 - -clean: - rm -f *~ - rm -f *.o - rm -f verif3_$(UQBT) - -codeVerif3: verif3_$(UQBT).o - $(CXX) verif3_$(UQBT).o -o verif3_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/brian_williams_verif5/src/Makefile_prudenci_mac b/examples/brian_williams_verif5/src/Makefile_prudenci_mac deleted file mode 100644 index 580e3c375..000000000 --- a/examples/brian_williams_verif5/src/Makefile_prudenci_mac +++ /dev/null @@ -1,50 +0,0 @@ -HOME_DIR = /Users/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/i_queso_0_45_trunk/ -BOOST_DIR = $(HOME_DIR)/Installations/i_boost_1_51_0/ -GSL_DIR = $(HOME_DIR)/Installations/i_gsl_1_15/ -GRVY_DIR = $(HOME_DIR)/Installations/i_grvy_0_32_0/ -HDF5_DIR = $(HOME_DIR)/Installations/i_hdf5_1_8_9/ -UQBT = gsl - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 \ - -L/opt/local/lib \ - -llapack -lblas - -CXX = mpic++ -CXXFLAGS += -O2 -Wall -c - -default: all - -.SUFFIXES: .o .C - -all: codeVerif5 - -clean: - rm -f *~ - rm -f *.o - rm -f verif5_$(UQBT) - -codeVerif5: verif5_$(UQBT).o - $(CXX) verif5_$(UQBT).o -o verif5_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/brian_williams_verif6/src/Makefile_prudenci_mac b/examples/brian_williams_verif6/src/Makefile_prudenci_mac deleted file mode 100644 index 5df9da6d5..000000000 --- a/examples/brian_williams_verif6/src/Makefile_prudenci_mac +++ /dev/null @@ -1,50 +0,0 @@ -HOME_DIR = /Users/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/i_queso_0_45_trunk/ -BOOST_DIR = $(HOME_DIR)/Installations/i_boost_1_51_0/ -GSL_DIR = $(HOME_DIR)/Installations/i_gsl_1_15/ -GRVY_DIR = $(HOME_DIR)/Installations/i_grvy_0_32_0/ -HDF5_DIR = $(HOME_DIR)/Installations/i_hdf5_1_8_9/ -UQBT = gsl - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 \ - -L/opt/local/lib \ - -llapack -lblas - -CXX = mpic++ -CXXFLAGS += -O2 -Wall -c - -default: all - -.SUFFIXES: .o .C - -all: codeVerif6 - -clean: - rm -f *~ - rm -f *.o - rm -f verif6_$(UQBT) - -codeVerif6: verif6_$(UQBT).o - $(CXX) verif6_$(UQBT).o -o verif6_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/gp/scalar/cobra-manual.py b/examples/gp/scalar/cobra-manual.py new file mode 100644 index 000000000..dddf2b089 --- /dev/null +++ b/examples/gp/scalar/cobra-manual.py @@ -0,0 +1,56 @@ +import sys +import re +from StringIO import StringIO +import numpy as np +import matplotlib + +matplotlib.use('pdf') +import matplotlib.pyplot as plt + +chain = str(sys.argv[2]) + +with open(str(sys.argv[1])+'/ip_raw_chain_sub'+chain+'.m', 'r') as fin: + header = fin.readline() + + # Sort out the dimension of the 2D array of samples + dims = re.findall(r'\d+', header) + dims = [int(dim) for dim in dims] + dims[0] -= 1 + + # Ignore first line because laziness + # for i in range(20): + samples = fin.readline() + + # Now read all and ignore last line + samples = fin.readlines() + samples = samples[0:-1] + + # Now join all up and use magic numpy read help + samples = ' '.join(samples) + samples = np.loadtxt(StringIO(samples)) + +num_samples = samples.shape[0] +N = 60000 + +mins = np.array([0.95, 0.9, 0.9, 0.9, 0.9]) +maxs = np.array([1.05, 1.1, 1.1, 1.1, 1.1]) + +samples[:,3] = mins[3] + (samples[:,3] * (maxs[3] - mins[3])) +samples[:,4] = mins[4] + (samples[:,4] * (maxs[4] - mins[4])) + +fig = plt.figure() +ax = fig.add_subplot(1, 1, 1) +ax.scatter(samples[N:,3], samples[N:,4]) +ax.set_xlabel('k_xkwlx') +ax.set_ylabel('k_cd') +fig.savefig('joint.pdf') + +ax.cla() + +eta = 0.906 * samples[:,3] + 0.478 * samples[:,4] + +ax.hist(eta[N:], bins=20) +fig.savefig('eta.pdf') + +print "eta mean:", np.mean(eta[N:]) +print "eta std:", np.std(eta[N:], ddof=1) # should be 0.00791 diff --git a/examples/gp/scalar/cobra-samples.py b/examples/gp/scalar/cobra-samples.py new file mode 100644 index 000000000..211433d82 --- /dev/null +++ b/examples/gp/scalar/cobra-samples.py @@ -0,0 +1,59 @@ +import sys +import re +from StringIO import StringIO +import numpy as np +import matplotlib + +matplotlib.use('pdf') +import matplotlib.pyplot as plt + +chain = str(sys.argv[2]) + +with open(str(sys.argv[1])+'/ip_raw_chain_sub'+chain+'.m', 'r') as fin: + header = fin.readline() + + # Sort out the dimension of the 2D array of samples + dims = re.findall(r'\d+', header) + dims = [int(dim) for dim in dims] + dims[0] -= 1 + + # Ignore first line because laziness + # for i in range(20): + samples = fin.readline() + + # Now read all and ignore last line + samples = fin.readlines() + samples = samples[0:-1] + + # Now join all up and use magic numpy read help + samples = ' '.join(samples) + samples = np.loadtxt(StringIO(samples)) + +num_samples = samples.shape[0] + +mins = np.array([0.95, 0.9, 0.9, 0.9, 0.9]) +maxs = np.array([1.05, 1.1, 1.1, 1.1, 1.1]) + +filenames = ('k_tmasl', 'k_tmoml', 'k_tnrgl', 'k_xkwlx', 'k_cd', + 'emulatorMean', + 'emulatorPrecision', + 'emulatorCorrelation1', + 'emulatorCorrelation2', + 'emulatorCorrelation3', + 'emulatorCorrelation4', + 'emulatorCorrelation5', + 'emulatorCorrelation6', + 'discrepancyPrecision', + 'discrepancyCorrelation1') + +fig = plt.figure() +ax = fig.add_subplot(1, 1, 1) +for i, name in enumerate(filenames): + savename = name + '_' + chain + '.pdf' + if i < 5: + theta_samples = mins[i] + (samples[:,i] * (maxs[i] - mins[i])) + else: + theta_samples = samples[:,i] + ax.plot(theta_samples) + fig.savefig(savename) + ax.cla() diff --git a/examples/gp/scalar/ctf_dat.txt b/examples/gp/scalar/ctf_dat.txt new file mode 100644 index 000000000..dd0f94205 --- /dev/null +++ b/examples/gp/scalar/ctf_dat.txt @@ -0,0 +1,10 @@ +1.21626870795683 +1.16149729403381 +1.1866271753701 +1.17683037400566 +1.17908641397936 +1.12766758338348 +1.17093102211906 +1.2072835297607 +1.18316329384675 +1.19396014684855 diff --git a/examples/gp/scalar/dakota_pstudy.dat b/examples/gp/scalar/dakota_pstudy.dat new file mode 100644 index 000000000..2d74abcdf --- /dev/null +++ b/examples/gp/scalar/dakota_pstudy.dat @@ -0,0 +1,51 @@ +%eval_id k_tmasl k_tmoml k_tnrgl k_xkwlx k_cd TotalPressure + 1 0.9504833265 0.9565031976 0.9070661904 0.9368767201 0.9902105699 1.11491 + 2 0.9704315633 0.9673671481 1.098333711 1.042833417 0.9396811465 1.18503 + 3 0.9668456402 0.9415691043 1.088395137 1.056229445 1.082078539 1.26852 + 4 1.021933049 1.081931608 1.055263162 1.002864582 1.097385647 1.22538 + 5 0.976719735 1.045242646 0.9803728644 1.01932066 1.023714908 1.20525 + 6 1.030644258 1.000261395 1.086072538 0.9619466614 0.999087427 1.14153 + 7 1.033479868 1.00547323 0.9794220783 1.071325006 0.9826118693 1.23193 + 8 0.9803928353 1.058941195 1.007968514 1.091684579 0.9608412905 1.2387 + 9 1.017384242 1.055874904 0.9202142133 0.9106698251 1.059631639 1.12142 + 10 1.039085017 0.9903868268 1.027248507 0.902328287 1.030390783 1.10115 + 11 0.9741611498 0.9513905754 0.9946802606 0.9421607145 0.9334849739 1.09416 + 12 0.9642257071 1.028441506 1.078605675 0.9301294486 0.9586327411 1.09475 + 13 0.979482743 1.026338606 0.9197225104 0.9918824665 1.027524616 1.18198 + 14 0.9821972744 1.033900319 0.9125861151 1.004675286 0.9099149855 1.13732 + 15 0.9920868386 1.019948426 0.9665838929 1.074701427 1.062491732 1.27586 + 16 1.003825877 0.9279219027 1.039942545 0.9750344064 0.9758580042 1.14256 + 17 1.001098361 1.094175733 1.035820862 0.9691751418 0.9436327119 1.12221 + 18 0.9907264763 1.037084636 1.062263521 1.033813525 1.005303491 1.20944 + 19 0.9955255765 0.9938034285 0.9313636533 0.9543762946 0.945913695 1.11046 + 20 1.01401808 0.9223914805 0.987550559 0.9049676054 0.9293711513 1.05997 + 21 1.01169168 1.066554864 1.00159988 1.04832432 1.040735732 1.24028 + 22 0.9896416018 0.9040699829 0.9584220241 0.9864903757 1.076077307 1.19996 + 23 1.026651322 0.9629350077 0.9016065396 1.052200845 1.094490151 1.27099 + 24 0.9522009452 0.9995354981 0.9979826914 0.9234223933 1.002902883 1.10837 + 25 1.042210059 0.916662705 0.9510966253 0.9344222067 0.9079090081 1.07606 + 26 1.008565411 1.048817115 1.064460124 0.983195362 1.048049029 1.18361 + 27 0.9861303865 1.062122937 0.9431387667 0.979262433 1.086095516 1.19771 + 28 0.9682748981 0.9690028597 1.008818441 1.0977804 1.074730428 1.30385 + 29 0.997538715 1.073585489 1.074838609 1.067970087 0.9876920528 1.23136 + 30 0.960891798 0.9796612269 0.9471908791 1.02824267 1.01713938 1.21027 + 31 1.035285687 0.9282637672 0.9112777017 1.060563117 0.9926884867 1.22743 + 32 0.9857119652 0.90075304 1.056745701 0.9189727839 1.091278093 1.14313 + 33 1.018023459 1.041474636 0.9358355433 0.9649328786 0.9276575505 1.11128 + 34 1.049918142 1.091619875 0.9526624009 0.9587045741 1.033133757 1.15403 + 35 0.9983256433 0.9747358926 1.094801076 1.02551546 0.9167120121 1.15864 + 36 1.013293136 0.9143560309 1.048997465 1.037015977 0.90309727 1.16188 + 37 1.029086735 1.022171889 1.082118374 0.9488425436 1.05299279 1.15406 + 38 0.9629704313 0.9381087132 0.9755750919 1.078985522 1.037980326 1.26729 + 39 1.047678387 0.9100415862 1.01454745 1.081305261 0.9234426563 1.21039 + 40 0.973992016 1.097494297 1.041668819 0.9989493167 0.9692778694 1.16052 + 41 1.037340321 0.9855375029 1.021844299 1.087688498 1.012046642 1.26188 + 42 1.02596132 1.015994577 1.016960221 1.092127074 0.9134742555 1.21451 + 43 0.9594901969 0.9827905876 1.069914524 0.9479300522 1.009465123 1.13353 + 44 0.954635897 1.009148953 0.9703234826 1.020890506 0.9543595717 1.17295 + 45 1.004448229 0.9355603683 0.9396882 0.9950812769 0.977432089 1.1612 + 46 1.041433693 0.9555272647 1.047644115 0.9128028486 1.06796733 1.12716 + 47 0.9573383712 1.086678783 0.9266088717 1.012186879 0.9514026049 1.1637 + 48 1.044161709 1.078649595 0.9902257044 1.011338967 0.9642182413 1.16921 + 49 1.022034669 0.9441188514 0.9605187868 1.044034489 1.071108179 1.2516 + 50 1.006757408 1.070436592 1.030513925 0.9269419437 1.047784872 1.1313 diff --git a/examples/gp/scalar/gpmsa_input.txt b/examples/gp/scalar/gpmsa_input.txt new file mode 100644 index 000000000..aca6255cc --- /dev/null +++ b/examples/gp/scalar/gpmsa_input.txt @@ -0,0 +1,59 @@ +############################################### +# 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 = -1 + +############################################### +# 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 = 100000 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 2000 +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 = 0 +ip_mh_tk_useLocalHessian = 0 +ip_mh_tk_useNewtonComponent = 0 +ip_mh_dr_maxNumExtraStages = 0 +ip_mh_dr_listOfScalesForExtraStages = 5. 10. 20. +ip_mh_am_initialNonAdaptInterval = 100000 +ip_mh_am_adaptInterval = 100000 +ip_mh_am_eta = 0.384 +ip_mh_am_epsilon = 1.e-5 + +ip_mh_filteredChain_generate = 0 + +gpmsa_emulator_precision_shape = 5.0 +gpmsa_emulator_precision_scale = 0.2 +gpmsa_emulator_correlation_strength_alpha = 1.0 +gpmsa_emulator_correlation_strength_beta = 0.1 +gpmsa_discrepancy_precision_shape = 1.0 +gpmsa_discrepancy_precision_scale = 1e4 +gpmsa_discrepancy_correlation_strength_alpha = 1.0 +gpmsa_discrepancy_correlation_strength_beta = 0.1 +gpmsa_emulator_data_precision_shape = 3.0 +gpmsa_emulator_data_precision_scale = 333.333 diff --git a/examples/gp/scalar/gpmsa_scalar.C b/examples/gp/scalar/gpmsa_scalar.C new file mode 100644 index 000000000..3ec9afbae --- /dev/null +++ b/examples/gp/scalar/gpmsa_scalar.C @@ -0,0 +1,279 @@ +#include +#include +#include +#include +#include +#include + +#include + +// Read in data files +double readData(const std::vector & simulationScenarios, + const std::vector & simulationParameters, + const std::vector & simulationOutputs, + const std::vector & experimentScenarios, + const std::vector & experimentOutputs) { + FILE * fp_in = fopen("gp/scalar/dakota_pstudy.dat", "r"); + unsigned int i, id, size = 512; + double k_tmasl, k_tmoml, k_tnrgl, k_xkwlx, k_cd, pressure; + char line[size]; + + // The user should know what the bounds on the data are + double mins[] = {0.95, 0.9, 0.9, 0.9, 0.9}; + double maxs[] = {1.05, 1.1, 1.1, 1.1, 1.1}; + + double meansim = 0; + double m2sim = 0; + + // First line is a header, so we ignore it + fgets(line, size, fp_in); + + i = 0; + while (fscanf(fp_in, "%d %lf %lf %lf %lf %lf %lf\n", &id, &k_tmasl, &k_tmoml, + &k_tnrgl, &k_xkwlx, &k_cd, &pressure) != EOF) { + (*(simulationScenarios[i]))[0] = 0.5; + + (*(simulationParameters[i]))[0] = (k_tmasl - mins[0]) / (maxs[0] - mins[0]); + (*(simulationParameters[i]))[1] = (k_tmoml - mins[1]) / (maxs[1] - mins[1]); + (*(simulationParameters[i]))[2] = (k_tnrgl - mins[2]) / (maxs[2] - mins[2]); + (*(simulationParameters[i]))[3] = (k_xkwlx - mins[3]) / (maxs[3] - mins[3]); + (*(simulationParameters[i]))[4] = (k_cd - mins[4]) / (maxs[4] - mins[4]); + + (*(simulationOutputs[i]))[0] = pressure; + i++; + + double delta = pressure - meansim; + meansim += delta / i; + m2sim += delta * (pressure - meansim); + } + + double stdsim = m2sim / (double)(i - 1); + + // The user is required to standardise the experimental and simulation data + for (unsigned int j = 0; j < i; j++) { + (*(simulationOutputs[j]))[0] -= meansim; + (*(simulationOutputs[j]))[0] /= stdsim; + } + + fclose(fp_in); // Done with simulation data + + // Read in experimental data + fp_in = fopen("gp/scalar/ctf_dat.txt", "r"); + i = 0; + while (fscanf(fp_in, "%lf\n", &pressure) != EOF) { + (*(experimentOutputs[i]))[0] = pressure; + i++; + } + + // Also required to standardise experimental data too + for (unsigned int j = 0; j < i; j++) { + (*(experimentOutputs[j]))[0] -= meansim; + (*(experimentOutputs[j]))[0] /= stdsim; + } + + fclose(fp_in); + + // Returning standard deviation of data (simulation data is treated as + // plentiful and the standard deviation of this data is treated as the 'true' + // standard deviation of the data. This can be argued.) + return stdsim; +} + +int main(int argc, char ** argv) { + // Step 0: Set up some variables + unsigned int numExperiments = 10; // Number of experiments + unsigned int numUncertainVars = 5; // Number of things to calibrate + unsigned int numSimulations = 50; // Number of simulations + unsigned int numConfigVars = 1; // Dimension of configuration space + unsigned int numEta = 1; // Number of responses the model is returning + unsigned int experimentSize = 1; // Size of each experiment + + MPI_Init(&argc, &argv); + + // Step 1: Set up QUESO environment + QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL); + + // Step 2: Set up prior for calibration parameters + QUESO::VectorSpace paramSpace(env, + "param_", numUncertainVars, NULL); + + // Parameter (theta) bounds: + // descriptors 'k_tmasl' 'k_xkle' 'k_xkwew' 'k_xkwlx' 'k_cd' + // upper_bounds 1.05 1.1 1.1 1.1 1.1 + // lower_bounds 0.95 0.9 0.9 0.9 0.9 + // + // These bounds are dealt with when reading in the data + QUESO::GslVector paramMins(paramSpace.zeroVector()); + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + + paramMins.cwSet(0.0); + paramMaxs.cwSet(1.0); + + QUESO::BoxSubset paramDomain("param_", + paramSpace, paramMins, paramMaxs); + + QUESO::UniformVectorRV priorRv("prior_", + paramDomain); + + // Step 3: Instantiate the 'scenario' and 'output' spaces for simulation + QUESO::VectorSpace configSpace(env, + "scenario_", numConfigVars, NULL); + + QUESO::VectorSpace nEtaSpace(env, + "output_", numEta, NULL); + + // Step 4: Instantiate the 'output' space for the experiments + QUESO::VectorSpace experimentSpace(env, + "experimentspace_", experimentSize, NULL); + + QUESO::VectorSpace totalExperimentSpace(env, + "experimentspace_", experimentSize * numExperiments, NULL); + + // Step 5: Instantiate the Gaussian process emulator object + // + // Regarding simulation scenario input values, the user should standardise + // them so that they exist inside a hypercube. + // + // Regarding simulation output data, the user should transform it so that the + // mean is zero and the variance is one. + // + // Regarding experimental scenario input values, the user should standardize + // them so that they exist inside a hypercube. + // + // Regarding experimental data, the user should transformed it so that it has + // zero mean and variance one. + + // GPMSA stores all the information about our simulation + // data and experimental data. It also stores default information about the + // hyperparameter distributions. + QUESO::GPMSAFactory + gpmsaFactory(env, + NULL, + priorRv, + configSpace, + paramSpace, + nEtaSpace, + experimentSpace, + numSimulations, + numExperiments); + + // std::vector containing all the points in scenario space where we have + // simulations + std::vector simulationScenarios(numSimulations, + (QUESO::GslVector *) NULL); + + // std::vector containing all the points in parameter space where we have + // simulations + std::vector paramVecs(numSimulations, + (QUESO::GslVector *) NULL); + + // std::vector containing all the simulation output data + std::vector outputVecs(numSimulations, + (QUESO::GslVector *) NULL); + + // std::vector containing all the points in scenario space where we have + // experiments + std::vector experimentScenarios(numExperiments, + (QUESO::GslVector *) NULL); + + // std::vector containing all the experimental output data + std::vector experimentVecs(numExperiments, + (QUESO::GslVector *) NULL); + + // The experimental output data observation error covariance matrix + QUESO::GslMatrix experimentMat(totalExperimentSpace.zeroVector()); + + // Instantiate each of the simulation points/outputs + for (unsigned int i = 0; i < numSimulations; i++) { + simulationScenarios[i] = new QUESO::GslVector(configSpace.zeroVector()); // 'x_{i+1}^*' in paper + paramVecs [i] = new QUESO::GslVector(paramSpace.zeroVector()); // 't_{i+1}^*' in paper + outputVecs [i] = new QUESO::GslVector(nEtaSpace.zeroVector()); // 'eta_{i+1}' in paper + } + + for (unsigned int i = 0; i < numExperiments; i++) { + experimentScenarios[i] = new QUESO::GslVector(configSpace.zeroVector()); // 'x_{i+1}' in paper + experimentVecs[i] = new QUESO::GslVector(experimentSpace.zeroVector()); + } + + // Read in data and store the standard deviation of the simulation data. We + // will need the standard deviation when we pass the experiment error + // covariance matrix to QUESO. + double stdsim = readData(simulationScenarios, + paramVecs, + outputVecs, + experimentScenarios, + experimentVecs); + + for (unsigned int i = 0; i < numExperiments; i++) { + // Passing in error of experiments (standardised). + experimentMat(i, i) = (0.025 / stdsim) * (0.025 / stdsim); + } + + // Add simulation and experimental data + gpmsaFactory.addSimulations(simulationScenarios, paramVecs, outputVecs); + gpmsaFactory.addExperiments(experimentScenarios, experimentVecs, &experimentMat); + + QUESO::GenericVectorRV postRv( + "post_", + gpmsaFactory.prior().imageSet().vectorSpace()); + QUESO::StatisticalInverseProblem ip("", + NULL, gpmsaFactory, postRv); + + QUESO::GslVector paramInitials( + gpmsaFactory.prior().imageSet().vectorSpace().zeroVector()); + + // Initial condition of the chain + // Have to set each of these by hand, *and* the sampler is sensitive to these + // values + paramInitials[0] = 0.5; // param 1 + paramInitials[1] = 0.5; // param 2 + paramInitials[2] = 0.5; // param 3 + paramInitials[3] = 0.5; // param 4 + paramInitials[4] = 0.5; // param 5 + paramInitials[5] = 0.0; // not used. emulator mean + paramInitials[6] = 1.0; // emulator corr str + paramInitials[7] = 0.97; // emulator corr str + paramInitials[8] = 0.97; // emulator corr str + paramInitials[9] = 0.97; // emulator corr str + paramInitials[10] = 0.97; // emulator corr str + paramInitials[11] = 0.97; // emulator corr str + paramInitials[12] = 0.97; // emulator corr str + paramInitials[13] = 10.0; // discrepancy precision + paramInitials[14] = 0.97; // discrepancy corr str + paramInitials[15] = 8000.0; // emulator data precision + + QUESO::GslMatrix proposalCovMatrix( + gpmsaFactory.prior().imageSet().vectorSpace().zeroVector()); + + // Setting the proposal covariance matrix by hand. This requires great + // forethough, and can generally be referred to as a massive hack. These + // values were taken from the gpmsa matlab code and fiddled with. + double scale = 600.0; + proposalCovMatrix(0, 0) = 3.1646 / 10.0; // param 1 + proposalCovMatrix(1, 1) = 3.1341 / 10.0; // param 2 + proposalCovMatrix(2, 2) = 3.1508 / 10.0; // param 3 + proposalCovMatrix(3, 3) = 0.3757 / 10.0; // param 4 + proposalCovMatrix(4, 4) = 0.6719 / 10.0; // param 5 + proposalCovMatrix(5, 5) = 0.1 / scale; // not used. emulator mean + proposalCovMatrix(6, 6) = 0.4953 / scale; // emulator precision + proposalCovMatrix(7, 7) = 0.6058 / scale; // emulator corr str + proposalCovMatrix(8, 8) = 7.6032e-04 / scale; // emulator corr str + proposalCovMatrix(9, 9) = 8.3815e-04 / scale; // emulator corr str + proposalCovMatrix(10, 10) = 7.5412e-04 / scale; // emulator corr str + proposalCovMatrix(11, 11) = 0.2682 / scale; // emulator corr str + proposalCovMatrix(12, 12) = 0.0572 / scale; // emulator corr str + proposalCovMatrix(13, 13) = 1.3417 / scale; // discrepancy precision + proposalCovMatrix(14, 14) = 0.3461 / scale; // discrepancy corr str + proposalCovMatrix(15, 15) = 495.3 / scale; // emulator data precision + + // Square to get variances + for (unsigned int i = 0; i < 16; i++) { + proposalCovMatrix(i, i) = proposalCovMatrix(i, i) * proposalCovMatrix(i, i); + } + + ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix); + + MPI_Finalize(); + + return 0; +} diff --git a/examples/gpmsaTower/src/Makefile_prudenci_lonestar b/examples/gpmsaTower/src/Makefile_prudenci_lonestar deleted file mode 100644 index 1d452899a..000000000 --- a/examples/gpmsaTower/src/Makefile_prudenci_lonestar +++ /dev/null @@ -1,52 +0,0 @@ -HOME_DIR = /home1/01001/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/queso_0_46 -#TRILINOS_DIR = $(HOME_DIR)/Installations/trilinos_9_0_2_dec_2010 -BOOST_DIR = $(TACC_BOOST_DIR) -GSL_DIR = $(TACC_GSL_DIR) -GRVY_DIR = $(TACC_GRVY_DIR) -HDF5_DIR = $(TACC_HDF5_DIR) -UQBT = gsl - -#include $(TRILINOS_DIR)/include/Makefile.export.epetra - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 - -CXX = mpicxx -CXXFLAGS += -g -Wall -wd383 -wd981 -wd1572 -c - -default: all - -.SUFFIXES: .o .C - -all: codeTower - -clean: - rm -f ../inc/*~ - rm -f *~ - rm -f *.o - rm -f tower_$(UQBT) - -codeTower: tower_$(UQBT).o - $(CXX) tower_$(UQBT).o -o tower_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/gpmsaTower/src/Makefile_prudenci_mac b/examples/gpmsaTower/src/Makefile_prudenci_mac deleted file mode 100644 index 7bfa6ba65..000000000 --- a/examples/gpmsaTower/src/Makefile_prudenci_mac +++ /dev/null @@ -1,50 +0,0 @@ -HOME_DIR = /Users/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/i_queso_0_45_trunk/ -BOOST_DIR = $(HOME_DIR)/Installations/i_boost_1_51_0/ -GSL_DIR = $(HOME_DIR)/Installations/i_gsl_1_15/ -GRVY_DIR = $(HOME_DIR)/Installations/i_grvy_0_32_0/ -HDF5_DIR = $(HOME_DIR)/Installations/i_hdf5_1_8_9/ -UQBT = gsl - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 \ - -L/opt/local/lib \ - -llapack -lblas - -CXX = mpic++ -CXXFLAGS += -O2 -Wall -c - -default: all - -.SUFFIXES: .o .C - -all: codeTower - -clean: - rm -f *~ - rm -f *.o - rm -f tower_$(UQBT) - -codeTower: tower_$(UQBT).o - $(CXX) tower_$(UQBT).o -o tower_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/gpmsaTower/src/Makefile_prudenci_uqm b/examples/gpmsaTower/src/Makefile_prudenci_uqm deleted file mode 100644 index a67f70ba8..000000000 --- a/examples/gpmsaTower/src/Makefile_prudenci_uqm +++ /dev/null @@ -1,56 +0,0 @@ -HOME_DIR = /h2/prudenci -QUESO_DIR = $(HOME_DIR)/Installations/queso_0_46/ -#BOOST_DIR = /opt/local/ -#GSL_DIR = /opt/local/ -#GRVY_DIR = $(HOME_DIR)/Installations/grvy_0_26_0/ -#HDF5_DIR = $(HOME_DIR)/Installations/hdf5_1_8_5/ -UQBT = gsl - -INC_PATHS = \ - -I. \ - -I../inc \ - -I$(QUESO_DIR)/include \ - -I$(MPI_DIR)/include \ - -I$(BOOST_DIR)/include \ - -I$(GSL_DIR)/include \ - -I$(GRVY_DIR)/include \ - -I$(HDF5_DIR)/include - -LIBS = \ - -L$(QUESO_DIR)/lib \ - -lqueso \ - -L$(MPI_DIR)/lib \ - -L$(BOOST_DIR)/lib \ - -lboost_program_options \ - -L$(GSL_DIR)/lib \ - -lgsl \ - -L$(GRVY_DIR)/lib \ - -lgrvy \ - -L$(HDF5_DIR)/lib \ - -lhdf5 \ - -L$(MKLROOT)/lib/em64t \ - -lmkl_intel_lp64 -lmkl_sequential -lmkl_core - -# -L/opt/local/lib \ -# -llapack -lblas - -CXX = mpicxx -CXXFLAGS += -O2 -Wall -c - -default: all - -.SUFFIXES: .o .C - -all: codeTower - -clean: - rm -f ../inc/*~ - rm -f *~ - rm -f *.o - rm -f tower_$(UQBT) - -codeTower: tower_$(UQBT).o - $(CXX) tower_$(UQBT).o -o tower_$(UQBT) $(LIBS) - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/gravity/src/Makefile_example_violeta b/examples/gravity/src/Makefile_example_violeta deleted file mode 100755 index 54e4643fe..000000000 --- a/examples/gravity/src/Makefile_example_violeta +++ /dev/null @@ -1,45 +0,0 @@ -########################################################## -# Using installed QUESO 0.50.0, from external release, in 'violeta' -########################################################## - -QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 -BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 -GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 -HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 - -INC_PATHS = \ - -I. \ - -I$(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: example_gravity_gsl - -clean: - rm -f *~ - rm -f *.o - rm -f gravity_gsl - -example_gravity_gsl: gravity_main.o gravity_likelihood.o gravity_compute.o gravity_qoi.o - $(CXX) gravity_main.o \ - gravity_likelihood.o \ - gravity_compute.o \ - gravity_qoi.o \ - -o gravity_gsl $(LIBS) - -%.o: %.C - $(CXX) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/hysteretic/src/Makefile_hysteretic_violeta b/examples/hysteretic/src/Makefile_hysteretic_violeta deleted file mode 100644 index 4bddee60b..000000000 --- a/examples/hysteretic/src/Makefile_hysteretic_violeta +++ /dev/null @@ -1,45 +0,0 @@ -########################################################## -# Using installed QUESO 0.50.0, from external release, in 'violeta' -########################################################## - -QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 -BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 -GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 -HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 - -INC_PATHS = \ - -I. \ - -I$(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) $< diff --git a/examples/modal/src/Makefile_modal_violeta b/examples/modal/src/Makefile_modal_violeta deleted file mode 100644 index eb5af9e35..000000000 --- a/examples/modal/src/Makefile_modal_violeta +++ /dev/null @@ -1,44 +0,0 @@ -########################################################## -# Using installed QUESO 0.47.1, from tarball, in 'violeta' -########################################################## - -QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 -BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 -GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 -HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 - -INC_PATHS = \ - -I. \ - -I$(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) $< diff --git a/examples/modal/src/example_likelihood.C b/examples/modal/src/example_likelihood.C index 68535773c..35b56bb4d 100644 --- a/examples/modal/src/example_likelihood.C +++ b/examples/modal/src/example_likelihood.C @@ -91,9 +91,5 @@ double likelihoodRoutine( double result = -0.5*((double) numModes)*5.*log(2.*M_PI*sigmaSq) - 0.5*(sum1+sum2)/sigmaSq; -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN return result; -#else - return -2.*result; -#endif } diff --git a/examples/simpleStatisticalForwardProblem/src/Makefile_sfp_example_margarida b/examples/simpleStatisticalForwardProblem/src/Makefile_sfp_example_margarida deleted file mode 100644 index b7692756f..000000000 --- a/examples/simpleStatisticalForwardProblem/src/Makefile_sfp_example_margarida +++ /dev/null @@ -1,44 +0,0 @@ -########################################################## -# Using installed QUESO 0.50.0, from external release in 'margarida' -########################################################## - -QUESO_DIR = /home/kemelli/LIBRARIES/QUESO-0.50.0 -BOOST_DIR = /home/kemelli/LIBRARIES/boost-1.53.0 -GSL_DIR = /home/kemelli/LIBRARIES/gsl-1.15 -HDF5_DIR = /home/kemelli/LIBRARIES/hdf5-1.8.10 - -INC_PATHS = \ - -I. \ - -I$(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: example_sfp - -clean: - rm -f *~ - rm -f *.o - rm -f simple_sfp_example - -example_sfp: simple_sfp_example_main.o simple_sfp_example_qoi.o simple_sfp_example_compute.o - $(CXX) simple_sfp_example_main.o \ - simple_sfp_example_qoi.o \ - simple_sfp_example_compute.o \ - -o simple_sfp_example $(LIBS) - -%.o: %.C - $(CXX) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.C b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.C index 8105c94ef..4af7faeb5 100644 --- a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.C +++ b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.C @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.h b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.h index c78fb2a79..51936b166 100644 --- a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.h +++ b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_compute.h @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_main.C b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_main.C index 97a6bb13e..9903fc9d9 100644 --- a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_main.C +++ b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_main.C @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.C b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.C index 32b5e2a57..484f21c49 100644 --- a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.C +++ b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.C @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.h b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.h index 78cf2aa60..5edd9377b 100644 --- a/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.h +++ b/examples/simpleStatisticalForwardProblem/src/simple_sfp_example_qoi.h @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalInverseProblem/src/Makefile_sip_example_margarida b/examples/simpleStatisticalInverseProblem/src/Makefile_sip_example_margarida deleted file mode 100644 index 9a83e2427..000000000 --- a/examples/simpleStatisticalInverseProblem/src/Makefile_sip_example_margarida +++ /dev/null @@ -1,44 +0,0 @@ -########################################################## -# Using installed QUESO 0.50.0, from external release in 'margarida' -########################################################## - -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: example_sip - -clean: - rm -f *~ - rm -f *.o - rm -f simple_sip_example - -example_sip: example_main.o example_likelihood.o example_compute.o - $(CXX) example_main.o \ - example_likelihood.o \ - example_compute.o \ - -o simple_sip_example $(LIBS) - -%.o: %.C - $(CXX) $(INC_PATHS) $(CXXFLAGS) $< diff --git a/examples/simpleStatisticalInverseProblem/src/example_compute.C b/examples/simpleStatisticalInverseProblem/src/example_compute.C index d14c3609d..8bdbc868e 100644 --- a/examples/simpleStatisticalInverseProblem/src/example_compute.C +++ b/examples/simpleStatisticalInverseProblem/src/example_compute.C @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalInverseProblem/src/example_compute.h b/examples/simpleStatisticalInverseProblem/src/example_compute.h index 913c51521..fcd66a87b 100644 --- a/examples/simpleStatisticalInverseProblem/src/example_compute.h +++ b/examples/simpleStatisticalInverseProblem/src/example_compute.h @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalInverseProblem/src/example_likelihood.C b/examples/simpleStatisticalInverseProblem/src/example_likelihood.C index 553b960e5..b657cf04b 100644 --- a/examples/simpleStatisticalInverseProblem/src/example_likelihood.C +++ b/examples/simpleStatisticalInverseProblem/src/example_likelihood.C @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- @@ -73,9 +73,5 @@ double likelihoodRoutine( // Do nothing; } -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN return -.5*result; -#else - return result; -#endif } diff --git a/examples/simpleStatisticalInverseProblem/src/example_likelihood.h b/examples/simpleStatisticalInverseProblem/src/example_likelihood.h index 17abd7ad5..4bd14f483 100644 --- a/examples/simpleStatisticalInverseProblem/src/example_likelihood.h +++ b/examples/simpleStatisticalInverseProblem/src/example_likelihood.h @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/simpleStatisticalInverseProblem/src/example_main.C b/examples/simpleStatisticalInverseProblem/src/example_main.C index 40c6610a8..379fcc10f 100644 --- a/examples/simpleStatisticalInverseProblem/src/example_main.C +++ b/examples/simpleStatisticalInverseProblem/src/example_main.C @@ -1,6 +1,6 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/statisticalInverseProblem/src/exStatisticalInverseProblem_likelihood.h b/examples/statisticalInverseProblem/src/exStatisticalInverseProblem_likelihood.h index 61cdef0c0..85eb1500a 100644 --- a/examples/statisticalInverseProblem/src/exStatisticalInverseProblem_likelihood.h +++ b/examples/statisticalInverseProblem/src/exStatisticalInverseProblem_likelihood.h @@ -63,10 +63,6 @@ likelihoodRoutine( result = scalarProduct(diffVec, matrix * diffVec); } -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN return -.5*result; -#else - return result; -#endif } #endif // EX_STATISTICAL_INVERSE_PROBLEM_LIKELIHOOD_H diff --git a/examples/statisticalInverse_Fortran/src/exStatisticalInverseProblem1_likelihood.h b/examples/statisticalInverse_Fortran/src/exStatisticalInverseProblem1_likelihood.h index ada08899f..e7a04b167 100644 --- a/examples/statisticalInverse_Fortran/src/exStatisticalInverseProblem1_likelihood.h +++ b/examples/statisticalInverse_Fortran/src/exStatisticalInverseProblem1_likelihood.h @@ -1,11 +1,11 @@ // -*-c++-*- //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // -// Copyright (C) 2008,2009,2010 The PECOS Development Team +// 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 @@ -18,7 +18,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/examples/template_example/template_example.cpp b/examples/template_example/template_example.cpp index 01ee6f29a..0b61a7776 100644 --- a/examples/template_example/template_example.cpp +++ b/examples/template_example/template_example.cpp @@ -12,6 +12,7 @@ class Likelihood : public QUESO::BaseScalarFunction public: Likelihood(const char * prefix, const QUESO::VectorSet & domain) + : QUESO::BaseScalarFunction(prefix, domain) { // Setup here } @@ -35,17 +36,16 @@ class Likelihood : public QUESO::BaseScalarFunction // // 3) Return below -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN - // return -0.5 * misfit; -#else - // return misfit; -#endif + double misfit = 0.0; + + return -0.5 * misfit; } virtual double actualValue(const V & domainVector, const V * domainDirection, V * gradVector, M * hessianMatrix, V * hessianEffect) const { - // return exp(this->lnValue(...)) + return std::exp(this->lnValue(domainVector, domainDirection, gradVector, + hessianMatrix, hessianEffect)); } private: @@ -62,6 +62,9 @@ int main(int argc, char ** argv) { QUESO::VectorSpace paramSpace(env, "param_", 1, NULL); + double min_val = 0.0; + double max_val = 1.0; + // Step 2 of 5: Set up the prior QUESO::GslVector paramMins(paramSpace.zeroVector()); paramMins.cwSet(min_val); diff --git a/examples/validationCycle/src/exTgaValidationCycle_likelihood.h b/examples/validationCycle/src/exTgaValidationCycle_likelihood.h index c08e504e3..f38df8a75 100644 --- a/examples/validationCycle/src/exTgaValidationCycle_likelihood.h +++ b/examples/validationCycle/src/exTgaValidationCycle_likelihood.h @@ -431,11 +431,7 @@ likelihoodRoutine( env.subComm().Barrier(); //env.syncPrintDebugMsg("Leaving likelihoodRoutine()",1,env.fullComm()); -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN return -.5*resultValue; -#else - return resultValue; -#endif } #endif // EX_TGA_VALIDATION_CYCLE_LIKELIHOOD_H diff --git a/examples/validationPyramid/Makefile.old b/examples/validationPyramid/Makefile.old deleted file mode 100644 index 110e57a4f..000000000 --- a/examples/validationPyramid/Makefile.old +++ /dev/null @@ -1,20 +0,0 @@ -ifeq ($(UQBT),) -override UQBT = gsl -else ifeq ($(UQBT),gsl) -else -override UQBT = tri -endif - -all: all_src - -echo_basic_type: - @echo 'UQBT = ' $(UQBT) - -clean_all: echo_basic_type clean_src - -clean_src: - cd src; make clean_all UQBT=$(UQBT) - -all_src: - cd src; make UQBT=$(UQBT) - diff --git a/inc/queso/Makefile.am b/inc/queso/Makefile.am index f9218bf11..ba68c347b 100644 --- a/inc/queso/Makefile.am +++ b/inc/queso/Makefile.am @@ -37,6 +37,7 @@ BUILT_SOURCES += EnvironmentOptions.h BUILT_SOURCES += FunctionBase.h BUILT_SOURCES += FunctionOperatorBuilder.h BUILT_SOURCES += GslMatrix.h +BUILT_SOURCES += GslOptimizer.h BUILT_SOURCES += GslVector.h BUILT_SOURCES += InfiniteDimensionalGaussian.h BUILT_SOURCES += InfiniteDimensionalLikelihoodBase.h @@ -50,6 +51,8 @@ BUILT_SOURCES += Map.h BUILT_SOURCES += Matrix.h BUILT_SOURCES += MpiComm.h BUILT_SOURCES += OperatorBase.h +BUILT_SOURCES += Optimizer.h +BUILT_SOURCES += OptimizerMonitor.h BUILT_SOURCES += RngBase.h BUILT_SOURCES += RngBoost.h BUILT_SOURCES += RngGsl.h @@ -61,6 +64,8 @@ BUILT_SOURCES += exceptions.h BUILT_SOURCES += ExperimentModel.h BUILT_SOURCES += ExperimentModelOptions.h BUILT_SOURCES += ExperimentStorage.h +BUILT_SOURCES += GPMSA.h +BUILT_SOURCES += GPMSAOptions.h BUILT_SOURCES += GcmExperimentInfo.h BUILT_SOURCES += GcmJointInfo.h BUILT_SOURCES += GcmJointTildeInfo.h @@ -113,9 +118,15 @@ BUILT_SOURCES += GenericVectorRV.h BUILT_SOURCES += GenericVectorRealizer.h BUILT_SOURCES += HessianCovMatricesTKGroup.h BUILT_SOURCES += InfoTheory.h +BUILT_SOURCES += InvLogitGaussianJointPdf.h +BUILT_SOURCES += InvLogitGaussianVectorRV.h +BUILT_SOURCES += InvLogitGaussianVectorRealizer.h BUILT_SOURCES += InverseGammaJointPdf.h BUILT_SOURCES += InverseGammaVectorRV.h BUILT_SOURCES += InverseGammaVectorRealizer.h +BUILT_SOURCES += JeffreysJointPdf.h +BUILT_SOURCES += JeffreysVectorRV.h +BUILT_SOURCES += JeffreysVectorRealizer.h BUILT_SOURCES += JointPdf.h BUILT_SOURCES += LogNormalJointPdf.h BUILT_SOURCES += LogNormalVectorRV.h @@ -145,6 +156,7 @@ BUILT_SOURCES += StatisticalInverseProblem.h BUILT_SOURCES += StatisticalInverseProblemOptions.h BUILT_SOURCES += StdScalarCdf.h BUILT_SOURCES += TKGroup.h +BUILT_SOURCES += TransformedScaledCovMatrixTKGroup.h BUILT_SOURCES += UniformJointPdf.h BUILT_SOURCES += UniformVectorRV.h BUILT_SOURCES += UniformVectorRealizer.h @@ -226,6 +238,8 @@ FunctionOperatorBuilder.h: $(top_srcdir)/src/core/inc/FunctionOperatorBuilder.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ GslMatrix.h: $(top_srcdir)/src/core/inc/GslMatrix.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +GslOptimizer.h: $(top_srcdir)/src/core/inc/GslOptimizer.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ GslVector.h: $(top_srcdir)/src/core/inc/GslVector.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ InfiniteDimensionalGaussian.h: $(top_srcdir)/src/core/inc/InfiniteDimensionalGaussian.h @@ -252,6 +266,10 @@ MpiComm.h: $(top_srcdir)/src/core/inc/MpiComm.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ OperatorBase.h: $(top_srcdir)/src/core/inc/OperatorBase.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +Optimizer.h: $(top_srcdir)/src/core/inc/Optimizer.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +OptimizerMonitor.h: $(top_srcdir)/src/core/inc/OptimizerMonitor.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ RngBase.h: $(top_srcdir)/src/core/inc/RngBase.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ RngBoost.h: $(top_srcdir)/src/core/inc/RngBoost.h @@ -274,6 +292,10 @@ ExperimentModelOptions.h: $(top_srcdir)/src/gp/inc/ExperimentModelOptions.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ ExperimentStorage.h: $(top_srcdir)/src/gp/inc/ExperimentStorage.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +GPMSA.h: $(top_srcdir)/src/gp/inc/GPMSA.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +GPMSAOptions.h: $(top_srcdir)/src/gp/inc/GPMSAOptions.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ GcmExperimentInfo.h: $(top_srcdir)/src/gp/inc/GcmExperimentInfo.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ GcmJointInfo.h: $(top_srcdir)/src/gp/inc/GcmJointInfo.h @@ -378,12 +400,24 @@ HessianCovMatricesTKGroup.h: $(top_srcdir)/src/stats/inc/HessianCovMatricesTKGro $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ InfoTheory.h: $(top_srcdir)/src/stats/inc/InfoTheory.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +InvLogitGaussianJointPdf.h: $(top_srcdir)/src/stats/inc/InvLogitGaussianJointPdf.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +InvLogitGaussianVectorRV.h: $(top_srcdir)/src/stats/inc/InvLogitGaussianVectorRV.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +InvLogitGaussianVectorRealizer.h: $(top_srcdir)/src/stats/inc/InvLogitGaussianVectorRealizer.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ InverseGammaJointPdf.h: $(top_srcdir)/src/stats/inc/InverseGammaJointPdf.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ InverseGammaVectorRV.h: $(top_srcdir)/src/stats/inc/InverseGammaVectorRV.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ InverseGammaVectorRealizer.h: $(top_srcdir)/src/stats/inc/InverseGammaVectorRealizer.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +JeffreysJointPdf.h: $(top_srcdir)/src/stats/inc/JeffreysJointPdf.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +JeffreysVectorRV.h: $(top_srcdir)/src/stats/inc/JeffreysVectorRV.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +JeffreysVectorRealizer.h: $(top_srcdir)/src/stats/inc/JeffreysVectorRealizer.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ JointPdf.h: $(top_srcdir)/src/stats/inc/JointPdf.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ LogNormalJointPdf.h: $(top_srcdir)/src/stats/inc/LogNormalJointPdf.h @@ -442,6 +476,8 @@ StdScalarCdf.h: $(top_srcdir)/src/stats/inc/StdScalarCdf.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ TKGroup.h: $(top_srcdir)/src/stats/inc/TKGroup.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ +TransformedScaledCovMatrixTKGroup.h: $(top_srcdir)/src/stats/inc/TransformedScaledCovMatrixTKGroup.h + $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ UniformJointPdf.h: $(top_srcdir)/src/stats/inc/UniformJointPdf.h $(AM_V_GEN)rm -f $@ && $(LN_S) $< $@ UniformVectorRV.h: $(top_srcdir)/src/stats/inc/UniformVectorRV.h diff --git a/m4/common/antioch.m4 b/m4/common/antioch.m4 deleted file mode 100644 index 6abcb9453..000000000 --- a/m4/common/antioch.m4 +++ /dev/null @@ -1,200 +0,0 @@ -# SYNOPSIS -# -# Test for Antioch -# -# AX_PATH_ANTIOCH( , ) -# -# DESCRIPTION -# -# Provides a --with-antioch=DIR option. Searches --with-antioch, -# $ANTIOCH_DIR, and the usual places for ANTIOCH headers and libraries. -# -# On success, sets ANTIOCH_CPPFLAGS, ANTIOCH_LIBS, and #defines HAVE_ANTIOCH. -# Also defines automake conditional ANTIOCH_ENABLED. Assumes package -# is optional unless overridden with $2=yes. -# -# COPYLEFT -# -# Copyright (c) 2013 Roy H. Stogner -# Copyright (c) 2013 Paul T. Bauman -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AX_PATH_ANTIOCH], -[ - -AC_ARG_VAR(ANTIOCH_DIR,[root directory of Antioch installation]) - -AC_ARG_WITH(antioch, - [AS_HELP_STRING([--with-antioch[=DIR]],[root directory of Antioch installation (default = ANTIOCH_DIR)])], - [with_antioch=$withval -if test "${with_antioch}" != yes; then - ANTIOCH_PREFIX=$withval -fi -],[ -with_antioch=$withval -if test "x${ANTIOCH_DIR}" != "x"; then - ANTIOCH_PREFIX=${ANTIOCH_DIR} -fi -]) - -# package requirement; if not specified, the default is to assume that -# the package is optional - -is_package_required=ifelse([$2], ,no, $2 ) - -HAVE_ANTIOCH=0 - -# logic change: if the user called the macro, check for package, -# decide what to do based on whether the package is required or not. - -# if test "${with_antioch}" != no ; then - - if test -d "${ANTIOCH_PREFIX}/lib" ; then - ANTIOCH_LDFLAGS="-L${ANTIOCH_PREFIX}/lib -Wl,-rpath,${ANTIOCH_PREFIX}/lib" - ANTIOCH_LIBS="-lantioch" - fi - - if test -d "${ANTIOCH_PREFIX}/include" ; then - ANTIOCH_CPPFLAGS="-I${ANTIOCH_PREFIX}/include -I${ANTIOCH_PREFIX}/src" - fi - - ac_ANTIOCH_save_CPPFLAGS="$CPPFLAGS" - ac_ANTIOCH_save_LDFLAGS="$LDFLAGS" - ac_ANTIOCH_save_LIBS="$LIBS" - - CPPFLAGS="${ANTIOCH_CPPFLAGS} ${CPPFLAGS}" - LDFLAGS="${ANTIOCH_LDFLAGS} ${LDFLAGS}" - LIBS="${ANTIOCH_LIBS} ${LIBS}" - - AC_LANG_PUSH([C++]) - AC_CHECK_HEADER([antioch/antioch_version.h],[found_header=yes],[found_header=no]) - AC_LANG_POP([C++]) - - #----------------------- - # Minimum version check - #---------------------- - - min_antioch_version=ifelse([$1], ,0.0.0, $1) - - # looking for major.minor.micro style versioning - - MAJOR_VER=`echo $min_antioch_version | sed 's/^\([[0-9]]*\).*/\1/'` - if test "x${MAJOR_VER}" = "x" ; then - MAJOR_VER=0 - fi - - MINOR_VER=`echo $min_antioch_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\2/'` - if test "x${MINOR_VER}" = "x" ; then - MINOR_VER=0 - fi - - MICRO_VER=`echo $min_antioch_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\3/'` - if test "x${MICRO_VER}" = "x" ; then - MICRO_VER=0 - fi - - # begin additional test(s) if header if available - - if test "x${found_header}" = "xyes" ; then - - AC_MSG_CHECKING(for antioch - version >= $min_antioch_version) - version_succeeded=no - - AC_LANG_PUSH([C++]) - AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[ - @%:@include "antioch/antioch_version.h" - ]], [[ - #if ANTIOCH_MAJOR_VERSION > $MAJOR_VER - /* Sweet nibblets */ - #elif (ANTIOCH_MAJOR_VERSION >= $MAJOR_VER) && (ANTIOCH_MINOR_VERSION > $MINOR_VER) - /* Winner winner, chicken dinner */ - #elif (ANTIOCH_MAJOR_VERSION >= $MAJOR_VER) && (ANTIOCH_MINOR_VERSION >= $MINOR_VER) && (ANTIOCH_MICRO_VERSION >= $MICRO_VER) - /* I feel like chicken tonight, like chicken tonight? */ - #else - # error version is too old - #endif - ]])],[ - AC_MSG_RESULT(yes) - version_succeeded=yes - ],[ - AC_MSG_RESULT(no) - ]) - AC_LANG_POP([C++]) - - if test "$version_succeeded" != "yes";then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([ - - Your Antioch version does not meet the minimum versioning - requirements ($min_antioch_version). Please use --with-antioch to specify the location - of an updated installation or consider upgrading the system version. - - ]) - fi - fi - - # Library availability - - AC_MSG_CHECKING([for -lantioch linkage]) - - AC_LANG_PUSH([C++]) - - AC_LINK_IFELSE( - [AC_LANG_PROGRAM([#include "antioch/antioch_version.h"], - [Antioch::get_antioch_version()])], - [AC_MSG_RESULT(yes) - found_library=yes], - [AC_MSG_RESULT(no) - found_library=no]) - - fi dnl end test if header if available - - AC_LANG_POP([C++]) - - CPPFLAGS="$ac_ANTIOCH_save_CPPFLAGS" - LDFLAGS="$ac_ANTIOCH_save_LDFLAGS" - LIBS="$ac_ANTIOCH_save_LIBS" - - succeeded=no - if test "$found_header" = yes; then - if test "$version_succeeded" = yes; then - if test "$found_library" = yes; then - succeeded=yes - fi - fi - fi - - if test "$succeeded" = no; then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([Antioch not found. Try either --with-antioch or setting ANTIOCH_DIR.]) - else - AC_MSG_NOTICE([optional Antioch library not found]) - ANTIOCH_CPPFLAGS="" # ANTIOCH_CFLAGS empty on failure - ANTIOCH_LDFLAGS="" # ANTIOCH_LDFLAGS empty on failure - ANTIOCH_LIBS="" # ANTIOCH_LIBS empty on failure - fi - else - HAVE_ANTIOCH=1 - AC_DEFINE(HAVE_ANTIOCH,1,[Define if Antioch is available]) - AC_SUBST(ANTIOCH_CPPFLAGS) - AC_SUBST(ANTIOCH_LDFLAGS) - AC_SUBST(ANTIOCH_LIBS) - AC_SUBST(ANTIOCH_PREFIX) - fi - - AC_SUBST(HAVE_ANTIOCH) - -# fi - -AM_CONDITIONAL(ANTIOCH_ENABLED,test x$HAVE_ANTIOCH = x1) - -]) diff --git a/m4/common/chemay.m4 b/m4/common/chemay.m4 deleted file mode 100644 index 390418f44..000000000 --- a/m4/common/chemay.m4 +++ /dev/null @@ -1,206 +0,0 @@ -# SYNOPSIS -# -# Test for libchemay -# -# AM_PATH_CHEMAY( , ) -# -# DESCRIPTION -# -# Provides a --with-chemay=DIR option. Searches --with-chemay, -# $CHEMAY_DIR, and the usual places for Chemay headers and libraries. -# -# On success, sets CHEMAY_CFLAGS, CHEMAY_LIBS, and #defines HAVE_CHEMAY. -# Also defines automake conditional CHEMAY_ENABLED. Assumes package -# is optional unless overridden with $2=yes. -# -# COPYLEFT -# -# Copyright (c) 2010 Roy H. Stogner -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AX_PATH_CHEMAY], -[ - -AC_ARG_VAR(CHEMAY_DIR,[root directory of Chemay installation]) - -AC_ARG_WITH(chemay, - [AS_HELP_STRING([--with-chemay[=DIR]],[root directory of CHEMAY installation (default = CHEMAY_DIR)])], - [with_chemay=$withval -if test "${with_chemay}" != yes; then - CHEMAY_PREFIX=$withval -fi -],[ -with_chemay=$withval -if test "x${CHEMAY_DIR}" != "x"; then - CHEMAY_PREFIX=${CHEMAY_DIR} -fi -]) - -# package requirement; if not specified, the default is to assume that -# the package is optional - -is_package_required=ifelse([$2], ,no, $2 ) - -HAVE_CHEMAY=0 - -if test "${with_chemay}" != no ; then - - BOOST_SMART_PTR - - AX_PATH_GSL - - if test ! -d "${CHEMAY_PREFIX}/" ; then - AC_MSG_NOTICE([Chemay directory ${CHEMAY_PREFIX} not found]) - fi - - if test -d "${CHEMAY_PREFIX}/lib" ; then - CHEMAY_LIBS="-L${CHEMAY_PREFIX}/lib -lchemay -lcantera_compare -ltinyxml -Wl,-rpath,${CHEMAY_PREFIX}/lib ${GSL_LIBS}" - CHEMAY_FCFLAGS="-I${CHEMAY_PREFIX}/lib" - else - AC_MSG_NOTICE([Chemay library directory ${CHEMAY_PREFIX}/lib not found]) - fi - - if test -d "${CHEMAY_PREFIX}/include" ; then - CHEMAY_CPPFLAGS="-I${CHEMAY_PREFIX}/include ${BOOST_CPPFLAGS}" - else - AC_MSG_NOTICE([Chemay include directory ${CHEMAY_PREFIX}/include not found]) - fi - - ac_CHEMAY_save_CFLAGS="$CFLAGS" - ac_CHEMAY_save_CPPFLAGS="$CPPFLAGS" - ac_CHEMAY_save_LDFLAGS="$LDFLAGS" - ac_CHEMAY_save_LIBS="$LIBS" - - CPPFLAGS="${CHEMAY_CPPFLAGS} ${CPPFLAGS}" - LDFLAGS="${CHEMAY_LIBS} ${LDFLAGS}" - AC_LANG_PUSH([C++]) - AC_CHECK_HEADER([chemay/Arrhenius.hh],[found_header=yes],[found_header=no]) - -dnl #----------------------- -dnl # Minimum version check -dnl #---------------------- -dnl -dnl min_chemay_version=ifelse([$1], ,0.29, $1) -dnl -dnl # looking for major.minor.micro style versioning -dnl -dnl MAJOR_VER=`echo $min_chemay_version | sed 's/^\([[0-9]]*\).*/\1/'` -dnl if test "x${MAJOR_VER}" = "x" ; then -dnl MAJOR_VER=0 -dnl fi -dnl -dnl MINOR_VER=`echo $min_chemay_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\2/'` -dnl if test "x${MINOR_VER}" = "x" ; then -dnl MINOR_VER=0 -dnl fi -dnl -dnl MICRO_VER=`echo $min_chemay_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\3/'` -dnl if test "x${MICRO_VER}" = "x" ; then -dnl MICRO_VER=0 -dnl fi -dnl -dnl # begin additional test(s) if header if available -dnl -dnl if test "x${found_header}" = "xyes" ; then -dnl -dnl AC_MSG_CHECKING(for Chemay - version >= $min_chemay_version) -dnl version_succeeded=no -dnl -dnl AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[ -dnl @%:@include -dnl ]], [[ -dnl #if CHEMAY_MAJOR_VERSION > $MAJOR_VER -dnl /* Sweet nibblets */ -dnl #elif (CHEMAY_MAJOR_VERSION >= $MAJOR_VER) && (CHEMAY_MINOR_VERSION > $MINOR_VER) -dnl /* Winner winner, chicken dinner */ -dnl #elif (CHEMAY_MAJOR_VERSION >= $MAJOR_VER) && (CHEMAY_MINOR_VERSION >= $MINOR_VER) && (CHEMAY_MICRO_VERSION >= $MICRO_VER) -dnl /* I feel like chicken tonight, like chicken tonight? */ -dnl #else -dnl # error version is too old -dnl #endif -dnl ]])],[ -dnl AC_MSG_RESULT(yes) -dnl version_succeeded=yes -dnl ],[ -dnl AC_MSG_RESULT(no) -dnl ]) -dnl -dnl if test "$version_succeeded" != "yes";then -dnl if test "$is_package_required" = yes; then -dnl AC_MSG_ERROR([ -dnl -dnl Your Chemay library version does not meet the minimum versioning -dnl requirements ($min_chemay_version). Please use --with-chemay to specify the location -dnl of an updated installation or consider upgrading the system version. -dnl -dnl ]) -dnl fi -dnl fi - - # Library availability - - AC_MSG_CHECKING([for -lchemay linkage]) - - AC_LINK_IFELSE( - [AC_LANG_PROGRAM([#include ], - [Chemay::Arrhenius dummy])], - [AC_MSG_RESULT(yes)] - [found_library=yes], - [AC_MSG_RESULT(no)] - [AC_MSG_WARN([Failed to link to libchemay.])] - [found_library=no]) - -dnl AC_CHECK_LIB([chemay],chemay_input_fopen,[found_library=yes],[found_library=no]) - -dnl fi dnl end test if header if available - - AC_LANG_POP([C++]) - - CFLAGS="$ac_CHEMAY_save_CFLAGS" - CPPFLAGS="$ac_CHEMAY_save_CPPFLAGS" - LDFLAGS="$ac_CHEMAY_save_LDFLAGS" - LIBS="$ac_CHEMAY_save_LIBS" - - succeeded=no - if test "$found_header" = yes; then -dnl if test "$version_succeeded" = yes; then - if test "$found_library" = yes; then - succeeded=yes - fi -dnl fi - fi - - if test "$succeeded" = no; then - CHEMAY_CPPFLAGS="" - CHEMAY_FCFLAGS="" - CHEMAY_LIBS="" - CHEMAY_PREFIX="" - if test "$is_package_required" = yes; then - AC_MSG_ERROR([libchemay not found. Try either --with-chemay or setting CHEMAY_DIR.]) - else - AC_MSG_NOTICE([optional Chemay library not found]) - fi - else - HAVE_CHEMAY=1 - AC_DEFINE(HAVE_CHEMAY,1,[Define if Chemay is available]) - fi - - AC_SUBST(CHEMAY_CPPFLAGS) - AC_SUBST(CHEMAY_FCFLAGS) - AC_SUBST(CHEMAY_LIBS) - AC_SUBST(CHEMAY_PREFIX) - AC_SUBST(HAVE_CHEMAY) -fi - -AM_CONDITIONAL(CHEMAY_ENABLED,test x$HAVE_CHEMAY = x1) - -]) diff --git a/m4/common/config_environment.m4 b/m4/common/config_environment.m4 index 1ecf47638..882b1dfb1 100644 --- a/m4/common/config_environment.m4 +++ b/m4/common/config_environment.m4 @@ -58,7 +58,7 @@ AC_DEFINE_UNQUOTED([BUILD_ARCH], "${BUILD_ARCH}", [Architecture of the b AC_DEFINE_UNQUOTED([BUILD_HOST], "${BUILD_HOST}", [Build host name]) AC_DEFINE_UNQUOTED([BUILD_VERSION], "${BUILD_VERSION}", [SVN revision]) AC_DEFINE_UNQUOTED([BUILD_DEVSTATUS],"${BUILD_DEVSTATUS}",[Dev/Release build]) -AC_DEFINE( [BUILD_DATE], __DATE__" "__TIME__, [Build date]) +AC_DEFINE( [BUILD_DATE], __DATE__ " " __TIME__, [Build date]) AC_SUBST(BUILD_USER) AC_SUBST(BUILD_ARCH) diff --git a/m4/common/config_git_environment.m4 b/m4/common/config_git_environment.m4 index 11b220df8..c06c41d2b 100644 --- a/m4/common/config_git_environment.m4 +++ b/m4/common/config_git_environment.m4 @@ -57,7 +57,7 @@ AC_DEFINE_UNQUOTED([BUILD_ARCH], "${BUILD_ARCH}", [Architecture of the b AC_DEFINE_UNQUOTED([BUILD_HOST], "${BUILD_HOST}", [Build host name]) AC_DEFINE_UNQUOTED([BUILD_VERSION], "${BUILD_VERSION}", [git revision]) AC_DEFINE_UNQUOTED([BUILD_DEVSTATUS],"${BUILD_DEVSTATUS}",[Dev/Release build]) -AC_DEFINE( [BUILD_DATE], __DATE__" "__TIME__, [Build date]) +AC_DEFINE( [BUILD_DATE], __DATE__ " " __TIME__, [Build date]) AC_SUBST(BUILD_USER) AC_SUBST(BUILD_ARCH) diff --git a/m4/common/eigen.m4 b/m4/common/eigen.m4 deleted file mode 100644 index efa85429b..000000000 --- a/m4/common/eigen.m4 +++ /dev/null @@ -1,172 +0,0 @@ -# ---------------------------------------------------------------- -# Locate header files for the C++ linear algebra library Eigen. -# Eigen is a header-only template library. By default we check for the -# Eigen files in the --with-eigen-include=xxx argument provided to -# configure, or if those don't exist in the $EIGEN_INC/Eigen directory, -# or in /usr/include. -# -# If an argument to this macro is specified, it should give the minimum useful -# version number. -# -# If a second argument to this macro is specified as 'yes', then this is a -# required dependency and configure will exit with an error if it is not -# satisfied. -# -# Note: Eigen is installed (by default) at the location -# /path/to/eigen/Eigen, i.e. with path ending in capital 'Eigen'. -# You should specify --with-eigen-include=/path/to/eigen -# during configure, or set your $EIGEN_INC environment variable -# to /path/to/eigen. -# ---------------------------------------------------------------- - -AC_DEFUN([CONFIGURE_EIGEN], -[ - AC_ARG_ENABLE(eigen, - AC_HELP_STRING([--enable-eigen], - [build with Eigen linear algebra support]), - [case "${enableval}" in - yes) enableeigen=yes ;; - no) enableeigen=no ;; - *) AC_MSG_ERROR(bad value ${enableval} for --enable-eigen) ;; - esac], - [enableeigen=$enableoptional]) - - # package requirement; if not specified, the default is to assume that - # the package is optional - - is_package_required=ifelse([$2], ,no, $2 ) - - install_internal_eigen=no - if (test x$enableeigen = xyes); then - - # User-specific include path - AC_ARG_WITH(eigen-include, - AC_HELP_STRING([--with-eigen-include=PATH],[Specify the path for EIGEN header files (for example $EIGEN_PATH/include/eigen3)]), - witheigeninc=$withval, - witheigeninc=no) - - # Fall back on default paths to Eigen's include files - if (test $witheigeninc != no); then - EIGEN_INC="$witheigeninc" - - elif (test "x$EIGEN_INC" != x -a -f $EIGEN_INC/Eigen/Eigen); then - echo "Environment EIGEN_INC=$EIGEN_INC" - - elif (test "x$EIGEN3_INCLUDE" != x -a -f $EIGEN3_INCLUDE/Eigen/Eigen); then - EIGEN_INC=$EIGEN3_INCLUDE - echo "Environment EIGEN_INC=$EIGEN_INC" - - elif (test "x$EIGEN_INCLUDE" != x -a -f $EIGEN_INCLUDE/Eigen/Eigen); then - EIGEN_INC=$EIGEN_INCLUDE - echo "Environment EIGEN_INC=$EIGEN_INC" - - elif (test -f /usr/include/eigen3/Eigen/Eigen); then - EIGEN_INC="/usr/include/eigen3" - - else - EIGEN_INC="/usr/include" - fi - - # Initialize Makefile/config.h substitution variables - EIGEN_INCLUDE="" - - # Check for existence of a header file in the specified location. Note: here - # we are checking for the header file "Eigen" in the Eigen directory. - AC_LANG_SAVE - AC_LANG_CPLUSPLUS - - externaleigenincFound=no; - AC_CHECK_HEADERS($EIGEN_INC/Eigen/Eigen, externaleigenincFound=yes) - - if (test x$externaleigenincFound = xyes); then - EIGEN_INCLUDE="-I$EIGEN_INC" - elif (test -d $top_srcdir/contrib/eigen/eigen); then - AC_MSG_RESULT([<<< external Eigen header files not found, using Eigen from ./contrib >>>]) - EIGEN_INC=$top_srcdir/contrib/eigen/eigen - EIGEN_INCLUDE="-I\$(top_srcdir)/contrib/eigen/eigen" - install_internal_eigen=yes - else - enableeigen=no - fi - - - # OK, we have a usable eigen path, make sure the headers we want are good. - if (test x$enableeigen = xyes); then - - ac_eigen_save_CPPFLAGS="$CPPFLAGS" - CPPFLAGS="-I${EIGEN_INC} ${CPPFLAGS}" - - AC_CHECK_HEADERS([Eigen/Dense],[],[enableeigen=no]) - - if (test x$enableeigensparse = xyes); then - AC_CHECK_HEADERS([Eigen/Sparse],[],[enableeigen=no]) - fi - - #----------------------- - # Minimum version check - #---------------------- - - min_eigen_version=ifelse([$1], ,0.0.0, $1) - - # looking for major.minor.micro (which Eigen calls world.major.minor) style versioning - - MAJOR_VER=`echo $min_eigen_version | sed 's/^\([[0-9]]*\).*/\1/'` - if test "x${MAJOR_VER}" = "x" ; then - MAJOR_VER=0 - fi - - MINOR_VER=`echo $min_eigen_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\2/'` - if test "x${MINOR_VER}" = "x" ; then - MINOR_VER=0 - fi - - MICRO_VER=`echo $min_eigen_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\3/'` - if test "x${MICRO_VER}" = "x" ; then - MICRO_VER=0 - fi - - - AC_MSG_CHECKING(for eigen - version >= $min_eigen_version) - - AC_LANG_PUSH([C++]) - AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[ - @%:@include "Eigen/Core" - ]], [[ - #if EIGEN_WORLD_VERSION > $MAJOR_VER - #elif (EIGEN_WORLD_VERSION >= $MAJOR_VER) && (EIGEN_MAJOR_VERSION > $MINOR_VER) - #elif (EIGEN_WORLD_VERSION >= $MAJOR_VER) && (EIGEN_MAJOR_VERSION >= $MINOR_VER) && (EIGEN_MINOR_VERSION >= $MICRO_VER) - #else - # error version is too old - #endif - ]])],[ - AC_MSG_RESULT(yes) - ],[ - AC_MSG_RESULT(no) - enableeigen=no - ]) - AC_LANG_POP([C++]) - - CPPFLAGS="${ac_eigen_save_CPPFLAGS}" - - # if we survived, we really have Eigen - if (test x$enableeigen = xyes); then - HAVE_EIGEN=1 - AC_DEFINE(HAVE_EIGEN, 1, [Flag indicating whether the library will be compiled with Eigen support]) - AC_MSG_RESULT(<<< Configuring library with Eigen support >>>) - elif test "$is_package_required" = yes; then - AC_MSG_ERROR([ - - Your EIGEN version ($EIGEN_INC) does not meet the minimum versioning - requirements ($min_eigen_version). Please use --with-eigen-include to - specify the location of an updated installation. - - ]) - fi - fi - - AC_LANG_RESTORE - fi - - # Substitute the substitution variables - AC_SUBST(EIGEN_INCLUDE) -]) diff --git a/m4/common/grins.m4 b/m4/common/grins.m4 deleted file mode 100644 index 1c10bebc0..000000000 --- a/m4/common/grins.m4 +++ /dev/null @@ -1,201 +0,0 @@ -# SYNOPSIS -# -# Test for GRINS -# -# AX_PATH_GRINS( , ) -# -# DESCRIPTION -# -# Provides a --with-grins=DIR option. Searches --with-grins, -# $GRINS_DIR, and the usual places for GRINS headers and libraries. -# -# On success, sets GRINS_CPPFLAGS, GRINS_LIBS, and #defines HAVE_GRINS. -# Also defines automake conditional GRINS_ENABLED. Assumes package -# is optional unless overridden with $2=yes. -# -# COPYLEFT -# -# Copyright (c) 2012 Paul T. Bauman -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AX_PATH_GRINS], -[ - -AC_ARG_VAR(GRINS_DIR,[root directory of GRINS installation]) - -AC_ARG_WITH(grins, - [AS_HELP_STRING([--with-grins[=DIR]],[root directory of GRINS installation (default = GRINS_DIR)])], - [with_grins=$withval -if test "${with_grins}" != yes; then - GRINS_PREFIX=$withval -fi -],[ -with_grins=$withval -if test "x${GRINS_DIR}" != "x"; then - GRINS_PREFIX=${GRINS_DIR} -fi -]) - -# package requirement; if not specified, the default is to assume that -# the package is optional - -is_package_required=ifelse([$2], ,no, $2 ) - -HAVE_GRINS=0 - -# logic change: if the user called the macro, check for package, -# decide what to do based on whether the package is required or not. - -# if test "${with_grins}" != no ; then - - AC_REQUIRE([AX_PATH_LIBMESH_NEW(0.9.1, yes)]) - - if test -d "${GRINS_PREFIX}/lib" ; then - GRINS_LDFLAGS="-L${GRINS_PREFIX}/lib -Wl,-rpath,${GRINS_PREFIX}/lib ${LIBMESH_LDFLAGS}" - GRINS_LIBS="-lgrins ${LIBMESH_LIBS}" - fi - - if test -d "${GRINS_PREFIX}/include" ; then - GRINS_CPPFLAGS="-I${GRINS_PREFIX}/include -I${GRINS_PREFIX}/src ${LIBMESH_CPPFLAGS}" - fi - - ac_GRINS_save_CPPFLAGS="$CPPFLAGS" - ac_GRINS_save_LDFLAGS="$LDFLAGS" - ac_GRINS_save_LIBS="$LIBS" - - CPPFLAGS="${GRINS_CPPFLAGS} ${CPPFLAGS}" - LDFLAGS="${GRINS_LDFLAGS} ${LDFLAGS}" - LIBS="${GRINS_LIBS} ${LIBS}" - - AC_LANG_PUSH([C++]) - AC_CHECK_HEADER([grins/grins_version.h],[found_header=yes],[found_header=no]) - AC_LANG_POP([C++]) - - #----------------------- - # Minimum version check - #---------------------- - - min_grins_version=ifelse([$1], ,0.2.0, $1) - - # looking for major.minor.micro style versioning - - MAJOR_VER=`echo $min_grins_version | sed 's/^\([[0-9]]*\).*/\1/'` - if test "x${MAJOR_VER}" = "x" ; then - MAJOR_VER=0 - fi - - MINOR_VER=`echo $min_grins_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\2/'` - if test "x${MINOR_VER}" = "x" ; then - MINOR_VER=0 - fi - - MICRO_VER=`echo $min_grins_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\3/'` - if test "x${MICRO_VER}" = "x" ; then - MICRO_VER=0 - fi - - # begin additional test(s) if header if available - - if test "x${found_header}" = "xyes" ; then - - AC_MSG_CHECKING(for grins - version >= $min_grins_version) - version_succeeded=no - - AC_LANG_PUSH([C++]) - AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[ - @%:@include "grins/grins_version.h" - ]], [[ - #if GRINS_MAJOR_VERSION > $MAJOR_VER - /* Sweet nibblets */ - #elif (GRINS_MAJOR_VERSION >= $MAJOR_VER) && (GRINS_MINOR_VERSION > $MINOR_VER) - /* Winner winner, chicken dinner */ - #elif (GRINS_MAJOR_VERSION >= $MAJOR_VER) && (GRINS_MINOR_VERSION >= $MINOR_VER) && (GRINS_MICRO_VERSION >= $MICRO_VER) - /* I feel like chicken tonight, like chicken tonight? */ - #else - # error version is too old - #endif - ]])],[ - AC_MSG_RESULT(yes) - version_succeeded=yes - ],[ - AC_MSG_RESULT(no) - ]) - AC_LANG_POP([C++]) - - if test "$version_succeeded" != "yes";then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([ - - Your GRINS version does not meet the minimum versioning - requirements ($min_grins_version). Please use --with-grins to specify the location - of an updated installation or consider upgrading the system version. - - ]) - fi - fi - - # Library availability - - AC_MSG_CHECKING([for -lgrins linkage]) - - AC_LANG_PUSH([C++]) - - AC_LINK_IFELSE( - [AC_LANG_PROGRAM([#include "grins/grins_version.h"], - [GRINS::get_grins_version()])], - [AC_MSG_RESULT(yes) - found_library=yes], - [AC_MSG_RESULT(no) - found_library=no]) - - fi dnl end test if header if available - - AC_LANG_POP([C++]) - - CPPFLAGS="$ac_GRINS_save_CPPFLAGS" - LDFLAGS="$ac_GRINS_save_LDFLAGS" - LIBS="$ac_GRINS_save_LIBS" - - succeeded=no - if test "$found_header" = yes; then - if test "$version_succeeded" = yes; then - if test "$found_library" = yes; then - succeeded=yes - fi - fi - fi - - if test "$succeeded" = no; then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([GRINS not found. Try either --with-grins or setting GRINS_DIR.]) - else - AC_MSG_NOTICE([optional GRINS library not found]) - GRINS_CPPFLAGS="" # GRINS_CFLAGS empty on failure - GRINS_LDFLAGS="" # GRINS_LDFLAGS empty on failure - GRINS_LIBS="" # GRINS_LIBS empty on failure - fi - else - HAVE_GRINS=1 - AC_DEFINE(HAVE_GRINS,1,[Define if GRINS is available]) - AC_SUBST(GRINS_CPPFLAGS) - AC_SUBST(GRINS_LDFLAGS) - AC_SUBST(GRINS_LIBS) - AC_SUBST(GRINS_PREFIX) - fi - - AC_SUBST(HAVE_GRINS) - -# fi - -AM_CONDITIONAL(GRINS_ENABLED,test x$HAVE_GRINS = x1) - -]) diff --git a/m4/common/pecos_ablation.m4 b/m4/common/pecos_ablation.m4 deleted file mode 100644 index 069616eac..000000000 --- a/m4/common/pecos_ablation.m4 +++ /dev/null @@ -1,102 +0,0 @@ -# SYNOPSIS -# -# Test for PECOS 1D Ablation library -# -# AM_PATH_ABLATION([, ACTION-IF-FOUND [, ACTION-IF-NOT-FOUND]]]) -# -# DESCRIPTION -# -# Provides a --with-ablation=DIR option. Searches --with-ablation, -# $ABLATION_DIR, and the usual places for ablation headers and libraries. -# -# On success, sets ABLATION_CFLAGS, ABLATION_FCFLAGS, ABLATION_LIBS, and -# #defines HAVE_ABLATION. When ACTION-IF-NOT-FOUND is not specified, -# the default behavior is for configure to fail. -# -# COPYLEFT -# -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AM_PATH_ABLATION], -[ - -AC_ARG_VAR(ABLATION_DIR,[root directory of PECOS ablation installation]) - -AC_ARG_WITH(ablation, - [AS_HELP_STRING([--with-ablation[=DIR]],[root directory of PECOS ablation installation (default = ABLATION_DIR)])], - [with_ablation=$withval -if test "${with_ablation}" != yes; then - ABLATION_PREFIX=$withval -fi -],[ -# assume a sensible default of --with-ablation=yes -with_ablation=yes -if test "x${ABLATION_DIR}" != "x"; then - ABLATION_PREFIX=${ABLATION_DIR} -fi -]) - -if test "${with_ablation}" != no -a "x${ABLATION_PREFIX}" != "x" ; then - if test -d "${ABLATION_PREFIX}/conf" ; then - ABLATION_LIBS=`{ echo "include ${ABLATION_PREFIX}/conf/Makefile.export.ablation"; echo "echo:"; echo " @echo \\$(PECOS_ABLATION_LIBS)"; } | make -s -f -` - elif test -d "${ABLATION_PREFIX}/lib" ; then - # This only works for ablation libraries without PETSc enabled, and requires AM_PATH_GRVY - ABLATION_LIBS="-L${ABLATION_PREFIX}/lib -lablation -Wl,-rpath,${ABLATION_PREFIX}/lib ${GRVY_LIBS}" - fi - - if test -d "${ABLATION_PREFIX}/include" ; then - ABLATION_CFLAGS="-I${ABLATION_PREFIX}/include" - fi - - ac_ABLATION_save_CFLAGS="$CFLAGS" - ac_ABLATION_save_CPPFLAGS="$CPPFLAGS" - ac_ABLATION_save_LDFLAGS="$LDFLAGS" - ac_ABLATION_save_LIBS="$LIBS" - - CFLAGS="${ABLATION_CFLAGS} ${CFLAGS}" - CPPFLAGS="${ABLATION_CFLAGS} ${CPPFLAGS}" - LDFLAGS="${LDFLAGS}" - AC_LANG_PUSH([C]) - AC_CHECK_HEADER([ablation1d.h],[found_header=yes],[found_header=no]) - AC_CHECK_LIB([ablation],[ablationC1d_version],[found_library=yes],[found_library=no], [ ${ABLATION_LIBS} ]) - AC_LANG_POP([C]) - - CFLAGS="$ac_ABLATION_save_CFLAGS" - CPPFLAGS="$ac_ABLATION_save_CPPFLAGS" - LDFLAGS="$ac_ABLATION_save_LDFLAGS" - LIBS="$ac_ABLATION_save_LIBS" - - succeeded=no - if test "$found_header" = yes; then - if test "$found_library" = yes; then - succeeded=yes - fi - fi - - if test "$succeeded" = no; then - ENABLE_ABLATION=0 - ifelse([$2],,AC_MSG_ERROR([libablation not found. Try either --with-ablation or setting ABLATION_DIR.]), - [$2]) - else - ENABLE_ABLATION=1 - AC_DEFINE(HAVE_ABLATION,1,[Define if PECOS libablation is available]) - AC_SUBST(ABLATION_CFLAGS) - AC_SUBST(ABLATION_LIBS) - AC_SUBST(ABLATION_PREFIX) - ifelse([$1],,,[$1]) - fi -else - ENABLE_ABLATION=0 - AC_MSG_NOTICE([Disabling optional ablation library]) -fi - -]) diff --git a/m4/common/pecos_ablation_new.m4 b/m4/common/pecos_ablation_new.m4 deleted file mode 100644 index 197502825..000000000 --- a/m4/common/pecos_ablation_new.m4 +++ /dev/null @@ -1,184 +0,0 @@ -# SYNOPSIS -# -# Test for PECOS 1D Ablation library -# -# AM_PATH_ABLATION1D( , ) -# -# DESCRIPTION -# -# Provides a --with-ablation=DIR option. Searches --with-ablation, -# $ABLATION_DIR, and the usual places for ablation headers and libraries. -# -# The macro performs a minimum version check and on success, sets -# ABLATION_CFLAGS, ABLATION_FCFLAGS, ABLATION_LIBS, and #defines -# HAVE_ABLATION. -# -# Assumes package is optional unless overridden with $2=yes -# -# COPYLEFT -# -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AM_PATH_ABLATION1D], -[ - -AC_ARG_VAR(ABLATION_DIR,[root directory of PECOS ablation installation]) - -AC_ARG_WITH(ablation, - [AS_HELP_STRING([--with-ablation[=DIR]],[root directory of PECOS ablation installation (default = ABLATION_DIR)])], - [with_ablation=$withval -if test "${with_ablation}" != yes; then - ABLATION_PREFIX=$withval -fi -],[ -# assume a sensible default of --with-ablation=yes -with_ablation=yes -if test "x${ABLATION_DIR}" != "x"; then - ABLATION_PREFIX=${ABLATION_DIR} -fi -]) - -# package requirement; if not specified, the default is to assume that -# the package is optional - -is_package_required=ifelse([$2], ,no, $2 ) - -HAVE_ABLATION=0 - -# if test "${with_ablation}" != no ; then - - if test -d "${ABLATION_PREFIX}/lib" ; then - ABLATION_LIBS="-L${ABLATION_PREFIX}/lib -lablation -Wl,-rpath,${ABLATION_PREFIX}/lib" - ABLATION_FCFLAGS="-I${ABLATION_PREFIX}/lib" - fi - if test -d "${ABLATION_PREFIX}/include" ; then - ABLATION_CFLAGS="-I${ABLATION_PREFIX}/include" - fi - - ac_ABLATION_save_CFLAGS="$CFLAGS" - ac_ABLATION_save_CPPFLAGS="$CPPFLAGS" - ac_ABLATION_save_LDFLAGS="$LDFLAGS" - ac_ABLATION_save_LIBS="$LIBS" - - CFLAGS="${ABLATION_CFLAGS} ${CFLAGS}" - CPPFLAGS="${ABLATION_CFLAGS} ${CPPFLAGS}" - LDFLAGS="${LDFLAGS}" - AC_LANG_PUSH([C]) - AC_CHECK_HEADER([ablation1d.h],[found_header=yes],[found_header=no]) - - #----------------------- - # Minimum version check - #---------------------- - - min_ablation_version=ifelse([$1], ,0.25.1, $1) - - # looking for major.minor.micro style versioning - - MAJOR_VER=`echo $min_ablation_version | sed 's/^\([[0-9]]*\).*/\1/'` - if test "x${MAJOR_VER}" = "x" ; then - MAJOR_VER=0 - fi - - MINOR_VER=`echo $min_ablation_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\2/'` - if test "x${MINOR_VER}" = "x" ; then - MINOR_VER=0 - fi - - MICRO_VER=`echo $min_ablation_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\3/'` - if test "x${MICRO_VER}" = "x" ; then - MICRO_VER=0 - fi - - dnl begin additional test(s) if header if available - - if test "x${found_header}" = "xyes" ; then - - AC_MSG_CHECKING(for ablation - version >= $min_ablation_version) - version_succeeded=no - - AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[ - @%:@include - ]], [[ - #if ABLATION_MAJOR_VERSION > $MAJOR_VER - /* Sweet nibblets */ - #elif (ABLATION_MAJOR_VERSION >= $MAJOR_VER) && (ABLATION_MINOR_VERSION > $MINOR_VER) - /* Winner winner, chicken dinner */ - #elif (ABLATION_MAJOR_VERSION >= $MAJOR_VER) && (ABLATION_MINOR_VERSION >= $MINOR_VER) && (ABLATION_MICRO_VERSION >= $MICRO_VER) - /* I feel like chicken tonight, like chicken tonight? */ - #else - # error version is too old - #endif - ]])],[ - AC_MSG_RESULT(yes) - version_succeeded=yes - ],[ - AC_MSG_RESULT(no) - ]) - - if test "$version_succeeded" != "yes";then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([ - - Your Ablation library version does not meet the minimum versioning - requirements ($min_ablation_version). Please use --with-ablation to specify the location - of an updated installation or consider upgrading the system version. - - ]) - fi - fi - - # Library availability - - AC_CHECK_LIB([ablation],[ablationC1d_version],[found_library=yes],[found_library=no], [ ${ABLATION_LIBS} ]) - - fi dnl end test if header if available - - AC_LANG_POP([C]) - - CFLAGS="$ac_ABLATION_save_CFLAGS" - CPPFLAGS="$ac_ABLATION_save_CPPFLAGS" - LDFLAGS="$ac_ABLATION_save_LDFLAGS" - LIBS="$ac_ABLATION_save_LIBS" - - succeeded=no - if test "$found_header" = yes; then - if test "$version_succeeded" = yes; then - if test "$found_library" = yes; then - succeeded=yes - fi - fi - fi - - if test "$succeeded" = no; then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([Ablation library not found. Try either --with-ablation or setting ABLATION_DIR.]) - else - AC_MSG_NOTICE([optional Ablation library not found]) - ABLATION_CFLAGS="" # GRVY_CFLAGS empty on failure - ABLATION_FCFLAGS="" # GRVY_FCFLAGS empty on failure - ABLATION_LIBS="" # GRVY_LIBS empty on failure - fi - else - HAVE_ABLATION=1 - AC_DEFINE(HAVE_ABLATION,1,[Define if PECOS libablation is available]) - AC_SUBST(ABLATION_CFLAGS) - AC_SUBST(ABLATION_LIBS) - AC_SUBST(ABLATION_PREFIX) - fi - - AC_SUBST(HAVE_ABLATION) - -# fi - -AM_CONDITIONAL(ABLATION_ENABLED,test x$HAVE_ABLATION = x1) - -]) diff --git a/m4/common/pecos_radiation.m4 b/m4/common/pecos_radiation.m4 deleted file mode 100644 index 90aa1a1b1..000000000 --- a/m4/common/pecos_radiation.m4 +++ /dev/null @@ -1,194 +0,0 @@ -# SYNOPSIS -# -# Test for pecos libradiation -# -# AM_PATH_RADIATION( , ) -# -# DESCRIPTION -# -# Provides a --with-radiation=DIR option. Searches --with-radiation, -# $RADIATION_DIR, and the usual places for libradiation headers and -# libraries. -# -# On success, sets RADIATION_CFLAGS, RADIATION_LIBS, and #defines -# HAVE_RADIATION. -# Also defines automake conditional RADIATION_ENABLED. Assumes -# package is optional unless overridden with $2=yes. -# -# COPYLEFT -# -# Copyright (c) 2012 Roy H. Stogner -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AX_PATH_RADIATION], -[ - -AC_ARG_VAR(RADIATION_DIR,[root directory of libradiation installation]) - -AC_ARG_WITH(radiation, - [AS_HELP_STRING([--with-radiation[=DIR]],[root directory of libradiation installation (default = RADIATION_DIR)])], - [with_radiation=$withval -if test "${with_radiation}" != yes; then - RADIATION_PREFIX=$withval -fi -],[ -# assume a sensible default of --with-radiation=yes -with_radiation=yes -if test "x${RADIATION_DIR}" != "x"; then - RADIATION_PREFIX=${RADIATION_DIR} -fi -]) - -# package requirement; if not specified, the default is to assume that -# the package is optional - -is_package_required=ifelse([$2], ,no, $2 ) - -HAVE_RADIATION=0 - -# logic change: if the user called the macro, check for package, -# decide what to do based on whether the package is required or not. - -# if test "${with_radiation}" != no ; then - - if test -d "${RADIATION_PREFIX}/lib" ; then - RADIATION_LIBS="-L${RADIATION_PREFIX}/lib -lradiation -Wl,-rpath,${RADIATION_PREFIX}/lib ${GRVY_FLIBS}" - RADIATION_FLIBS="$RADIATION_LIBS" - RADIATION_FCFLAGS="-I${RADIATION_PREFIX}/lib" - fi - - if test -d "${RADIATION_PREFIX}/include" ; then - RADIATION_CFLAGS="-I${RADIATION_PREFIX}/include" - fi - - ac_RADIATION_save_CFLAGS="$CFLAGS" - ac_RADIATION_save_CPPFLAGS="$CPPFLAGS" - ac_RADIATION_save_LDFLAGS="$LDFLAGS" - ac_RADIATION_save_LIBS="$LIBS" - - CFLAGS="${RADIATION_CFLAGS} ${CFLAGS}" - CPPFLAGS="${RADIATION_CFLAGS} ${CPPFLAGS}" - LDFLAGS="${RADIATION_LIBS} ${LDFLAGS}" - AC_LANG_PUSH([C]) - AC_CHECK_HEADER([radiation.h],[found_header=yes],[found_header=no]) - - #----------------------- - # Minimum version check - #---------------------- - - min_radiation_version=ifelse([$1], ,0.20, $1) - - # looking for major.minor.micro style versioning - - MAJOR_VER=`echo $min_radiation_version | sed 's/^\([[0-9]]*\).*/\1/'` - if test "x${MAJOR_VER}" = "x" ; then - MAJOR_VER=0 - fi - - MINOR_VER=`echo $min_radiation_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\2/'` - if test "x${MINOR_VER}" = "x" ; then - MINOR_VER=0 - fi - - MICRO_VER=`echo $min_radiation_version | sed 's/^\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\)\.\{0,1\}\([[0-9]]*\).*/\3/'` - if test "x${MICRO_VER}" = "x" ; then - MICRO_VER=0 - fi - - # begin additional test(s) if header if available - - if test "x${found_header}" = "xyes" ; then - - AC_MSG_CHECKING(for radiation - version >= $min_radiation_version) - version_succeeded=no - - AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[ - @%:@include - ]], [[ - #if RADIATION_MAJOR_VERSION > $MAJOR_VER - /* Sweet nibblets */ - #elif (RADIATION_MAJOR_VERSION >= $MAJOR_VER) && (RADIATION_MINOR_VERSION > $MINOR_VER) - /* Winner winner, chicken dinner */ - #elif (RADIATION_MAJOR_VERSION >= $MAJOR_VER) && (RADIATION_MINOR_VERSION >= $MINOR_VER) && (RADIATION_MICRO_VERSION >= $MICRO_VER) - /* I feel like chicken tonight, like chicken tonight? */ - #else - # error version is too old - #endif - ]])],[ - AC_MSG_RESULT(yes) - version_succeeded=yes - ],[ - AC_MSG_RESULT(no) - ]) - - if test "$version_succeeded" != "yes";then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([ - - Your libradiation version does not meet the minimum versioning - requirements ($min_radiation_version). Please use --with-radiation - to specify the location of an updated installation or consider - upgrading the system version. - - ]) - fi - fi - - # Library availability - - AC_MSG_CHECKING([for -lradiation linkage]) - - AC_CHECK_LIB([radiation],absorption_coefficient_,[found_library=yes],[found_library=no], [ ${RADIATION_LIBS} ]) - - fi dnl end test if header if available - - AC_LANG_POP([C]) - - CFLAGS="$ac_RADIATION_save_CFLAGS" - CPPFLAGS="$ac_RADIATION_save_CPPFLAGS" - LDFLAGS="$ac_RADIATION_save_LDFLAGS" - LIBS="$ac_RADIATION_save_LIBS" - - succeeded=no - if test "$found_header" = yes; then - if test "$version_succeeded" = yes; then - if test "$found_library" = yes; then - succeeded=yes - fi - fi - fi - - if test "$succeeded" = no; then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([libradiation not found. Try either --with-radiation or setting RADIATION_DIR.]) - else - AC_MSG_NOTICE([optional radiation library not found]) - RADIATION_CFLAGS="" # RADIATION_CFLAGS empty on failure - RADIATION_FCFLAGS="" # RADIATION_FCFLAGS empty on failure - RADIATION_LIBS="" # RADIATION_LIBS empty on failure - RADIATION_FLIBS="" # RADIATION_FLIBS empty on failure - fi - else - HAVE_RADIATION=1 - AC_DEFINE(HAVE_RADIATION,1,[Define if RADIATION is available]) - AC_SUBST(RADIATION_CFLAGS) - AC_SUBST(RADIATION_FCFLAGS) - AC_SUBST(RADIATION_LIBS) - AC_SUBST(RADIATION_PREFIX) - AC_SUBST(RADIATION_FLIBS) - fi - - AC_SUBST(HAVE_RADIATION) - -# fi - -]) diff --git a/m4/common/vexcl.m4 b/m4/common/vexcl.m4 deleted file mode 100644 index e9f6e8286..000000000 --- a/m4/common/vexcl.m4 +++ /dev/null @@ -1,136 +0,0 @@ -# SYNOPSIS -# -# Test for VEXCL -# -# AX_PATH_VEXCL( , ) -# -# DESCRIPTION -# -# Provides a --with-vexcl=DIR option. Searches --with-vexcl, -# $VEXCL_DIR, and the usual places for VEXCL headers and libraries. -# -# On success, sets VEXCL_CPPFLAGS, VEXCL_LIBS, and #defines HAVE_VEXCL. -# Also defines automake conditional VEXCL_ENABLED. Assumes package -# is optional unless overridden with $2=yes. -# -# COPYLEFT -# -# Copyright (c) 2013 Roy H. Stogner -# Copyright (c) 2012 Paul T. Bauman -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AX_PATH_VEXCL], -[ - -AC_ARG_VAR(VEXCL_DIR,[root directory of VEXCL installation]) - -AC_ARG_WITH(vexcl, - [AS_HELP_STRING([--with-vexcl[=DIR]],[root directory of VEXCL installation (default = VEXCL_DIR)])], - [with_vexcl=$withval -if test "${with_vexcl}" != yes; then - VEXCL_PREFIX=$withval -elif test "x${VEXCL_DIR}" != "x"; then - VEXCL_PREFIX=${VEXCL_DIR} -else - VEXCL_PREFIX=/usr -fi -],[ -with_vexcl=$withval -if test "x${VEXCL_DIR}" != "x"; then - VEXCL_PREFIX=${VEXCL_DIR} -fi -]) - -# package requirement; if not specified, the default is to assume that -# the package is optional - -is_package_required=ifelse([$2], ,no, $2 ) - -HAVE_VEXCL=0 - -VEXCL_INCLUDE="$VEXCL_PREFIX" - -if test "${with_vexcl}" != no ; then - - # If we can see the vexcl headers, then we know where to get them - # and we'll need C++11 to compile them - if test -e "${VEXCL_PREFIX}/vexcl/vexcl.hpp" ; then - VEXCL_CPPFLAGS="-I${VEXCL_PREFIX}" - AX_CXX_COMPILE_STDCXX_11(noext) - elif test -e "${VEXCL_PREFIX}/include/vexcl/vexcl.hpp" ; then - VEXCL_INCLUDE="${VEXCL_PREFIX}/include" - VEXCL_CPPFLAGS="-I${VEXCL_PREFIX}/include" - AX_CXX_COMPILE_STDCXX_11(noext) - else - AC_MSG_NOTICE([${VEXCL_PREFIX}/vexcl/vexcl.hpp not found]) - AC_MSG_NOTICE([${VEXCL_PREFIX}/include/vexcl/vexcl.hpp not found]) - fi - - # Make sure we have OpenCL support - AX_CHECK_CL([C++]) - - found_boost=yes - BOOST_REQUIRE([1.47],[found_boost=no]) # Chrono introduced in 1.47 - BOOST_CHRONO - BOOST_DATE_TIME - BOOST_FILESYSTEM - BOOST_SYSTEM - BOOST_THREADS - - # Make sure we have OpenCL support - AX_CHECK_CL([C++]) - - AC_LANG_PUSH([C++]) - ac_VEXCL_save_CPPFLAGS="$CPPFLAGS" - CPPFLAGS="$CPPFLAGS $VEXCL_CPPFLAGS $BOOST_CPPFLAGS $CL_CFLAGS $CL_CPPFLAGS" - AC_CHECK_HEADER([vexcl/vexcl.hpp],[found_header=yes],[found_header=no]) - CPPFLAGS="$ac_VEXCL_save_CPPFLAGS" - AC_LANG_POP([C++]) - - #----------------------- - # Minimum version check skipped - there's no versioning - # information in vexcl headers as of 0.7.0 - #---------------------- - - succeeded=yes - if test x$found_header = xyes -a x"$no_cl" != xyes; then - VEXCL_CPPFLAGS="$VEXCL_CPPFLAGS $BOOST_CPPFLAGS $CL_CFLAGS $CL_CPPFLAGS" - VEXCL_LDFLAGS="$BOOST_CHRONO_LDFLAGS $BOOST_DATE_TIME_LDFLAGS $BOOST_FILESYSTEM_LDFLAGS $BOOST_SYSTEM_LDFLAGS $CL_LDFLAGS" - VEXCL_LIBS="$BOOST_CHRONO_LIBS $BOOST_DATE_TIME_LIBS $BOOST_FILESYSTEM_LIBS $BOOST_SYSTEM_LIBS $CL_LIBS" - - HAVE_VEXCL=1 - AC_DEFINE(HAVE_VEXCL,1,[Define if VEXCL is available]) - AC_SUBST(VEXCL_CPPFLAGS) - AC_SUBST(VEXCL_LDFLAGS) - AC_SUBST(VEXCL_LIBS) - AC_SUBST(VEXCL_PREFIX) - AC_SUBST(VEXCL_INCLUDE) - else - succeeded=no - - if test "$is_package_required" = yes; then - AC_MSG_ERROR([VEXCL not found. Try either --with-vexcl or setting VEXCL_DIR.]) - else - AC_MSG_NOTICE([optional VEXCL library not found]) - VEXCL_CPPFLAGS="" # VEXCL_CPPFLAGS empty on failure - VEXCL_LDFLAGS="" # VEXCL_LDFLAGS empty on failure - VEXCL_LIBS="" # VEXCL_LIBS empty on failure - fi - fi - - AC_SUBST(HAVE_VEXCL) - -fi - -AM_CONDITIONAL(VEXCL_ENABLED,test x$HAVE_VEXCL = x1) - -]) diff --git a/m4/common/viennacl.m4 b/m4/common/viennacl.m4 deleted file mode 100644 index dcee28ae5..000000000 --- a/m4/common/viennacl.m4 +++ /dev/null @@ -1,136 +0,0 @@ -# SYNOPSIS -# -# Test for ViennaCL -# -# AX_PATH_VIENNACL( , ) -# -# DESCRIPTION -# -# Provides a --with-viennacl=DIR option. Searches --with-viennacl, -# $VIENNACL_DIR, and the usual places for VIENNACL headers and libraries. -# -# On success, sets VIENNACL_CPPFLAGS, VIENNACL_LIBS, and #defines HAVE_VIENNACL. -# Also defines automake conditional VIENNACL_ENABLED. Assumes package -# is optional unless overridden with $2=yes. -# -# COPYLEFT -# -# Copyright (c) 2013 Roy H. Stogner -# Copyright (c) 2012 Paul T. Bauman -# Copyright (c) 2010 Karl W. Schulz -# Copyright (c) 2009 Rhys Ulerich -# Copyright (c) 2008 Thomas Porschberg -# Copyright (c) 2008 Caolan McNamara -# Copyright (c) 2008 Alexandre Duret-Lutz -# Copyright (c) 2008 Matthew Mueller -# -# Copying and distribution of this file, with or without modification, are -# permitted in any medium without royalty provided the copyright notice -# and this notice are preserved. - -AC_DEFUN([AX_PATH_VIENNACL], -[ - -AC_ARG_VAR(VIENNACL_DIR,[root directory of ViennaCL installation]) - -AC_ARG_WITH(viennacl, - [AS_HELP_STRING([--with-viennacl[=DIR]],[root directory of ViennaCL installation (default = VIENNACL_DIR)])], - [with_viennacl=$withval -if test "${with_viennacl}" != yes; then - VIENNACL_PREFIX=$withval -elif test "x${VIENNACL_DIR}" != "x"; then - VIENNACL_PREFIX=${VIENNACL_DIR} -else - VIENNACL_PREFIX=/usr -fi -],[ -with_viennacl=$withval -if test "x${VIENNACL_DIR}" != "x"; then - VIENNACL_PREFIX=${VIENNACL_DIR} -fi -]) - -# package requirement; if not specified, the default is to assume that -# the package is optional - -is_package_required=ifelse([$2], ,no, $2 ) - -HAVE_VIENNACL=0 - -# logic change: if the user called the macro, check for package, -# decide what to do based on whether the package is required or not. - -# if test "${with_viennacl}" != no ; then - - # If we can see the ViennaCL headers, then we know where to get them - # and we'll need C++11 to compile them - if test -e "${VIENNACL_PREFIX}/viennacl/vector.hpp" ; then - VIENNACL_CPPFLAGS="-I${VIENNACL_PREFIX}" - AX_CXX_COMPILE_STDCXX_11(noext) - fi - - ac_VIENNACL_save_CPPFLAGS="$CPPFLAGS" - - CPPFLAGS="${VIENNACL_CPPFLAGS} ${CPPFLAGS}" - - AC_LANG_PUSH([C++]) - AC_CHECK_HEADER([viennacl/vector.hpp],[found_header=yes],[found_header=no]) - AC_LANG_POP([C++]) - - # Make sure we have OpenCL support - AX_CHECK_CL([C++]) - - # ViennaCL requires Boost headers. - # There doesn't seem to be any way to simply *test* for Boost with - # boost.m4, so at this point we'll assume that if we've seen - # ViennaCL then you really wanted to use ViennaCL. - if test x$found_header = xyes; then - BOOST_REQUIRE([1.36]) - BOOST_NUMERIC_UBLAS - - VIENNACL_CPPFLAGS="$VIENNACL_CPPFLAGS $BOOST_CPPFLAGS" - fi - - #----------------------- - # Minimum version check skipped - there's no versioning - # information in Viennacl headers as of 1.4.2 - #---------------------- - - CPPFLAGS="$ac_VIENNACL_save_CPPFLAGS" - - succeeded=yes - if test "$found_header" != yes; then - succeeded=no - fi - if test "$no_cl" = yes; then - succeeded=no - else - VIENNACL_CPPFLAGS="$VIENNACL_CPPFLAGS $CL_CFLAGS" - VIENNACL_LIBS="$VIENNACL_LIBS $CL_LIBS" - fi - - if test "$succeeded" = no; then - if test "$is_package_required" = yes; then - AC_MSG_ERROR([ViennaCL not found. Try either --with-viennacl or setting VIENNACL_DIR.]) - else - AC_MSG_NOTICE([optional ViennaCL library not found]) - VIENNACL_CPPFLAGS="" # VIENNACL_CPPFLAGS empty on failure - VIENNACL_LDFLAGS="" # VIENNACL_LDFLAGS empty on failure - VIENNACL_LIBS="" # VIENNACL_LIBS empty on failure - fi - else - HAVE_VIENNACL=1 - AC_DEFINE(HAVE_VIENNACL,1,[Define if VIENNACL is available]) - AC_SUBST(VIENNACL_CPPFLAGS) - AC_SUBST(VIENNACL_LDFLAGS) - AC_SUBST(VIENNACL_LIBS) - AC_SUBST(VIENNACL_PREFIX) - fi - - AC_SUBST(HAVE_VIENNACL) - -# fi - -AM_CONDITIONAL(VIENNACL_ENABLED,test x$HAVE_VIENNACL = x1) - -]) diff --git a/m4/libtool.m4 b/m4/libtool.m4 deleted file mode 100644 index 44e0ecff1..000000000 --- a/m4/libtool.m4 +++ /dev/null @@ -1,7982 +0,0 @@ -# libtool.m4 - Configure libtool for the host system. -*-Autoconf-*- -# -# Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, -# 2006, 2007, 2008, 2009, 2010, 2011 Free Software -# Foundation, Inc. -# Written by Gordon Matzigkeit, 1996 -# -# This file is free software; the Free Software Foundation gives -# unlimited permission to copy and/or distribute it, with or without -# modifications, as long as this notice is preserved. - -m4_define([_LT_COPYING], [dnl -# Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, -# 2006, 2007, 2008, 2009, 2010, 2011 Free Software -# Foundation, Inc. -# Written by Gordon Matzigkeit, 1996 -# -# This file is part of GNU Libtool. -# -# GNU Libtool 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 2 of -# the License, or (at your option) any later version. -# -# As a special exception to the GNU General Public License, -# if you distribute this file as part of a program or library that -# is built using GNU Libtool, you may include this file under the -# same distribution terms that you use for the rest of that program. -# -# GNU Libtool 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 GNU Libtool; see the file COPYING. 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It appears that all Alpha - # Linux and Compaq Tru64 Unix objects are PIC. - _LT_TAGVAR(lt_prog_compiler_pic, $1)= - _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' - ;; - xlc* | xlC* | bgxl[[cC]]* | mpixl[[cC]]*) - # IBM XL 8.0, 9.0 on PPC and BlueGene - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-qpic' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-qstaticlink' - ;; - *) - case `$CC -V 2>&1 | sed 5q` in - *Sun\ C*) - # Sun C++ 5.9 - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' - ;; - esac - ;; - esac - ;; - lynxos*) - ;; - m88k*) - ;; - mvs*) - case $cc_basename in - cxx*) - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-W c,exportall' - ;; - *) - ;; - esac - ;; - netbsd*) - ;; - *qnx* | *nto*) - # QNX uses GNU C++, but need to define -shared option too, otherwise - # it will coredump. - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' - ;; - osf3* | osf4* | osf5*) - case $cc_basename in - KCC*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='--backend -Wl,' - ;; - RCC*) - # Rational C++ 2.4.1 - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' - ;; - cxx*) - # Digital/Compaq C++ - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - # Make sure the PIC flag is empty. It appears that all Alpha - # Linux and Compaq Tru64 Unix objects are PIC. - _LT_TAGVAR(lt_prog_compiler_pic, $1)= - _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' - ;; - *) - ;; - esac - ;; - psos*) - ;; - solaris*) - case $cc_basename in - CC* | sunCC*) - # Sun C++ 4.2, 5.x and Centerline C++ - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' - ;; - gcx*) - # Green Hills C++ Compiler - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' - ;; - *) - ;; - esac - ;; - sunos4*) - case $cc_basename in - CC*) - # Sun C++ 4.x - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' - ;; - lcc*) - # Lucid - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' - ;; - *) - ;; - esac - ;; - sysv5* | unixware* | sco3.2v5* | sco5v6* | OpenUNIX*) - case $cc_basename in - CC*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' - ;; - esac - ;; - tandem*) - case $cc_basename in - NCC*) - # NonStop-UX NCC 3.20 - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' - ;; - *) - ;; - esac - ;; - vxworks*) - ;; - *) - _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no - ;; - esac - fi -], -[ - if test "$GCC" = yes; then - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' - - case $host_os in - aix*) - # All AIX code is PIC. - if test "$host_cpu" = ia64; then - # AIX 5 now supports IA64 processor - _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' - fi - ;; - - amigaos*) - case $host_cpu in - powerpc) - # see comment about AmigaOS4 .so support - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' - ;; - m68k) - # FIXME: we need at least 68020 code to build shared libraries, but - # adding the `-m68020' flag to GCC prevents building anything better, - # like `-m68040'. - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-m68020 -resident32 -malways-restore-a4' - ;; - esac - ;; - - beos* | irix5* | irix6* | nonstopux* | osf3* | osf4* | osf5*) - # PIC is the default for these OSes. - ;; - - mingw* | cygwin* | pw32* | os2* | cegcc*) - # This hack is so that the source file can tell whether it is being - # built for inclusion in a dll (and should export symbols for example). - # Although the cygwin gcc ignores -fPIC, still need this for old-style - # (--disable-auto-import) libraries - m4_if([$1], [GCJ], [], - [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) - ;; - - darwin* | rhapsody*) - # PIC is the default on this platform - # Common symbols not allowed in MH_DYLIB files - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fno-common' - ;; - - haiku*) - # PIC is the default for Haiku. - # The "-static" flag exists, but is broken. - _LT_TAGVAR(lt_prog_compiler_static, $1)= - ;; - - hpux*) - # PIC is the default for 64-bit PA HP-UX, but not for 32-bit - # PA HP-UX. On IA64 HP-UX, PIC is the default but the pic flag - # sets the default TLS model and affects inlining. - case $host_cpu in - hppa*64*) - # +Z the default - ;; - *) - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' - ;; - esac - ;; - - interix[[3-9]]*) - # Interix 3.x gcc -fpic/-fPIC options generate broken code. - # Instead, we relocate shared libraries at runtime. - ;; - - msdosdjgpp*) - # Just because we use GCC doesn't mean we suddenly get shared libraries - # on systems that don't support them. - _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no - enable_shared=no - ;; - - *nto* | *qnx*) - # QNX uses GNU C++, but need to define -shared option too, otherwise - # it will coredump. - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' - ;; - - sysv4*MP*) - if test -d /usr/nec; then - _LT_TAGVAR(lt_prog_compiler_pic, $1)=-Kconform_pic - fi - ;; - - *) - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' - ;; - esac - - case $cc_basename in - nvcc*) # Cuda Compiler Driver 2.2 - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Xlinker ' - if test -n "$_LT_TAGVAR(lt_prog_compiler_pic, $1)"; then - _LT_TAGVAR(lt_prog_compiler_pic, $1)="-Xcompiler $_LT_TAGVAR(lt_prog_compiler_pic, $1)" - fi - ;; - esac - else - # PORTME Check for flag to pass linker flags through the system compiler. - case $host_os in - aix*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - if test "$host_cpu" = ia64; then - # AIX 5 now supports IA64 processor - _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' - else - _LT_TAGVAR(lt_prog_compiler_static, $1)='-bnso -bI:/lib/syscalls.exp' - fi - ;; - - mingw* | cygwin* | pw32* | os2* | cegcc*) - # This hack is so that the source file can tell whether it is being - # built for inclusion in a dll (and should export symbols for example). - m4_if([$1], [GCJ], [], - [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) - ;; - - hpux9* | hpux10* | hpux11*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - # PIC is the default for IA64 HP-UX and 64-bit HP-UX, but - # not for PA HP-UX. - case $host_cpu in - hppa*64*|ia64*) - # +Z the default - ;; - *) - _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' - ;; - esac - # Is there a better lt_prog_compiler_static that works with the bundled CC? - _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' - ;; - - irix5* | irix6* | nonstopux*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - # PIC (with -KPIC) is the default. - _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' - ;; - - linux* | k*bsd*-gnu | kopensolaris*-gnu) - case $cc_basename in - # old Intel for x86_64 which still supported -KPIC. - ecc*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' - ;; - # icc used to be incompatible with GCC. - # ICC 10 doesn't accept -KPIC any more. - icc* | ifort*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' - _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' - ;; - # Lahey Fortran 8.1. - lf95*) - _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' - _LT_TAGVAR(lt_prog_compiler_pic, $1)='--shared' - _LT_TAGVAR(lt_prog_compiler_static, $1)='--static' - ;; 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[AC_DEFUN([_LT_PROG_F77])]) -m4_ifndef([_LT_PROG_FC], [AC_DEFUN([_LT_PROG_FC])]) -m4_ifndef([_LT_PROG_CXX], [AC_DEFUN([_LT_PROG_CXX])]) diff --git a/manual/users/users.tex b/manual/users/users.tex index 6986cee17..0e0aee367 100644 --- a/manual/users/users.tex +++ b/manual/users/users.tex @@ -78,7 +78,7 @@ \newcommand{\D}{ {\bf D}} \newcommand{\myverb}[1]{ \indent{ \begin{verbatim} #1 \end{verbatim} } } -\newcommand{\QUESOversion}{0.50.0} +\newcommand{\QUESOversion}{0.51.0} \usepackage{color} \definecolor{dkgreen}{rgb}{0,0.6,0} diff --git a/manual/users/users_2_installation.tex b/manual/users/users_2_installation.tex index 1c2e3c2f0..683c27584 100644 --- a/manual/users/users_2_installation.tex +++ b/manual/users/users_2_installation.tex @@ -177,16 +177,16 @@ \section{Configure QUESO Building Environment}\label{sec:Queso_configure} -Therefore, the basic steps to configure QUESO using Boost, GSL (required), HDF5 and Trilinos (optional) for installation at `\verb+$HOME/LIBRARIES/QUESO-0.50.0+' are: +Therefore, the basic steps to configure QUESO using Boost, GSL (required), HDF5 and Trilinos (optional) for installation at `\verb+$HOME/LIBRARIES/QUESO-0.51.0+' are: \begin{lstlisting}[{basicstyle=\footnotesize\ttfamily}] -$ ./configure --prefix=$HOME/LIBRARIES/QUESO-0.50.0 \ +$ ./configure --prefix=$HOME/LIBRARIES/QUESO-0.51.0 \ --with-boost=$HOME/LIBRARIES/boost-1.53.0 \ --with-gsl=$HOME/LIBRARIES/gsl-1.15 \ --with-hdf5=$HOME/LIBRARIES/hdf5-1.8.10 \ --with-trilinos=$HOME/LIBRARIES/trilinos-11.2.4 \end{lstlisting} -Note: the directory `\verb+$HOME/LIBRARIES/QUESO-0.50.0+' does not need to exist in advance, since it will be created by the command \verb+make install+ described in Section \ref{sec:install_Queso_make}. +Note: the directory `\verb+$HOME/LIBRARIES/QUESO-0.51.0+' does not need to exist in advance, since it will be created by the command \verb+make install+ described in Section \ref{sec:install_Queso_make}. \section{Compile, Check and Install \Queso{}}\label{sec:install_Queso_make} @@ -266,7 +266,7 @@ \section{Summary of Installation Steps}\label{sec:summary} Supposing you have downloaded the file `queso-0.47.1.tar.gz' into \texttt{\$HOME/queso\_download/}. % In a BASH shell, the basic steps to configure QUESO using GRVY, Boost and GSL for installation at -`\verb+$HOME/LIBRARIES/QUESO-0.50.0+' are: +`\verb+$HOME/LIBRARIES/QUESO-0.51.0+' are: \begin{lstlisting}[{basicstyle=\footnotesize\ttfamily}] $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\ @@ -283,7 +283,7 @@ \section{Summary of Installation Steps}\label{sec:summary} $ cd $HOME/queso_download/ #enter source dir $ gunzip < queso-0.47.1.tar.gz | tar xf - $ cd $HOME/queso_download/queso-0.47.1 #enter the build dir -$ ./configure --prefix=$HOME/LIBRARIES/QUESO-0.50.0 \ +$ ./configure --prefix=$HOME/LIBRARIES/QUESO-0.51.0 \ --with-boost=$HOME/LIBRARIES/boost-1.53.0 \ --with-gsl=$HOME/LIBRARIES/gsl-1.15 \ --with-hdf5=$HOME/LIBRARIES/hdf5-1.8.10 \ @@ -292,13 +292,13 @@ \section{Summary of Installation Steps}\label{sec:summary} $ make check $ make install $ make docs -$ ls $HOME/LIBRARIES/QUESO-0.50.0 #listing QUESO installation dir +$ ls $HOME/LIBRARIES/QUESO-0.51.0 #listing QUESO installation dir >> bin include lib examples \end{lstlisting} % % \paragraph*{Note:} According to Section \ref{sec:Queso_tree}, \texttt{\$HOME/queso\_download/} is the source tree, \\ \verb+$HOME/queso_download/queso-0.47.1+ is the build tree, and \newline -% \verb+$HOME/LIBRARIES/QUESO-0.50.0+ is the installation tree. +% \verb+$HOME/LIBRARIES/QUESO-0.51.0+ is the installation tree. % @@ -361,7 +361,7 @@ \section{The Installed Directory Structure} \label{sc-installed-dir-structure} \item \verb+bin+: contains the executable \verb+queso_version+, which provides information about the installed library. The code bellow presents a sample output: \begin{lstlisting}[{basicstyle=\footnotesize\ttfamily}] -kemelli@margarida:~/LIBRARIES/QUESO-0.50.0/bin$ ./queso_version +kemelli@margarida:~/LIBRARIES/QUESO-0.51.0/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-0.50.0/bin$ +kemelli@margarida:~/LIBRARIES/QUESO-0.51.0/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+$HOME/LIBRARIES/QUESO-0.50.0/lib+ should be added to the user's \verb+LD_LIBRARY_PATH+ environmental variable in order to use QUESO library with his/her application code: + \item \verb+lib+: contains the static and dynamic versions of the library. The full to path to this directory, e.g., \verb+$HOME/LIBRARIES/QUESO-0.51.0/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-0.50.0/lib + $HOME/LIBRARIES/QUESO-0.51.0/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-0.50.0/lib + $HOME/LIBRARIES/QUESO-0.51.0/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-0.50.0/lib +% $HOME/LIBRARIES/QUESO-0.51.0/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-0.50.0/ +QUESO_DIR = $HOME/LIBRARIES/QUESO-0.51.0/ BOOST_DIR = $HOME/LIBRARIES/boost-1.53.0/ GSL_DIR = $HOME/LIBRARIES/gsl-1.15/ GRVY_DIR = $HOME/LIBRARIES/grvy-0.32.0 diff --git a/manual/users/users_5a_example_sip.tex b/manual/users/users_5a_example_sip.tex index a5d495862..a1cb904ca 100644 --- a/manual/users/users_5a_example_sip.tex +++ b/manual/users/users_5a_example_sip.tex @@ -98,7 +98,7 @@ \subsection{Running the Example}\label{sec:sip-run} To run the executable provided (available after QUESO installation), enter the following commands: \begin{lstlisting}[label={},caption={}] -$ cd $HOME/LIBRARIES/QUESO-0.50.0/ +$ cd $HOME/LIBRARIES/QUESO-0.51.0/ $ 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 $HOME/LIBRARIES/QUESO-0.50.0/examples/simpleStatisticalInverseProblem/ +$ cd $HOME/LIBRARIES/QUESO-0.51.0/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-0.50.0/lib + $HOME/LIBRARIES/QUESO-0.51.0/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 382cd42de..0b3287e1d 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 $HOME/LIBRARIES/QUESO-0.50.0/ +$ cd $HOME/LIBRARIES/QUESO-0.51.0/ $ 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-0.50.0/examples/simpleStatisticalForwardProblem +$ cd HOME/LIBRARIES/QUESO-0.51.0/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-0.50.0/lib + $HOME/LIBRARIES/QUESO-0.51.0/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 1f84b60b5..8c5e657db 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 $HOME/LIBRARIES/QUESO-0.50.0/examples/gravity +$ cd $HOME/LIBRARIES/QUESO-0.51.0/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-0.50.0/examples/gravity$ ./gravity_gsl gravity_inv_fwd.inp +kemelli@violeta:~/LIBRARIES/QUESO-0.51.0/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-0.50.0/examples/gravity$ +kemelli@violeta:~/LIBRARIES/QUESO-0.51.0/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-0.50.0/examples/gravity/ +cd $HOME/LIBRARIES/QUESO-0.51.0/examples/gravity/ mpirun -np 8 ./gravity_gsl gravity_inv_fwd.inp \end{lstlisting} diff --git a/manual/users/users_5e_example_ml_modal.tex b/manual/users/users_5e_example_ml_modal.tex index c5a78c965..00d8381d1 100644 --- a/manual/users/users_5e_example_ml_modal.tex +++ b/manual/users/users_5e_example_ml_modal.tex @@ -9,7 +9,7 @@ \section{\texttt{modal}}\label{sec:example_modal} Choosing between a one-mode or a two-mode target distribution is done at execution level, as presented in the following code line: \begin{lstlisting}[label={},caption={}] -$ cd $HOME/LIBRARIES/QUESO-0.50.0/ +$ cd $HOME/LIBRARIES/QUESO-0.51.0/ $ cd examples/modal $ rm outputData/* $ ./modal_gsl example.inp @@ -57,7 +57,7 @@ \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 $HOME/LIBRARIES/QUESO-0.51.0/ $ cd examples/modal $ rm outputData/* $ ./modal_gsl example.inp 1 #one mode! @@ -113,7 +113,7 @@ \subsubsection{Running the Two-Mode Example} 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 $HOME/LIBRARIES/QUESO-0.51.0/ $ cd examples/modal $ rm outputData/* $ ./modal_gsl example.inp 2 # two modes! @@ -194,12 +194,12 @@ \subsection{Create your own Makefile}\label{sec:modal-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 $HOME/LIBRARIES/QUESO-0.50.0/examples/modal/ +$ cd $HOME/LIBRARIES/QUESO-0.51.0/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 + $HOME/LIBRARIES/QUESO-0.51.0/lib $ make -f Makefile_modal_violeta $ ./modal_gsl example.inp \end{lstlisting} @@ -318,4 +318,4 @@ \subsubsection{Autocorrelation Plots} \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 +\end{figure} diff --git a/manual/users/users_5f_example_ml_bimodal.tex b/manual/users/users_5f_example_ml_bimodal.tex index 5a7556606..6ee86cdd1 100644 --- a/manual/users/users_5f_example_ml_bimodal.tex +++ b/manual/users/users_5f_example_ml_bimodal.tex @@ -35,7 +35,7 @@ \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 $HOME/LIBRARIES/QUESO-0.50.0/examples/bimodal +$ cd $HOME/LIBRARIES/QUESO-0.51.0/examples/bimodal $ rm outputData/* $ ./bimodal_gsl bimodal_1chain.inp $ matlab @@ -113,12 +113,12 @@ \subsection{Create your own Makefile}\label{sec:bimodal-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 $HOME/LIBRARIES/QUESO-0.50.0/examples/bimodal/ +$ cd $HOME/LIBRARIES/QUESO-0.51.0/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 + $HOME/LIBRARIES/QUESO-0.51.0/lib $ make -f Makefile_bimodal_violeta $ ./bimodal_gsl example.inp \end{lstlisting} diff --git a/manual/users/users_5g_example_ml_hysteretic.tex b/manual/users/users_5g_example_ml_hysteretic.tex index 0edc8dad8..13322ef64 100644 --- a/manual/users/users_5g_example_ml_hysteretic.tex +++ b/manual/users/users_5g_example_ml_hysteretic.tex @@ -118,7 +118,7 @@ \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 $HOME/LIBRARIES/QUESO-0.50.0/examples/example +$ cd $HOME/LIBRARIES/QUESO-0.51.0/examples/example $ rm outputData/* $ ./example_gsl example_1chain.inp $ matlab @@ -178,12 +178,12 @@ \subsection{Create your own Makefile}\label{sec:hysteretic-makefile} Thus to compile, build and execute the code, commands similar to the following should be entered: \begin{lstlisting} -$ cd $HOME/LIBRARIES/QUESO-0.50.0/examples/hysteretic/ +$ cd $HOME/LIBRARIES/QUESO-0.51.0/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 + $HOME/LIBRARIES/QUESO-0.51.0/lib $ make -f Makefile_hysteretic_violeta $ ./hysteretic_gsl example.inp \end{lstlisting} diff --git a/queso-deps.pc.in b/queso-deps.pc.in new file mode 100644 index 000000000..db86e6901 --- /dev/null +++ b/queso-deps.pc.in @@ -0,0 +1,17 @@ +# @PACKAGE_NAME@ installation details +prefix=@prefix@ +exec_prefix=@exec_prefix@ +libdir=@libdir@ +includedir=@includedir@ + +# Dependency installation used to build @PACKAGE_NAME@ +# Retrieve these using `pkg-config --variable=name @PACKAGE_NAME@` +mpicxx=@MPICXX@ + +Name: @PACKAGE_NAME@ +Description: @PACKAGE_DESCRIPTION@ +URL: @PACKAGE_URL@ +Requires.private: gsl +Version: @VERSION@ +Libs: -L${libdir} -l@PACKAGE_NAME@ @BOOST_PROGRAM_OPTIONS_LDFLAGS@ @BOOST_PROGRAM_OPTIONS_LIBS@ +Cflags: -I${includedir} @BOOST_CPPFLAGS@ diff --git a/queso.pc.in b/queso.pc.in new file mode 100644 index 000000000..d902f191b --- /dev/null +++ b/queso.pc.in @@ -0,0 +1,17 @@ +# @PACKAGE_NAME@ installation details +prefix=@prefix@ +exec_prefix=@exec_prefix@ +libdir=@libdir@ +includedir=@includedir@ + +# Dependency installation used to build @PACKAGE_NAME@ +# Retrieve these using `pkg-config --variable=name @PACKAGE_NAME@` +mpicxx=@MPICXX@ + +Name: @PACKAGE_NAME@ +Description: @PACKAGE_DESCRIPTION@ +URL: @PACKAGE_URL@ +Requires: +Version: @VERSION@ +Libs: -L${libdir} -l@PACKAGE_NAME@ +Cflags: -I${includedir} diff --git a/src/Makefile.am b/src/Makefile.am index 99b329fe6..29c8ad18c 100644 --- a/src/Makefile.am +++ b/src/Makefile.am @@ -1,6 +1,3 @@ -# Build in these directories: -# DIST_SUBDIRS = basic stats - AM_CPPFLAGS = AM_CPPFLAGS += -I$(top_builddir)/inc AM_CPPFLAGS += -I$(top_builddir)/src/core/inc # For queso.h @@ -8,8 +5,6 @@ AM_CPPFLAGS += $(BOOST_CPPFLAGS) AM_CPPFLAGS += $(GSL_CFLAGS) AM_CPPFLAGS += $(ANN_CFLAGS) -### -I$(top_srcdir)/src/interface - if GRVY_ENABLED AM_CPPFLAGS += $(GRVY_CFLAGS) endif @@ -61,370 +56,392 @@ if LIBMESH_ENABLED libqueso_la_LDFLAGS += $(LIBMESH_LIBS) endif -#libqueso_la_LDFLAGS += -Wl,-rpath,$(TRILINOS_HOME)/lib -lepetra - # Sources from core/src libqueso_la_SOURCES = -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/Defines.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/Environment.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/EnvironmentOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/RngBase.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/RngGsl.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/RngBoost.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/BasicPdfsBase.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/BasicPdfsGsl.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/BasicPdfsBoost.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/Vector.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/Matrix.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/TeuchosVector.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/TeuchosMatrix.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/MpiComm.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/Map.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/DistArray.C +libqueso_la_SOURCES += core/src/Defines.C +libqueso_la_SOURCES += core/src/Environment.C +libqueso_la_SOURCES += core/src/EnvironmentOptions.C +libqueso_la_SOURCES += core/src/RngBase.C +libqueso_la_SOURCES += core/src/RngGsl.C +libqueso_la_SOURCES += core/src/RngBoost.C +libqueso_la_SOURCES += core/src/BasicPdfsBase.C +libqueso_la_SOURCES += core/src/BasicPdfsGsl.C +libqueso_la_SOURCES += core/src/BasicPdfsBoost.C +libqueso_la_SOURCES += core/src/Vector.C +libqueso_la_SOURCES += core/src/Matrix.C +libqueso_la_SOURCES += core/src/TeuchosVector.C +libqueso_la_SOURCES += core/src/TeuchosMatrix.C +libqueso_la_SOURCES += core/src/MpiComm.C +libqueso_la_SOURCES += core/src/Map.C +libqueso_la_SOURCES += core/src/DistArray.C +libqueso_la_SOURCES += core/src/Optimizer.C +libqueso_la_SOURCES += core/src/GslOptimizer.C +libqueso_la_SOURCES += core/src/OptimizerMonitor.C # Sources that need libmesh -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/FunctionBase.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/LibMeshFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/OperatorBase.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/LibMeshOperatorBase.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/LibMeshNegativeLaplacianOperator.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/InfiniteDimensionalGaussian.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/InfiniteDimensionalMeasureBase.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/InfiniteDimensionalMCMCSampler.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/InfiniteDimensionalMCMCSamplerOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/InfiniteDimensionalLikelihoodBase.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/FunctionOperatorBuilder.C +libqueso_la_SOURCES += core/src/FunctionBase.C +libqueso_la_SOURCES += core/src/LibMeshFunction.C +libqueso_la_SOURCES += core/src/OperatorBase.C +libqueso_la_SOURCES += core/src/LibMeshOperatorBase.C +libqueso_la_SOURCES += core/src/LibMeshNegativeLaplacianOperator.C +libqueso_la_SOURCES += core/src/InfiniteDimensionalGaussian.C +libqueso_la_SOURCES += core/src/InfiniteDimensionalMeasureBase.C +libqueso_la_SOURCES += core/src/InfiniteDimensionalMCMCSampler.C +libqueso_la_SOURCES += core/src/InfiniteDimensionalMCMCSamplerOptions.C +libqueso_la_SOURCES += core/src/InfiniteDimensionalLikelihoodBase.C +libqueso_la_SOURCES += core/src/FunctionOperatorBuilder.C # Sources from core/src with gsl conditional if UQBT_GSL -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/GslVector.C -libqueso_la_SOURCES += $(top_srcdir)/src/core/src/GslMatrix.C +libqueso_la_SOURCES += core/src/GslVector.C +libqueso_la_SOURCES += core/src/GslMatrix.C endif # Sources from misc/src -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/1D1DFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/1DQuadrature.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/ComplexFft.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/Miscellaneous.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/RealFft.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/2dArrayOfStuff.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/OneDGrid.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/UniformOneDGrid.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/StdOneDGrid.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/Fft.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/CovCond.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/AsciiTable.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/ArrayOfOneDTables.C -libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/ArrayOfOneDGrids.C +libqueso_la_SOURCES += misc/src/1D1DFunction.C +libqueso_la_SOURCES += misc/src/1DQuadrature.C +libqueso_la_SOURCES += misc/src/ComplexFft.C +libqueso_la_SOURCES += misc/src/Miscellaneous.C +libqueso_la_SOURCES += misc/src/RealFft.C +libqueso_la_SOURCES += misc/src/2dArrayOfStuff.C +libqueso_la_SOURCES += misc/src/OneDGrid.C +libqueso_la_SOURCES += misc/src/UniformOneDGrid.C +libqueso_la_SOURCES += misc/src/StdOneDGrid.C +libqueso_la_SOURCES += misc/src/Fft.C +libqueso_la_SOURCES += misc/src/CovCond.C +libqueso_la_SOURCES += misc/src/AsciiTable.C +libqueso_la_SOURCES += misc/src/ArrayOfOneDTables.C +libqueso_la_SOURCES += misc/src/ArrayOfOneDGrids.C # Sources from misc/src withn gsl conditional if UQBT_GSL - libqueso_la_SOURCES += $(top_srcdir)/src/misc/src/GslAsciiTable.C + libqueso_la_SOURCES += misc/src/GslAsciiTable.C endif # Sources from basic/src -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/TeuchosVectorSpace.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/SequenceStatisticalOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/ArrayOfSequences.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/VectorSpace.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/VectorSet.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/BoxSubset.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/ConcatenationSubset.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/DiscreteSubset.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/IntersectionSubset.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/VectorSubset.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/ScalarFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/GenericScalarFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/ConstantScalarFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/ScalarFunctionSynchronizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/InstantiateIntersection.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/SequenceOfVectors.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/VectorFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/GenericVectorFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/ConstantVectorFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/ScalarSequence.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/VectorFunctionSynchronizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/VectorSequence.C +libqueso_la_SOURCES += basic/src/TeuchosVectorSpace.C +libqueso_la_SOURCES += basic/src/SequenceStatisticalOptions.C +libqueso_la_SOURCES += basic/src/ArrayOfSequences.C +libqueso_la_SOURCES += basic/src/VectorSpace.C +libqueso_la_SOURCES += basic/src/VectorSet.C +libqueso_la_SOURCES += basic/src/BoxSubset.C +libqueso_la_SOURCES += basic/src/ConcatenationSubset.C +libqueso_la_SOURCES += basic/src/DiscreteSubset.C +libqueso_la_SOURCES += basic/src/IntersectionSubset.C +libqueso_la_SOURCES += basic/src/VectorSubset.C +libqueso_la_SOURCES += basic/src/ScalarFunction.C +libqueso_la_SOURCES += basic/src/GenericScalarFunction.C +libqueso_la_SOURCES += basic/src/ConstantScalarFunction.C +libqueso_la_SOURCES += basic/src/ScalarFunctionSynchronizer.C +libqueso_la_SOURCES += basic/src/InstantiateIntersection.C +libqueso_la_SOURCES += basic/src/SequenceOfVectors.C +libqueso_la_SOURCES += basic/src/VectorFunction.C +libqueso_la_SOURCES += basic/src/GenericVectorFunction.C +libqueso_la_SOURCES += basic/src/ConstantVectorFunction.C +libqueso_la_SOURCES += basic/src/ScalarSequence.C +libqueso_la_SOURCES += basic/src/VectorFunctionSynchronizer.C +libqueso_la_SOURCES += basic/src/VectorSequence.C # Sources from basic/src with gsl conditional if UQBT_GSL - libqueso_la_SOURCES += $(top_srcdir)/src/basic/src/GslVectorSpace.C + libqueso_la_SOURCES += basic/src/GslVectorSpace.C endif # Sources from stats/src -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/FiniteDistribution.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MetropolisHastingsSG.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MetropolisHastingsSGOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MLSampling.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MLSamplingOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MLSamplingLevelOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MonteCarloSG.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MonteCarloSGOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/StatisticalInverseProblemOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/StatisticalForwardProblem.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/StatisticalInverseProblem.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/StatisticalForwardProblemOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/InfoTheory.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/JointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/BayesianJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/BetaJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ConcatenatedJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GammaJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GaussianJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GenericJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/InverseGammaJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/LogNormalJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/PoweredJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/UniformJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/WignerJointPdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MarkovChainPositionData.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/MatrixCovarianceFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ExponentialMatrixCovarianceFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GenericMatrixCovarianceFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ModelValidation.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ScalarCdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/SampledScalarCdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/StdScalarCdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ScalarCovarianceFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ExponentialScalarCovarianceFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GenericScalarCovarianceFunction.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ScalarGaussianRandomField.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ValidationCycle.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/VectorGaussianRandomField.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/TKGroup.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ScaledCovMatrixTKGroup.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/HessianCovMatricesTKGroup.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/VectorCdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GenericVectorCdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/SampledVectorCdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GaussianVectorCdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/VectorMdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GenericVectorMdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GaussianVectorMdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/SampledVectorMdf.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/BetaVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ConcatenatedVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GammaVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GaussianVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GenericVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/InverseGammaVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/LogNormalVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/SequentialVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/UniformVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/VectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/WignerVectorRealizer.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/VectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/BetaVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/ConcatenatedVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GammaVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GaussianVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/GenericVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/InverseGammaVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/LogNormalVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/UniformVectorRV.C -libqueso_la_SOURCES += $(top_srcdir)/src/stats/src/WignerVectorRV.C +libqueso_la_SOURCES += stats/src/FiniteDistribution.C +libqueso_la_SOURCES += stats/src/MetropolisHastingsSG.C +libqueso_la_SOURCES += stats/src/MetropolisHastingsSGOptions.C +libqueso_la_SOURCES += stats/src/MLSampling.C +libqueso_la_SOURCES += stats/src/MLSamplingOptions.C +libqueso_la_SOURCES += stats/src/MLSamplingLevelOptions.C +libqueso_la_SOURCES += stats/src/MonteCarloSG.C +libqueso_la_SOURCES += stats/src/MonteCarloSGOptions.C +libqueso_la_SOURCES += stats/src/StatisticalInverseProblemOptions.C +libqueso_la_SOURCES += stats/src/StatisticalForwardProblem.C +libqueso_la_SOURCES += stats/src/StatisticalInverseProblem.C +libqueso_la_SOURCES += stats/src/StatisticalForwardProblemOptions.C +libqueso_la_SOURCES += stats/src/InfoTheory.C +libqueso_la_SOURCES += stats/src/JointPdf.C +libqueso_la_SOURCES += stats/src/BayesianJointPdf.C +libqueso_la_SOURCES += stats/src/BetaJointPdf.C +libqueso_la_SOURCES += stats/src/ConcatenatedJointPdf.C +libqueso_la_SOURCES += stats/src/GammaJointPdf.C +libqueso_la_SOURCES += stats/src/GaussianJointPdf.C +libqueso_la_SOURCES += stats/src/InvLogitGaussianJointPdf.C +libqueso_la_SOURCES += stats/src/GenericJointPdf.C +libqueso_la_SOURCES += stats/src/InverseGammaJointPdf.C +libqueso_la_SOURCES += stats/src/JeffreysJointPdf.C +libqueso_la_SOURCES += stats/src/LogNormalJointPdf.C +libqueso_la_SOURCES += stats/src/PoweredJointPdf.C +libqueso_la_SOURCES += stats/src/UniformJointPdf.C +libqueso_la_SOURCES += stats/src/WignerJointPdf.C +libqueso_la_SOURCES += stats/src/MarkovChainPositionData.C +libqueso_la_SOURCES += stats/src/MatrixCovarianceFunction.C +libqueso_la_SOURCES += stats/src/ExponentialMatrixCovarianceFunction.C +libqueso_la_SOURCES += stats/src/GenericMatrixCovarianceFunction.C +libqueso_la_SOURCES += stats/src/ModelValidation.C +libqueso_la_SOURCES += stats/src/ScalarCdf.C +libqueso_la_SOURCES += stats/src/SampledScalarCdf.C +libqueso_la_SOURCES += stats/src/StdScalarCdf.C +libqueso_la_SOURCES += stats/src/ScalarCovarianceFunction.C +libqueso_la_SOURCES += stats/src/ExponentialScalarCovarianceFunction.C +libqueso_la_SOURCES += stats/src/GenericScalarCovarianceFunction.C +libqueso_la_SOURCES += stats/src/ScalarGaussianRandomField.C +libqueso_la_SOURCES += stats/src/ValidationCycle.C +libqueso_la_SOURCES += stats/src/VectorGaussianRandomField.C +libqueso_la_SOURCES += stats/src/TKGroup.C +libqueso_la_SOURCES += stats/src/ScaledCovMatrixTKGroup.C +libqueso_la_SOURCES += stats/src/TransformedScaledCovMatrixTKGroup.C +libqueso_la_SOURCES += stats/src/HessianCovMatricesTKGroup.C +libqueso_la_SOURCES += stats/src/VectorCdf.C +libqueso_la_SOURCES += stats/src/GenericVectorCdf.C +libqueso_la_SOURCES += stats/src/SampledVectorCdf.C +libqueso_la_SOURCES += stats/src/GaussianVectorCdf.C +libqueso_la_SOURCES += stats/src/VectorMdf.C +libqueso_la_SOURCES += stats/src/GenericVectorMdf.C +libqueso_la_SOURCES += stats/src/GaussianVectorMdf.C +libqueso_la_SOURCES += stats/src/SampledVectorMdf.C +libqueso_la_SOURCES += stats/src/BetaVectorRealizer.C +libqueso_la_SOURCES += stats/src/ConcatenatedVectorRealizer.C +libqueso_la_SOURCES += stats/src/GammaVectorRealizer.C +libqueso_la_SOURCES += stats/src/GaussianVectorRealizer.C +libqueso_la_SOURCES += stats/src/InvLogitGaussianVectorRealizer.C +libqueso_la_SOURCES += stats/src/GenericVectorRealizer.C +libqueso_la_SOURCES += stats/src/InverseGammaVectorRealizer.C +libqueso_la_SOURCES += stats/src/JeffreysVectorRealizer.C +libqueso_la_SOURCES += stats/src/LogNormalVectorRealizer.C +libqueso_la_SOURCES += stats/src/SequentialVectorRealizer.C +libqueso_la_SOURCES += stats/src/UniformVectorRealizer.C +libqueso_la_SOURCES += stats/src/VectorRealizer.C +libqueso_la_SOURCES += stats/src/WignerVectorRealizer.C +libqueso_la_SOURCES += stats/src/VectorRV.C +libqueso_la_SOURCES += stats/src/BetaVectorRV.C +libqueso_la_SOURCES += stats/src/ConcatenatedVectorRV.C +libqueso_la_SOURCES += stats/src/GammaVectorRV.C +libqueso_la_SOURCES += stats/src/GaussianVectorRV.C +libqueso_la_SOURCES += stats/src/InvLogitGaussianVectorRV.C +libqueso_la_SOURCES += stats/src/GenericVectorRV.C +libqueso_la_SOURCES += stats/src/InverseGammaVectorRV.C +libqueso_la_SOURCES += stats/src/JeffreysVectorRV.C +libqueso_la_SOURCES += stats/src/LogNormalVectorRV.C +libqueso_la_SOURCES += stats/src/UniformVectorRV.C +libqueso_la_SOURCES += stats/src/WignerVectorRV.C # Sources from gp/src -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/ExperimentModel.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/ExperimentModelOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/ExperimentStorage.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GpmsaComputerModel.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GpmsaComputerModelOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/SimulationModelOptions.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmExperimentInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmJointInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmJointTildeInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmSimulationInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmSimulationTildeInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmTotalInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmZInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/GcmZTildeInfo.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/SimulationModel.C -libqueso_la_SOURCES += $(top_srcdir)/src/gp/src/SimulationStorage.C +libqueso_la_SOURCES += gp/src/ExperimentModel.C +libqueso_la_SOURCES += gp/src/ExperimentModelOptions.C +libqueso_la_SOURCES += gp/src/ExperimentStorage.C +libqueso_la_SOURCES += gp/src/GpmsaComputerModel.C +libqueso_la_SOURCES += gp/src/GpmsaComputerModelOptions.C +libqueso_la_SOURCES += gp/src/SimulationModelOptions.C +libqueso_la_SOURCES += gp/src/GcmExperimentInfo.C +libqueso_la_SOURCES += gp/src/GcmJointInfo.C +libqueso_la_SOURCES += gp/src/GcmJointTildeInfo.C +libqueso_la_SOURCES += gp/src/GcmSimulationInfo.C +libqueso_la_SOURCES += gp/src/GcmSimulationTildeInfo.C +libqueso_la_SOURCES += gp/src/GcmTotalInfo.C +libqueso_la_SOURCES += gp/src/GcmZInfo.C +libqueso_la_SOURCES += gp/src/GcmZTildeInfo.C +libqueso_la_SOURCES += gp/src/SimulationModel.C +libqueso_la_SOURCES += gp/src/SimulationStorage.C +libqueso_la_SOURCES += gp/src/GPMSA.C +libqueso_la_SOURCES += gp/src/GPMSAOptions.C # Headers to install from core/inc libqueso_include_HEADERS = libqueso_include_HEADERS += $(top_builddir)/src/core/inc/queso.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/Defines.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/Environment.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/EnvironmentOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/RngBase.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/RngGsl.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/RngBoost.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/BasicPdfsBase.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/BasicPdfsGsl.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/BasicPdfsBoost.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/GslMatrix.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/GslVector.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/TeuchosMatrix.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/TeuchosVector.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/Matrix.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/MpiComm.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/Map.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/DistArray.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/asserts.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/exceptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/Vector.h +libqueso_include_HEADERS += core/inc/Defines.h +libqueso_include_HEADERS += core/inc/Environment.h +libqueso_include_HEADERS += core/inc/EnvironmentOptions.h +libqueso_include_HEADERS += core/inc/RngBase.h +libqueso_include_HEADERS += core/inc/RngGsl.h +libqueso_include_HEADERS += core/inc/RngBoost.h +libqueso_include_HEADERS += core/inc/BasicPdfsBase.h +libqueso_include_HEADERS += core/inc/BasicPdfsGsl.h +libqueso_include_HEADERS += core/inc/BasicPdfsBoost.h +libqueso_include_HEADERS += core/inc/GslMatrix.h +libqueso_include_HEADERS += core/inc/GslVector.h +libqueso_include_HEADERS += core/inc/TeuchosMatrix.h +libqueso_include_HEADERS += core/inc/TeuchosVector.h +libqueso_include_HEADERS += core/inc/Matrix.h +libqueso_include_HEADERS += core/inc/MpiComm.h +libqueso_include_HEADERS += core/inc/Map.h +libqueso_include_HEADERS += core/inc/DistArray.h +libqueso_include_HEADERS += core/inc/asserts.h +libqueso_include_HEADERS += core/inc/exceptions.h +libqueso_include_HEADERS += core/inc/Vector.h +libqueso_include_HEADERS += core/inc/Optimizer.h +libqueso_include_HEADERS += core/inc/GslOptimizer.h +libqueso_include_HEADERS += core/inc/OptimizerMonitor.h # Headers that need libmesh -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/FunctionBase.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/LibMeshFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/OperatorBase.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/LibMeshOperatorBase.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/LibMeshNegativeLaplacianOperator.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/InfiniteDimensionalMeasureBase.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/InfiniteDimensionalGaussian.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/InfiniteDimensionalMCMCSampler.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/InfiniteDimensionalMCMCSamplerOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/InfiniteDimensionalLikelihoodBase.h -libqueso_include_HEADERS += $(top_srcdir)/src/core/inc/FunctionOperatorBuilder.h +libqueso_include_HEADERS += core/inc/FunctionBase.h +libqueso_include_HEADERS += core/inc/LibMeshFunction.h +libqueso_include_HEADERS += core/inc/OperatorBase.h +libqueso_include_HEADERS += core/inc/LibMeshOperatorBase.h +libqueso_include_HEADERS += core/inc/LibMeshNegativeLaplacianOperator.h +libqueso_include_HEADERS += core/inc/InfiniteDimensionalMeasureBase.h +libqueso_include_HEADERS += core/inc/InfiniteDimensionalGaussian.h +libqueso_include_HEADERS += core/inc/InfiniteDimensionalMCMCSampler.h +libqueso_include_HEADERS += core/inc/InfiniteDimensionalMCMCSamplerOptions.h +libqueso_include_HEADERS += core/inc/InfiniteDimensionalLikelihoodBase.h +libqueso_include_HEADERS += core/inc/FunctionOperatorBuilder.h # Headers to install from misc/inc -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/1D1DFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/1DQuadrature.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/2dArrayOfStuff.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/ArrayOfOneDGrids.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/ArrayOfOneDTables.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/AsciiTable.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/CovCond.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/Fft.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/Miscellaneous.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/OneDGrid.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/UniformOneDGrid.h -libqueso_include_HEADERS += $(top_srcdir)/src/misc/inc/StdOneDGrid.h +libqueso_include_HEADERS += misc/inc/1D1DFunction.h +libqueso_include_HEADERS += misc/inc/1DQuadrature.h +libqueso_include_HEADERS += misc/inc/2dArrayOfStuff.h +libqueso_include_HEADERS += misc/inc/ArrayOfOneDGrids.h +libqueso_include_HEADERS += misc/inc/ArrayOfOneDTables.h +libqueso_include_HEADERS += misc/inc/AsciiTable.h +libqueso_include_HEADERS += misc/inc/CovCond.h +libqueso_include_HEADERS += misc/inc/Fft.h +libqueso_include_HEADERS += misc/inc/Miscellaneous.h +libqueso_include_HEADERS += misc/inc/OneDGrid.h +libqueso_include_HEADERS += misc/inc/UniformOneDGrid.h +libqueso_include_HEADERS += misc/inc/StdOneDGrid.h # Headers to install from basic/inc -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/ArrayOfSequences.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/InstantiateIntersection.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/ScalarFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/GenericScalarFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/ConstantScalarFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/ScalarFunctionSynchronizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/ScalarSequence.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/SequenceOfVectors.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/SequenceStatisticalOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/VectorFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/VectorFunctionSynchronizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/VectorSequence.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/VectorSet.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/VectorSpace.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/VectorSubset.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/BoxSubset.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/ConcatenationSubset.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/DiscreteSubset.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/IntersectionSubset.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/GenericVectorFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/basic/inc/ConstantVectorFunction.h +libqueso_include_HEADERS += basic/inc/ArrayOfSequences.h +libqueso_include_HEADERS += basic/inc/InstantiateIntersection.h +libqueso_include_HEADERS += basic/inc/ScalarFunction.h +libqueso_include_HEADERS += basic/inc/GenericScalarFunction.h +libqueso_include_HEADERS += basic/inc/ConstantScalarFunction.h +libqueso_include_HEADERS += basic/inc/ScalarFunctionSynchronizer.h +libqueso_include_HEADERS += basic/inc/ScalarSequence.h +libqueso_include_HEADERS += basic/inc/SequenceOfVectors.h +libqueso_include_HEADERS += basic/inc/SequenceStatisticalOptions.h +libqueso_include_HEADERS += basic/inc/VectorFunction.h +libqueso_include_HEADERS += basic/inc/VectorFunctionSynchronizer.h +libqueso_include_HEADERS += basic/inc/VectorSequence.h +libqueso_include_HEADERS += basic/inc/VectorSet.h +libqueso_include_HEADERS += basic/inc/VectorSpace.h +libqueso_include_HEADERS += basic/inc/VectorSubset.h +libqueso_include_HEADERS += basic/inc/BoxSubset.h +libqueso_include_HEADERS += basic/inc/ConcatenationSubset.h +libqueso_include_HEADERS += basic/inc/DiscreteSubset.h +libqueso_include_HEADERS += basic/inc/IntersectionSubset.h +libqueso_include_HEADERS += basic/inc/GenericVectorFunction.h +libqueso_include_HEADERS += basic/inc/ConstantVectorFunction.h # Headers to install from stats/inc -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/FiniteDistribution.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/JointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/BayesianJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/BetaJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ConcatenatedJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GammaJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GaussianJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GenericJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/InverseGammaJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/LogNormalJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/PoweredJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/UniformJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/WignerJointPdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MarkovChainPositionData.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MetropolisHastingsSG.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MetropolisHastingsSGOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MLSampling.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MLSamplingOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MLSamplingLevelOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ModelValidation.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MonteCarloSG.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MonteCarloSGOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ScalarCdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/SampledScalarCdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/StdScalarCdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/StatisticalForwardProblem.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/StatisticalForwardProblemOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/StatisticalInverseProblem.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/StatisticalInverseProblemOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/TKGroup.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ScaledCovMatrixTKGroup.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/HessianCovMatricesTKGroup.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ValidationCycle.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/VectorCdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GenericVectorCdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/SampledVectorCdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GaussianVectorCdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/VectorMdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GenericVectorMdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GaussianVectorMdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/SampledVectorMdf.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/BetaVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ConcatenatedVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GammaVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GaussianVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GenericVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/InverseGammaVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/LogNormalVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/SequentialVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/UniformVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/VectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/WignerVectorRealizer.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/VectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/BetaVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ConcatenatedVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GammaVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GaussianVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GenericVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/InverseGammaVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/LogNormalVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/UniformVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/WignerVectorRV.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/InfoTheory.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ScalarCovarianceFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ExponentialScalarCovarianceFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GenericScalarCovarianceFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/MatrixCovarianceFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ExponentialMatrixCovarianceFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/GenericMatrixCovarianceFunction.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/ScalarGaussianRandomField.h -libqueso_include_HEADERS += $(top_srcdir)/src/stats/inc/VectorGaussianRandomField.h +libqueso_include_HEADERS += stats/inc/FiniteDistribution.h +libqueso_include_HEADERS += stats/inc/JointPdf.h +libqueso_include_HEADERS += stats/inc/BayesianJointPdf.h +libqueso_include_HEADERS += stats/inc/BetaJointPdf.h +libqueso_include_HEADERS += stats/inc/ConcatenatedJointPdf.h +libqueso_include_HEADERS += stats/inc/GammaJointPdf.h +libqueso_include_HEADERS += stats/inc/GaussianJointPdf.h +libqueso_include_HEADERS += stats/inc/InvLogitGaussianJointPdf.h +libqueso_include_HEADERS += stats/inc/GenericJointPdf.h +libqueso_include_HEADERS += stats/inc/InverseGammaJointPdf.h +libqueso_include_HEADERS += stats/inc/JeffreysJointPdf.h +libqueso_include_HEADERS += stats/inc/LogNormalJointPdf.h +libqueso_include_HEADERS += stats/inc/PoweredJointPdf.h +libqueso_include_HEADERS += stats/inc/UniformJointPdf.h +libqueso_include_HEADERS += stats/inc/WignerJointPdf.h +libqueso_include_HEADERS += stats/inc/MarkovChainPositionData.h +libqueso_include_HEADERS += stats/inc/MetropolisHastingsSG.h +libqueso_include_HEADERS += stats/inc/MetropolisHastingsSGOptions.h +libqueso_include_HEADERS += stats/inc/MLSampling.h +libqueso_include_HEADERS += stats/inc/MLSamplingOptions.h +libqueso_include_HEADERS += stats/inc/MLSamplingLevelOptions.h +libqueso_include_HEADERS += stats/inc/ModelValidation.h +libqueso_include_HEADERS += stats/inc/MonteCarloSG.h +libqueso_include_HEADERS += stats/inc/MonteCarloSGOptions.h +libqueso_include_HEADERS += stats/inc/ScalarCdf.h +libqueso_include_HEADERS += stats/inc/SampledScalarCdf.h +libqueso_include_HEADERS += stats/inc/StdScalarCdf.h +libqueso_include_HEADERS += stats/inc/StatisticalForwardProblem.h +libqueso_include_HEADERS += stats/inc/StatisticalForwardProblemOptions.h +libqueso_include_HEADERS += stats/inc/StatisticalInverseProblem.h +libqueso_include_HEADERS += stats/inc/StatisticalInverseProblemOptions.h +libqueso_include_HEADERS += stats/inc/TKGroup.h +libqueso_include_HEADERS += stats/inc/ScaledCovMatrixTKGroup.h +libqueso_include_HEADERS += stats/inc/TransformedScaledCovMatrixTKGroup.h +libqueso_include_HEADERS += stats/inc/HessianCovMatricesTKGroup.h +libqueso_include_HEADERS += stats/inc/ValidationCycle.h +libqueso_include_HEADERS += stats/inc/VectorCdf.h +libqueso_include_HEADERS += stats/inc/GenericVectorCdf.h +libqueso_include_HEADERS += stats/inc/SampledVectorCdf.h +libqueso_include_HEADERS += stats/inc/GaussianVectorCdf.h +libqueso_include_HEADERS += stats/inc/VectorMdf.h +libqueso_include_HEADERS += stats/inc/GenericVectorMdf.h +libqueso_include_HEADERS += stats/inc/GaussianVectorMdf.h +libqueso_include_HEADERS += stats/inc/SampledVectorMdf.h +libqueso_include_HEADERS += stats/inc/BetaVectorRealizer.h +libqueso_include_HEADERS += stats/inc/ConcatenatedVectorRealizer.h +libqueso_include_HEADERS += stats/inc/GammaVectorRealizer.h +libqueso_include_HEADERS += stats/inc/GaussianVectorRealizer.h +libqueso_include_HEADERS += stats/inc/InvLogitGaussianVectorRealizer.h +libqueso_include_HEADERS += stats/inc/GenericVectorRealizer.h +libqueso_include_HEADERS += stats/inc/InverseGammaVectorRealizer.h +libqueso_include_HEADERS += stats/inc/JeffreysVectorRealizer.h +libqueso_include_HEADERS += stats/inc/LogNormalVectorRealizer.h +libqueso_include_HEADERS += stats/inc/SequentialVectorRealizer.h +libqueso_include_HEADERS += stats/inc/UniformVectorRealizer.h +libqueso_include_HEADERS += stats/inc/VectorRealizer.h +libqueso_include_HEADERS += stats/inc/WignerVectorRealizer.h +libqueso_include_HEADERS += stats/inc/VectorRV.h +libqueso_include_HEADERS += stats/inc/BetaVectorRV.h +libqueso_include_HEADERS += stats/inc/ConcatenatedVectorRV.h +libqueso_include_HEADERS += stats/inc/GammaVectorRV.h +libqueso_include_HEADERS += stats/inc/GaussianVectorRV.h +libqueso_include_HEADERS += stats/inc/InvLogitGaussianVectorRV.h +libqueso_include_HEADERS += stats/inc/GenericVectorRV.h +libqueso_include_HEADERS += stats/inc/InverseGammaVectorRV.h +libqueso_include_HEADERS += stats/inc/JeffreysVectorRV.h +libqueso_include_HEADERS += stats/inc/LogNormalVectorRV.h +libqueso_include_HEADERS += stats/inc/UniformVectorRV.h +libqueso_include_HEADERS += stats/inc/WignerVectorRV.h +libqueso_include_HEADERS += stats/inc/InfoTheory.h +libqueso_include_HEADERS += stats/inc/ScalarCovarianceFunction.h +libqueso_include_HEADERS += stats/inc/ExponentialScalarCovarianceFunction.h +libqueso_include_HEADERS += stats/inc/GenericScalarCovarianceFunction.h +libqueso_include_HEADERS += stats/inc/MatrixCovarianceFunction.h +libqueso_include_HEADERS += stats/inc/ExponentialMatrixCovarianceFunction.h +libqueso_include_HEADERS += stats/inc/GenericMatrixCovarianceFunction.h +libqueso_include_HEADERS += stats/inc/ScalarGaussianRandomField.h +libqueso_include_HEADERS += stats/inc/VectorGaussianRandomField.h # Headers to install from gp/inc -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/ExperimentModel.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/ExperimentModelOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/ExperimentStorage.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmExperimentInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmJointInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmJointTildeInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmSimulationInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmSimulationTildeInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmTotalInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmZInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GcmZTildeInfo.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GpmsaComputerModel.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/GpmsaComputerModelOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/SimulationModel.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/SimulationModelOptions.h -libqueso_include_HEADERS += $(top_srcdir)/src/gp/inc/SimulationStorage.h +libqueso_include_HEADERS += gp/inc/ExperimentModel.h +libqueso_include_HEADERS += gp/inc/ExperimentModelOptions.h +libqueso_include_HEADERS += gp/inc/ExperimentStorage.h +libqueso_include_HEADERS += gp/inc/GcmExperimentInfo.h +libqueso_include_HEADERS += gp/inc/GcmJointInfo.h +libqueso_include_HEADERS += gp/inc/GcmJointTildeInfo.h +libqueso_include_HEADERS += gp/inc/GcmSimulationInfo.h +libqueso_include_HEADERS += gp/inc/GcmSimulationTildeInfo.h +libqueso_include_HEADERS += gp/inc/GcmTotalInfo.h +libqueso_include_HEADERS += gp/inc/GcmZInfo.h +libqueso_include_HEADERS += gp/inc/GcmZTildeInfo.h +libqueso_include_HEADERS += gp/inc/GpmsaComputerModel.h +libqueso_include_HEADERS += gp/inc/GpmsaComputerModelOptions.h +libqueso_include_HEADERS += gp/inc/SimulationModel.h +libqueso_include_HEADERS += gp/inc/SimulationModelOptions.h +libqueso_include_HEADERS += gp/inc/SimulationStorage.h +libqueso_include_HEADERS += gp/inc/GPMSA.h +libqueso_include_HEADERS += gp/inc/GPMSAOptions.h # Install the magic header -libqueso_include_HEADERS += $(top_srcdir)/src/contrib/inc/all.h +libqueso_include_HEADERS += contrib/inc/all.h # Headers to install, but not part of dist @@ -435,7 +452,7 @@ nodist_libqueso_include_HEADERS += $(top_builddir)/src/core/inc/queso.h # Standalone binaries bin_PROGRAMS = queso_version -queso_version_SOURCES = $(top_srcdir)/src/core/src/version.C +queso_version_SOURCES = core/src/version.C queso_version_LDADD = libqueso.la if TRILINOS_ENABLED @@ -455,7 +472,6 @@ endif if GIT_CLONE EXTRA_DIST = $(top_srcdir)/src/common/lic_utils/update_license.pl .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE \ - $(libqueso_include_HEADERS) $(libqueso_la_SOURCES) $(top_srcdir)/src/core/inc/queso.h + @$(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/src/basic/inc/ScalarFunction.h b/src/basic/inc/ScalarFunction.h index b1602e959..f90608fa9 100644 --- a/src/basic/inc/ScalarFunction.h +++ b/src/basic/inc/ScalarFunction.h @@ -34,47 +34,51 @@ namespace QUESO { /*! \file ScalarFunction.h * \brief Set of classes for handling vector functions. - * + * * \class BaseScalarFunction * \brief A templated (base) class for handling scalar functions. * * This class allows the mathematical definition of a scalar function such as: - * \f$ f: B \subset R \rightarrow R \f$. A function of one or more variables - * has always one-dimensional range. PDFs (marginal, joint) and CDFs are examples - * of scalar functions. */ - + * \f$ f: B \subset R \rightarrow R \f$. A function of one or more variables + * has always one-dimensional range. PDFs (marginal, joint) and CDFs are examples + * of scalar functions. + */ template class BaseScalarFunction { public: //! @name Constructor/Destructor methods. - //@{ + //@{ //! Default constructor. - /*! Instantiates an object of the class, i.e. a scalar function, given a prefix and its domain.*/ - BaseScalarFunction(const char* prefix, - const VectorSet& domainSet); - //! Destructor + /*! + * Instantiates an object of the class, i.e. a scalar function, given a + * prefix and its domain. + */ + BaseScalarFunction(const char * prefix, const VectorSet & domainSet); + + //! Destructor virtual ~BaseScalarFunction(); //@} - - //! @name Mathematical methods. - //@{ + + //! @name Mathematical methods. + //@{ //! Access to the protected attribute \c m_domainSet: domain set of the scalar function. - const VectorSet& domainSet () const; - - + const VectorSet & domainSet() const; + //! Actual value of the scalar function. - virtual double actualValue(const V& domainVector, const V* domainDirection, V* gradVector, M* hessianMatrix, V* hessianEffect) const = 0; - + virtual double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const = 0; + //! Logarithm of the value of the scalar function. - virtual double lnValue (const V& domainVector, const V* domainDirection, V* gradVector, M* hessianMatrix, V* hessianEffect) const = 0; + virtual double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const = 0; //@} protected: - const BaseEnvironment& m_env; - std::string m_prefix; - - //! Domain set of the scalar function. - const VectorSet& m_domainSet; + const BaseEnvironment & m_env; + std::string m_prefix; + + //! Domain set of the scalar function. + const VectorSet & m_domainSet; }; } // End namespace QUESO diff --git a/src/basic/src/GenericScalarFunction.C b/src/basic/src/GenericScalarFunction.C index 0d6d38b96..9c6680015 100644 --- a/src/basic/src/GenericScalarFunction.C +++ b/src/basic/src/GenericScalarFunction.C @@ -69,11 +69,7 @@ double GenericScalarFunction::actualValue(const V& domainVector, double value = m_valueRoutinePtr(domainVector, domainDirection, m_routinesDataPtr, gradVector, hessianMatrix, hessianEffect); if (m_routineIsForLn) { -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN value = std::exp(value); -#else - value = std::exp(-.5*value); -#endif UQ_FATAL_TEST_MACRO((domainDirection != NULL) || (gradVector != NULL) || (hessianMatrix != NULL) || @@ -100,11 +96,7 @@ double GenericScalarFunction::lnValue(const V& domainVector, double value = m_valueRoutinePtr(domainVector, domainDirection, m_routinesDataPtr, gradVector, hessianMatrix, hessianEffect); if (m_routineIsForLn == false) { -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN value = log(value); -#else - value = -2.*log(value); -#endif UQ_FATAL_TEST_MACRO((domainDirection != NULL) || (gradVector != NULL) || (hessianMatrix != NULL) || diff --git a/src/basic/src/ScalarFunction.C b/src/basic/src/ScalarFunction.C index 1517d727a..065e184b0 100644 --- a/src/basic/src/ScalarFunction.C +++ b/src/basic/src/ScalarFunction.C @@ -30,24 +30,24 @@ namespace QUESO { // Default constructor -template -BaseScalarFunction::BaseScalarFunction(const char* prefix, - const VectorSet& domainSet) +template +BaseScalarFunction::BaseScalarFunction(const char * prefix, + const VectorSet & domainSet) : m_env(domainSet.env()), - m_prefix((std::string)(prefix)+"func_"), + m_prefix((std::string)(prefix) + "func_"), m_domainSet(domainSet) { } // Destructor -template -BaseScalarFunction::~BaseScalarFunction() +template +BaseScalarFunction::~BaseScalarFunction() { } // Math methods -template -const VectorSet& BaseScalarFunction::domainSet() const +template +const VectorSet & BaseScalarFunction::domainSet() const { return m_domainSet; } diff --git a/src/contrib/ANN/Makefile.am b/src/contrib/ANN/Makefile.am index 21e288ce6..1cd0ed9db 100644 --- a/src/contrib/ANN/Makefile.am +++ b/src/contrib/ANN/Makefile.am @@ -1,10 +1,32 @@ ## Process this file with automake to produce Makefile.in +if HAVE_ANN -#Build in these directories: +SUBDIRS = test -if HAVE_ANN - SUBDIRS = lib test -endif +lib_LTLIBRARIES = libANN.la +libANN_includedir = $(prefix)/include/ANN -##### EXTRA_DIST = test +AM_CPPFLAGS = -I$(top_srcdir)/src -I$(top_srcdir)/include +libANN_la_SOURCES = +libANN_la_SOURCES += src/ANN.cpp +libANN_la_SOURCES += src/brute.cpp +libANN_la_SOURCES += src/kd_tree.cpp +libANN_la_SOURCES += src/kd_util.cpp +libANN_la_SOURCES += src/kd_split.cpp +libANN_la_SOURCES += src/kd_dump.cpp +libANN_la_SOURCES += src/kd_search.cpp +libANN_la_SOURCES += src/kd_pr_search.cpp +libANN_la_SOURCES += src/kd_fix_rad_search.cpp +libANN_la_SOURCES += src/bd_tree.cpp +libANN_la_SOURCES += src/bd_search.cpp +libANN_la_SOURCES += src/bd_pr_search.cpp +libANN_la_SOURCES += src/bd_fix_rad_search.cpp +libANN_la_SOURCES += src/perf.cpp + +libANN_include_HEADERS = +libANN_include_HEADERS += include/ANN/ANN.h +libANN_include_HEADERS += include/ANN/ANNx.h +libANN_include_HEADERS += include/ANN/ANNperf.h + +endif diff --git a/src/contrib/ANN/lib/Makefile.am b/src/contrib/ANN/lib/Makefile.am deleted file mode 100644 index 8c5ae2405..000000000 --- a/src/contrib/ANN/lib/Makefile.am +++ /dev/null @@ -1,29 +0,0 @@ -## Process this file with automake to produce Makefile.in - -lib_LTLIBRARIES = libANN.la -libANN_includedir = $(prefix)/include/ANN - -ANN_DIR = $(top_srcdir)/src/contrib/ANN - -AM_CPPFLAGS = -I$(ANN_DIR)/src -I$(ANN_DIR)/include - -libANN_la_SOURCES = -libANN_la_SOURCES += $(ANN_DIR)/src/ANN.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/brute.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/kd_tree.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/kd_util.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/kd_split.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/kd_dump.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/kd_search.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/kd_pr_search.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/kd_fix_rad_search.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/bd_tree.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/bd_search.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/bd_pr_search.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/bd_fix_rad_search.cpp -libANN_la_SOURCES += $(ANN_DIR)/src/perf.cpp - -libANN_include_HEADERS = -libANN_include_HEADERS += $(ANN_DIR)/include/ANN/ANN.h -libANN_include_HEADERS += $(ANN_DIR)/include/ANN/ANNx.h -libANN_include_HEADERS += $(ANN_DIR)/include/ANN/ANNperf.h diff --git a/src/contrib/inc/all.h b/src/contrib/inc/all.h index bcdc1e40d..59f789efe 100644 --- a/src/contrib/inc/all.h +++ b/src/contrib/inc/all.h @@ -22,154 +22,162 @@ // //-----------------------------------------------------------------------el- -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include #include -#include -#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include #include -#include -#include -#include -#include +#include #include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include +#include +#include +#include #include -#include -#include -#include -#include -#include -#include -#include -#include +#include #include -#include -#include -#include -#include #include -#include -#include -#include -#include -#include #include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include +#include +#include +#include +#include #include -#include -#include -#include -#include -#include #include #include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include #include -#include -#include -#include -#include -#include #include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include #include +#include #include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include +#include +#include +#include +#include #include +#include #include -#include -#include #include -#include -#include +#include #include #include +#include +#include #include #include -#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include diff --git a/src/core/inc/Defines.h b/src/core/inc/Defines.h index 96b35df0f..75fe86848 100644 --- a/src/core/inc/Defines.h +++ b/src/core/inc/Defines.h @@ -28,8 +28,6 @@ #include #include -//! Defines available optional libraries (GLPK, HDF5, Trilinos) - #ifdef QUESO_HAVE_GLPK #define QUESO_HAS_GLPK #endif @@ -46,8 +44,12 @@ #define QUESO_HAS_ANN #endif +//! This define is deprecated. Remove any #ifdef statements in user code. #define QUESO_HAS_MPI +//! This define is deprecated. Remove any #ifdef statements in user code. +#define QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN + #include #include // For exit() #include @@ -64,7 +66,6 @@ int MyWorldfullRank(); #undef QUESO_MEMORY_DEBUGGING #undef UQ_DEBUG_PARALLEL_RUNS_IN_DETAIL #undef UQ_ALSO_COMPUTE_MDFS_WITHOUT_KDE -#define QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN #define QUESO_CLASSES_INSTANTIATE_NEW_MAPS #undef QUESO_USES_SEQUENCE_STATISTICAL_OPTIONS #undef QUESO_COMPUTES_EXTRA_POST_PROCESSING_STATISTICS diff --git a/src/core/inc/GslOptimizer.h b/src/core/inc/GslOptimizer.h new file mode 100644 index 000000000..a63f4fab6 --- /dev/null +++ b/src/core/inc/GslOptimizer.h @@ -0,0 +1,147 @@ +//-----------------------------------------------------------------------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 UQ_GSL_OPTIMIZER_H +#define UQ_GSL_OPTIMIZER_H + +#include + +namespace QUESO { + +/*! + * \file GslOptimizer.h + * \brief Class for handling optimization of scalar functions + * + * \class GslOptimizer + * \brief A base class for handling optimisation of scalar functions + * + * WRITE DOCS HERE + */ + +class Vector; +class GslVector; +class GslMatrix; +class OptimizerMonitor; + +template +class BaseScalarFunction; + +class GslOptimizer : public BaseOptimizer { +public: + //! Constructs an object that will maximize a scalar function + /*! + * The function \c objectiveFunction is the function that will be maximized. + */ + GslOptimizer( + const BaseScalarFunction & objectiveFunction); + + //! Destructor + virtual ~GslOptimizer(); + + //! Minimize the objective function, starting at \c m_initialPoint + /*! + * m_initialPoint is handled in the derived class + */ + virtual void minimize(OptimizerMonitor* monitor = NULL); + + //! Returns the objective function + const BaseScalarFunction & objectiveFunction() const; + + enum SolverType { FLETCHER_REEVES_CG, + POLAK_RIBIERE_CG, + BFGS, + BFGS2, + STEEPEST_DECENT, + NELDER_MEAD, + NELDER_MEAD2, + NELDER_MEAD2_RAND }; + + //! Set the point at which the optimization starts + void setInitialPoint(const GslVector & intialPoint); + + //! Return the point that minimizes the objective function + /*! + * This state will be filled with GSL_NAN if, for some reason, the + * optimization failed + */ + const GslVector & minimizer() const; + + void set_solver_type( SolverType solver ); + + void set_solver_type( std::string& solver ); + + SolverType string_to_enum( std::string& solver ); + + //! Sets step size used in gradient-free solvers + /*! + * By default, the step size used will be a vector of 0.1. + * Use this method to reset the step_size to the desired + * values. + */ + void set_step_size( const GslVector& step_size ); + + //! Sets step size used in gradient-based solvers + /*! + * GSL doesn't document this parameter well, but it seems to be related + * to the line search, so we default to 1.0 for full step. + */ + void set_step_size( double step_size ); + + //! Set GSL line minimization tolerance + /*! + * Applicable only to gradient-based solvers. Default is 0.1, as + * recommended by GSL documentation. See GSL documentation + * for more details. + */ + void set_line_tol( double tol ); + +private: + const BaseScalarFunction & m_objectiveFunction; + + GslVector * m_initialPoint; + GslVector * m_minimizer; + + SolverType m_solver_type; + + //! For use in gradient-free algorithms + GslVector m_fstep_size; + + //! For use in gradient-based algorithms + double m_fdfstep_size; + + //! Line minimization tolerance in gradient-based algorithms + double m_line_tol; + + //! Helper function + bool solver_needs_gradient(SolverType solver); + + void minimize_with_gradient( unsigned int dim, OptimizerMonitor* monitor ); + + void minimize_no_gradient( unsigned int dim, OptimizerMonitor* monitor ); + +}; + +} // End namespace QUESO + +#endif // UQ_GSL_OPTIMIZER_H diff --git a/src/core/inc/Optimizer.h b/src/core/inc/Optimizer.h new file mode 100644 index 000000000..530571f53 --- /dev/null +++ b/src/core/inc/Optimizer.h @@ -0,0 +1,94 @@ +//-----------------------------------------------------------------------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 UQ_BASE_OPTIMIZER_H +#define UQ_BASE_OPTIMIZER_H + +namespace QUESO { + +/*! + * \file Optimizer.h + * \brief Class for handling optimization of scalar functions + * + * \class BaseOptimizer + * \brief A base class for handling optimisation of scalar functions + * + * This class defines the interface every optimizer must adhere to. All + * optimizers should subclass this class. + */ + +class Vector; +class OptimizerMonitor; + +class BaseOptimizer { +public: + //! Default constructor. + BaseOptimizer(); + + //! Destructor + virtual ~BaseOptimizer(); + + //! Minimize the objective function, starting at \c m_initialPoint + /*! + * m_initialPoint is handled in the derived class + */ + virtual void minimize(OptimizerMonitor* monitor) = 0; + + //! Returns the maximum number of iterations the optimizer will do + /*! + * Default value is 100 + */ + unsigned int getMaxIterations() const; + + //! Returns the tolerance used to test for an extremum in the optimizer + /*! + * Default value is 1e-3 + */ + double getTolerance() const; + + //! Returns the step size used in the finite difference formula + /*! + * Default value is 1e-4 + */ + double getFiniteDifferenceStepSize() const; + + //! Sets the maximum number of iterations to be used by the optimizer + void setMaxIterations(unsigned int maxIterations); + + //! Sets the tolerance the optimizer will use to test for an extremum + void setTolerance(double tolerance); + + //! Sets the step to use in the finite difference derivative + void setFiniteDifferenceStepSize(double h); + +protected: + + unsigned int m_maxIterations; + double m_tolerance; + double m_finiteDifferenceStepSize; +}; + +} // End namespace QUESO + +#endif // UQ_BASE_OPTIMIZER_H diff --git a/src/core/inc/OptimizerMonitor.h b/src/core/inc/OptimizerMonitor.h new file mode 100644 index 000000000..dd63d7865 --- /dev/null +++ b/src/core/inc/OptimizerMonitor.h @@ -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- + +#ifndef UQ_OPTIMIZER_MONITOR_H +#define UQ_OPTIMIZER_MONITOR_H + +#include +#include + +namespace QUESO +{ + class BaseEnvironment; + + //! Object to monitor convergence of optimizers + class OptimizerMonitor + { + public: + + //! Constructor + /*! + * Input paramter n_iters will reserve space for monitor data + * for n_iters. This is for efficiency - if the number of iterations + * goes beyond n_iters, there will not be an error, just less performance + * by the monitor. Defaults to reserving space for 100 iterations + */ + OptimizerMonitor( const BaseEnvironment& env, unsigned int n_iters = 100 ); + + ~OptimizerMonitor(); + + //! Enabling output to std::cout everytime append is called + /*! + * Helpful when wanting to monitor convergence progress in real time. + * The print_xmin argument controls whether or not the current estimate + * of the minimizer is printed. Not recommended if you have more than a + * handful of dimensions over which you are optimizing. + */ + void set_display_output( bool enable_output, bool print_xmin ); + + //! Add current value of minimizer, objective, and error norm + void append( std::vector& x_min, double objective, double norm ); + + //! Clears internal datastructures and resets display variables to false. + void reset(); + + //! Prints entire convergence history. + /*! + * The print_xmin argument controls whether or not the estimate + * of the minimizer (all components) is printed. + */ + void print( std::ostream& output, bool print_xmin = false ) const; + + private: + + const BaseEnvironment& m_env; + + bool m_display_conv; + bool m_print_xmin; + + std::vector > m_minimizer_hist; + std::vector m_objective_hist; + std::vector m_norm_hist; + + void print_header( std::ostream& output, bool print_xmin ) const; + void print_iteration( unsigned int iter, std::ostream& output, + bool print_xmin ) const; + + }; + +} // end namespace QUESO + +#endif // UQ_OPTIMIZER_MONITOR_H diff --git a/src/core/inc/asserts.h b/src/core/inc/asserts.h index cae171698..0d49fd510 100644 --- a/src/core/inc/asserts.h +++ b/src/core/inc/asserts.h @@ -84,8 +84,12 @@ #endif // NDEBUG -#define queso_error() do { queso_here(); QUESO_THROW(QUESO::LogicError()); } while(0) -#define queso_not_implemented() do { queso_here(); QUESO_THROW(QUESO::NotImplemented()); } while(0) -#define queso_file_error(filename) do { queso_here(); QUESO_THROW(QUESO::FileError(filename)); } while(0) +#define queso_error() do { queso_here(); QUESO_THROW(QUESO::LogicError()); } while(0) +#define queso_not_implemented() do { queso_here(); QUESO_THROW(QUESO::NotImplemented()); } while(0) +#define queso_file_error(filename) do { queso_here(); QUESO_THROW(QUESO::FileError(filename)); } while(0) + +#define queso_error_msg(msg) do { queso_here(); std::cerr << msg << std::endl; QUESO_THROW(QUESO::LogicError()); } while(0) +#define queso_not_implemented_msg(msg) do { queso_here(); std::cerr << msg << std::endl; QUESO_THROW(QUESO::NotImplemented()); } while(0) +#define queso_file_error_msg(filename, msg) do { queso_here(); std::cerr << msg << std::endl; QUESO_THROW(QUESO::FileError(filename)); } while(0) #endif // QUESO_ASSERTS_H diff --git a/src/core/src/GslOptimizer.C b/src/core/src/GslOptimizer.C new file mode 100644 index 000000000..2c6c8c238 --- /dev/null +++ b/src/core/src/GslOptimizer.C @@ -0,0 +1,520 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +// We need to extern "C" because gsl needs a pointer to a C function to +// minimize +extern "C" { + // This evaluate -log posterior + double c_evaluate(const gsl_vector * x, void * context) { + + GslOptimizer * optimizer = static_cast(context); + + GslVector state( + optimizer->objectiveFunction().domainSet().vectorSpace().zeroVector()); + + // DM: Doing this copy sucks, but whatever. It'll do for now. + for (unsigned int i = 0; i < state.sizeLocal(); i++) { + state[i] = gsl_vector_get(x, i); + } + + // Bail early if GSL tries to evaluate outside of the domain + if (!optimizer->objectiveFunction().domainSet().contains(state)) { + return GSL_NAN; + } + + // Should cache derivative here so we don't a) segfault in the user's code + // and b) so we don't recompute stuff + double result = -optimizer->objectiveFunction().lnValue(state, NULL, NULL, + NULL, NULL); + + return result; + } + + // This evaluates the derivative of -log posterior + void c_evaluate_derivative(const gsl_vector * x, void * context, + gsl_vector * derivative) { + GslOptimizer * optimizer = static_cast(context); + + GslVector state( + optimizer->objectiveFunction().domainSet().vectorSpace().zeroVector()); + GslVector deriv( + optimizer->objectiveFunction().domainSet().vectorSpace().zeroVector()); + + // DM: Doing this copy sucks, but whatever. It'll do for now. + for (unsigned int i = 0; i < state.sizeLocal(); i++) { + state[i] = gsl_vector_get(x, i); + + // We fill with GSL_NAN and use it as a flag to check later that the user + // actually fills the derivative vector with stuff + deriv[i] = GSL_NAN; + } + + if (!optimizer->objectiveFunction().domainSet().contains(state)) { + // Fill derivative with error codes if the point is outside of the + // domain + for (unsigned int i = 0; i < deriv.sizeLocal(); i++) { + gsl_vector_set(derivative, i, GSL_NAN); + } + } + else { + // We should cache the return value here so we don't recompute stuff + double fx = -optimizer->objectiveFunction().lnValue(state, NULL, &deriv, + NULL, NULL); + + // Decide whether or not we need to do a finite difference based on + // whether the user actually filled deriv with values that are not + // GSL_NAN + // + // We're currently doing this check every time this function gets called. + // We could probably pull this logic out of here and put it somewhere + // where it only happens once + bool userComputedDerivative = true; + for (unsigned int i = 0; i < deriv.sizeLocal(); i++) { + // If the user missed out a derivative in any direction, fall back to + // a finite difference + if (gsl_isnan(deriv[i])) { + userComputedDerivative = false; + break; + } + } + + if (userComputedDerivative) { + for (unsigned int i = 0; i < deriv.sizeLocal(); i++) { + gsl_vector_set(derivative, i, -deriv[i]); // We need the minus sign + } + } + else { + // Finite difference step-size + double h = optimizer->getFiniteDifferenceStepSize(); + + // User did not provide a derivative, so do a finite difference + for (unsigned int i = 0; i < deriv.sizeLocal(); i++) { + double tempState = state[i]; + state[i] += h; + + // User didn't provide a derivative, so we don't bother passing in + // the derivative vector again + double fxph = -optimizer->objectiveFunction().lnValue(state, NULL, + NULL, NULL, NULL); + + // Reset the state back to what it was before + state[i] = tempState; + + // Make sure we didn't do anything dumb and tell gsl if we did + if (!gsl_isnan(fx) && !gsl_isnan(fxph)) { + gsl_vector_set(derivative, i, (fxph - fx) / h); + } + else { + gsl_vector_set(derivative, i, GSL_NAN); + } + } + } + } + } + + // This evaluates -log posterior and the derivative of -log posterior + void c_evaluate_with_derivative(const gsl_vector * x, void * context, + double * f, gsl_vector * derivative) { + // We don't need to call both of these + *f = c_evaluate(x, context); + c_evaluate_derivative(x, context, derivative); + } +} // End extern "C" + +GslOptimizer::GslOptimizer( + const BaseScalarFunction & objectiveFunction) + : BaseOptimizer(), + m_objectiveFunction(objectiveFunction), + m_initialPoint(new GslVector(objectiveFunction.domainSet(). + vectorSpace().zeroVector())), + m_minimizer(new GslVector(this->m_objectiveFunction.domainSet(). + vectorSpace().zeroVector())), + m_solver_type(BFGS2), + m_fstep_size(this->m_objectiveFunction.domainSet().vectorSpace().zeroVector()), + m_fdfstep_size(1.0), + m_line_tol(0.1) +{ + // We initialize the minimizer to GSL_NAN just in case the optimization fails + m_minimizer->cwSet(GSL_NAN); + + // Set to documented default value. + m_fstep_size.cwSet(0.1); +} + +GslOptimizer::~GslOptimizer() +{ + delete this->m_initialPoint; +} + +void GslOptimizer::minimize(OptimizerMonitor* monitor) { + + // First check that initial guess is reasonable + if (!this->m_objectiveFunction.domainSet().contains(*(this->m_initialPoint))) + { + if( m_objectiveFunction.domainSet().env().fullRank() == 0 ) + { + std::cerr << "Minimization was given initial point outside of domain" + << std::endl; + } + queso_error(); + } + + unsigned int dim = this->m_objectiveFunction.domainSet().vectorSpace(). + zeroVector().sizeLocal(); + + if( this->solver_needs_gradient(m_solver_type) ) + { + this->minimize_with_gradient( dim, monitor ); + } + else + { + this->minimize_no_gradient( dim, monitor ); + } + + return; +} + +const BaseScalarFunction & +GslOptimizer::objectiveFunction() const +{ + return this->m_objectiveFunction; +} + +void +GslOptimizer::setInitialPoint(const GslVector & initialPoint) +{ + for (unsigned int i = 0; i < initialPoint.sizeLocal(); i++) { + (*(this->m_initialPoint))[i] = initialPoint[i]; + } +} + +const GslVector & +GslOptimizer::minimizer() const +{ + return *(this->m_minimizer); +} + + void GslOptimizer::set_solver_type( SolverType solver ) + { + m_solver_type = solver; + } + + bool GslOptimizer::solver_needs_gradient( SolverType solver ) + { + bool gradient_needed = false; + + switch(solver) + { + case(FLETCHER_REEVES_CG): + case(POLAK_RIBIERE_CG): + case(BFGS): + case(BFGS2): + case(STEEPEST_DECENT): + { + gradient_needed = true; + break; + } + case(NELDER_MEAD): + case(NELDER_MEAD2): + case(NELDER_MEAD2_RAND): + { + break; + } + default: + { + // Wat?! + queso_error(); + } + } // switch(solver) + + return gradient_needed; + } + + void GslOptimizer::minimize_with_gradient( unsigned int dim, OptimizerMonitor* monitor ) + { + // Set initial point + gsl_vector * x = gsl_vector_alloc(dim); + for (unsigned int i = 0; i < dim; i++) { + gsl_vector_set(x, i, (*m_initialPoint)[i]); + } + + // Tell GSL which solver we're using + const gsl_multimin_fdfminimizer_type* type = NULL; + + switch(m_solver_type) + { + case(FLETCHER_REEVES_CG): + type = gsl_multimin_fdfminimizer_conjugate_fr; + break; + case(POLAK_RIBIERE_CG): + type = gsl_multimin_fdfminimizer_conjugate_pr; + break; + case(BFGS): + type = gsl_multimin_fdfminimizer_vector_bfgs; + break; + case(BFGS2): + type = gsl_multimin_fdfminimizer_vector_bfgs2; + break; + case(STEEPEST_DECENT): + type = gsl_multimin_fdfminimizer_steepest_descent; + break; + case(NELDER_MEAD): + case(NELDER_MEAD2): + case(NELDER_MEAD2_RAND): + default: + // Wat?! + queso_error(); + } + + // Init solver + gsl_multimin_fdfminimizer * solver = + gsl_multimin_fdfminimizer_alloc(type, dim); + + // Point GSL to the right functions + gsl_multimin_function_fdf minusLogPosterior; + minusLogPosterior.n = dim; + minusLogPosterior.f = &c_evaluate; + minusLogPosterior.df = &c_evaluate_derivative; + minusLogPosterior.fdf = &c_evaluate_with_derivative; + minusLogPosterior.params = (void *)(this); + + gsl_multimin_fdfminimizer_set(solver, &minusLogPosterior, x, m_fdfstep_size, m_line_tol); + + int status; + size_t iter = 0; + + do { + iter++; + status = gsl_multimin_fdfminimizer_iterate(solver); + + if (status) { + if( m_objectiveFunction.domainSet().env().fullRank() == 0 ) + { + std::cerr << "Error while GSL does optimisation. " + << "See below for GSL error type." << std::endl; + std::cerr << "Gsl error: " << gsl_strerror(status) << std::endl; + } + break; + } + + status = gsl_multimin_test_gradient(solver->gradient, this->getTolerance()); + + if(monitor) + { + gsl_vector* x = gsl_multimin_fdfminimizer_x(solver); + std::vector x_min(dim); + for( unsigned int i = 0; i < dim; i++) + x_min[i] = gsl_vector_get(x,i); + + double f = gsl_multimin_fdfminimizer_minimum(solver); + + gsl_vector* grad = gsl_multimin_fdfminimizer_gradient(solver); + double grad_norm = gsl_blas_dnrm2(grad); + + monitor->append( x_min, f, grad_norm ); + } + + } while ((status == GSL_CONTINUE) && (iter < this->getMaxIterations())); + + for (unsigned int i = 0; i < dim; i++) { + (*m_minimizer)[i] = gsl_vector_get(solver->x, i); + } + + // We're being good human beings and cleaning up the memory we allocated + gsl_multimin_fdfminimizer_free(solver); + gsl_vector_free(x); + + return; + } + + void GslOptimizer::minimize_no_gradient( unsigned int dim, OptimizerMonitor* monitor ) + { + // Set initial point + gsl_vector* x = gsl_vector_alloc(dim); + for (unsigned int i = 0; i < dim; i++) { + gsl_vector_set(x, i, (*m_initialPoint)[i]); + } + + // Tell GSL which solver we're using + const gsl_multimin_fminimizer_type* type = NULL; + + switch(m_solver_type) + { + case(NELDER_MEAD): + type = gsl_multimin_fminimizer_nmsimplex; + break; + case(NELDER_MEAD2): + type = gsl_multimin_fminimizer_nmsimplex2; + break; + case(NELDER_MEAD2_RAND): + type = gsl_multimin_fminimizer_nmsimplex2rand; + break; + case(FLETCHER_REEVES_CG): + case(POLAK_RIBIERE_CG): + case(BFGS): + case(BFGS2): + case(STEEPEST_DECENT): + default: + // Wat?! + queso_error(); + } + + // Init solver + gsl_multimin_fminimizer* solver = + gsl_multimin_fminimizer_alloc(type, dim); + + // Point GSL at the right functions + gsl_multimin_function minusLogPosterior; + minusLogPosterior.n = dim; + minusLogPosterior.f = &c_evaluate; + minusLogPosterior.params = (void *)(this); + + // Needed for these gradient free algorithms. + gsl_vector* step_size = gsl_vector_alloc(dim); + + for(unsigned int i = 0; i < dim; i++) { + gsl_vector_set(step_size, i, m_fstep_size[i]); + } + + gsl_multimin_fminimizer_set(solver, &minusLogPosterior, x, step_size); + + int status; + size_t iter = 0; + double size = 0.0; + + do + { + iter++; + status = gsl_multimin_fminimizer_iterate(solver); + + if (status) { + if( m_objectiveFunction.domainSet().env().fullRank() == 0 ) + { + std::cerr << "Error while GSL does optimisation. " + << "See below for GSL error type." << std::endl; + std::cerr << "Gsl error: " << gsl_strerror(status) << std::endl; + } + break; + } + + size = gsl_multimin_fminimizer_size(solver); + + status = gsl_multimin_test_size (size, this->getTolerance()); + + if(monitor) + { + gsl_vector* x = gsl_multimin_fminimizer_x(solver); + std::vector x_min(dim); + for( unsigned int i = 0; i < dim; i++) + x_min[i] = gsl_vector_get(x,i); + + double f = gsl_multimin_fminimizer_minimum(solver); + + monitor->append( x_min, f, size ); + } + + } + + while ((status == GSL_CONTINUE) && (iter < this->getMaxIterations())); + + for (unsigned int i = 0; i < dim; i++) { + (*m_minimizer)[i] = gsl_vector_get(solver->x, i); + } + + // We're being good human beings and cleaning up the memory we allocated + gsl_vector_free(step_size); + gsl_multimin_fminimizer_free(solver); + gsl_vector_free(x); + + return; + } + + void GslOptimizer::set_step_size( const GslVector& step_size ) + { + m_fstep_size = step_size; + } + + void GslOptimizer::set_step_size( double step_size ) + { + m_fdfstep_size = step_size; + } + + GslOptimizer::SolverType GslOptimizer::string_to_enum( std::string& solver ) + { + SolverType solver_type; + + if( solver == std::string("fletcher_reeves_cg") ) + solver_type = FLETCHER_REEVES_CG; + else if( solver == std::string("polak_ribiere_cg") ) + solver_type = POLAK_RIBIERE_CG; + else if( solver == std::string("bfgs") ) + solver_type = BFGS; + else if( solver == std::string("bfgs2") ) + solver_type = BFGS2; + else if( solver == std::string("steepest_decent") ) + solver_type = STEEPEST_DECENT; + else if( solver == std::string("nelder_mead") ) + solver_type = NELDER_MEAD; + else if( solver == std::string("nelder_mead2") ) + solver_type = NELDER_MEAD2; + else if( solver == std::string("nelder_mead2_rand") ) + solver_type = NELDER_MEAD2_RAND; + else + { + if( m_objectiveFunction.domainSet().env().fullRank() == 0 ) + { + std::cerr << "Error: Invalid GslOptimizer solver name: " << solver << std::endl + << " Valids choices are: fletcher_reeves_cg" << std::endl + << " polak_ribiere_cg" << std::endl + << " bfgs" << std::endl + << " bfgs2" << std::endl + << " steepest_decent" << std::endl + << " nelder_mead" << std::endl + << " nelder_mead2" << std::endl + << " nelder_mead2_rand" << std::endl; + } + queso_error(); + } + + return solver_type; + } + + void GslOptimizer::set_solver_type( std::string& solver ) + { + this->set_solver_type( this->string_to_enum(solver) ); + } + +} // End namespace QUESO diff --git a/src/core/src/Optimizer.C b/src/core/src/Optimizer.C new file mode 100644 index 000000000..7047832c4 --- /dev/null +++ b/src/core/src/Optimizer.C @@ -0,0 +1,76 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +BaseOptimizer::BaseOptimizer() + : m_maxIterations(100), + m_tolerance(1e-3), + m_finiteDifferenceStepSize(1e-4) +{ +} + +BaseOptimizer::~BaseOptimizer() +{ +} + +unsigned int +BaseOptimizer::getMaxIterations() const +{ + return this->m_maxIterations; +} + +double +BaseOptimizer::getTolerance() const +{ + return this->m_tolerance; +} + +double +BaseOptimizer::getFiniteDifferenceStepSize() const +{ + return this->m_finiteDifferenceStepSize; +} + +void +BaseOptimizer::setMaxIterations(unsigned int maxIterations) +{ + this->m_maxIterations = maxIterations; +} + +void +BaseOptimizer::setTolerance(double tolerance) +{ + this->m_tolerance = tolerance; +} + +void +BaseOptimizer::setFiniteDifferenceStepSize(double h) +{ + this->m_finiteDifferenceStepSize = h; +} + +} // End namespace QUESO diff --git a/src/core/src/OptimizerMonitor.C b/src/core/src/OptimizerMonitor.C new file mode 100644 index 000000000..6ac2b1bf1 --- /dev/null +++ b/src/core/src/OptimizerMonitor.C @@ -0,0 +1,165 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO +{ + + OptimizerMonitor::OptimizerMonitor( const BaseEnvironment& env, unsigned int n_iters ) + : m_env(env), + m_display_conv(false), + m_print_xmin(false) + { + m_minimizer_hist.reserve(n_iters); + m_objective_hist.reserve(n_iters); + m_norm_hist.reserve(n_iters); + } + + OptimizerMonitor::~OptimizerMonitor() + {} + + void OptimizerMonitor::set_display_output( bool enable_output, bool print_xmin ) + { + m_display_conv = enable_output; + m_print_xmin = print_xmin; + } + + void OptimizerMonitor::append( std::vector& x_min, double objective, double norm ) + { + // This needs to be done before trying to print + m_minimizer_hist.push_back(x_min); + m_objective_hist.push_back(objective); + m_norm_hist.push_back(norm); + + // Print out to screen if the user set the option + if( m_display_conv ) + { + // If we are appending the first entry, print a nice header + if( m_minimizer_hist.size() == 1 ) + { + this->print_header(std::cout, m_print_xmin); + } + + /* We're assuming here the size of the array is the current iteration. + Shift by -1 since we count from 0 in C++. */ + this->print_iteration(m_norm_hist.size()-1,std::cout,m_print_xmin); + } + + } + + void OptimizerMonitor::print_header( std::ostream& output, bool print_xmin ) const + { + unsigned int width = 37; + + if( print_xmin) width += (m_minimizer_hist[0]).size()*15; + + // Only print output on processor 0 + if( m_env.fullRank() == 0 ) + { + output.width(5); + output << "i"; + + if( print_xmin) + { + for( unsigned int i = 0; i < m_minimizer_hist[0].size(); i++ ) + { + output.width(9); + output << "x" << i << std::string(5,' '); + } + } + + output.width(9); + output << "f" << std::string(5,' '); + + output.width(12); + output << "norm" << std::endl; + output << std::string(width,'-') << std::endl; + } + } + + void OptimizerMonitor::print_iteration( unsigned int iter, std::ostream& output, + bool print_xmin ) const + { + if( m_env.fullRank() == 0 ) + { + output.width(5); + output << iter; + + if( print_xmin) + { + for( unsigned int i = 0; i < m_minimizer_hist[iter].size(); i++ ) + { + output.width(2); + output << " "; + output.width(13); + output << std::scientific << m_minimizer_hist[iter][i]; + } + } + + output.width(2); + output << " "; + output.width(13); + output << std::scientific << m_objective_hist[iter]; + + output.width(2); + output << " "; + output.width(13); + output << m_norm_hist[iter] << std::endl; + } + } + + void OptimizerMonitor::reset() + { + m_display_conv = false; + m_print_xmin = false; + + m_minimizer_hist.clear(); + m_objective_hist.clear(); + m_norm_hist.clear(); + } + + void OptimizerMonitor::print( std::ostream& output, bool print_xmin ) const + { + // First check that there's something to print. + if( m_norm_hist.empty() ) + { + std::cerr << "Nothing to print from OptimizerMonitor!" << std::endl; + queso_error(); + } + + this->print_header(output,print_xmin); + + unsigned int size = m_norm_hist.size(); + for(unsigned int i = 0; i < size; i++ ) + { + this->print_iteration(i,output,print_xmin); + } + } + +} // end namespace QUESO diff --git a/src/core/src/RngGsl.C b/src/core/src/RngGsl.C index 754416408..a1527522a 100644 --- a/src/core/src/RngGsl.C +++ b/src/core/src/RngGsl.C @@ -115,4 +115,10 @@ RngGsl::gammaSample(double a, double b) const return gsl_ran_gamma(m_rng,a,b); } +const gsl_rng* +RngGsl::rng() const +{ + return m_rng; +} + } // End namespace QUESO diff --git a/src/core/src/version.C b/src/core/src/version.C index 74b591b77..585c8ff2b 100644 --- a/src/core/src/version.C +++ b/src/core/src/version.C @@ -1,10 +1,10 @@ //-----------------------------------------------------------------------bl- //-------------------------------------------------------------------------- -// +// // QUESO - a library to support the Quantification of Uncertainty // for Estimation, Simulation and Optimization // -// Copyright (C) 2008,2009,2010 The PECOS Development Team +// 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 @@ -17,7 +17,7 @@ // // 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, +// Foundation, Inc. 51 Franklin Street, Fifth Floor, // Boston, MA 02110-1301 USA // //-----------------------------------------------------------------------el- diff --git a/src/gp/inc/GPMSA.h b/src/gp/inc/GPMSA.h new file mode 100644 index 000000000..51ec0057e --- /dev/null +++ b/src/gp/inc/GPMSA.h @@ -0,0 +1,366 @@ +//-----------------------------------------------------------------------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 UQ_GPMSA_HELPER_H +#define UQ_GPMSA_HELPER_H + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace QUESO { + +template +class GPMSAEmulator : public BaseScalarFunction +{ +public: + GPMSAEmulator(const VectorSet & domain, + const VectorSpace & m_scenarioSpace, + const VectorSpace & m_parameterSpace, + const VectorSpace & m_simulationOutputSpace, + const VectorSpace & m_experimentOutputSpace, + const unsigned int m_numSimulations, + const unsigned int m_numExperiments, + const std::vector & m_simulationScenarios, + const std::vector & m_simulationParameters, + const std::vector & m_simulationOutputs, + const std::vector & m_experimentScenarios, + const std::vector & m_experimentOutputs, + const M & m_experimentErrors, + const ConcatenatedVectorRV & m_totalPrior); + + virtual ~GPMSAEmulator(); + + virtual double lnValue(const V & domainVector, + const V * domainDirection, + V * gradVector, + M * hessianMatrix, + V * hessianEffect) const; + + virtual double actualValue(const V & domainVector, + const V * domainDirection, + V * gradVector, + M * hessianMatrix, + V * hessianEffect) const; + + const VectorSpace & m_scenarioSpace; + const VectorSpace & m_parameterSpace; + const VectorSpace & m_simulationOutputSpace; + const VectorSpace & m_experimentOutputSpace; + + const unsigned int m_numSimulations; + const unsigned int m_numExperiments; + + const std::vector & m_simulationScenarios; + const std::vector & m_simulationParameters; + const std::vector & m_simulationOutputs; + const std::vector & m_experimentScenarios; + const std::vector & m_experimentOutputs; + + // Total observation error covriance matrix + const M & m_experimentErrors; + + const ConcatenatedVectorRV & m_totalPrior; +}; + +template +class GPMSAFactory +{ +public: + //! Constructor + GPMSAFactory(const BaseEnvironment & env, + GPMSAOptions * opts, + const BaseVectorRV & parameterPrior, + const VectorSpace & scenarioSpace, + const VectorSpace & parameterSpace, + const VectorSpace & simulationOutputSpace, + const VectorSpace & experimentOutputSpace, + unsigned int numSimulations, + unsigned int numExperiments); + + //! Destructor + ~GPMSAFactory(); + + //! @name Getters + //@{ + + //! Return number of simulations + unsigned int numSimulations() const; + + //! Return number of experiments + unsigned int numExperiments() const; + + //! Return the vector space in which scenarios live + const VectorSpace & scenarioSpace() const; + + //! Return the vector space in which parameters live + const VectorSpace & parameterSpace() const; + + //! Return the vector space in which simulations live + const VectorSpace & simulationOutputSpace() const; + + //! Return the vector space in which experiments live + const VectorSpace & experimentOutputSpace() const; + + //! Return the point in \c scenarioSpace for simulation \c simulationId + /*! + * This returns the point in scenario space at which simulation + * \c simulationId was executed + */ + const V & simulationScenario(unsigned int simulationId) const; + + //! Return all points in \c scenarioSpace for all simulations + /*! + * This returns all points in scenario space at which simulations were + * executed + */ + const std::vector & simulationScenarios() const; + + //! Return the point in \c parameterSpace for simulation \c simulationId + /*! + * This returns the point in parameter space at which simulation + * \c simulationId was executed + */ + const V & simulationParameter(unsigned int simulationId) const; + + //! Return all points in \c parameterSpace for all simulations + /*! + * This returns all points in parameter space at which simulations were + * executed + */ + const std::vector & simulationParameters() const; + + //! Return the simulation output for simulation \c simulationId + /*! + * The returned vector is a point in \c simulationOutputSpace + */ + const V & simulationOutput(unsigned int simulationId) const; + + //! Return all points in \c simulationOutputSpace for all simulations + /*! + * This returns all points in simulation output space at which simulations + * were executed + */ + const std::vector & simulationOutputs() const; + + //! Return the point in \c scenarioSpace for experiment \c experimentId + /*! + * This returns the point in scenario space at which experiment + * \c experimentId was executed + */ + const V & experimentScenario(unsigned int experimentId) const; + + //! Return all points in \c scenarioSpace for all experiments + /*! + * This returns all points in scenario space at which experiments were + * executed + */ + const std::vector & experimentScenarios() const; + + //! Return the experiment output for experiment \c experimentId + /*! + * The returned vector is a point in \c experimentOutputSpace + */ + const V & experimentOutput(unsigned int experimentId) const; + + //! Return all points in \c experimentOutputSpace for all experiments + /*! + * This returns all points in experiment output space at which experiments + * were executed + */ + const std::vector & experimentOutputs() const; + + //! Return all observation error covarince matrices for all experiments + const M & experimentErrors() const; + + //! Return the QUESO environment + const BaseEnvironment & env() const; + + //! Return the GPMSAEmulator likelihood object + const GPMSAEmulator & getGPMSAEmulator() const; + + //@} + + //! Add a simulation to \c this + /*! + * The simulation added to \c this is assumed to correspond to the point + * \c simulationScenario in scenario space and \c simulationParameter in + * parameter space. The simulation output is assumed to be stored in + * \c simulationOutput. + */ + void addSimulation(V & simulationScenario, + V & simulationParameter, + V & simulationOutput); + + //! Adds multiple simulations to \c this + /*! + * This method takes a vector of simulations and calls \c addSimulation on + * each element + */ + void addSimulations(const std::vector & simulationScenarios, + const std::vector & simulationParameters, + const std::vector & simulationOutputs); + + //! Add all experiments to \c this + /*! + * This method takes a vector of *all* the experimental data and associated + * observation errors/correlations and stores them. This cannot be done + * piecemeal like the simulation data. + * + * Each experiment (\experimentOutputs[i]) is assumed to correspond to the + * point \c expermientScenarios[i] in scenario space. The observation error + * covariance matrix is assumed to be stored in \c experimentErrors. + */ + void addExperiments(const std::vector & experimentScenarios, + const std::vector & experimentOutputs, + const M * experimentErrors); + + const ConcatenatedVectorRV & prior() const; + + void print(std::ostream& os) const; + friend std::ostream & operator<<(std::ostream& os, + const GPMSAFactory & obj) + { + obj.print(os); + return os; + } + + const BaseEnvironment & m_env; + + const BaseVectorRV & m_parameterPrior; + + const VectorSpace & m_scenarioSpace; + const VectorSpace & m_parameterSpace; + const VectorSpace & m_simulationOutputSpace; + const VectorSpace & m_experimentOutputSpace; + + unsigned int m_numSimulations; + unsigned int m_numExperiments; + + std::vector m_simulationScenarios; + std::vector m_simulationParameters; + std::vector m_simulationOutputs; + std::vector m_experimentScenarios; + std::vector m_experimentOutputs; + + // Total observation error covriance matrix + const M * m_experimentErrors; + + // Counter for the number of adds that happen + unsigned int m_numSimulationAdds; + unsigned int m_numExperimentAdds; + + // The space in which the emulator (simulator) lives + // const VectorSpace & m_emulatorSpace; + + // The emulator state + // const V & m_emulator; + + // All the GP priors information for a scalar GP follows: + void setUpHyperpriors(); + + // Domains for all the hyperpriors + VectorSpace * oneDSpace; + + // Emulator mean + V * emulatorMeanMin; + V * emulatorMeanMax; + BoxSubset * emulatorMeanDomain; + + // Emulator precision + V * emulatorPrecisionMin; + V * emulatorPrecisionMax; + BoxSubset * emulatorPrecisionDomain; + + // Emulator correlation strength + VectorSpace * emulatorCorrelationSpace; + V * emulatorCorrelationMin; + V * emulatorCorrelationMax; + BoxSubset * emulatorCorrelationDomain; + + // Discrepancy precision + V * discrepancyPrecisionMin; + V * discrepancyPrecisionMax; + BoxSubset * discrepancyPrecisionDomain; + + // Discrepancy correlation strength + VectorSpace * discrepancyCorrelationSpace; + V * discrepancyCorrelationMin; + V * discrepancyCorrelationMax; + BoxSubset * discrepancyCorrelationDomain; + + // Emulator data precision + V * emulatorDataPrecisionMin; + V * emulatorDataPrecisionMax; + BoxSubset * emulatorDataPrecisionDomain; + + // Now form full prior + VectorSpace * totalSpace; + V * totalMins; + V * totalMaxs; + + BoxSubset * totalDomain; + + std::vector *> priors; + + // The hyperpriors + UniformVectorRV * m_emulatorMean; // scalar + GammaVectorRV * m_emulatorPrecision; // (scalar) gamma(a, b) shape-rate + BetaVectorRV * m_emulatorCorrelationStrength; // (dim scenariosspace + dim parameterspace) + GammaVectorRV * m_discrepancyPrecision; // (scalar) shape-rate + BetaVectorRV * m_discrepancyCorrelationStrength; // (dim scenariospace) + GammaVectorRV * m_emulatorDataPrecision; // (scalar) shape-rate + ConcatenatedVectorRV * m_totalPrior; // prior for joint parameters and hyperparameters + + V * m_emulatorPrecisionShapeVec; + V * m_emulatorPrecisionScaleVec; + V * m_emulatorCorrelationStrengthAlphaVec; + V * m_emulatorCorrelationStrengthBetaVec; + V * m_discrepancyPrecisionShapeVec; + V * m_discrepancyPrecisionScaleVec; + V * m_discrepancyCorrelationStrengthAlphaVec; + V * m_discrepancyCorrelationStrengthBetaVec; + V * m_emulatorDataPrecisionShapeVec; + V * m_emulatorDataPrecisionScaleVec; + + // The gaussian process object to build + GPMSAEmulator * gpmsaEmulator; + bool m_constructedGP; + +private: + GPMSAOptions * m_opts; + +}; + +} // End namespace QUESO + +#endif // UQ_GPMSA_HELPER_H diff --git a/src/gp/inc/GPMSAOptions.h b/src/gp/inc/GPMSAOptions.h new file mode 100644 index 000000000..3f4b90fff --- /dev/null +++ b/src/gp/inc/GPMSAOptions.h @@ -0,0 +1,118 @@ +//-------------------------------------------------------------------------- +// +// 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 UQ_GPMSA_OPTIONS_H +#define UQ_GPMSA_OPTIONS_H + +#include + +namespace QUESO { + +/*! + * \file GPMSAOptions.h + * \brief This class defines the options that specify the behaviour of the Gaussian process emulator + * + * \class GPMSAOptions + * \brief This class defines the options that specify the behaviour of the Gaussian process emulator + */ + +class GPMSAOptions +{ +public: + //! Given prefix, read the input file for parameters named prefix_* + GPMSAOptions(const BaseEnvironment& env, const char* prefix); + + //! Destructor + ~GPMSAOptions(); + + //! Scans the input file for options prefixed with \c prefix + void scanOptionsValues(); + + //! Prints \c this to \c os + void print(std::ostream& os) const; + + //! The prefix to look for in the input file + std::string m_prefix; + + //! The shape parameter for the Gamma hyperprior for the emulator precision + double m_emulatorPrecisionShape; + + //! The scale parameter for the Gamma hyperprior for the emulator precision + double m_emulatorPrecisionScale; + + //! The alpha paramter for the Beta hyperprior for the emulator correlation strength + double m_emulatorCorrelationStrengthAlpha; + + //! The beta paramter for the Beta hyperprior for the emulator correlation strength + double m_emulatorCorrelationStrengthBeta; + + //! The shape parameter for the Gamma hyperprior for the discrepancy precision + double m_discrepancyPrecisionShape; + + //! The scale parameter for the Gamma hyperprior for the discrepancy precision + double m_discrepancyPrecisionScale; + + //! The alpha paramter for the Beta hyperprior for the discrepancy correlation strength + double m_discrepancyCorrelationStrengthAlpha; + + //! The beta paramter for the Beta hyperprior for the discrepancy correlation strength + double m_discrepancyCorrelationStrengthBeta; + + //! Returns the QUESO environment + const BaseEnvironment& env() const; + + //! The shape parameter for the Gamma hyperprior for the emulator data precision + double m_emulatorDataPrecisionShape; + + //! The scale parameter for the Gamma hyperprior for the emulator data precision + double m_emulatorDataPrecisionScale; + + friend std::ostream & operator<<(std::ostream& os, const GPMSAOptions & obj) + { + obj.print(os); + return os; + } + +private: + void defineMyOptions(po::options_description& optionsDesc) const; + void getMyOptionValues(po::options_description& optionsDesc); + + const BaseEnvironment& m_env; + + po::options_description* m_optionsDesc; + std::string m_option_help; + std::string m_option_emulatorPrecisionShape; + std::string m_option_emulatorPrecisionScale; + std::string m_option_emulatorCorrelationStrengthAlpha; + std::string m_option_emulatorCorrelationStrengthBeta; + std::string m_option_discrepancyPrecisionShape; + std::string m_option_discrepancyPrecisionScale; + std::string m_option_discrepancyCorrelationStrengthAlpha; + std::string m_option_discrepancyCorrelationStrengthBeta; + std::string m_option_emulatorDataPrecisionShape; + std::string m_option_emulatorDataPrecisionScale; +}; + +} // End namespace QUESO + +#endif // UQ_GPMSA_OPTIONS_H diff --git a/src/gp/src/GPMSA.C b/src/gp/src/GPMSA.C new file mode 100644 index 000000000..da445355f --- /dev/null +++ b/src/gp/src/GPMSA.C @@ -0,0 +1,807 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +template +GPMSAEmulator::GPMSAEmulator( + const VectorSet & domain, + const VectorSpace & m_scenarioSpace, + const VectorSpace & m_parameterSpace, + const VectorSpace & m_simulationOutputSpace, + const VectorSpace & m_experimentOutputSpace, + const unsigned int m_numSimulations, + const unsigned int m_numExperiments, + const std::vector & m_simulationScenarios, + const std::vector & m_simulationParameters, + const std::vector & m_simulationOutputs, + const std::vector & m_experimentScenarios, + const std::vector & m_experimentOutputs, + const M & m_experimentErrors, + const ConcatenatedVectorRV & m_totalPrior) + : + BaseScalarFunction("", m_totalPrior.imageSet()), + m_scenarioSpace(m_scenarioSpace), + m_parameterSpace(m_parameterSpace), + m_simulationOutputSpace(m_simulationOutputSpace), + m_experimentOutputSpace(m_experimentOutputSpace), + m_numSimulations(m_numSimulations), + m_numExperiments(m_numExperiments), + m_simulationScenarios(m_simulationScenarios), + m_simulationParameters(m_simulationParameters), + m_simulationOutputs(m_simulationOutputs), + m_experimentScenarios(m_experimentScenarios), + m_experimentOutputs(m_experimentOutputs), + m_experimentErrors(m_experimentErrors), + m_totalPrior(m_totalPrior) +{ +} + +template +GPMSAEmulator::~GPMSAEmulator() +{ + // Do nothing? +} + +template +double +GPMSAEmulator::lnValue(const V & domainVector, + const V * domainDirection, + V * gradVector, + M * hessianMatrix, + V * hessianEffect) const +{ + // Components of domainVector: + // theta(1) + // theta(2) + // ... + // theta(dimParameterSpace) + // emulator_mean + // emulator_precision + // emulator_corr_strength(1) + // ... + // emulator_corr_strength(dimScenario + dimParameter) + // discrepancy_precision + // discrepancy_corr_strength(1) + // ... + // discrepancy_corr_strength(dimScenario) + // emulator_data_precision(1) + + // Construct covariance matrix + unsigned int totalDim = this->m_numExperiments + this->m_numSimulations; + double prodScenario = 1.0; + double prodParameter = 1.0; + double prodDiscrepancy = 1.0; + unsigned int dimScenario = (this->m_scenarioSpace).dimLocal(); + unsigned int dimParameter = (this->m_parameterSpace).dimLocal(); + + // This is cumbersome. All I want is a matrix. + VectorSpace gpSpace(this->m_scenarioSpace.env(), "", totalDim, NULL); + V residual(gpSpace.zeroVector()); + M covMatrix(residual); + + V *scenario1; + V *scenario2; + V *parameter1; + V *parameter2; + + // This for loop is a disaster and could do with a *lot* of optimisation + for (unsigned int i = 0; i < totalDim; i++) { + for (unsigned int j = 0; j < totalDim; j++) { + // Decide whether to do experiment part of the covariance matrix + // Get i-th simulation-parameter pair + if (i < this->m_numExperiments) { + // Experiment scenario (known) + scenario1 = new V(*((this->m_experimentScenarios)[i])); + + // Experiment parameter (unknown) + parameter1 = new V(*((this->m_simulationParameters)[0])); + for (unsigned int k = 0; k < dimParameter; k++) { + (*parameter1)[k] = domainVector[k]; + } + } + else { + scenario1 = + new V(*((this->m_simulationScenarios)[i-this->m_numExperiments])); + parameter1 = + new V(*((this->m_simulationParameters)[i-this->m_numExperiments])); + } + + if (j < this->m_numExperiments) { + scenario2 = new V(*((this->m_experimentScenarios)[j])); + parameter2 = new V(*((this->m_simulationParameters)[0])); + for (unsigned int k = 0; k < dimParameter; k++) { + (*parameter2)[k] = domainVector[k]; + } + } + else { + scenario2 = + new V(*((this->m_simulationScenarios)[j-this->m_numExperiments])); + parameter2 = + new V(*((this->m_simulationParameters)[j-this->m_numExperiments])); + } + + // Emulator component + prodScenario = 1.0; + prodParameter = 1.0; + unsigned int emulatorCorrStrStart = dimParameter + 2; + for (unsigned int k = 0; k < dimScenario; k++) { + prodScenario *= std::pow(domainVector[emulatorCorrStrStart+k], + 4.0 * ((*scenario1)[k] - (*scenario2)[k]) * + ((*scenario1)[k] - (*scenario2)[k])); + } + + for (unsigned int k = 0; k < dimParameter; k++) { + prodParameter *= std::pow( + domainVector[emulatorCorrStrStart+dimScenario+k], + 4.0 * ((*parameter1)[k] - (*parameter2)[k]) * + ((*parameter1)[k] - (*parameter2)[k])); + } + + double emPrecision = domainVector[dimParameter+1]; + covMatrix(i, j) = prodScenario * prodParameter / + emPrecision; // emulator precision + + delete scenario1; + delete scenario2; + delete parameter1; + delete parameter2; + + // If we're in the experiment cross correlation part, need extra foo + if (i < this->m_numExperiments && j < this->m_numExperiments) { + scenario1 = new V(*((this->m_simulationScenarios)[i])); + scenario2 = new V(*((this->m_simulationScenarios)[j])); + prodDiscrepancy = 1.0; + unsigned int discrepancyCorrStrStart = dimParameter + + dimParameter + + dimScenario + 3; + for (unsigned int k = 0; k < dimScenario; k++) { + prodDiscrepancy *= std::pow(domainVector[discrepancyCorrStrStart+k], + 4.0 * ((*scenario1)[k] - (*scenario2)[k]) * + ((*scenario1)[k] - (*scenario2)[k])); + } + + covMatrix(i, j) += prodDiscrepancy / + domainVector[discrepancyCorrStrStart-1]; + covMatrix(i, j) += (this->m_experimentErrors)(i, j); + + delete scenario1; + delete scenario2; + } + } + + // Add small white noise component to diagonal to make stuff +ve def + unsigned int dimSum = 4 + + dimParameter + + dimParameter + + dimScenario + + dimScenario; // yum + double nugget = 1.0 / domainVector[dimSum-1]; + covMatrix(i, i) += nugget; + } + + // Form residual = D - mean + for (unsigned int i = 0; i < this->m_numExperiments; i++) { + // Scalar so ok -- will need updating for nonscalar case + residual[i] = (*((this->m_experimentOutputs)[i]))[0]; + } + for (unsigned int i = 0; i < this->m_numSimulations; i++) { + // Scalar so ok -- will need updating for nonscalar case + residual[i+this->m_numExperiments] = (*((this->m_simulationOutputs)[i]))[0]; + } + + // Solve covMatrix * sol = residual + V sol(covMatrix.invertMultiply(residual)); + + // There's no dot product function in GslVector. + double minus_2_log_lhd = 0.0; + for (unsigned int i = 0; i < totalDim; i++) { + minus_2_log_lhd += sol[i] * residual[i]; + } + + return -0.5 * minus_2_log_lhd; +} + +template +double +GPMSAEmulator::actualValue(const V & domainVector, + const V * domainDirection, + V * gradVector, + M * hessianMatrix, + V * hessianEffect) const +{ + // Do nothing? + return 1.0; +} + +template +GPMSAFactory::GPMSAFactory( + const BaseEnvironment & env, + GPMSAOptions * opts, + const BaseVectorRV & parameterPrior, + const VectorSpace & scenarioSpace, + const VectorSpace & parameterSpace, + const VectorSpace & simulationOutputSpace, + const VectorSpace & experimentOutputSpace, + unsigned int numSimulations, + unsigned int numExperiments) + : + m_env(env), + m_parameterPrior(parameterPrior), + m_scenarioSpace(scenarioSpace), + m_parameterSpace(parameterSpace), + m_simulationOutputSpace(simulationOutputSpace), + m_experimentOutputSpace(experimentOutputSpace), + m_numSimulations(numSimulations), + m_numExperiments(numExperiments), + m_simulationScenarios(numSimulations, (V *)NULL), + m_simulationParameters(numSimulations, (V *)NULL), + m_simulationOutputs(numSimulations, (V *)NULL), + m_experimentScenarios(numExperiments, (V *)NULL), + m_experimentOutputs(numExperiments, (V *)NULL), + m_numSimulationAdds(0), + m_numExperimentAdds(0), + priors(7, NULL) +{ + // DM: Not sure if the logic in these 3 if-blocks is correct + if ((opts == NULL) && (this->m_env.optionsInputFileName() == "")) { + queso_error_msg("Must options object or an input file"); + } + + if (opts != NULL) { + this->m_opts = opts; + } + else { + // Create a default one + this->m_opts = new GPMSAOptions(this->m_env, ""); + } + + if (this->m_env.optionsInputFileName() != "") { + this->m_opts->scanOptionsValues(); + } + + this->setUpHyperpriors(); + this->m_constructedGP = false; +} + +template +GPMSAFactory::~GPMSAFactory() +{ + // Do nothing +} + +template +unsigned int +GPMSAFactory::numSimulations() const +{ + return this->m_numSimulations; +} + +template +unsigned int +GPMSAFactory::numExperiments() const +{ + return this->m_numExperiments; +} + +template +const VectorSpace & +GPMSAFactory::scenarioSpace() const +{ + return this->m_scenarioSpace; +} + +template +const VectorSpace & +GPMSAFactory::parameterSpace() const +{ + return this->m_parameterSpace; +} + +template +const VectorSpace & +GPMSAFactory::simulationOutputSpace() const +{ + return this->m_simulationOutputSpace; +} + +template +const VectorSpace & +GPMSAFactory::experimentOutputSpace() const +{ + return this->m_experimentOutputSpace; +} + +template +const V & +GPMSAFactory::simulationScenario( + unsigned int simulationId) const +{ + UQ_FATAL_TEST_MACRO(simulationId >= m_simulationScenarios.size(), + m_env.worldRank(), + "GPMSAFactory::scenarioVec_original()", + "simulationId is too large"); + + UQ_FATAL_TEST_MACRO(m_simulationScenarios[simulationId] == NULL, + m_env.worldRank(), + "GPMSAFactory::scenarioVec_original()", + "vector is NULL"); + + return *(this->m_simulationScenarios[simulationId]); +} + +template +const std::vector & +GPMSAFactory::simulationScenarios() const +{ + return this->m_simulationScenarios; +} + +template +const V & +GPMSAFactory::simulationParameter( + unsigned int simulationId) const +{ + UQ_FATAL_TEST_MACRO(simulationId >= m_simulationParameters.size(), + m_env.worldRank(), + "GPMSAFactory::parameterVec_original()", + "simulationId is too large"); + + UQ_FATAL_TEST_MACRO(m_simulationParameters[simulationId] == NULL, + m_env.worldRank(), + "GPMSAFactory::parameterVec_original()", + "vector is NULL"); + + return *(this->m_simulationParameters[simulationId]); +} + +template +const std::vector & +GPMSAFactory::simulationParameters() const +{ + return this->m_simulationParameters; +} + +template +const V & +GPMSAFactory::simulationOutput( + unsigned int simulationId) const +{ + UQ_FATAL_TEST_MACRO(simulationId >= m_simulationOutputs.size(), + m_env.worldRank(), + "GPMSAFactory::outputVec_original()", + "simulationId is too large"); + + UQ_FATAL_TEST_MACRO(m_simulationOutputs[simulationId] == NULL, + m_env.worldRank(), + "GPMSAFactory::outputVec_original()", + "vector is NULL"); + + return *(this->m_simulationOutputs[simulationId]); +} + +template +const std::vector & +GPMSAFactory::simulationOutputs() const +{ + return this->m_simulationOutputs; +} + +template +const V & +GPMSAFactory::experimentScenario( + unsigned int experimentId) const +{ + UQ_FATAL_TEST_MACRO(experimentId >= (this->m_experimentScenarios).size(), + m_env.worldRank(), + "GPMSAFactory::experimentScenario()", + "experimentId is too large"); + + UQ_FATAL_TEST_MACRO(this->m_experimentScenarios[experimentId] == NULL, + m_env.worldRank(), + "GPMSAFactory::experimentScenario()", + "vector is NULL"); + + return *(this->m_experimentScenarios[experimentId]); +} + +template +const std::vector & +GPMSAFactory::experimentScenarios() const +{ + return this->m_experimentScenarios; +} + +template +const V & +GPMSAFactory::experimentOutput( + unsigned int experimentId) const +{ + UQ_FATAL_TEST_MACRO(experimentId >= (this->m_experimentOutputs).size(), + m_env.worldRank(), + "GPMSAFactory::experimentOutput()", + "experimentId is too large"); + + UQ_FATAL_TEST_MACRO(this->m_experimentOutputs[experimentId] == NULL, + m_env.worldRank(), + "GPMSAFactory::experimentOutput()", + "vector is NULL"); + + return *(this->m_experimentOutputs[experimentId]); +} + +template +const std::vector & +GPMSAFactory::experimentOutputs() const +{ + return this->m_experimentOutputs; +} + +template +const M & +GPMSAFactory::experimentErrors() const +{ + return *(this->m_experimentErrors); +} + +template +const BaseEnvironment & +GPMSAFactory::env() const +{ + return this->m_env; +} + +template +const GPMSAEmulator & +GPMSAFactory::getGPMSAEmulator() const +{ + return *(this->gpmsaEmulator); +} + +template +void +GPMSAFactory::addSimulation(V & simulationScenario, + V & simulationParameter, + V & simulationOutput) +{ + UQ_FATAL_TEST_MACRO(this->m_numSimulationAdds >= this->m_numSimulations, + this->m_env.worldRank(), + "GPMSAFactory::addSimulation()", + "too many simulation adds..."); + + this->m_simulationScenarios[this->m_numSimulationAdds] = &simulationScenario; + this->m_simulationParameters[this->m_numSimulationAdds] = &simulationParameter; + this->m_simulationOutputs[this->m_numSimulationAdds] = &simulationOutput; + this->m_numSimulationAdds++; + + if ((this->m_numSimulationAdds == this->m_numSimulations) && + (this->m_numExperimentAdds == this->m_numExperiments) && + (this->m_constructedGP == false)) { + this->m_constructedGP = true; + this->gpmsaEmulator = new GPMSAEmulator( + this->prior().imageSet(), + this->m_scenarioSpace, + this->m_parameterSpace, + this->m_simulationOutputSpace, + this->m_experimentOutputSpace, + this->m_numSimulations, + this->m_numExperiments, + this->m_simulationScenarios, + this->m_simulationParameters, + this->m_simulationOutputs, + this->m_experimentScenarios, + this->m_experimentOutputs, + *(this->m_experimentErrors), + *(this->m_totalPrior)); + } +} + +template +void +GPMSAFactory::addSimulations( + const std::vector & simulationScenarios, + const std::vector & simulationParameters, + const std::vector & simulationOutputs) +{ + for (unsigned int i = 0; i < this->m_numSimulations; i++) { + this->addSimulation(*(simulationScenarios[i]), *(simulationParameters[i]), + *(simulationOutputs[i])); + } +} + +template +void +GPMSAFactory::addExperiments( + const std::vector & experimentScenarios, + const std::vector & experimentOutputs, + const M * experimentErrors) +{ + UQ_FATAL_TEST_MACRO(experimentScenarios.size() > this->m_numExperiments, + this->m_env.worldRank(), + "GPMSAFactory::addExperiment()", + "too many experiments..."); + + for (unsigned int i = 0; i < this->m_experimentScenarios.size(); i++) { + this->m_experimentScenarios[i] = experimentScenarios[i]; + this->m_experimentOutputs[i] = experimentOutputs[i]; + } + this->m_experimentErrors = experimentErrors; + this->m_numExperimentAdds += experimentScenarios.size(); + + if ((this->m_numSimulationAdds == this->m_numSimulations) && + (this->m_numExperimentAdds == this->m_numExperiments) && + (this->m_constructedGP == false)) { + this->m_constructedGP = true; + this->gpmsaEmulator = new GPMSAEmulator( + this->prior().imageSet(), + this->m_scenarioSpace, + this->m_parameterSpace, + this->m_simulationOutputSpace, + this->m_experimentOutputSpace, + this->m_numSimulations, + this->m_numExperiments, + this->m_simulationScenarios, + this->m_simulationParameters, + this->m_simulationOutputs, + this->m_experimentScenarios, + this->m_experimentOutputs, + *(this->m_experimentErrors), + *(this->m_totalPrior)); + } +} + +template +const ConcatenatedVectorRV & +GPMSAFactory::prior() const +{ + return *(this->m_totalPrior); +} + +template +void +GPMSAFactory::print(std::ostream& os) const +{ + // Do nothing +} + +// Private methods follow +template +void +GPMSAFactory::setUpHyperpriors() +{ + double emulatorPrecisionShape = this->m_opts->m_emulatorPrecisionShape; + double emulatorPrecisionScale = this->m_opts->m_emulatorPrecisionScale; + double emulatorCorrelationStrengthAlpha = this->m_opts->m_emulatorCorrelationStrengthAlpha; + double emulatorCorrelationStrengthBeta = this->m_opts->m_emulatorCorrelationStrengthBeta; + double discrepancyPrecisionShape = this->m_opts->m_discrepancyPrecisionShape; + double discrepancyPrecisionScale = this->m_opts->m_discrepancyPrecisionScale; + double discrepancyCorrelationStrengthAlpha = this->m_opts->m_discrepancyCorrelationStrengthAlpha; + double discrepancyCorrelationStrengthBeta = this->m_opts->m_discrepancyCorrelationStrengthBeta; + double emulatorDataPrecisionShape = this->m_opts->m_emulatorDataPrecisionShape; + double emulatorDataPrecisionScale = this->m_opts->m_emulatorDataPrecisionScale; + + this->oneDSpace = new VectorSpace(this->m_env, "", 1, NULL); + + // Emulator mean + this->emulatorMeanMin = new V(this->oneDSpace->zeroVector()); + this->emulatorMeanMax = new V(this->oneDSpace->zeroVector()); + this->emulatorMeanMin->cwSet(-INFINITY); + this->emulatorMeanMax->cwSet(INFINITY); + + this->emulatorMeanDomain = new BoxSubset( + "", + *(this->oneDSpace), + *(this->emulatorMeanMin), + *(this->emulatorMeanMax)); + + this->m_emulatorMean = new UniformVectorRV( + "", + *(this->emulatorMeanDomain)); + + // Emulator precision + this->emulatorPrecisionMin = new V(this->oneDSpace->zeroVector()); + this->emulatorPrecisionMax = new V(this->oneDSpace->zeroVector()); + this->m_emulatorPrecisionShapeVec = new V(this->oneDSpace->zeroVector()); + this->m_emulatorPrecisionScaleVec = new V(this->oneDSpace->zeroVector()); + this->emulatorPrecisionMin->cwSet(0.3); + this->emulatorPrecisionMax->cwSet(INFINITY); + this->m_emulatorPrecisionShapeVec->cwSet(emulatorPrecisionShape); + this->m_emulatorPrecisionScaleVec->cwSet(emulatorPrecisionScale); + + this->emulatorPrecisionDomain = new BoxSubset( + "", + *(this->oneDSpace), + *(this->emulatorPrecisionMin), + *(this->emulatorPrecisionMax)); + + this->m_emulatorPrecision = new GammaVectorRV("", + *(this->emulatorPrecisionDomain), + *(this->m_emulatorPrecisionShapeVec), + *(this->m_emulatorPrecisionScaleVec)); + + // Emulator correlation strength + unsigned int dimScenario = (this->scenarioSpace()).dimLocal(); + unsigned int dimParameter = (this->parameterSpace()).dimLocal(); + this->emulatorCorrelationSpace = new VectorSpace( + this->m_env, + "", + dimScenario + dimParameter, + NULL); + + this->emulatorCorrelationMin = new V( + this->emulatorCorrelationSpace->zeroVector()); + this->emulatorCorrelationMax = new V( + this->emulatorCorrelationSpace->zeroVector()); + this->m_emulatorCorrelationStrengthAlphaVec = new V( + this->emulatorCorrelationSpace->zeroVector()); + this->m_emulatorCorrelationStrengthBetaVec = new V( + this->emulatorCorrelationSpace->zeroVector()); + this->emulatorCorrelationMin->cwSet(0); + this->emulatorCorrelationMax->cwSet(1); + this->m_emulatorCorrelationStrengthAlphaVec->cwSet(emulatorCorrelationStrengthAlpha); + this->m_emulatorCorrelationStrengthBetaVec->cwSet(emulatorCorrelationStrengthBeta); + + this->emulatorCorrelationDomain = new BoxSubset( + "", + *(this->emulatorCorrelationSpace), + *(this->emulatorCorrelationMin), + *(this->emulatorCorrelationMax)); + + this->m_emulatorCorrelationStrength = new BetaVectorRV("", + *(this->emulatorCorrelationDomain), + *(this->m_emulatorCorrelationStrengthAlphaVec), + *(this->m_emulatorCorrelationStrengthBetaVec)); + + // Discrepancy precision + this->discrepancyPrecisionMin = new V(this->oneDSpace->zeroVector()); + this->discrepancyPrecisionMax = new V(this->oneDSpace->zeroVector()); + this->m_discrepancyPrecisionShapeVec = new V(this->oneDSpace->zeroVector()); + this->m_discrepancyPrecisionScaleVec = new V(this->oneDSpace->zeroVector()); + this->discrepancyPrecisionMin->cwSet(0); + this->discrepancyPrecisionMax->cwSet(INFINITY); + this->m_discrepancyPrecisionShapeVec->cwSet(discrepancyPrecisionShape); + this->m_discrepancyPrecisionScaleVec->cwSet(discrepancyPrecisionScale); + + this->discrepancyPrecisionDomain = new BoxSubset( + "", + *(this->oneDSpace), + *(this->discrepancyPrecisionMin), + *(this->emulatorPrecisionMax)); + + this->m_discrepancyPrecision = new GammaVectorRV("", + *(this->discrepancyPrecisionDomain), + *(this->m_discrepancyPrecisionShapeVec), + *(this->m_discrepancyPrecisionScaleVec)); + + // Discrepancy correlation strength + this->discrepancyCorrelationSpace = new VectorSpace( + this->m_env, + "", + dimScenario, + NULL); + + this->discrepancyCorrelationMin = new V( + this->discrepancyCorrelationSpace->zeroVector()); + this->discrepancyCorrelationMax = new V( + this->discrepancyCorrelationSpace->zeroVector()); + this->m_discrepancyCorrelationStrengthAlphaVec = new V( + this->discrepancyCorrelationSpace->zeroVector()); + this->m_discrepancyCorrelationStrengthBetaVec = new V( + this->discrepancyCorrelationSpace->zeroVector()); + this->discrepancyCorrelationMin->cwSet(0); + this->discrepancyCorrelationMax->cwSet(1); + this->m_discrepancyCorrelationStrengthAlphaVec->cwSet(discrepancyCorrelationStrengthAlpha); + this->m_discrepancyCorrelationStrengthBetaVec->cwSet(discrepancyCorrelationStrengthBeta); + + this->discrepancyCorrelationDomain = new BoxSubset( + "", + *(this->discrepancyCorrelationSpace), + *(this->discrepancyCorrelationMin), + *(this->discrepancyCorrelationMax)); + + this->m_discrepancyCorrelationStrength = new BetaVectorRV("", + *(this->discrepancyCorrelationDomain), + *(this->m_discrepancyCorrelationStrengthAlphaVec), + *(this->m_discrepancyCorrelationStrengthBetaVec)); + + // Emulator data precision + this->emulatorDataPrecisionMin = new V(this->oneDSpace->zeroVector()); + this->emulatorDataPrecisionMax = new V(this->oneDSpace->zeroVector()); + this->m_emulatorDataPrecisionShapeVec = new V(this->oneDSpace->zeroVector()); + this->m_emulatorDataPrecisionScaleVec = new V(this->oneDSpace->zeroVector()); + this->emulatorDataPrecisionMin->cwSet(60.0); + this->emulatorDataPrecisionMax->cwSet(1e5); + this->m_emulatorDataPrecisionShapeVec->cwSet(emulatorDataPrecisionShape); + this->m_emulatorDataPrecisionScaleVec->cwSet(emulatorDataPrecisionScale); + + this->emulatorDataPrecisionDomain = new BoxSubset( + "", + *(this->oneDSpace), + *(this->emulatorDataPrecisionMin), + *(this->emulatorDataPrecisionMax)); + + this->m_emulatorDataPrecision = new GammaVectorRV("", + *(this->emulatorDataPrecisionDomain), + *(this->m_emulatorDataPrecisionShapeVec), + *(this->m_emulatorDataPrecisionScaleVec)); + + // Now form full prior + unsigned int dimSum = 4 + + dimParameter + + dimParameter + + dimScenario + + dimScenario; // yum + + this->totalSpace = new VectorSpace( + this->m_env, + "", + dimSum, + NULL); + this->totalMins = new V(this->totalSpace->zeroVector()); + this->totalMaxs = new V(this->totalSpace->zeroVector()); + + // Hackety hack McHackington. There's no better way to do this unfortunately + this->totalMins->cwSet(0); + this->totalMaxs->cwSet(1); + + (*(this->totalMins))[dimParameter] = -INFINITY; // Min mean + (*(this->totalMaxs))[dimParameter] = INFINITY; // Max mean + (*(this->totalMins))[dimParameter+1] = 0.3; // Min emulator precision + (*(this->totalMaxs))[dimParameter+1] = INFINITY; // Max emulator precision + (*(this->totalMins))[dimSum-1] = 60.0; // Min emulator data precision + (*(this->totalMaxs))[dimSum-1] = 1e5; // Max emulator data precision + + // Min discrepancy precision + (*(this->totalMins))[dimScenario+dimParameter+dimParameter+2] = 0; + // Max discrepancy precision + (*(this->totalMaxs))[dimScenario+dimParameter+dimParameter+2] = INFINITY; + + this->totalDomain = new BoxSubset( + "", + *(this->totalSpace), + *(this->totalMins), + *(this->totalMaxs)); + + this->priors[0] = &(this->m_parameterPrior); + this->priors[1] = this->m_emulatorMean; + this->priors[2] = this->m_emulatorPrecision; + this->priors[3] = this->m_emulatorCorrelationStrength; + this->priors[4] = this->m_discrepancyPrecision; + this->priors[5] = this->m_discrepancyCorrelationStrength; + this->priors[6] = this->m_emulatorDataPrecision; + + // Finally + this->m_totalPrior = new ConcatenatedVectorRV( + "", + this->priors, + *(this->totalDomain)); +} + +} // End namespace QUESO + +template class QUESO::GPMSAFactory; diff --git a/src/gp/src/GPMSAOptions.C b/src/gp/src/GPMSAOptions.C new file mode 100644 index 000000000..9d54d5517 --- /dev/null +++ b/src/gp/src/GPMSAOptions.C @@ -0,0 +1,176 @@ +//-----------------------------------------------------------------------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 + +// ODV = option default value +#define UQ_GPMSA_EMULATOR_PRECISION_SHAPE_ODV 5.0 +#define UQ_GPMSA_EMULATOR_PRECISION_SCALE_ODV 0.2 +#define UQ_GPMSA_EMULATOR_CORRELATION_STRENGTH_ALPHA_ODV 1.0 +#define UQ_GPMSA_EMULATOR_CORRELATION_STRENGTH_BETA_ODV 0.1 +#define UQ_GPMSA_DISCREPANCY_PRECISION_SHAPE_ODV 1.0 +#define UQ_GPMSA_DISCREPANCY_PRECISION_SCALE_ODV 1e4 +#define UQ_GPMSA_DISCREPANCY_CORRELATION_STRENGTH_ALPHA_ODV 1.0 +#define UQ_GPMSA_DISCREPANCY_CORRELATION_STRENGTH_BETA_ODV 0.1 +#define UQ_GPMSA_EMULATOR_DATA_PRECISION_SHAPE_ODV 3.0 +#define UQ_GPMSA_EMULATOR_DATA_PRECISION_SCALE_ODV 333.333 + +namespace QUESO { + +GPMSAOptions::GPMSAOptions( + const BaseEnvironment & env, + const char * prefix) + : + m_prefix((std::string)(prefix) + "gpmsa_"), + m_env(env), + m_optionsDesc(new po::options_description("Gaussian process emulator options")), + m_option_help(m_prefix + "help"), + m_option_emulatorPrecisionShape(m_prefix + "emulator_precision_shape"), + m_option_emulatorPrecisionScale(m_prefix + "emulator_precision_scale"), + m_option_emulatorCorrelationStrengthAlpha(m_prefix + "emulator_correlation_strength_alpha"), + m_option_emulatorCorrelationStrengthBeta(m_prefix + "emulator_correlation_strength_beta"), + m_option_discrepancyPrecisionShape(m_prefix + "discrepancy_precision_shape"), + m_option_discrepancyPrecisionScale(m_prefix + "discrepancy_precision_scale"), + m_option_discrepancyCorrelationStrengthAlpha(m_prefix + "discrepancy_correlation_strength_alpha"), + m_option_discrepancyCorrelationStrengthBeta(m_prefix + "discrepancy_correlation_strength_beta"), + m_option_emulatorDataPrecisionShape(m_prefix + "emulator_data_precision_shape"), + m_option_emulatorDataPrecisionScale(m_prefix + "emulator_data_precision_scale") +{ + if (m_env.optionsInputFileName() == "") { + queso_error_msg("Missing input file is required"); + } +} + +GPMSAOptions::~GPMSAOptions() +{ + if (m_optionsDesc) { + delete m_optionsDesc; + } +} + +void +GPMSAOptions::scanOptionsValues() +{ + if (m_optionsDesc == NULL) { + queso_error_msg("m_optionsDesc variable is NULL"); + } + + defineMyOptions(*m_optionsDesc); + m_env.scanInputFileForMyOptions(*m_optionsDesc); + getMyOptionValues(*m_optionsDesc); + + if (m_env.subDisplayFile() != NULL) { + *m_env.subDisplayFile() << "In GPMSAOptions::scanOptionsValues()" + << ": after reading values of options with prefix '" << m_prefix + << "', state of object is:" + << "\n" << *this + << std::endl; + } +} + +void +GPMSAOptions::defineMyOptions(po::options_description& optionsDesc) const +{ + optionsDesc.add_options() + (m_option_help.c_str(), "produce help message Gaussian process emulator") + (m_option_emulatorPrecisionShape.c_str(), po::value()->default_value(UQ_GPMSA_EMULATOR_PRECISION_SHAPE_ODV), "shape hyperprior (Gamma) parameter for emulator precision") + (m_option_emulatorPrecisionScale.c_str(), po::value()->default_value(UQ_GPMSA_EMULATOR_PRECISION_SCALE_ODV), "scale hyperprior (Gamma) parameter for emulator precision") + (m_option_emulatorCorrelationStrengthAlpha.c_str(), po::value()->default_value(UQ_GPMSA_EMULATOR_CORRELATION_STRENGTH_ALPHA_ODV), "alpha hyperprior (Beta) parameter for emulator correlation strength") + (m_option_emulatorCorrelationStrengthBeta.c_str(), po::value()->default_value(UQ_GPMSA_EMULATOR_CORRELATION_STRENGTH_BETA_ODV), "beta hyperprior (Beta) parameter for emulator correlation strength") + (m_option_discrepancyPrecisionShape.c_str(), po::value()->default_value(UQ_GPMSA_DISCREPANCY_PRECISION_SHAPE_ODV), "shape hyperprior (Gamma) parameter for discrepancy precision") + (m_option_discrepancyPrecisionScale.c_str(), po::value()->default_value(UQ_GPMSA_DISCREPANCY_PRECISION_SCALE_ODV), "scale hyperprior (Gamma) parameter for discrepancy precision") + (m_option_discrepancyCorrelationStrengthAlpha.c_str(), po::value()->default_value(UQ_GPMSA_DISCREPANCY_CORRELATION_STRENGTH_ALPHA_ODV), "alpha hyperprior (Beta) parameter for discrepancy correlation strength") + (m_option_discrepancyCorrelationStrengthBeta.c_str(), po::value()->default_value(UQ_GPMSA_DISCREPANCY_CORRELATION_STRENGTH_BETA_ODV), "beta hyperprior (Beta) parameter for discrepancy correlation strength") + (m_option_emulatorDataPrecisionShape.c_str(), po::value()->default_value(UQ_GPMSA_EMULATOR_DATA_PRECISION_SHAPE_ODV), "shape hyperprior (Gamma) parameter for emulator data precision") + (m_option_emulatorDataPrecisionScale.c_str(), po::value()->default_value(UQ_GPMSA_EMULATOR_DATA_PRECISION_SCALE_ODV), "scale hyperprior (Gamma) parameter for emulator data precision"); +} + +void +GPMSAOptions::getMyOptionValues(po::options_description& optionsDesc) +{ + if (m_env.allOptionsMap().count(m_option_help)) { + if (m_env.subDisplayFile()) { + *m_env.subDisplayFile() << optionsDesc + << std::endl; + } + } + + if (m_env.allOptionsMap().count(m_option_emulatorPrecisionShape)) { + this->m_emulatorPrecisionShape = ((const po::variable_value &) m_env.allOptionsMap()[m_option_emulatorPrecisionShape]).as(); + } + + if (m_env.allOptionsMap().count(m_option_emulatorPrecisionScale)) { + this->m_emulatorPrecisionScale = ((const po::variable_value &) m_env.allOptionsMap()[m_option_emulatorPrecisionScale]).as(); + } + + if (m_env.allOptionsMap().count(m_option_emulatorCorrelationStrengthAlpha)) { + this->m_emulatorCorrelationStrengthAlpha = ((const po::variable_value &) m_env.allOptionsMap()[m_option_emulatorCorrelationStrengthAlpha]).as(); + } + + if (m_env.allOptionsMap().count(m_option_emulatorCorrelationStrengthBeta)) { + this->m_emulatorCorrelationStrengthBeta = ((const po::variable_value &) m_env.allOptionsMap()[m_option_emulatorCorrelationStrengthBeta]).as(); + } + + if (m_env.allOptionsMap().count(m_option_discrepancyPrecisionShape)) { + this->m_discrepancyPrecisionShape = ((const po::variable_value &) m_env.allOptionsMap()[m_option_discrepancyPrecisionShape]).as(); + } + + if (m_env.allOptionsMap().count(m_option_discrepancyPrecisionScale)) { + this->m_discrepancyPrecisionScale = ((const po::variable_value &) m_env.allOptionsMap()[m_option_discrepancyPrecisionScale]).as(); + } + + if (m_env.allOptionsMap().count(m_option_discrepancyCorrelationStrengthAlpha)) { + this->m_discrepancyCorrelationStrengthAlpha = ((const po::variable_value &) m_env.allOptionsMap()[m_option_discrepancyCorrelationStrengthAlpha]).as(); + } + + if (m_env.allOptionsMap().count(m_option_discrepancyCorrelationStrengthBeta)) { + this->m_discrepancyCorrelationStrengthBeta = ((const po::variable_value &) m_env.allOptionsMap()[m_option_discrepancyCorrelationStrengthBeta]).as(); + } + + if (m_env.allOptionsMap().count(m_option_emulatorDataPrecisionShape)) { + this->m_emulatorDataPrecisionShape = ((const po::variable_value &) m_env.allOptionsMap()[m_option_emulatorDataPrecisionShape]).as(); + } + + if (m_env.allOptionsMap().count(m_option_emulatorDataPrecisionScale)) { + this->m_emulatorDataPrecisionScale = ((const po::variable_value &) m_env.allOptionsMap()[m_option_emulatorDataPrecisionScale]).as(); + } +} + +void +GPMSAOptions::print(std::ostream& os) const +{ + os << "\n" << m_option_emulatorPrecisionShape << " = " << this->m_emulatorPrecisionShape + << "\n" << m_option_emulatorPrecisionScale << " = " << this->m_emulatorPrecisionScale + << "\n" << m_option_emulatorCorrelationStrengthAlpha << " = " << this->m_emulatorCorrelationStrengthAlpha + << "\n" << m_option_emulatorCorrelationStrengthBeta << " = " << this->m_emulatorCorrelationStrengthBeta + << "\n" << m_option_discrepancyPrecisionShape << " = " << this->m_discrepancyPrecisionShape + << "\n" << m_option_discrepancyPrecisionScale << " = " << this->m_discrepancyPrecisionScale + << "\n" << m_option_discrepancyCorrelationStrengthAlpha << " = " << this->m_discrepancyCorrelationStrengthAlpha + << "\n" << m_option_discrepancyCorrelationStrengthBeta << " = " << this->m_discrepancyCorrelationStrengthBeta + << "\n" << m_option_emulatorDataPrecisionShape << " = " << this->m_emulatorDataPrecisionShape + << "\n" << m_option_emulatorDataPrecisionScale << " = " << this->m_emulatorDataPrecisionScale + << std::endl; +} + +} // End namespace QUESO diff --git a/src/gp/src/GpmsaComputerModel.C b/src/gp/src/GpmsaComputerModel.C index 215747c96..10d290880 100644 --- a/src/gp/src/GpmsaComputerModel.C +++ b/src/gp/src/GpmsaComputerModel.C @@ -2122,9 +2122,8 @@ GpmsaComputerModel::likelihoodRoutine( } if (m_like_counter == 0) { - std::cout << "Exiting in likelihoodRoutine(), on purpose..." << std::endl; sleep(1); - queso_error(); + queso_error_msg("Exiting in likelihoodRoutine(), on purpose..."); } return lnLikelihoodValue; diff --git a/src/stats/inc/HessianCovMatricesTKGroup.h b/src/stats/inc/HessianCovMatricesTKGroup.h index 5b38e4c0f..b85cbd1bd 100644 --- a/src/stats/inc/HessianCovMatricesTKGroup.h +++ b/src/stats/inc/HessianCovMatricesTKGroup.h @@ -59,7 +59,7 @@ class HessianCovMatricesTKGroup : public BaseTKGroup { bool symmetric () const; //! Gaussian increment property to construct a transition kernel. - const GaussianVectorRV& rv (unsigned int stageId ); + const GaussianVectorRV& rv (unsigned int stageId ) const; //! Gaussian increment property to construct a transition kernel. const GaussianVectorRV& rv (const std::vector& stageIds); diff --git a/src/stats/inc/InvLogitGaussianJointPdf.h b/src/stats/inc/InvLogitGaussianJointPdf.h new file mode 100644 index 000000000..13655704e --- /dev/null +++ b/src/stats/inc/InvLogitGaussianJointPdf.h @@ -0,0 +1,155 @@ +//-----------------------------------------------------------------------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 UQ_INVLOGIT_GAUSSIAN_JOINT_PROB_DENSITY_H +#define UQ_INVLOGIT_GAUSSIAN_JOINT_PROB_DENSITY_H + +#include +#include +#include + +namespace QUESO { + +/*! + * \class InvLogitGaussianJointPdf + * \brief A class for handling hybrid (transformed) Gaussians with bounds + * + * This class allows the mathematical definition of a Gaussian PDF in + * dimensions of a VectorSubset that are unbounded, and a \c logit transformed + * Gaussian PDF in dimensions that have a lower or upper (or both) bound. For + * the bounded directions, the PDF is + * + * \f[ + * p(x) = \frac{1}{2 \sigma^2 \pi} \exp \left( - \frac12 (\mbox{logit} x - \mu)^2 \right) \mbox{logit}'(x), \quad x \in [a, b], + * \f] + * + * where \c logit is defined as + * + * \f[ + * \mbox{logit}(x) = \log \left( \frac{x - a}{b - x} \right), \quad x \in [a, b]. + * \f] + */ + +template +class InvLogitGaussianJointPdf : public BaseJointPdf { +public: + //! @name Constructor/Destructor methods + //@{ + //! Constructor + /*! + * Constructs a new object, given a prefix and the domain of the PDF, a + * vector of mean values, \c lawExpVector (for the Gaussian, not the + * transformed Gaussian), and a vector of covariance values + * \c lawVarVector (an alternative representation for a diagonal covariance + * matrix). + */ + InvLogitGaussianJointPdf(const char * prefix, + const BoxSubset & domainBoxSubset, const V & lawExpVector, + const V & lawVarVector); + + //! Constructor + /*! + * Constructs a new object, given a prefix and the image set of the vector + * realizer, a vector of mean values, \c lawExpVector (for the Gaussian, not + * the transformed Gaussian), and a covariance matrix, \c lawCovMatrix. + */ + InvLogitGaussianJointPdf(const char * prefix, + const BoxSubset & domainBoxSubset, const V & lawExpVector, + const M & lawCovMatrix); + + //! Destructor + ~InvLogitGaussianJointPdf(); + //@} + + //! @name Math methods + //@{ + + //! Actual value of the (transformed) Gaussian PDF + /*! This method calls lnValue() and applies the exponential to it.*/ + double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const; + + //! Logarithm of the value of the (transformed) Gaussian PDF (scalar function). + /*! + * The ln(value) comes from a summation of the Gaussian density: + * + * \f[ + * lnValue =- \sum_i \frac{1}{\sqrt{|covMatrix|} \sqrt{2 \pi}} exp(-\frac{(domainVector_i - lawExpVector_i) * covMatrix^{-1} * + * (domainVector_i - lawExpVector_i) }{2} + + * \log \mbox{logit}'(domainVector_i), + * \f] + * + * where the \f$ covMatrix \f$ may recovered via \c this->lawVarVector(), in + * case of diagonal matrices or via \c this->m_lawCovMatrix, otherwise. + */ + double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const; + + //! Computes the logarithm of the normalization factor. + /*! + * This routine calls BaseJointPdf::commonComputeLogOfNormalizationFactor(). + */ + double computeLogOfNormalizationFactor(unsigned int numSamples, + bool updateFactorInternally) const; + + //! Updates the mean of the Gaussian (not transformed) with the new value \c newLawExpVector. + /*! + * This method deletes old expected values (allocated at construction or last + * call to this method). + */ + void updateLawExpVector(const V & newLawExpVector); + + //! Updates the lower triangular matrix from Cholesky decomposition of the covariance matrix to the new value \c newLowerCholLawCovMatrix. + /*! + * This method deletes old expected values (allocated at construction or + * last call to this method). + */ + void updateLawCovMatrix(const M & newLawCovMatrix); + + //! Returns the covariance matrix; access to protected attribute m_lawCovMatrix. + const M & lawCovMatrix() const; + + //! Access to the vector of mean values of the Gaussian (not transformed) and private attribute: m_lawExpVector. + const V & lawExpVector() const; + + //! Access to the vector of variance values and private attribute: m_lawVarVector. + const V & lawVarVector() const; + //@} +private: + using BaseScalarFunction::m_env; + using BaseScalarFunction::m_prefix; + using BaseScalarFunction::m_domainSet; + using BaseJointPdf::m_normalizationStyle; + using BaseJointPdf::m_logOfNormalizationFactor; + V * m_lawExpVector; + V * m_lawVarVector; + bool m_diagonalCovMatrix; + const M * m_lawCovMatrix; + + const BoxSubset & m_domainBoxSubset; +}; + +} // End namespace QUESO + +#endif // UQ_INVLOGIT_GAUSSIAN_JOINT_PROB_DENSITY_H diff --git a/src/stats/inc/InvLogitGaussianVectorRV.h b/src/stats/inc/InvLogitGaussianVectorRV.h new file mode 100644 index 000000000..5dfa125e7 --- /dev/null +++ b/src/stats/inc/InvLogitGaussianVectorRV.h @@ -0,0 +1,103 @@ +//-----------------------------------------------------------------------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 UQ_INVLOGIT_GAUSSIAN_VECTOR_RV_H +#define UQ_INVLOGIT_GAUSSIAN_VECTOR_RV_H + +#include +#include + +namespace QUESO { + +/*! + * \class InvLogitGaussianVectorRV + * \brief A class representing a (transformed) Gaussian vector RV with bounds + * + * This class allows the user to compute the value of a (transoformed) Gaussian + * PDF and to generate realizations (samples) from it. + */ + +template +class InvLogitGaussianVectorRV : public BaseVectorRV { +public: + //! @name Constructor/Destructor methods + //@{ + //! Constructor + /*! + * Construct a (transformed) Gaussian vector RV with mean \c lawExpVector + * (of the Gaussian, not the transformed Gaussian) and diagonal covariance + * matrix \c lawVarVector whose variates live in \c imageBoxSubset. + */ + InvLogitGaussianVectorRV(const char * prefix, + const BoxSubset & imageBoxSubset, const V & lawExpVector, + const V & lawVarVector); + + //! Constructor + /*! + * Construct a (transformed) Gaussian vector RV with mean \c lawExpVector + * (of the Gaussian, not the transformed Gaussian) and covariance matrix + * \c lawCovMatrix whose variates live in \c imageBoxSubset. + */ + InvLogitGaussianVectorRV(const char * prefix, + const BoxSubset & imageBoxSubset, const V & lawExpVector, + const M & lawCovMatrix); + + //! Virtual destructor + virtual ~InvLogitGaussianVectorRV(); + //@} + + //! @name Statistical methods + //@{ + //! Updates the vector that contains the mean values for the underlying Gaussian. + void updateLawExpVector(const V & newLawExpVector); + + //! Updates the covariance matrix. + /*! + * This method tries to use Cholesky decomposition; and if it fails, the + * method then calls a SVD decomposition. + */ + void updateLawCovMatrix(const M & newLawCovMatrix); + //@} + + //! @name I/O methods + //@{ + //! TODO: Prints the vector RV. + /*! \todo: implement me!*/ + void print(std::ostream & os) const; + //@} + +private: + using BaseVectorRV::m_env; + using BaseVectorRV::m_prefix; + using BaseVectorRV::m_imageSet; + using BaseVectorRV::m_pdf; + using BaseVectorRV::m_realizer; + using BaseVectorRV::m_subCdf; + using BaseVectorRV::m_unifiedCdf; + using BaseVectorRV::m_mdf; +}; + +} // End namespace QUESO + +#endif // UQ_INVLOGIT_GAUSSIAN_VECTOR_RV_H diff --git a/src/stats/inc/InvLogitGaussianVectorRealizer.h b/src/stats/inc/InvLogitGaussianVectorRealizer.h new file mode 100644 index 000000000..dd436eebc --- /dev/null +++ b/src/stats/inc/InvLogitGaussianVectorRealizer.h @@ -0,0 +1,134 @@ +//-----------------------------------------------------------------------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 UQ_INV_LOGIT_GAUSSIAN_REALIZER_H +#define UQ_INV_LOGIT_GAUSSIAN_REALIZER_H + +#include + +namespace QUESO { + +/*! + * \class InvLogitGaussianVectorRealizer + * \brief A class for handling sampling from (transformed) Gaussian probability density distributions with bounds + * + * To get a realization from a transformed Gaussian (i.e. with bounds), an + * inverse \c logit transform is applied. I.e., samples are \f$ f(X) \f$ where + * \f$ X \f$ is drawn from a Gaussian and where + * + * \f[ + * f(x) = \frac{b \exp(x) + a}{1 + \exp(x)}. + * \f] + * + * This will produce a sample in the closed interval [a, b]. + */ + +template +class InvLogitGaussianVectorRealizer : public BaseVectorRealizer { +public: + + //! @name Constructor/Destructor methods + //@{ + //! Constructor + /*! + * Constructs a new object, given a prefix and the image set of the vector + * realizer, a vector of mean values, \c lawExpVector (of the Gaussian, not + * the transformed Gaussian), and a lower triangular matrix resulting from + * Cholesky decomposition of the covariance matrix, \c lowerCholLawCovMatrix. + */ + InvLogitGaussianVectorRealizer(const char * prefix, + const BoxSubset & unifiedImageBoxSubset, const V & lawExpVector, + const M & lowerCholLawCovMatrix); + + //! Constructor + /*! + * Constructs a new object, given a prefix and the image set of the vector + * realizer, a vector of mean values, \c lawExpVector (of the Gaussian, not + * the transformed Gaussian), and a set of two matrices and one vector + * resulting from the Single Value Decomposition of the covariance matrix, + * \c matU, \c vecSsqrt and \c matVt. + */ + InvLogitGaussianVectorRealizer(const char * prefix, + const BoxSubset & unifiedImageBoxSubset, const V & lawExpVector, + const M & matU, const V & vecSsqrt, const M & matVt); + + //! Destructor + ~InvLogitGaussianVectorRealizer(); + //@} + + //! @name Realization-related methods + //@{ + //! Access to the vector of mean values of the Gaussian and private attribute: m_unifiedLawExpVector. + const V & unifiedLawExpVector() const; + + //! Access to the vector of variance values and private attribute: m_unifiedLawVarVector. + const V & unifiedLawVarVector() const; + + //! Draws a realization. + /*! + * This function draws a realization of a (transformed) Gaussian + * distribution with mean \c m_unifiedLawExpVector and variance + * \c m_unifiedLawVarVector and saves it in \c nextValues. + */ + void realization(V & nextValues) const; + + //! Updates the mean of the Gaussian with the new value \c newLawExpVector. + void updateLawExpVector(const V & newLawExpVector); + + //! Updates the lower triangular matrix from Cholesky decomposition of the covariance matrix to the new value \c newLowerCholLawCovMatrix. + /*! The lower triangular matrix results resulting from a Cholesky + * decomposition of the covariance matrix. This routine deletes old expected + * values: m_lowerCholLawCovMatrix; m_matU, m_vecSsqrt, m_matVt. + */ + void updateLowerCholLawCovMatrix(const M & newLowerCholLawCovMatrix); + + //! Updates the SVD matrices from SVD decomposition of the covariance matrix to the new values: \c matU, \c vecSsqrt, and \c matVt. + /*! The lower triangular matrix results resulting from a Cholesky + * decomposition of the covariance matrix. This routine deletes old expected + * values: m_lowerCholLawCovMatrix; m_matU, m_vecSsqrt, m_matVt. + */ + void updateLowerCholLawCovMatrix(const M & matU, const V & vecSsqrt, + const M & matVt); + //@} + +private: + V * m_unifiedLawExpVector; + V * m_unifiedLawVarVector; + M * m_lowerCholLawCovMatrix; + M * m_matU; + V * m_vecSsqrt; + M * m_matVt; + + using BaseVectorRealizer::m_env; + using BaseVectorRealizer::m_prefix; + using BaseVectorRealizer::m_unifiedImageSet; + using BaseVectorRealizer::m_subPeriod; + + // For easy access to the bounds of the domain + const BoxSubset & m_unifiedImageBoxSubset; +}; + +} // End namespace QUESO + +#endif // UQ_INV_LOGIT_GAUSSIAN_REALIZER_H diff --git a/src/stats/inc/JeffreysJointPdf.h b/src/stats/inc/JeffreysJointPdf.h new file mode 100644 index 000000000..2f69aced1 --- /dev/null +++ b/src/stats/inc/JeffreysJointPdf.h @@ -0,0 +1,89 @@ +//-----------------------------------------------------------------------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 UQ_JEFFREYS_JOINT_PROB_DENSITY_H +#define UQ_JEFFREYS_JOINT_PROB_DENSITY_H + +#include +#include +#include +#include + +namespace QUESO { + +/*! + * \class JeffreysJointPdf + * \brief A class for handling jeffreys joint PDFs. + * + * This class allows the mathematical definition of a Jeffreys Joint PDF. +*/ + +template +class JeffreysJointPdf : public BaseJointPdf { +public: + //! @name Constructor/Destructor methods + //@{ + //! Constructor + /*! Constructs a new object of the class, given a prefix and the domain set of the jeffreys PDF. */ + JeffreysJointPdf(const char* prefix, + const VectorSet& domainSet); + //! Destructor + ~JeffreysJointPdf(); + //@} + + //! @name Math methods + //@{ + //! Actual value of the jeffreys PDF. + /*! + * If the domain of the PDF is well defined (neither negative nor infinite), + * then the actual value is given by + * 1.0 / (\c domainVector[0] * ... * \c domainVector[n-1]), otherwise the + * actual value is 0.0 + */ + double actualValue(const V& domainVector, const V* domainDirection, V* + gradVector, M* hessianMatrix, V* hessianEffect) const; + + //! Logarithm of the value of the jeffreys PDF. + /*! + * Analogous to the \c actualValue routine, except that the logarithm of the + * calculated value is returned. + */ + double lnValue (const V& domainVector, const V* domainDirection, V* gradVector, M* hessianMatrix, V* hessianEffect) const; + + //TODO: do we want this part? + //! Computes the logarithm of the normalization factor. + /*! This routine calls BaseJointPdf::commonComputeLogOfNormalizationFactor().*/ + double computeLogOfNormalizationFactor(unsigned int numSamples, bool updateFactorInternally) const; + //@} +protected: + using BaseScalarFunction::m_env; + using BaseScalarFunction::m_prefix; + using BaseScalarFunction::m_domainSet; + using BaseJointPdf::m_normalizationStyle; + using BaseJointPdf::m_logOfNormalizationFactor; +}; + +} // End namespace QUESO + +#endif // UQ_JEFFREYS_JOINT_PROB_DENSITY_H diff --git a/src/stats/inc/JeffreysVectorRV.h b/src/stats/inc/JeffreysVectorRV.h new file mode 100644 index 000000000..364854ba7 --- /dev/null +++ b/src/stats/inc/JeffreysVectorRV.h @@ -0,0 +1,79 @@ +//-----------------------------------------------------------------------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 UQ_JEFFREYS_VECTOR_RV_H +#define UQ_JEFFREYS_VECTOR_RV_H + +#include +#include +#include +#include +#include +#include +#include +#include // todo: take specificity of gsl_, i.e., make it general (gsl or boost or etc) + +namespace QUESO { + +/*! + * \class JeffreysVectorRV + * \brief A class representing a jeffreys vector RV. + * + * This class allows the user to compute the value of a jeffreys PDF and to generate realizations + * (samples) from it. It is used, for instance, to create a jeffreys prior PDF. */ + +template +class JeffreysVectorRV : public BaseVectorRV { +public: + + //! @name Constructor/Destructor methods + //@{ + //! Default constructor + /*! Constructs a uniform vector RV, given a prefix and the image set of the vector RV.*/ + JeffreysVectorRV(const char* prefix, + const VectorSet& imageSet); + //! Virtual destructor + virtual ~JeffreysVectorRV(); + //@} + + //! @name I/O methods + //@{ + //! TODO: Prints the vector RV. + /*! \todo: implement me!*/ + void print(std::ostream& os) const; + //@} + +private: + using BaseVectorRV::m_env; + using BaseVectorRV::m_prefix; + using BaseVectorRV::m_imageSet; + using BaseVectorRV::m_pdf; + using BaseVectorRV::m_realizer; + using BaseVectorRV::m_subCdf; + using BaseVectorRV::m_unifiedCdf; + using BaseVectorRV::m_mdf; +}; + +} // End namespace QUESO + +#endif // UQ_JEFFREYS_VECTOR_RV_H diff --git a/src/stats/inc/JeffreysVectorRealizer.h b/src/stats/inc/JeffreysVectorRealizer.h new file mode 100644 index 000000000..72e7b34a5 --- /dev/null +++ b/src/stats/inc/JeffreysVectorRealizer.h @@ -0,0 +1,74 @@ +//-----------------------------------------------------------------------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 UQ_JEFFREYS_REALIZER_H +#define UQ_JEFFREYS_REALIZER_H + +#include +#include +#include +#include + +namespace QUESO { + +/*! + * \class JeffreysVectorRealizer + * \brief A class for handling sampling from a jeffreys probability density + * distribution. + * + * This class handles sampling from a jeffreys probability density distribution.*/ + +template +class JeffreysVectorRealizer : public BaseVectorRealizer { +public: + + //! @name Constructor/Destructor methods + //@{ + //! Constructor + /*! Constructs a new object, given a prefix and the image set of the vector realizer. */ + JeffreysVectorRealizer(const char* prefix, + const VectorSet& unifiedImageSet); + //! Destructor + ~JeffreysVectorRealizer(); + //@} + + //! @name Realization-related methods + //@{ + //! Draws a realization. + /*! This function draws a realization of a jeffreys distribution and saves it in \c nextValues. It + * internally finds the minimum and the maximum values of the distribution. + */ + void realization(V& nextValues) const; + //@} + +private: + using BaseVectorRealizer::m_env; + using BaseVectorRealizer::m_prefix; + using BaseVectorRealizer::m_unifiedImageSet; + using BaseVectorRealizer::m_subPeriod; +}; + +} // End namespace QUESO + +#endif // UQ_JEFFREYS_REALIZER_H diff --git a/src/stats/inc/MLSampling.h b/src/stats/inc/MLSampling.h index 5b97a41ae..c5e3a33ea 100644 --- a/src/stats/inc/MLSampling.h +++ b/src/stats/inc/MLSampling.h @@ -74,7 +74,9 @@ struct ExchangeInfoStruct template struct BalancedLinkedChainControlStruct { - P_V* initialPosition; // KEY new: add logPrior and logLikelihood + P_V* initialPosition; + double initialLogPrior; + double initialLogLikelihood; unsigned int numberOfPositions; }; @@ -297,7 +299,7 @@ class MLSampling //! Plans for number of linked chains for each node so that all nodes generate the closest possible to the same number of positions (Step 07 from ML algorithm). /*! This method is responsible for the Step 07 in the ML algorithm implemented/described in the method MLSampling::generateSequence.*/ - /*! @param[in] useBalancedChains,indexOfFirstWeight,indexOfLastWeight, unifiedIndexCountersAtProc0Only,currOptions,prevChain + /*! @param[in] useBalancedChains,indexOfFirstWeight,indexOfLastWeight, unifiedIndexCountersAtProc0Only,currOptions,prevChain,prevExponent,currExponent,prevLogLikelihoodValues,prevLogTargetValues @param[in,out] exchangeStdVec @param[out] unbalancedLinkControl, balancedLinkControl*/ void generateSequence_Step07_inter0(bool useBalancedChains, // input @@ -307,6 +309,10 @@ class MLSampling UnbalancedLinkedChainsPerNodeStruct& unbalancedLinkControl, // (possible) output const MLSamplingLevelOptions* currOptions, // input const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input std::vector& exchangeStdVec, // (possible) input/output BalancedLinkedChainsPerNodeStruct& balancedLinkControl); // (possible) output @@ -323,6 +329,10 @@ class MLSampling @param[in,out] unifiedCovMatrix @param[out] currEta */ void generateSequence_Step09_all (const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input unsigned int indexOfFirstWeight, // input unsigned int indexOfLastWeight, // input const std::vector& unifiedWeightStdVectorAtProc0Only, // input @@ -344,6 +354,10 @@ class MLSampling const UnbalancedLinkedChainsPerNodeStruct& unbalancedLinkControl, // input // Round Rock unsigned int indexOfFirstWeight, // input // Round Rock const SequenceOfVectors& prevChain, // input // Round Rock + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input const BalancedLinkedChainsPerNodeStruct& balancedLinkControl, // input // Round Rock SequenceOfVectors & currChain, // output double& cumulativeRawChainRunTime, // output @@ -381,9 +395,13 @@ class MLSampling /*! @param[in] currOptions, prevChain, exchangeStdVec * @param[out] exchangeStdVec, balancedLinkControl*/ void prepareBalLinkedChains_inter0 (const MLSamplingLevelOptions* currOptions, // input - const SequenceOfVectors& prevChain, // input - std::vector& exchangeStdVec, // input/output - BalancedLinkedChainsPerNodeStruct& balancedLinkControl); // output + const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input + std::vector& exchangeStdVec, // input/output + BalancedLinkedChainsPerNodeStruct& balancedLinkControl); // output /*! @param[in] indexOfFirstWeight, indexOfLastWeight, unifiedIndexCountersAtProc0Only * @param[out] unbalancedLinkControl*/ @@ -407,16 +425,20 @@ class MLSampling /*! @param[in] inputOptions, unifiedCovMatrix, rv, unbalancedLinkControl, indexOfFirstWeight, prevChain * @param[out] workingChain, cumulativeRunTime, cumulativeRejections, currLogLikelihoodValues, currLogTargetValues*/ void generateUnbLinkedChains_all (MLSamplingLevelOptions& inputOptions, // input, only m_rawChainSize changes - const P_M& unifiedCovMatrix, // input - const GenericVectorRV & rv, // input - const UnbalancedLinkedChainsPerNodeStruct& unbalancedLinkControl, // input // Round Rock - unsigned int indexOfFirstWeight, // input // Round Rock - const SequenceOfVectors& prevChain, // input // Round Rock - SequenceOfVectors & workingChain, // output - double& cumulativeRunTime, // output - unsigned int& cumulativeRejections, // output - ScalarSequence * currLogLikelihoodValues, // output - ScalarSequence * currLogTargetValues); // output + const P_M& unifiedCovMatrix, // input + const GenericVectorRV & rv, // input + const UnbalancedLinkedChainsPerNodeStruct& unbalancedLinkControl, // input // Round Rock + unsigned int indexOfFirstWeight, // input // Round Rock + const SequenceOfVectors& prevChain, // input // Round Rock + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input + SequenceOfVectors & workingChain, // output + double& cumulativeRunTime, // output + unsigned int& cumulativeRejections, // output + ScalarSequence * currLogLikelihoodValues, // output + ScalarSequence * currLogTargetValues); // output #ifdef QUESO_HAS_GLPK /*! @param[in] exchangeStdVec @@ -432,6 +454,10 @@ class MLSampling /*! @param[in] prevChain, exchangeStdVec, finalNumChainsPerNode, finalNumPositionsPerNode * @param[out] balancedLinkControl*/ void mpiExchangePositions_inter0 (const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input const std::vector& exchangeStdVec, // input const std::vector& finalNumChainsPerNode, // input const std::vector& finalNumPositionsPerNode, // input diff --git a/src/stats/inc/MLSamplingLevelOptions.h b/src/stats/inc/MLSamplingLevelOptions.h index 0b364402b..e7665c9b9 100644 --- a/src/stats/inc/MLSamplingLevelOptions.h +++ b/src/stats/inc/MLSamplingLevelOptions.h @@ -54,6 +54,7 @@ #define UQ_ML_SAMPLING_L_INITIAL_POSITION_DATA_INPUT_FILE_TYPE_ODV UQ_FILE_EXTENSION_FOR_MATLAB_FORMAT #define UQ_ML_SAMPLING_L_INITIAL_PROPOSAL_COV_MATRIX_DATA_INPUT_FILE_NAME_ODV UQ_ML_SAMPLING_L_FILENAME_FOR_NO_FILE #define UQ_ML_SAMPLING_L_INITIAL_PROPOSAL_COV_MATRIX_DATA_INPUT_FILE_TYPE_ODV UQ_FILE_EXTENSION_FOR_MATLAB_FORMAT +#define UQ_ML_SAMPLING_L_INITIAL_POSITION_USE_PREVIOUS_LEVEL_LIKELIHOOD_ODV 0 #define UQ_ML_SAMPLING_L_RAW_CHAIN_DATA_INPUT_FILE_NAME_ODV UQ_ML_SAMPLING_L_FILENAME_FOR_NO_FILE #define UQ_ML_SAMPLING_L_RAW_CHAIN_DATA_INPUT_FILE_TYPE_ODV UQ_FILE_EXTENSION_FOR_MATLAB_FORMAT #define UQ_ML_SAMPLING_L_LIST_OF_DISABLED_PARAMETERS_ODV "" @@ -97,6 +98,7 @@ #define UQ_ML_SAMPLING_L_AM_ADAPTED_MATRICES_DATA_OUTPUT_ALLOWED_SET_ODV "" #define UQ_ML_SAMPLING_L_AM_ETA_ODV 1. #define UQ_ML_SAMPLING_L_AM_EPSILON_ODV 1.e-5 +#define UQ_ML_SAMPLING_L_DO_LOGIT_TRANSFORM 0 namespace QUESO { @@ -204,6 +206,9 @@ class MLSamplingLevelOptions //! Type of input file for initial proposal covariance matrix. std::string m_initialProposalCovMatrixDataInputFileType; + + //! Use previous level likelihood for initial chain position instead of re-computing it from target pdf + bool m_initialPositionUsePreviousLevelLikelihood; // ml_likelihood_caching std::set m_parameterDisabledSet; // gpmsa2 @@ -335,6 +340,9 @@ class MLSamplingLevelOptions //! 'am' epsilon. double m_amEpsilon; + //! Whether or not a logit transform will be done for bounded domains + bool m_doLogitTransform; + private: //! Copies the option values from \c srcOptions to \c this. void copyOptionsValues(const MLSamplingLevelOptions& srcOptions); @@ -366,14 +374,15 @@ class MLSamplingLevelOptions std::string m_option_minRejectionRate; std::string m_option_maxRejectionRate; std::string m_option_covRejectionRate; - std::string m_option_minAcceptableEta; // gpmsa1 + std::string m_option_minAcceptableEta; // gpmsa1 std::string m_option_totallyMute; std::string m_option_initialPosition_dataInputFileName; std::string m_option_initialPosition_dataInputFileType; std::string m_option_initialProposalCovMatrix_dataInputFileName; std::string m_option_initialProposalCovMatrix_dataInputFileType; - std::string m_option_listOfDisabledParameters; // gpmsa2 - std::string m_option_initialValuesOfDisabledParameters; // gpmsa2 + std::string m_option_initialPositionUsePreviousLevelLikelihood; // ml_likelihood_caching + std::string m_option_listOfDisabledParameters; // gpmsa2 + std::string m_option_initialValuesOfDisabledParameters; // gpmsa2 std::string m_option_rawChain_dataInputFileName; std::string m_option_rawChain_dataInputFileType; std::string m_option_rawChain_size; @@ -417,6 +426,7 @@ class MLSamplingLevelOptions std::string m_option_am_adaptedMatrices_dataOutputAllowedSet; std::string m_option_am_eta; std::string m_option_am_epsilon; + std::string m_option_doLogitTransform; }; std::ostream& operator<<(std::ostream& os, const MLSamplingLevelOptions& obj); diff --git a/src/stats/inc/MetropolisHastingsSG.h b/src/stats/inc/MetropolisHastingsSG.h index 5ba126c45..c35e34b29 100644 --- a/src/stats/inc/MetropolisHastingsSG.h +++ b/src/stats/inc/MetropolisHastingsSG.h @@ -132,16 +132,33 @@ class MetropolisHastingsSG * 'pROblem_775_ip_', then the constructor will read all options that begin with 'pROblem_775_ip_mh_'. Options reading is handled by class 'MetropolisHastingsOptions'.*/ MetropolisHastingsSG(const char* prefix, - const MhOptionsValues* alternativeOptionsValues, // dakota - const BaseVectorRV& sourceRv, - const P_V& initialPosition, - const P_M* inputProposalCovMatrix); + const MhOptionsValues* alternativeOptionsValues, // dakota + const BaseVectorRV& sourceRv, + const P_V& initialPosition, + const P_M* inputProposalCovMatrix); + + //! Constructor. + MetropolisHastingsSG(const char* prefix, + const MhOptionsValues* alternativeOptionsValues, // dakota + const BaseVectorRV& sourceRv, + const P_V& initialPosition, + double initialLogPrior, + double initialLogLikelihood, + const P_M* inputProposalCovMatrix); //! Constructor. MetropolisHastingsSG(const MLSamplingLevelOptions& mlOptions, - const BaseVectorRV& sourceRv, - const P_V& initialPosition, - const P_M* inputProposalCovMatrix); + const BaseVectorRV& sourceRv, + const P_V& initialPosition, + const P_M* inputProposalCovMatrix); + + //! Constructor. + MetropolisHastingsSG(const MLSamplingLevelOptions& mlOptions, + const BaseVectorRV& sourceRv, + const P_V& initialPosition, + double initialLogPrior, + double initialLogLikelihood, + const P_M* inputProposalCovMatrix); //! Destructor ~MetropolisHastingsSG(); @@ -178,6 +195,9 @@ class MetropolisHastingsSG //@} + //! Returns the underlying transition kernel for this sequence generator + const BaseTKGroup & transitionKernel() const; + //! @name I/O methods //@{ //! TODO: Prints the sequence. @@ -251,31 +271,37 @@ class MetropolisHastingsSG int writeInfo (const BaseVectorSequence& workingChain, std::ofstream& ofsvar) const; - const BaseEnvironment& m_env; - const VectorSpace & m_vectorSpace; - const BaseJointPdf& m_targetPdf; - P_V m_initialPosition; - P_M m_initialProposalCovMatrix; - bool m_nullInputProposalCovMatrix; - unsigned int m_numDisabledParameters; // gpmsa2 - std::vector m_parameterEnabledStatus; // gpmsa2 - const ScalarFunctionSynchronizer* m_targetPdfSynchronizer; - - BaseTKGroup* m_tk; - unsigned int m_positionIdForDebugging; - unsigned int m_stageIdForDebugging; - std::vector m_idsOfUniquePositions; - std::vector m_logTargets; - std::vector m_alphaQuotients; - double m_lastChainSize; - P_V* m_lastMean; - P_M* m_lastAdaptedCovMatrix; - unsigned int m_numPositionsNotSubWritten; - - MHRawChainInfoStruct m_rawChainInfo; - - MhOptionsValues m_alternativeOptionsValues; - MetropolisHastingsSGOptions* m_optionsObj; + const BaseEnvironment & m_env; + const VectorSpace & m_vectorSpace; + const BaseJointPdf & m_targetPdf; + P_V m_initialPosition; + P_M m_initialProposalCovMatrix; + bool m_nullInputProposalCovMatrix; + unsigned int m_numDisabledParameters; // gpmsa2 + std::vector m_parameterEnabledStatus; // gpmsa2 + const ScalarFunctionSynchronizer * m_targetPdfSynchronizer; + + BaseTKGroup * m_tk; + unsigned int m_positionIdForDebugging; + unsigned int m_stageIdForDebugging; + std::vector m_idsOfUniquePositions; + std::vector m_logTargets; + std::vector m_alphaQuotients; + double m_lastChainSize; + P_V * m_lastMean; + P_M * m_lastAdaptedCovMatrix; + unsigned int m_numPositionsNotSubWritten; + + MHRawChainInfoStruct m_rawChainInfo; + + MhOptionsValues m_alternativeOptionsValues; + MetropolisHastingsSGOptions * m_optionsObj; + bool m_computeInitialPriorAndLikelihoodValues; + double m_initialLogPriorValue; + double m_initialLogLikelihoodValue; + + void transformInitialCovMatrixToGaussianSpace(const BoxSubset & + boxSubset); }; } // End namespace QUESO diff --git a/src/stats/inc/MetropolisHastingsSGOptions.h b/src/stats/inc/MetropolisHastingsSGOptions.h index 4d3c2469c..49129e950 100644 --- a/src/stats/inc/MetropolisHastingsSGOptions.h +++ b/src/stats/inc/MetropolisHastingsSGOptions.h @@ -88,6 +88,9 @@ #define UQ_MH_SG_AM_EPSILON_ODV 1.e-5 #define UQ_MH_SG_ENABLE_BROOKS_GELMAN_CONV_MONITOR 0 #define UQ_MH_SG_BROOKS_GELMAN_LAG 100 +#define UQ_MH_SG_OUTPUT_LOG_LIKELIHOOD 1 +#define UQ_MH_SG_OUTPUT_LOG_TARGET 1 +#define UQ_MH_SG_DO_LOGIT_TRANSFORM 1 namespace QUESO { @@ -95,11 +98,14 @@ namespace QUESO { \brief Classes to allow options to be passed to a Metropolis-Hastings algorithm. */ -/*! \class MhOptionsValues - * \brief This class provides options for the Metropolis-Hastings generator of samples if no input file is available. +/*! + * \class MhOptionsValues + * \brief This class provides options for the Metropolis-Hastings generator of samples if no input file is available. * - * Metropolis-Hastings generator of samples expects some options for its methods to be fully defined. - * This class provides default values for such options if no input file is available. */ + * Metropolis-Hastings generator of samples expects some options for its + * methods to be fully defined. This class provides default values for such + * options if no input file is available. + */ class MhOptionsValues { @@ -208,6 +214,15 @@ class MhOptionsValues unsigned int m_enableBrooksGelmanConvMonitor; unsigned int m_BrooksGelmanLag; + //! Flag for deciding whether or not to dump log likelihood values in output + bool m_outputLogLikelihood; + + //! Flag for deciding whether or not to dump log target values in output + bool m_outputLogTarget; + + //! Flag for deciding whether or not to do logit transform of bounded domains + bool m_doLogitTransform; + private: //! Copies the option values from \c src to \c this. void copy(const MhOptionsValues& src); @@ -341,6 +356,10 @@ class MetropolisHastingsSGOptions std::string m_option_enableBrooksGelmanConvMonitor; std::string m_option_BrooksGelmanLag; + + std::string m_option_outputLogLikelihood; + std::string m_option_outputLogTarget; + std::string m_option_doLogitTransform; }; std::ostream& operator<<(std::ostream& os, const MetropolisHastingsSGOptions& obj); diff --git a/src/stats/inc/ScaledCovMatrixTKGroup.h b/src/stats/inc/ScaledCovMatrixTKGroup.h index 98a7233e8..8aab8cae0 100644 --- a/src/stats/inc/ScaledCovMatrixTKGroup.h +++ b/src/stats/inc/ScaledCovMatrixTKGroup.h @@ -36,12 +36,12 @@ namespace QUESO { //***************************************************** /*! \class ScaledCovMatrixTKGroup * \brief This class allows the representation of a transition kernel with a scaled covariance matrix. */ - + template class ScaledCovMatrixTKGroup : public BaseTKGroup { public: //! @name Constructor/Destructor methods - //@{ + //@{ //! Default constructor. ScaledCovMatrixTKGroup(const char* prefix, const VectorSpace& vectorSpace, @@ -50,33 +50,33 @@ class ScaledCovMatrixTKGroup : public BaseTKGroup { //! Destructor. ~ScaledCovMatrixTKGroup(); //@} - + //! @name Statistical/Mathematical methods //@{ //! Whether or not the matrix is symmetric. Always 'true'. /*! \todo: It only returns 'true', thus a test for its symmetricity must be included.*/ bool symmetric () const; - + //! Gaussian increment property to construct a transition kernel. - const GaussianVectorRV& rv (unsigned int stageId ); - + const GaussianVectorRV& rv (unsigned int stageId ) const; + //! Gaussian increment property to construct a transition kernel. const GaussianVectorRV& rv (const std::vector& stageIds); - + //! Scales the covariance matrix. /*! The covariance matrix is scaled by a factor of \f$ 1/scales^2 \f$.*/ void updateLawCovMatrix (const M& covMatrix); //@} - + //! @name Misc methods //@{ - //! Sets the pre-computing positions \c m_preComputingPositions[stageId] with a new vector of size \c position. + //! Sets the pre-computing positions \c m_preComputingPositions[stageId] with a new vector of size \c position. bool setPreComputingPosition (const V& position, unsigned int stageId); - + //! Clears the pre-computing positions \c m_preComputingPositions[stageId] void clearPreComputingPositions(); //@} - + //! @name I/O methods //@{ //! TODO: Prints the transition kernel. diff --git a/src/stats/inc/StatisticalInverseProblem.h b/src/stats/inc/StatisticalInverseProblem.h index bab9463ff..7e398cdf8 100644 --- a/src/stats/inc/StatisticalInverseProblem.h +++ b/src/stats/inc/StatisticalInverseProblem.h @@ -33,6 +33,7 @@ #include #include #include +#include namespace QUESO { @@ -42,11 +43,11 @@ namespace QUESO { /*! \class StatisticalInverseProblem * \brief This templated class represents a Statistical Inverse Problem. - * - * This class is templated on the type 'P_V' of vector and type 'P_M' of matrix, where - * 'P_' stands for 'parameter'. Some templated classes might also use 'Q_V' and 'Q_M' - * when referring to a vector type and a matrix type, where 'Q_' stands for 'quantities - * of interest'. Conceptually, a statistical inverse problem has two input entities and + * + * This class is templated on the type 'P_V' of vector and type 'P_M' of matrix, where + * 'P_' stands for 'parameter'. Some templated classes might also use 'Q_V' and 'Q_M' + * when referring to a vector type and a matrix type, where 'Q_' stands for 'quantities + * of interest'. Conceptually, a statistical inverse problem has two input entities and * one output entity.\n * * The input entities of a statistical inverse problem are: @@ -54,16 +55,16 @@ namespace QUESO { the prior RV, an instance of class 'BaseVectorRV', and the likelihood function, an instance of class 'BaseScalarFunction'. - * Let \f$ \pi()\f$ denote the mathematical likelihood function and \f$ x \f$ denote a - * vector of parameters. The likelihood function object stores the routine that computes - * \f$ \pi(x) \f$ and whatever data necessary by such routine. The routine in the + * Let \f$ \pi()\f$ denote the mathematical likelihood function and \f$ x \f$ denote a + * vector of parameters. The likelihood function object stores the routine that computes + * \f$ \pi(x) \f$ and whatever data necessary by such routine. The routine in the * likelihood function object can compute either the actual value \f$ \pi(x) \f$ or the - * value \f$ \ln[\pi(x)] \f$. See files + * value \f$ \ln[\pi(x)] \f$. See files * 'basic/inc/ScalarFunction.h' and 'stats/inc/JointPdf.h' for more details.\n * - * The output entity of a statistical inverse problem is the posterior RV, another instance - * of class 'BaseVectorRV', which stores the solution according to the Bayesian - * approach. Upon return from a solution operation, the posterior RV is available through the + * The output entity of a statistical inverse problem is the posterior RV, another instance + * of class 'BaseVectorRV', which stores the solution according to the Bayesian + * approach. Upon return from a solution operation, the posterior RV is available through the * operation 'postRv()'. Such posterior RV is able to provide: a joint pdf (up to a multiplicative constant) through the operation 'postRv().pdf()', @@ -72,8 +73,8 @@ namespace QUESO { instance of the class 'BaseVectorRealizer'. */ -/* OLD STUFF: The constructor of the 'scalar function' asks for the user to specify which - * value the routine is actually computing, so that the 'scalar function' class can properly +/* OLD STUFF: The constructor of the 'scalar function' asks for the user to specify which + * value the routine is actually computing, so that the 'scalar function' class can properly * implements both class operations 'actualValue()' and 'minus2LnValue()'*/ template @@ -82,66 +83,109 @@ class StatisticalInverseProblem public: //! @name Constructor/Destructor methods //@{ - //! Constructor. - /*! Requirements: 1) the image set of the vector random variable 'priorRv', 2) the domain set of - * the likelihood function 'likelihoodFunction' and 3) the image set of the vector random variable - * 'postRv' should belong to vector spaces of equal dimensions. If the requirements are satisfied, - * the constructor then reads input options that begin with the string '\ip_'. If no - * options input file is provided, the construction assigns \c alternativeOptionsValues to the + //! Constructor. + /*! Requirements: 1) the image set of the vector random variable 'priorRv', 2) the domain set of + * the likelihood function 'likelihoodFunction' and 3) the image set of the vector random variable + * 'postRv' should belong to vector spaces of equal dimensions. If the requirements are satisfied, + * the constructor then reads input options that begin with the string '\ip_'. If no + * options input file is provided, the construction assigns \c alternativeOptionsValues to the * options of the SIP.*/ StatisticalInverseProblem(const char* prefix, const SipOptionsValues* alternativeOptionsValues, // dakota - const BaseVectorRV & priorRv, - const BaseScalarFunction& likelihoodFunction, + const BaseVectorRV & priorRv, + const BaseScalarFunction& likelihoodFunction, GenericVectorRV & postRv); - + + //! Constructor for statistical inverse problems to be sovled using GPMSA + /*! + * Requirements: + * 1) the factory for the GPMSA object + * 2) the image set of the vector random variable \c postRv (obtainable via + * GaussianProcessFactory::prior method) should be equal to the full + * prior image set (including all the hyperparameters) + * + * If the requirements are satisfied, the constructor then reads input + * options that begin with the string '\ip_'. If no options input + * file is provided, the construction assigns \c alternativeOptionsValues to + * the options of the statistical inverse problem. + */ + StatisticalInverseProblem(const char * prefix, + const SipOptionsValues * alternativeOptionsValues, + const GPMSAFactory & gpmsaFactory, + GenericVectorRV & postRv); + //! Destructor ~StatisticalInverseProblem(); //@} //! @name Statistical methods //@{ - //! Whether or not compute the solution. + //! Whether or not compute the solution. bool computeSolutionFlag () const; - - //! Solves the problem through Bayes formula and a Metropolis-Hastings algorithm. - /*! Requirements: 1) 'initialValues' should have the same number of components as member variable - * 'm_priorRv' and 2) if 'initialProposalCovMatrix' is not NULL, it should be square and its size - * should be equal to the size of 'initialValues'. If the requirements are satisfied, this methods - * checks the member flag 'm_computeSolution' (one of the options read from the input file during - * construction). If the flag is 'false', the operation returns immediately, computing nothing; - * otherwise, the operation sets the member variable 'm_postRv' accordingly. The operation: - - sets the pdf of 'm_postRv' equal to an instance of 'BayesianJointPdf', - instantiates 'SequenceOfVectors' (the chain), - instantiates 'MetropolisHastingsSG' (the Metropolis-Hastings algorithm), - populates the chain with the Metropolis-Hastings algorithm, and - sets the realizer of 'm_postRv' with the contents of the chain. - */ + + //! Solves the problem via Bayes formula and a Metropolis-Hastings algorithm. + /*! + * Requirements: + * + * 'initialValues' should have the same number of components as member + * variable 'm_priorRv' + * if 'initialProposalCovMatrix' is not NULL, it should be square and + * its size should be equal to the size of 'initialValues'. + * + * + * If the requirements are satisfied, this methods checks the member flag + * 'm_computeSolution' (one of the options read from the input file during + * construction). If the flag is 'false', the operation returns immediately, + * computing nothing; otherwise, the operation sets the member variable + * 'm_postRv' accordingly. The operation: + * + * sets the pdf of 'm_postRv' equal to an instance of + * BayesianJointPdf, + * instantiates SequenceOfVectors (the chain), + * instantiates MetropolisHastingsSG (the Metropolis-Hastings + * algorithm), + * populates the chain with the Metropolis-Hastings algorithm, and + * sets the realizer of 'm_postRv' with the contents of the chain. + * + * + * If \c seedWithMapEstimator() is called before this method, then + * \c initialValues is used as the initial condition for an optimization + * procedure, the result of which will be used as the seed for the chain. + */ void solveWithBayesMetropolisHastings(const MhOptionsValues* alternativeOptionsValues, // dakota const P_V& initialValues, const P_M* initialProposalCovMatrix); - + + //! Seeds the chain with the result of a deterministic optimisation + /*! + * This only works for Metropolis-Hastings right now. Multi-level is not + * currently supported. + */ + void seedWithMAPEstimator(); + //! Solves with Bayes Multi-Level (ML) sampling. void solveWithBayesMLSampling (); - + + //! Return the underlying MetropolisHastingSG object + const MetropolisHastingsSG & sequenceGenerator() const; + //! Returns the Prior RV; access to private attribute m_priorRv. const BaseVectorRV & priorRv () const; - + //! Returns the Posterior RV; access to private attribute m_postrRv. /*! The Posterior RV contains the solution of the Bayes problem.*/ const GenericVectorRV& postRv () const; - + //! Returns the logarithm value of the evidence. Related to ML. double logEvidence () const; //! Returns the mean of the logarithm value of the likelihood. Related to ML. double meanLogLikelihood () const; - + //\TODO Related to ML. double eig () const; - //@} - + //@} + //! @name I/O methods //@{ //! TODO: Prints the sequence. @@ -171,10 +215,12 @@ class StatisticalInverseProblem BaseVectorSequence * m_chain; ScalarSequence * m_logLikelihoodValues; ScalarSequence * m_logTargetValues; - + SipOptionsValues m_alternativeOptionsValues; StatisticalInverseProblemOptions* m_optionsObj; + bool m_seedWithMAPEstimator; + #ifdef UQ_ALSO_COMPUTE_MDFS_WITHOUT_KDE ArrayOfOneDGrids * m_subMdfGrids; ArrayOfOneDTables * m_subMdfValues; diff --git a/src/stats/inc/StatisticalInverseProblemOptions.h b/src/stats/inc/StatisticalInverseProblemOptions.h index 10c290a47..85fee0056 100644 --- a/src/stats/inc/StatisticalInverseProblemOptions.h +++ b/src/stats/inc/StatisticalInverseProblemOptions.h @@ -42,15 +42,19 @@ namespace QUESO { -/*! \file uqStatisticalInverseProblemOptions.h - \brief Classes to allow options to be passed to a Statistical Inverse Problem. -*/ - -/*! \class SipOptionsValues - * \brief This class provides options for a Statistical Inverse Problem if no input file is available. - * - * In order to solve a Statistical Inverse Problem (SIP), QUESO expects some options for its methods to be - * fully defined. This class provides default values for such options if no input file is available. */ +/*! + * \file StatisticalInverseProblemOptions.h + * \brief Classes to allow options to be passed to a Statistical Inverse Problem + */ + +/*! + * \class SipOptionsValues + * \brief This class provides options for a Statistical Inverse Problem if no input file is available. + * + * In order to solve a Statistical Inverse Problem (SIP), QUESO expects some + * options for its methods to be fully defined. This class provides default + * values for such options if no input file is available. + */ class SipOptionsValues { diff --git a/src/stats/inc/TKGroup.h b/src/stats/inc/TKGroup.h index d2ee2cb40..6b8833dd4 100644 --- a/src/stats/inc/TKGroup.h +++ b/src/stats/inc/TKGroup.h @@ -25,6 +25,7 @@ #ifndef UQ_TRANSITION_KERNEL_GROUP_H #define UQ_TRANSITION_KERNEL_GROUP_H +#include #include #include @@ -66,10 +67,10 @@ class BaseTKGroup { virtual bool symmetric () const = 0; //! Gaussian increment property to construct a transition kernel. See template specialization. - virtual const GaussianVectorRV& rv (unsigned int stageId ) = 0; + virtual const BaseVectorRV& rv (unsigned int stageId ) const = 0; //! Gaussian increment property to construct a transition kernel. See template specialization. - virtual const GaussianVectorRV& rv (const std::vector& stageIds) = 0; + virtual const BaseVectorRV& rv (const std::vector& stageIds) = 0; //@} //! @name Misc methods @@ -100,7 +101,7 @@ class BaseTKGroup { const VectorSpace* m_vectorSpace; std::vector m_scales; std::vector m_preComputingPositions; - std::vector* > m_rvs; // Gaussian, not Base... And nothing const... + std::vector* > m_rvs; // Gaussian, not Base... And nothing const... }; } // End namespace QUESO diff --git a/src/stats/inc/TransformedScaledCovMatrixTKGroup.h b/src/stats/inc/TransformedScaledCovMatrixTKGroup.h new file mode 100644 index 000000000..1facad393 --- /dev/null +++ b/src/stats/inc/TransformedScaledCovMatrixTKGroup.h @@ -0,0 +1,120 @@ +//-----------------------------------------------------------------------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 UQ_TRANSFORMED_SCALEDCOV_TK_GROUP_H +#define UQ_TRANSFORMED_SCALEDCOV_TK_GROUP_H + +#include +#include +#include +#include + +namespace QUESO { + +/*! + * \class TransformedScaledCovMatrixTKGroup + * \brief This class represents a transition kernel with a scaled covariance matrix on hybrid bounded/unbounded state spaces. + * + * The unbounded directions utilise a standard Gaussian proposal. The bounded + * or half-bounded directions utilise a transformed Gaussian proposal, so that + * no realizations are generated outside of the state space. + */ + +template +class TransformedScaledCovMatrixTKGroup : public BaseTKGroup { +public: + //! @name Constructor/Destructor methods + //@{ + //! Default constructor. + TransformedScaledCovMatrixTKGroup(const char * prefix, + const BoxSubset & boxSubset, const std::vector & scales, + const M & covMatrix); + + //! Destructor. + ~TransformedScaledCovMatrixTKGroup(); + //@} + + //! @name Statistical/Mathematical methods + //@{ + //! Whether or not the matrix is symmetric. Always 'false'. + bool symmetric() const; + + //! InvLogitGaussian increment property to construct a transition kernel. + const InvLogitGaussianVectorRV & rv(unsigned int stageId) const; + + //! InvLogitGaussian increment property to construct a transition kernel. + const InvLogitGaussianVectorRV & rv( + const std::vector & stageIds); + + //! Scales the covariance matrix of the underlying Gaussian distribution. + /*! The covariance matrix is scaled by a factor of \f$ 1/scales^2 \f$.*/ + void updateLawCovMatrix(const M & covMatrix); + //@} + + //! @name Misc methods + //@{ + //! Sets the pre-computing positions \c m_preComputingPositions[stageId] with a new vector of size \c position. + /*! + * The vector \c position is in *physical* space. This is then transformed + * using transformToGaussianSpace to map to a point in Gaussian space where + * we can, for example, update the mean of the underlying Gaussian RV. + */ + bool setPreComputingPosition(const V & position, unsigned int stageId); + + //! Clears the pre-computing positions \c m_preComputingPositions[stageId] + void clearPreComputingPositions(); + //@} + + //! @name I/O methods + //@{ + //! TODO: Prints the transition kernel. + /*! \todo: implement me!*/ + void print(std::ostream & os) const; + //@} + + // Convenience method that transforms a point in physical (user) space to a + // space with no boundaries and where the proposal covariance matrix is + // Gaussian + void transformToGaussianSpace(const V & physicalPoint, + V & transformedPoint) const; + +private: + //! Sets the mean of the underlying Gaussian RVs to zero. + void setRVsWithZeroMean(); + + using BaseTKGroup::m_env; + using BaseTKGroup::m_prefix; + using BaseTKGroup::m_vectorSpace; + using BaseTKGroup::m_scales; + using BaseTKGroup::m_preComputingPositions; + using BaseTKGroup::m_rvs; + + const BoxSubset & m_boxSubset; + M m_originalCovMatrix; + +}; + +} // End namespace QUESO + +#endif // UQ_TRANSFORMED_SCALEDCOV_TK_GROUP_H diff --git a/src/stats/inc/VectorRV.h b/src/stats/inc/VectorRV.h index c1c9a7762..d3d8db258 100644 --- a/src/stats/inc/VectorRV.h +++ b/src/stats/inc/VectorRV.h @@ -25,37 +25,18 @@ #ifndef UQ_VECTOR_RV_H #define UQ_VECTOR_RV_H +#include +#include #include #include -#include -#include -#include -#include -#include -#include -#include -#include #include -#include -#include -#include -#include -#include -#include -#include -#include #include #include #include #include -#include // todo: take specificity of gsl_, i.e., make it general (gsl or boost or etc) namespace QUESO { -//***************************************************** -// Base class [RV-00] -//***************************************************** - /*! \file VectorRV.h * \brief A templated class for handling vector random variables (RV). * @@ -63,7 +44,8 @@ namespace QUESO { * \brief A templated base class for handling vector RV. * * This class allows two basic but quite crucial functionalities: to compute the value of the - * PDF of a random variable (RV) at a point and to generate realizations (samples) from such PDF. */ + * PDF of a random variable (RV) at a point and to generate realizations (samples) from such PDF. + */ template class BaseVectorRV { diff --git a/src/stats/src/BayesianJointPdf.C b/src/stats/src/BayesianJointPdf.C index ca7923855..d92f3bb8b 100644 --- a/src/stats/src/BayesianJointPdf.C +++ b/src/stats/src/BayesianJointPdf.C @@ -158,7 +158,16 @@ BayesianJointPdf::lnValue( } V* gradVLike = NULL; - if (gradVector) gradVLike = &m_tmpVector1; + if (gradVector) { + gradVLike = &m_tmpVector1; + + // DM: We do this because we want to be able to test whether the user + // actually filled gradVector, rather than passing a different + // instance of V to the likelihood code + for (unsigned int i = 0; i < m_tmpVector1.sizeLocal(); i++) { + m_tmpVector1[i] = (*gradVector)[i]; + } + } M* hessianMLike = NULL; if (hessianMatrix) hessianMLike = m_tmpMatrix; diff --git a/src/stats/src/BetaVectorRV.C b/src/stats/src/BetaVectorRV.C index 3ef60007c..752fdc94e 100644 --- a/src/stats/src/BetaVectorRV.C +++ b/src/stats/src/BetaVectorRV.C @@ -22,10 +22,13 @@ // //-----------------------------------------------------------------------el- -#include #include #include +#include +#include +#include + namespace QUESO { // Constructor--------------------------------------- diff --git a/src/stats/src/ConcatenatedVectorRV.C b/src/stats/src/ConcatenatedVectorRV.C index 2785937d1..58bc72959 100644 --- a/src/stats/src/ConcatenatedVectorRV.C +++ b/src/stats/src/ConcatenatedVectorRV.C @@ -23,6 +23,8 @@ //-----------------------------------------------------------------------el- #include +#include +#include #include #include diff --git a/src/stats/src/GammaVectorRV.C b/src/stats/src/GammaVectorRV.C index 4c7bf8440..33f01c9c3 100644 --- a/src/stats/src/GammaVectorRV.C +++ b/src/stats/src/GammaVectorRV.C @@ -23,6 +23,8 @@ //-----------------------------------------------------------------------el- #include +#include +#include #include #include diff --git a/src/stats/src/GaussianVectorRV.C b/src/stats/src/GaussianVectorRV.C index 445e13956..538554512 100644 --- a/src/stats/src/GaussianVectorRV.C +++ b/src/stats/src/GaussianVectorRV.C @@ -23,6 +23,8 @@ //-----------------------------------------------------------------------el- #include +#include +#include #include #include diff --git a/src/stats/src/HessianCovMatricesTKGroup.C b/src/stats/src/HessianCovMatricesTKGroup.C index ed5077324..0e131c809 100644 --- a/src/stats/src/HessianCovMatricesTKGroup.C +++ b/src/stats/src/HessianCovMatricesTKGroup.C @@ -78,7 +78,7 @@ HessianCovMatricesTKGroup::symmetric() const //--------------------------------------------------- template const GaussianVectorRV& -HessianCovMatricesTKGroup::rv(unsigned int stageId) +HessianCovMatricesTKGroup::rv(unsigned int stageId) const { UQ_FATAL_TEST_MACRO(m_rvs.size() <= stageId, m_env.worldRank(), @@ -100,7 +100,11 @@ HessianCovMatricesTKGroup::rv(unsigned int stageId) "HessianCovMatricesTKGroup::rv1()", "m_preComputingPositions[stageId] == NULL"); - m_rvs[stageId]->updateLawExpVector(*m_preComputingPositions[stageId] + *m_originalNewtonSteps[stageId]); + GaussianVectorRV * gaussian_rv = + dynamic_cast * >(m_rvs[stageId]); + + gaussian_rv->updateLawExpVector(*m_preComputingPositions[stageId] + *m_originalNewtonSteps[stageId]); + if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 10)) { *m_env.subDisplayFile() << "In HessianCovMatrixTKGroup::rv1()" @@ -109,9 +113,10 @@ HessianCovMatricesTKGroup::rv(unsigned int stageId) << ", covMatrix = \n" << *m_originalCovMatrices[stageId] // FIX ME: might demand parallelism << std::endl; } - m_rvs[stageId]->updateLawCovMatrix(*m_originalCovMatrices[stageId]); - return *m_rvs[stageId]; + gaussian_rv->updateLawCovMatrix(*m_originalCovMatrices[stageId]); + + return *gaussian_rv; } //--------------------------------------------------- template @@ -140,7 +145,11 @@ HessianCovMatricesTKGroup::rv(const std::vector& stageIds) double factor = 1./m_scales[stageIds.size()-1]/m_scales[stageIds.size()-1]; - m_rvs[stageIds[0]]->updateLawExpVector(*m_preComputingPositions[stageIds[0]] + factor*(*m_originalNewtonSteps[stageIds[0]])); + GaussianVectorRV * gaussian_rv = + dynamic_cast * >(m_rvs[stageIds[0]]); + + gaussian_rv->updateLawExpVector(*m_preComputingPositions[stageIds[0]] + factor*(*m_originalNewtonSteps[stageIds[0]])); + if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 10)) { *m_env.subDisplayFile() << "In HessianCovMatrixTKGroup::rv2()" @@ -151,9 +160,9 @@ HessianCovMatricesTKGroup::rv(const std::vector& stageIds) << ", covMatrix = \n" << factor*(*m_originalCovMatrices[stageIds[0]]) // FIX ME: might demand parallelism << std::endl; } - m_rvs[stageIds[0]]->updateLawCovMatrix(factor*(*m_originalCovMatrices[stageIds[0]])); + gaussian_rv->updateLawCovMatrix(factor*(*m_originalCovMatrices[stageIds[0]])); - return *m_rvs[stageIds[0]]; + return *gaussian_rv; } // Misc methods-------------------------------------- template diff --git a/src/stats/src/InfoTheory.C b/src/stats/src/InfoTheory.C index 83ee249a5..e9caf6bac 100644 --- a/src/stats/src/InfoTheory.C +++ b/src/stats/src/InfoTheory.C @@ -316,8 +316,7 @@ double estimateKL_ANN( RV_1& xRV, // sanity check if( dimX != dimY ) { - std::cout << "Error-KL: the dimensions should agree" << std::endl; - queso_error(); + queso_error_msg("Error-KL: the dimensions should agree"); } // Allocate memory @@ -392,8 +391,7 @@ double estimateCE_ANN( RV_1& xRV, // sanity check if( dimX != dimY ) { - std::cout << "Error-CE: the dimensions should agree" << std::endl; - queso_error(); + queso_error_msg("Error-CE: the dimensions should agree"); } // Allocate memory diff --git a/src/stats/src/InvLogitGaussianJointPdf.C b/src/stats/src/InvLogitGaussianJointPdf.C new file mode 100644 index 000000000..273c9a7d7 --- /dev/null +++ b/src/stats/src/InvLogitGaussianJointPdf.C @@ -0,0 +1,252 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +// Constructor ------------------------------------- +template +InvLogitGaussianJointPdf::InvLogitGaussianJointPdf( + const char* prefix, + const BoxSubset& domainBoxSubset, + const V& lawExpVector, + const V& lawVarVector) + : + BaseJointPdf(((std::string)(prefix)+"invlogit_gau").c_str(), + domainBoxSubset), + m_lawExpVector(new V(lawExpVector)), + m_lawVarVector(new V(lawVarVector)), + m_diagonalCovMatrix(true), + m_lawCovMatrix(m_domainSet.vectorSpace().newDiagMatrix(lawVarVector)), + m_domainBoxSubset(domainBoxSubset) +{ +} + + +template +InvLogitGaussianJointPdf::InvLogitGaussianJointPdf( + const char* prefix, + const BoxSubset& domainBoxSubset, + const V& lawExpVector, + const M& lawCovMatrix) + : + BaseJointPdf(((std::string)(prefix)+"invlogit_gau").c_str(), + domainBoxSubset), + m_lawExpVector(new V(lawExpVector)), + m_lawVarVector(domainBoxSubset.vectorSpace().newVector(INFINITY)), // FIX ME + m_diagonalCovMatrix(false), + m_lawCovMatrix(new M(lawCovMatrix)), + m_domainBoxSubset(domainBoxSubset) +{ +} + +template +InvLogitGaussianJointPdf::~InvLogitGaussianJointPdf() +{ + delete m_lawCovMatrix; + delete m_lawVarVector; + delete m_lawExpVector; +} + +template +const V& +InvLogitGaussianJointPdf::lawExpVector() const +{ + return *m_lawExpVector; +} + +template +const V& +InvLogitGaussianJointPdf::lawVarVector() const +{ + return *m_lawVarVector; +} + +template +double +InvLogitGaussianJointPdf::actualValue( + const V& domainVector, + const V* domainDirection, + V* gradVector, + M* hessianMatrix, + V* hessianEffect) const +{ + double returnValue; + + returnValue = std::exp(this->lnValue(domainVector,domainDirection,gradVector,hessianMatrix,hessianEffect)); + + return returnValue; +} + +template +double +InvLogitGaussianJointPdf::lnValue( + const V& domainVector, + const V* domainDirection, + V* gradVector, + M* hessianMatrix, + V* hessianEffect) const +{ + double returnValue; + double lnDeterminant = 0.0; + V transformedDomainVector(domainVector); + + V min_domain_bounds(this->m_domainBoxSubset.minValues()); + V max_domain_bounds(this->m_domainBoxSubset.maxValues()); + + double lnjacobian = 0.0; + for (unsigned int i = 0; i < domainVector.sizeLocal(); i++) { + double min_val = min_domain_bounds[i]; + double max_val = max_domain_bounds[i]; + + if (boost::math::isfinite(min_val) && + boost::math::isfinite(max_val)) { + + if (domainVector[i] == min_val || domainVector[i] == max_val) { + // Exit early if we can + return -INFINITY; + } + + // Left- and right-hand sides are finite. Do full transform. + transformedDomainVector[i] = std::log(domainVector[i] - min_val) - + std::log(max_val - domainVector[i]); + + lnjacobian += std::log(max_val - min_val) - + std::log(domainVector[i] - min_val) - + std::log(max_val - domainVector[i]); + } + else if (boost::math::isfinite(min_val) && + !boost::math::isfinite(max_val)) { + + if (domainVector[i] == min_val) { + // Exit early if we can + return -INFINITY; + } + + // Left-hand side finite, but right-hand side is not. + // Do only left-hand transform. + transformedDomainVector[i] = std::log(domainVector[i] - min_val); + + lnjacobian += -std::log(domainVector[i] - min_val); + } + else if (!boost::math::isfinite(min_val) && + boost::math::isfinite(max_val)) { + + if (domainVector[i] == max_val) { + // Exit early if we can + return -INFINITY; + } + + // Right-hand side is finite, but left-hand side is not. + // Do only right-hand transform. + transformedDomainVector[i] = -std::log(max_val - domainVector[i]); + + lnjacobian += -std::log(max_val - domainVector[i]); + } + else { + // No transform. + transformedDomainVector[i] = domainVector[i]; + } + } + + V diffVec(transformedDomainVector - this->lawExpVector()); + if (m_diagonalCovMatrix) { + returnValue = ((diffVec * diffVec) / + this->lawVarVector()).sumOfComponents(); + if (m_normalizationStyle == 0) { + unsigned int iMax = this->lawVarVector().sizeLocal(); + for (unsigned int i = 0; i < iMax; ++i) { + lnDeterminant += log(this->lawVarVector()[i]); + } + } + } + else { + V tmpVec = this->m_lawCovMatrix->invertMultiply(diffVec); + returnValue = (diffVec * tmpVec).sumOfComponents(); + if (m_normalizationStyle == 0) { + lnDeterminant = this->m_lawCovMatrix->lnDeterminant(); + } + } + if (m_normalizationStyle == 0) { + returnValue += ((double) this->lawVarVector().sizeLocal()) * log(2 * M_PI); + returnValue += lnDeterminant; + } + returnValue *= -0.5; + returnValue += m_logOfNormalizationFactor; + returnValue += lnjacobian; + + return returnValue; +} + +template +double +InvLogitGaussianJointPdf::computeLogOfNormalizationFactor(unsigned int numSamples, bool updateFactorInternally) const +{ + double value = 0.; + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 2)) { + *m_env.subDisplayFile() << "Entering GaussianJointPdf::computeLogOfNormalizationFactor()" + << std::endl; + } + value = BaseJointPdf::commonComputeLogOfNormalizationFactor(numSamples, updateFactorInternally); + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 2)) { + *m_env.subDisplayFile() << "Leaving GaussianJointPdf::computeLogOfNormalizationFactor()" + << ", m_logOfNormalizationFactor = " << m_logOfNormalizationFactor + << std::endl; + } + + return value; +} + +template +void +InvLogitGaussianJointPdf::updateLawExpVector(const V& newLawExpVector) +{ + // delete old expected values (allocated at construction or last call to this function) + delete m_lawExpVector; + m_lawExpVector = new V(newLawExpVector); +} + +template +void +InvLogitGaussianJointPdf::updateLawCovMatrix(const M& newLawCovMatrix) +{ + // delete old expected values (allocated at construction or last call to this function) + delete m_lawCovMatrix; + m_lawCovMatrix = new M(newLawCovMatrix); +} + +template +const M& +InvLogitGaussianJointPdf::lawCovMatrix() const +{ + return *m_lawCovMatrix; +} + +} // End namespace QUESO + +template class QUESO::InvLogitGaussianJointPdf; diff --git a/src/stats/src/InvLogitGaussianVectorRV.C b/src/stats/src/InvLogitGaussianVectorRV.C new file mode 100644 index 000000000..cfee78dbf --- /dev/null +++ b/src/stats/src/InvLogitGaussianVectorRV.C @@ -0,0 +1,209 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +// Constructor--------------------------------------- +template +InvLogitGaussianVectorRV::InvLogitGaussianVectorRV( + const char * prefix, + const BoxSubset & imageBoxSubset, + const V & lawExpVector, + const V & lawVarVector) + : BaseVectorRV(((std::string)(prefix)+"invlogit_gau").c_str(), + imageBoxSubset) +{ + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Entering InvLogitGaussianVectorRV::constructor() [1]" + << ": prefix = " << m_prefix + << std::endl; + } + + UQ_FATAL_TEST_MACRO((lawVarVector.getMinValue() <= 0.0), + m_env.worldRank(), + "InvLogitGaussianVectorRV::constructor() [1]", + "Covariance matrix is not symmetric positive definite."); + + m_pdf = new InvLogitGaussianJointPdf(m_prefix.c_str(), + dynamic_cast & >(m_imageSet), lawExpVector, + lawVarVector); + + V cholDiag(lawVarVector); + cholDiag.cwSqrt(); + M lowerCholLawCovMatrix(cholDiag); + lowerCholLawCovMatrix.zeroUpper(false); + + m_realizer = new InvLogitGaussianVectorRealizer(m_prefix.c_str(), + dynamic_cast & >(m_imageSet), lawExpVector, + lowerCholLawCovMatrix); + + m_subCdf = NULL; // FIX ME: complete code + m_unifiedCdf = NULL; // FIX ME: complete code + m_mdf = NULL; // FIX ME: complete code + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Leaving InvLogitGaussianVectorRV::constructor() [1]" + << ": prefix = " << m_prefix + << std::endl; + } +} + +template +InvLogitGaussianVectorRV::InvLogitGaussianVectorRV( + const char * prefix, + const BoxSubset & imageBoxSubset, + const V & lawExpVector, + const M & lawCovMatrix) + : BaseVectorRV(((std::string)(prefix)+"invlogit_gau").c_str(), + imageBoxSubset) +{ + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Entering InvLogitGaussianVectorRV::constructor() [2]" + << ": prefix = " << m_prefix + << std::endl; + } + + m_pdf = new InvLogitGaussianJointPdf(m_prefix.c_str(), + dynamic_cast & >(m_imageSet), + lawExpVector, lawCovMatrix); + + M lowerCholLawCovMatrix(lawCovMatrix); + int iRC = lowerCholLawCovMatrix.chol(); + lowerCholLawCovMatrix.zeroUpper(false); + if (iRC) { + std::cerr << "In InvLogitGaussianVectorRV::constructor() [2]: chol failed, will use svd\n"; + if (m_env.subDisplayFile()) { + *m_env.subDisplayFile() << "In InvLogitGaussianVectorRV::constructor() [2]: chol failed; will use svd; lawCovMatrix contents are\n"; + *m_env.subDisplayFile() << lawCovMatrix; // FIX ME: might demand parallelism + *m_env.subDisplayFile() << std::endl; + } + M matU (lawCovMatrix); + M matVt(m_imageSet.vectorSpace().zeroVector()); + V vecS (m_imageSet.vectorSpace().zeroVector()); + iRC = lawCovMatrix.svd(matU,vecS,matVt); + UQ_FATAL_TEST_MACRO(iRC, + m_env.worldRank(), + "InvLogitGaussianVectorRV::constructor() [2]", + "Cholesky decomposition of covariance matrix failed."); + + vecS.cwSqrt(); + m_realizer = new InvLogitGaussianVectorRealizer(m_prefix.c_str(), + dynamic_cast & >(m_imageSet), lawExpVector, matU, + vecS, matVt); + } + else { + m_realizer = new InvLogitGaussianVectorRealizer(m_prefix.c_str(), + dynamic_cast & >(m_imageSet), lawExpVector, lowerCholLawCovMatrix); + } + + m_subCdf = NULL; // FIX ME: complete code + m_unifiedCdf = NULL; // FIX ME: complete code + m_mdf = NULL; // FIX ME: complete code + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Leaving InvLogitGaussianVectorRV::constructor() [2]" + << ": prefix = " << m_prefix + << std::endl; + } +} + +template +InvLogitGaussianVectorRV::~InvLogitGaussianVectorRV() +{ + delete m_mdf; + delete m_unifiedCdf; + delete m_subCdf; + delete m_realizer; + delete m_pdf; +} + +template +void +InvLogitGaussianVectorRV::updateLawExpVector(const V & newLawExpVector) +{ + // We are sure that m_pdf (and m_realizer, etc) point to associated Gaussian + // classes, so all is well + (dynamic_cast * >(m_pdf))->updateLawExpVector( + newLawExpVector); + (dynamic_cast * >(m_realizer))-> + updateLawExpVector(newLawExpVector); + return; +} + +template +void +InvLogitGaussianVectorRV::updateLawCovMatrix(const M & newLawCovMatrix) +{ + // We are sure that m_pdf (and m_realizer, etc) point to associated Gaussian + // classes, so all is well + (dynamic_cast * >(m_pdf))->updateLawCovMatrix( + newLawCovMatrix); + + M newLowerCholLawCovMatrix(newLawCovMatrix); + int iRC = newLowerCholLawCovMatrix.chol(); + newLowerCholLawCovMatrix.zeroUpper(false); + if (iRC) { + std::cerr << "In InvLogitGaussianVectorRV::updateLawCovMatrix(): chol failed, will use svd\n"; + if (m_env.subDisplayFile()) { + *m_env.subDisplayFile() << "In InvLogitGaussianVectorRV::updateLawCovMatrix(): chol failed; will use svd; newLawCovMatrix contents are\n"; + *m_env.subDisplayFile() << newLawCovMatrix; // FIX ME: might demand parallelism + *m_env.subDisplayFile() << std::endl; + } + M matU (newLawCovMatrix); + M matVt(m_imageSet.vectorSpace().zeroVector()); + V vecS (m_imageSet.vectorSpace().zeroVector()); + iRC = newLawCovMatrix.svd(matU,vecS,matVt); + UQ_FATAL_TEST_MACRO(iRC, + m_env.worldRank(), + "InvLogitGaussianVectorRV::updateLawCovMatrix()", + "Cholesky decomposition of covariance matrix failed."); + + vecS.cwSqrt(); + (dynamic_cast * >(m_realizer))-> + updateLowerCholLawCovMatrix(matU, vecS, matVt); + } + else { + (dynamic_cast * >(m_realizer))-> + updateLowerCholLawCovMatrix(newLowerCholLawCovMatrix); + } + return; +} + +template +void +InvLogitGaussianVectorRV::print(std::ostream & os) const +{ + os << "InvLogitGaussianVectorRV::print() says, 'Please implement me.'" << std::endl; + return; +} + +} // End namespace QUESO + +template class QUESO::InvLogitGaussianVectorRV; diff --git a/src/stats/src/InvLogitGaussianVectorRealizer.C b/src/stats/src/InvLogitGaussianVectorRealizer.C new file mode 100644 index 000000000..7be731666 --- /dev/null +++ b/src/stats/src/InvLogitGaussianVectorRealizer.C @@ -0,0 +1,197 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +template +InvLogitGaussianVectorRealizer::InvLogitGaussianVectorRealizer( + const char * prefix, + const BoxSubset & unifiedImageBoxSubset, + const V & lawExpVector, + const M & lowerCholLawCovMatrix) + : BaseVectorRealizer(((std::string)(prefix)+"invlogit_gau").c_str(), + unifiedImageBoxSubset, std::numeric_limits::max()), + m_unifiedLawExpVector(new V(lawExpVector)), + m_unifiedLawVarVector( + unifiedImageBoxSubset.vectorSpace().newVector(INFINITY)), // FIX ME + m_lowerCholLawCovMatrix(new M(lowerCholLawCovMatrix)), + m_matU(NULL), + m_vecSsqrt(NULL), + m_matVt(NULL), + m_unifiedImageBoxSubset(unifiedImageBoxSubset) +{ + *m_unifiedLawExpVector = lawExpVector; +} + +template +InvLogitGaussianVectorRealizer::InvLogitGaussianVectorRealizer( + const char * prefix, + const BoxSubset & unifiedImageBoxSubset, + const V & lawExpVector, + const M & matU, + const V & vecSsqrt, + const M & matVt) + : BaseVectorRealizer(((std::string)(prefix)+"invlogit_gau").c_str(), + unifiedImageBoxSubset, std::numeric_limits::max()), + m_unifiedLawExpVector(new V(lawExpVector)), + m_unifiedLawVarVector( + unifiedImageBoxSubset.vectorSpace().newVector( INFINITY)), // FIX ME + m_lowerCholLawCovMatrix(NULL), + m_matU(new M(matU)), + m_vecSsqrt(new V(vecSsqrt)), + m_matVt(new M(matVt)), + m_unifiedImageBoxSubset(unifiedImageBoxSubset) +{ + *m_unifiedLawExpVector = lawExpVector; // ???? +} + +template +InvLogitGaussianVectorRealizer::~InvLogitGaussianVectorRealizer() +{ + delete m_matVt; + delete m_vecSsqrt; + delete m_matU; + delete m_lowerCholLawCovMatrix; + delete m_unifiedLawVarVector; + delete m_unifiedLawExpVector; +} + +template +const V & +InvLogitGaussianVectorRealizer::unifiedLawExpVector() const +{ + return *m_unifiedLawExpVector; +} + +template +const V & +InvLogitGaussianVectorRealizer::unifiedLawVarVector() const +{ + return *m_unifiedLawVarVector; +} + +template +void +InvLogitGaussianVectorRealizer::realization(V & nextValues) const +{ + V iidGaussianVector(m_unifiedImageSet.vectorSpace().zeroVector()); + + iidGaussianVector.cwSetGaussian(0.0, 1.0); + + if (m_lowerCholLawCovMatrix) { + nextValues = (*m_unifiedLawExpVector) + + (*m_lowerCholLawCovMatrix) * iidGaussianVector; + } + else if (m_matU && m_vecSsqrt && m_matVt) { + nextValues = (*m_unifiedLawExpVector) + + (*m_matU) * ((*m_vecSsqrt) * ((*m_matVt) * iidGaussianVector)); + } + else { + queso_error_msg("GaussianVectorRealizer::realization() inconsistent internal state"); + } + + V min_domain_bounds(this->m_unifiedImageBoxSubset.minValues()); + V max_domain_bounds(this->m_unifiedImageBoxSubset.maxValues()); + + for (unsigned int i = 0; i < nextValues.sizeLocal(); i++) { + double temp = std::exp(nextValues[i]); + double min_val = min_domain_bounds[i]; + double max_val = max_domain_bounds[i]; + + if (boost::math::isfinite(min_val) && + boost::math::isfinite(max_val)) { + // Left- and right-hand sides are finite. Do full transform. + nextValues[i] = (max_val * temp + min_val) / (1.0 + temp); + } + else if (boost::math::isfinite(min_val) && + !boost::math::isfinite(max_val)) { + // Left-hand side finite, but right-hand side is not. + // Do only left-hand transform. + nextValues[i] = temp + min_val; + } + else if (!boost::math::isfinite(min_val) && + boost::math::isfinite(max_val)) { + // Right-hand side is finite, but left-hand side is not. + // Do only right-hand transform. + nextValues[i] = (max_val * temp - 1.0) / temp; + } + } +} + +template +void +InvLogitGaussianVectorRealizer::updateLawExpVector( + const V & newLawExpVector) +{ + // delete old expected values (allocated at construction or last call to this function) + delete m_unifiedLawExpVector; + + m_unifiedLawExpVector = new V(newLawExpVector); +} + +template +void +InvLogitGaussianVectorRealizer::updateLowerCholLawCovMatrix( + const M & newLowerCholLawCovMatrix) +{ + // delete old expected values (allocated at construction or last call to this function) + delete m_lowerCholLawCovMatrix; + delete m_matU; + delete m_vecSsqrt; + delete m_matVt; + + m_lowerCholLawCovMatrix = new M(newLowerCholLawCovMatrix); + m_matU = NULL; + m_vecSsqrt = NULL; + m_matVt = NULL; +} + +template +void +InvLogitGaussianVectorRealizer::updateLowerCholLawCovMatrix( + const M & matU, + const V & vecSsqrt, + const M & matVt) +{ + // delete old expected values (allocated at construction or last call to this function) + delete m_lowerCholLawCovMatrix; + delete m_matU; + delete m_vecSsqrt; + delete m_matVt; + + m_lowerCholLawCovMatrix = NULL; + m_matU = new M(matU); + m_vecSsqrt = new V(vecSsqrt); + m_matVt = new M(matVt); +} + +} // End namespace QUESO + +template class QUESO::InvLogitGaussianVectorRealizer; diff --git a/src/stats/src/InverseGammaVectorRV.C b/src/stats/src/InverseGammaVectorRV.C index 02402b2fe..759641b61 100644 --- a/src/stats/src/InverseGammaVectorRV.C +++ b/src/stats/src/InverseGammaVectorRV.C @@ -23,6 +23,8 @@ //-----------------------------------------------------------------------el- #include +#include +#include #include #include diff --git a/src/stats/src/JeffreysJointPdf.C b/src/stats/src/JeffreysJointPdf.C new file mode 100644 index 000000000..ac5cb3234 --- /dev/null +++ b/src/stats/src/JeffreysJointPdf.C @@ -0,0 +1,163 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +// Constructor ------------------------------------- +template +JeffreysJointPdf::JeffreysJointPdf( + const char* prefix, + const VectorSet& domainSet) + : + BaseJointPdf(((std::string)(prefix)+"jef").c_str(), + domainSet) +{ + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Entering JeffreysJointPdf::constructor()" + << ": prefix = " << m_prefix + << std::endl; + } + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Leaving JeffreysJointPdf::constructor()" + << ": prefix = " << m_prefix + << std::endl; + } +} +// Destructor -------------------------------------- +template +JeffreysJointPdf::~JeffreysJointPdf() +{ +} +// Math methods------------------------------------- +template +double +JeffreysJointPdf::actualValue( + const V& domainVector, + const V* domainDirection, + V* gradVector, + M* hessianMatrix, + V* hessianEffect) const +{ + if (domainVector.sizeLocal() != this->m_domainSet.vectorSpace().dimLocal()) { + queso_error_msg("There is an invalid input while computing JeffreysJointPdf::actualValue()"); + } + + if (gradVector ) *gradVector = m_domainSet.vectorSpace().zeroVector(); + if (hessianMatrix) *hessianMatrix *= 0.; + if (hessianEffect) *hessianEffect = m_domainSet.vectorSpace().zeroVector(); + + if (domainDirection) {}; // just to remove compiler warning + + double pdf = 1.0; + for (unsigned int i = 0; i < domainVector.sizeLocal(); ++i){ + if (domainVector[i] < 0.0 ) { + queso_error_msg("The domain for Jeffreys prior should be greater than zero."); + } + else if ((domainVector[i] == -INFINITY ) || + (domainVector[i] == INFINITY ) || + (m_normalizationStyle != 0 )) {//TODO: R: not sure what this is doing? + pdf = 0.0; + } + else { + pdf = pdf * (1.0 / domainVector[i]); + } + } + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << " return pdf " << std::endl; + } + return pdf; // No need to multiply by exp(m_logOfNormalizationFactor) [PDF-04] +} + +//-------------------------------------------------- + +template +double +JeffreysJointPdf::lnValue( + const V& domainVector, + const V* domainDirection, + V* gradVector, + M* hessianMatrix, + V* hessianEffect) const +{ + if (gradVector ) *gradVector = m_domainSet.vectorSpace().zeroVector(); + if (hessianMatrix) *hessianMatrix *= 0.0; + if (hessianEffect) *hessianEffect = m_domainSet.vectorSpace().zeroVector(); + + if (domainVector[0]) {}; // just to remove compiler warning + if (domainDirection) {}; // just to remove compiler warning + + double pdf = 1.0; + double result = 0.; + for (unsigned int i = 0; i < domainVector.sizeLocal(); ++i){ + if (domainVector[i] < 0.0) { + queso_error_msg("The domain for Jeffreys prior should be greater than zero."); + } + else if ((domainVector[i] == -INFINITY ) || + (domainVector[i] == INFINITY ) || + (m_normalizationStyle != 0 )) {//TODO: R: not sure what this is doing? + pdf = 0.0; + result = -INFINITY; //TODO: what do we do here? + } + else { + pdf = pdf * (1.0 / domainVector[i]); + result = -log(pdf); + } + } + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << " return log(pdf) " << std::endl; + } + return result; // No need to add m_logOfNormalizationFactor [PDF-04] +} +//-------------------------------------------------- +template +double +JeffreysJointPdf::computeLogOfNormalizationFactor(unsigned int numSamples, bool updateFactorInternally) const +{ + double value = 0.; + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 2)) { + *m_env.subDisplayFile() << "Entering JeffreysJointPdf::computeLogOfNormalizationFactor()" + << std::endl; + } + value = BaseJointPdf::commonComputeLogOfNormalizationFactor(numSamples, updateFactorInternally); + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 2)) { + *m_env.subDisplayFile() << "Leaving JeffreysJointPdf::computeLogOfNormalizationFactor()" + << ", m_logOfNormalizationFactor = " << m_logOfNormalizationFactor + << std::endl; + } + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << " return normalization factor " << std::endl; + } + return value; +} + +} // End namespace QUESO + +template class QUESO::JeffreysJointPdf; diff --git a/src/stats/src/JeffreysVectorRV.C b/src/stats/src/JeffreysVectorRV.C new file mode 100644 index 000000000..a50c333b6 --- /dev/null +++ b/src/stats/src/JeffreysVectorRV.C @@ -0,0 +1,83 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +// Default constructor------------------------------- +template +JeffreysVectorRV::JeffreysVectorRV( + const char* prefix, + const VectorSet& imageSet) + : + BaseVectorRV(((std::string)(prefix)+"jef").c_str(),imageSet) +{ + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Entering JeffreysVectorRV::constructor()" + << ": prefix = " << m_prefix + << std::endl; + } + + m_pdf = new JeffreysJointPdf(m_prefix.c_str(), + m_imageSet); + m_realizer = new JeffreysVectorRealizer(m_prefix.c_str(), + m_imageSet); + m_subCdf = NULL; // FIX ME: complete code + m_unifiedCdf = NULL; // FIX ME: complete code + m_mdf = NULL; // FIX ME: complete code + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) { + *m_env.subDisplayFile() << "Leaving JeffreysVectorRV::constructor()" + << ": prefix = " << m_prefix + << std::endl; + } +} +// Destructor --------------------------------------- +template +JeffreysVectorRV::~JeffreysVectorRV() +{ + delete m_mdf; + delete m_unifiedCdf; + delete m_subCdf; + delete m_realizer; + delete m_pdf; +} +// I/O methods -------------------------------------- +template +void +JeffreysVectorRV::print(std::ostream& os) const +{ + os << "JeffreysVectorRV::print() says, 'Please implement me.'" << std::endl; + return; +} + +} // End namespace QUESO + +template class QUESO::JeffreysVectorRV; diff --git a/src/stats/src/JeffreysVectorRealizer.C b/src/stats/src/JeffreysVectorRealizer.C new file mode 100644 index 000000000..43b30d684 --- /dev/null +++ b/src/stats/src/JeffreysVectorRealizer.C @@ -0,0 +1,96 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +// Constructor ------------------------------------- +template +JeffreysVectorRealizer::JeffreysVectorRealizer( + const char* prefix, + const VectorSet& unifiedImageSet) + : + BaseVectorRealizer(((std::string)(prefix)+"gen").c_str(),unifiedImageSet,std::numeric_limits::max()) +{ + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "Entering JeffreysVectorRealizer::constructor()" + << ": prefix = " << m_prefix + << std::endl; + } + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "Leaving JeffreysVectorRealizer::constructor()" + << ": prefix = " << m_prefix + << std::endl; + } +} +// Destructor -------------------------------------- +template +JeffreysVectorRealizer::~JeffreysVectorRealizer() +{ +} +// Realization-related methods---------------------- +template +void +JeffreysVectorRealizer::realization(V& nextValues) const +{ + const BoxSubset* imageBox = dynamic_cast* >(&m_unifiedImageSet); + + if (imageBox == NULL) { + queso_error_msg("For JeffreysVectorRealizer::realization(), only box images are supported right now"); + } + //take log of Jeffreys bounds to set uniform bounds + GslVector logMinValues(imageBox->minValues()); + for (unsigned int i = 0; i < logMinValues.sizeLocal(); ++i) { + if (logMinValues[i] < 0.0) { + queso_error_msg("The minimum value for a Jeffreys distribution should be greater than or equal to zero."); + } + else { + logMinValues[i] = std::log(logMinValues[i]); + } + } + + GslVector logMaxValues(imageBox->maxValues()); + for (unsigned int i = 0; i < logMaxValues.sizeLocal(); ++i) { + if (logMaxValues[i] <= 0.0) { + queso_error_msg("The maximum value for a Jeffreys distribution should be greater than zero."); + } + else { + logMaxValues[i] = std::log(logMaxValues[i]); + } + } + + nextValues.cwSetUniform(logMinValues,logMaxValues); + for (unsigned int i = 0; i < nextValues.sizeLocal(); ++i) { + nextValues[i] = std::exp(nextValues[i]); + } + return; +} + +} // End namespace QUESO + +template class QUESO::JeffreysVectorRealizer; diff --git a/src/stats/src/LogNormalVectorRV.C b/src/stats/src/LogNormalVectorRV.C index 2eb58bb36..6c8893f5e 100644 --- a/src/stats/src/LogNormalVectorRV.C +++ b/src/stats/src/LogNormalVectorRV.C @@ -23,6 +23,8 @@ //-----------------------------------------------------------------------el- #include +#include +#include #include #include diff --git a/src/stats/src/MLSampling.C b/src/stats/src/MLSampling.C index 8f2a6d552..6721f42fa 100644 --- a/src/stats/src/MLSampling.C +++ b/src/stats/src/MLSampling.C @@ -343,6 +343,10 @@ void MLSampling::prepareBalLinkedChains_inter0( // EXTRA FOR LOAD BALANCE const MLSamplingLevelOptions* currOptions, // input const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input std::vector& exchangeStdVec, // input/output BalancedLinkedChainsPerNodeStruct& balancedLinkControl) // output { @@ -468,6 +472,10 @@ MLSampling::prepareBalLinkedChains_inter0( // EXTRA FOR LOAD BALANCE // balancedLinkControl.linkedChains at each node ////////////////////////////////////////////////////////////////////////// mpiExchangePositions_inter0(prevChain, + prevExponent, + currExponent, + prevLogLikelihoodValues, + prevLogTargetValues, exchangeStdVec, finalNumChainsPerNode, finalNumPositionsPerNode, // It is already valid at all "management" nodes (not only at node 0) because of the sanity check above @@ -673,6 +681,8 @@ MLSampling::generateBalLinkedChains_all( // EXTRA FOR LOAD BALANCE } P_V auxInitialPosition(m_vectorSpace.zeroVector()); + double auxInitialLogPrior; + double auxInitialLogLikelihood; unsigned int chainIdMax = 0; if (m_env.inter0Rank() >= 0) { @@ -743,6 +753,8 @@ MLSampling::generateBalLinkedChains_all( // EXTRA FOR LOAD BALANCE if (m_env.inter0Rank() >= 0) { // aqui 4 auxInitialPosition = *(balancedLinkControl.balLinkedChains[chainId].initialPosition); // Round Rock + auxInitialLogPrior = balancedLinkControl.balLinkedChains[chainId].initialLogPrior; + auxInitialLogLikelihood = balancedLinkControl.balLinkedChains[chainId].initialLogLikelihood; tmpChainSize = balancedLinkControl.balLinkedChains[chainId].numberOfPositions+1; // IMPORTANT: '+1' in order to discard initial position afterwards if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 3)) { @@ -757,6 +769,7 @@ MLSampling::generateBalLinkedChains_all( // EXTRA FOR LOAD BALANCE } } auxInitialPosition.mpiBcast(0, m_env.subComm()); // Yes, 'subComm', important // KAUST + #if 0 // For debug only for (int r = 0; r < m_env.subComm().NumProc(); ++r) { if (r == m_env.subComm().MyPID()) { @@ -781,18 +794,37 @@ MLSampling::generateBalLinkedChains_all( // EXTRA FOR LOAD BALANCE ScalarSequence tmpLogLikelihoodValues(m_env,0,""); ScalarSequence tmpLogTargetValues (m_env,0,""); - // KAUST: all nodes should call here - MetropolisHastingsSG mcSeqGenerator(inputOptions, - rv, - auxInitialPosition, // KEY new: pass logPrior and logLikelihood - &unifiedCovMatrix); - - // KAUST: all nodes should call here - mcSeqGenerator.generateSequence(tmpChain, - &tmpLogLikelihoodValues, // likelihood is IMPORTANT - &tmpLogTargetValues); MHRawChainInfoStruct mcRawInfo; - mcSeqGenerator.getRawChainInfo(mcRawInfo); + if (inputOptions.m_initialPositionUsePreviousLevelLikelihood) { // ml_likelihood_caching + m_env.subComm().Bcast((void *) &auxInitialLogPrior, (int) 1, RawValue_MPI_DOUBLE, 0, // Yes, 'subComm', important + "MLSamplingClass::generateBalLinkedChains_all()", + "failed MPI.Bcast() for auxInitialLogPrior"); + m_env.subComm().Bcast((void *) &auxInitialLogLikelihood, (int) 1, RawValue_MPI_DOUBLE, 0, // Yes, 'subComm', important + "MLSamplingClass::generateBalLinkedChains_all()", + "failed MPI.Bcast() for auxInitialLogLikelihood"); + + MetropolisHastingsSG mcSeqGenerator(inputOptions, + rv, + auxInitialPosition, // KEY new: pass logPrior and logLikelihood + auxInitialLogPrior, + auxInitialLogLikelihood, + &unifiedCovMatrix); + mcSeqGenerator.generateSequence(tmpChain, + &tmpLogLikelihoodValues, // likelihood is IMPORTANT + &tmpLogTargetValues); + mcSeqGenerator.getRawChainInfo(mcRawInfo); + } + else { + MetropolisHastingsSG mcSeqGenerator(inputOptions, + rv, + auxInitialPosition, + &unifiedCovMatrix); + mcSeqGenerator.generateSequence(tmpChain, + &tmpLogLikelihoodValues, // likelihood is IMPORTANT + &tmpLogTargetValues); + mcSeqGenerator.getRawChainInfo(mcRawInfo); + } + cumulativeRunTime += mcRawInfo.runTime; cumulativeRejections += mcRawInfo.numRejections; @@ -894,6 +926,10 @@ MLSampling::generateUnbLinkedChains_all( const UnbalancedLinkedChainsPerNodeStruct& unbalancedLinkControl, // input // Round Rock unsigned int indexOfFirstWeight, // input // Round Rock const SequenceOfVectors& prevChain, // input // Round Rock + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input SequenceOfVectors & workingChain, // output double& cumulativeRunTime, // output unsigned int& cumulativeRejections, // output @@ -910,6 +946,8 @@ MLSampling::generateUnbLinkedChains_all( } P_V auxInitialPosition(m_vectorSpace.zeroVector()); + double auxInitialLogPrior; + double auxInitialLogLikelihood; unsigned int chainIdMax = 0; if (m_env.inter0Rank() >= 0) { @@ -972,12 +1010,18 @@ MLSampling::generateUnbLinkedChains_all( (m_currStep == 10)) { //m_env.setExceptionalCircumstance(true); } + double expRatio = currExponent; + if (prevExponent > 0.0) { + expRatio /= prevExponent; + } unsigned int cumulativeNumPositions = 0; for (unsigned int chainId = 0; chainId < chainIdMax; ++chainId) { unsigned int tmpChainSize = 0; if (m_env.inter0Rank() >= 0) { unsigned int auxIndex = unbalancedLinkControl.unbLinkedChains[chainId].initialPositionIndexInPreviousChain - indexOfFirstWeight; // KAUST4 // Round Rock prevChain.getPositionValues(auxIndex,auxInitialPosition); // Round Rock + auxInitialLogPrior = prevLogTargetValues[auxIndex] - prevLogLikelihoodValues[auxIndex]; + auxInitialLogLikelihood = expRatio * prevLogLikelihoodValues[auxIndex]; tmpChainSize = unbalancedLinkControl.unbLinkedChains[chainId].numberOfPositions+1; // IMPORTANT: '+1' in order to discard initial position afterwards if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 3)) { @@ -992,6 +1036,7 @@ MLSampling::generateUnbLinkedChains_all( } } auxInitialPosition.mpiBcast(0, m_env.subComm()); // Yes, 'subComm', important // KAUST + #if 0 // For debug only for (int r = 0; r < m_env.subComm().NumProc(); ++r) { if (r == m_env.subComm().MyPID()) { @@ -1017,17 +1062,36 @@ MLSampling::generateUnbLinkedChains_all( ScalarSequence tmpLogTargetValues (m_env,0,""); // KAUST: all nodes should call here - MetropolisHastingsSG mcSeqGenerator(inputOptions, - rv, - auxInitialPosition, // KEY new: pass logPrior and logLikelihood - &unifiedCovMatrix); - - // KAUST: all nodes should call here - mcSeqGenerator.generateSequence(tmpChain, - &tmpLogLikelihoodValues, // likelihood is IMPORTANT - &tmpLogTargetValues); MHRawChainInfoStruct mcRawInfo; - mcSeqGenerator.getRawChainInfo(mcRawInfo); + if (inputOptions.m_initialPositionUsePreviousLevelLikelihood) { // ml_likelihood_caching + m_env.subComm().Bcast((void *) &auxInitialLogPrior, (int) 1, RawValue_MPI_DOUBLE, 0, // Yes, 'subComm', important + "MLSamplingClass::generateUnbLinkedChains_all()", + "failed MPI.Bcast() for auxInitialLogPrior"); + m_env.subComm().Bcast((void *) &auxInitialLogLikelihood, (int) 1, RawValue_MPI_DOUBLE, 0, // Yes, 'subComm', important + "MLSamplingClass::generateUnbLinkedChains_all", + "failed MPI.Bcast() for auxInitialLogLikelihood"); + MetropolisHastingsSG mcSeqGenerator(inputOptions, + rv, + auxInitialPosition, // KEY new: pass logPrior and logLikelihood + auxInitialLogPrior, + auxInitialLogLikelihood, + &unifiedCovMatrix); + mcSeqGenerator.generateSequence(tmpChain, + &tmpLogLikelihoodValues, // likelihood is IMPORTANT + &tmpLogTargetValues); + mcSeqGenerator.getRawChainInfo(mcRawInfo); + } + else { + MetropolisHastingsSG mcSeqGenerator(inputOptions, + rv, + auxInitialPosition, + &unifiedCovMatrix); + mcSeqGenerator.generateSequence(tmpChain, + &tmpLogLikelihoodValues, // likelihood is IMPORTANT + &tmpLogTargetValues); + mcSeqGenerator.getRawChainInfo(mcRawInfo); + } + cumulativeRunTime += mcRawInfo.runTime; cumulativeRejections += mcRawInfo.numRejections; @@ -1820,16 +1884,27 @@ template void MLSampling::mpiExchangePositions_inter0( // EXTRA FOR LOAD BALANCE const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input const std::vector& exchangeStdVec, // input const std::vector& finalNumChainsPerNode, // input const std::vector& finalNumPositionsPerNode, // input BalancedLinkedChainsPerNodeStruct& balancedLinkControl) // output { - if (m_env.inter0Rank() < 0) return; + if (m_env.inter0Rank() < 0) { + return; + } unsigned int Np = (unsigned int) m_env.inter0Comm().NumProc(); unsigned int Nc = exchangeStdVec.size(); + double expRatio = currExponent; + if (prevExponent > 0.0) { + expRatio /= prevExponent; + } + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 0)) { *m_env.subDisplayFile() << "Entering MLSampling::mpiExchangePositions_inter0()" << ", level " << m_currLevel+LEVEL_REF_ID @@ -1935,27 +2010,30 @@ MLSampling::mpiExchangePositions_inter0( // EXTRA FOR LOAD BALANCE // Prepare counters and buffers for gatherv of initial positions ////////////////////////////////////////////////////////////////////////// unsigned int dimSize = m_vectorSpace.dimLocal(); + unsigned int nValuesPerInitialPosition = dimSize + 2; P_V auxInitialPosition(m_vectorSpace.zeroVector()); std::vector sendbuf(0); unsigned int sendcnt = 0; if (m_env.inter0Rank() != (int) r) { - sendcnt = numberOfInitialPositionsNodeRHasToReceiveFromNode[m_env.inter0Rank()] * dimSize; + sendcnt = numberOfInitialPositionsNodeRHasToReceiveFromNode[m_env.inter0Rank()] * nValuesPerInitialPosition; sendbuf.resize(sendcnt); for (unsigned int i = 0; i < numberOfInitialPositionsNodeRHasToReceiveFromNode[m_env.inter0Rank()]; ++i) { unsigned int auxIndex = indexesOfInitialPositionsNodeRHasToReceiveFromMe[i]; prevChain.getPositionValues(auxIndex,auxInitialPosition); for (unsigned int j = 0; j < dimSize; ++j) { - sendbuf[i*dimSize + j] = auxInitialPosition[j]; + sendbuf[i*nValuesPerInitialPosition + j] = auxInitialPosition[j]; } + sendbuf[i*nValuesPerInitialPosition + dimSize] = prevLogLikelihoodValues[auxIndex]; + sendbuf[i*nValuesPerInitialPosition + dimSize + 1] = prevLogTargetValues[auxIndex]; } } std::vector recvbuf(0); std::vector recvcnts(Np,0); // '0' is already the correct value for recvcnts[r] if (m_env.inter0Rank() == (int) r) { - recvbuf.resize(totalNumberOfInitialPositionsNodeRHasToReceive * dimSize); + recvbuf.resize(totalNumberOfInitialPositionsNodeRHasToReceive * nValuesPerInitialPosition); for (unsigned int nodeId = 0; nodeId < Np; ++nodeId) { // Yes, from '0' on (for 'r', numberOf...ToReceiveFromNode[r] = 0 anyway) - recvcnts[nodeId] = numberOfInitialPositionsNodeRHasToReceiveFromNode[nodeId/*m_env.inter0Rank()*/] * dimSize; + recvcnts[nodeId] = numberOfInitialPositionsNodeRHasToReceiveFromNode[nodeId/*m_env.inter0Rank()*/] * nValuesPerInitialPosition; } } @@ -1997,18 +2075,25 @@ MLSampling::mpiExchangePositions_inter0( // EXTRA FOR LOAD BALANCE for (unsigned int i = 0; i < Nc; ++i) { if ((exchangeStdVec[i].finalNodeOfInitialPosition == (int) r) && (exchangeStdVec[i].originalNodeOfInitialPosition == (int) r)) { - prevChain.getPositionValues(exchangeStdVec[i].originalIndexOfInitialPosition,auxInitialPosition); + unsigned int originalIndex = exchangeStdVec[i].originalIndexOfInitialPosition; + prevChain.getPositionValues(originalIndex, auxInitialPosition); balancedLinkControl.balLinkedChains[auxIndex].initialPosition = new P_V(auxInitialPosition); + balancedLinkControl.balLinkedChains[auxIndex].initialLogPrior = prevLogTargetValues[originalIndex] - prevLogLikelihoodValues[originalIndex]; + balancedLinkControl.balLinkedChains[auxIndex].initialLogLikelihood = expRatio*prevLogLikelihoodValues[originalIndex]; balancedLinkControl.balLinkedChains[auxIndex].numberOfPositions = exchangeStdVec[i].numberOfPositions; auxIndex++; - } + } } for (unsigned int i = 0; i < totalNumberOfInitialPositionsNodeRHasToReceive; ++i) { for (unsigned int j = 0; j < dimSize; ++j) { - auxInitialPosition[j] = recvbuf[i*dimSize + j]; + auxInitialPosition[j] = recvbuf[i*nValuesPerInitialPosition + j]; } balancedLinkControl.balLinkedChains[auxIndex].initialPosition = new P_V(auxInitialPosition); + double prevLogLikelihood = recvbuf[i*nValuesPerInitialPosition + dimSize]; + double prevLogTarget = recvbuf[i*nValuesPerInitialPosition + dimSize + 1]; + balancedLinkControl.balLinkedChains[auxIndex].initialLogPrior = prevLogTarget - prevLogLikelihood; + balancedLinkControl.balLinkedChains[auxIndex].initialLogLikelihood = expRatio*prevLogLikelihood; balancedLinkControl.balLinkedChains[auxIndex].numberOfPositions = chainLenghtsNodeRHasToInherit[i]; // aqui 3 auxIndex++; } @@ -3200,6 +3285,10 @@ MLSampling::generateSequence_Step07_inter0( UnbalancedLinkedChainsPerNodeStruct& unbalancedLinkControl, // (possible) output const MLSamplingLevelOptions* currOptions, // input const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input std::vector& exchangeStdVec, // (possible) input/output BalancedLinkedChainsPerNodeStruct& balancedLinkControl) // (possible) output { @@ -3219,6 +3308,10 @@ MLSampling::generateSequence_Step07_inter0( if (useBalancedChains) { prepareBalLinkedChains_inter0(currOptions, // input prevChain, // input + prevExponent, // input + currExponent, // input + prevLogLikelihoodValues, // input + prevLogTargetValues, // input exchangeStdVec, // input/output balancedLinkControl); // output } @@ -3287,6 +3380,10 @@ template void MLSampling::generateSequence_Step09_all( const SequenceOfVectors& prevChain, // input + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input unsigned int indexOfFirstWeight, // input unsigned int indexOfLastWeight, // input const std::vector& unifiedWeightStdVectorAtProc0Only, // input @@ -3536,6 +3633,10 @@ MLSampling::generateSequence_Step09_all( if (useBalancedChains) { prepareBalLinkedChains_inter0(currOptions, // input prevChain, // input + prevExponent, // input + currExponent, // input + prevLogLikelihoodValues, // input + prevLogTargetValues, // input exchangeStdVec, // input/output nowBalLinkControl); // output } @@ -3603,6 +3704,10 @@ MLSampling::generateSequence_Step09_all( nowUnbLinkControl, // input // Round Rock indexOfFirstWeight, // input // Round Rock prevChain, // input // Round Rock + prevExponent, // input + currExponent, // input + prevLogLikelihoodValues, // input + prevLogTargetValues, // input nowChain, // output nowRunTime, // output nowRejections, // output @@ -3763,6 +3868,10 @@ MLSampling::generateSequence_Step10_all( const UnbalancedLinkedChainsPerNodeStruct& unbalancedLinkControl, // input // Round Rock unsigned int indexOfFirstWeight, // input // Round Rock const SequenceOfVectors& prevChain, // input // Round Rock + double prevExponent, // input + double currExponent, // input + const ScalarSequence& prevLogLikelihoodValues, // input + const ScalarSequence& prevLogTargetValues, // input const BalancedLinkedChainsPerNodeStruct& balancedLinkControl, // input // Round Rock SequenceOfVectors & currChain, // output double& cumulativeRawChainRunTime, // output @@ -3821,6 +3930,10 @@ MLSampling::generateSequence_Step10_all( unbalancedLinkControl, // input // Round Rock indexOfFirstWeight, // input // Round Rock prevChain, // input // Round Rock + prevExponent, // input + currExponent, // input + prevLogLikelihoodValues, // input + prevLogTargetValues, // input currChain, // output cumulativeRawChainRunTime, // output cumulativeRawChainRejections, // output @@ -4314,6 +4427,9 @@ MLSampling::generateSequence( MLSamplingLevelOptions* currOptions = NULL; // step 1 SequenceOfVectors* prevChain = NULL; // step 2 + ScalarSequence prevLogLikelihoodValues(m_env, 0, ""); + ScalarSequence prevLogTargetValues (m_env, 0, ""); + double prevExponent = 0.; // step 3 unsigned int indexOfFirstWeight = 0; // step 2 unsigned int indexOfLastWeight = 0; // step 2 P_M* unifiedCovMatrix = NULL; // step 4 @@ -4361,14 +4477,12 @@ MLSampling::generateSequence( // Step 2 of 11: save [chain and corresponding target pdf values] from previous level //*********************************************************** m_currStep = 2; - double prevExponent = currExponent; + prevExponent = currExponent; double prevEta = currEta; unsigned int prevUnifiedRequestedNumSamples = currUnifiedRequestedNumSamples; prevChain = new SequenceOfVectors(m_vectorSpace, 0, m_options.m_prefix+"prev_chain"); - ScalarSequence prevLogLikelihoodValues(m_env,0,""); - ScalarSequence prevLogTargetValues (m_env,0,""); indexOfFirstWeight = 0; indexOfLastWeight = 0; @@ -4378,6 +4492,7 @@ MLSampling::generateSequence( generateSequence_Step02_inter0(currOptions, // input currChain, // input/output // restate currLogLikelihoodValues, // input/output // restate + currLogTargetValues, // input/output // restate *prevChain, // output prevLogLikelihoodValues, // output @@ -4500,6 +4615,10 @@ MLSampling::generateSequence( *unbalancedLinkControl, // (possible) output currOptions, // input *prevChain, // input + prevExponent, // input + currExponent, // input + prevLogLikelihoodValues, // input + prevLogTargetValues, // input exchangeStdVec, // (possible) input/output *balancedLinkControl); // (possible) output } @@ -4528,6 +4647,10 @@ MLSampling::generateSequence( //*********************************************************** m_currStep = 9; generateSequence_Step09_all(*prevChain, // input + prevExponent, // input + currExponent, // input + prevLogLikelihoodValues, // input + prevLogTargetValues, // input indexOfFirstWeight, // input indexOfLastWeight, // input unifiedWeightStdVectorAtProc0Only, // input @@ -4581,7 +4704,7 @@ MLSampling::generateSequence( currLogTargetValues = prevLogTargetValues; } } // while (tryExponentEta) // gpmsa1 - + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 0)) { // gpmsa1 *m_env.subDisplayFile() << "In MLSampling::generateSequence()" << ", level " << m_currLevel+LEVEL_REF_ID @@ -4603,6 +4726,10 @@ MLSampling::generateSequence( *unbalancedLinkControl, // input // Round Rock indexOfFirstWeight, // input // Round Rock *prevChain, // input // Round Rock + prevExponent, // input + currExponent, // input + prevLogLikelihoodValues, // input + prevLogTargetValues, // input *balancedLinkControl, // input // Round Rock currChain, // output cumulativeRawChainRunTime, // output diff --git a/src/stats/src/MLSamplingLevelOptions.C b/src/stats/src/MLSamplingLevelOptions.C index 8f6cfea46..2b2d30496 100644 --- a/src/stats/src/MLSamplingLevelOptions.C +++ b/src/stats/src/MLSamplingLevelOptions.C @@ -55,6 +55,7 @@ MLSamplingLevelOptions::MLSamplingLevelOptions( m_initialPositionDataInputFileType (UQ_ML_SAMPLING_L_INITIAL_POSITION_DATA_INPUT_FILE_TYPE_ODV), m_initialProposalCovMatrixDataInputFileName(UQ_ML_SAMPLING_L_INITIAL_PROPOSAL_COV_MATRIX_DATA_INPUT_FILE_NAME_ODV), m_initialProposalCovMatrixDataInputFileType(UQ_ML_SAMPLING_L_INITIAL_PROPOSAL_COV_MATRIX_DATA_INPUT_FILE_TYPE_ODV), + m_initialPositionUsePreviousLevelLikelihood(UQ_ML_SAMPLING_L_INITIAL_POSITION_USE_PREVIOUS_LEVEL_LIKELIHOOD_ODV), // ml_likelihood_caching //m_parameterDisabledSet (), // gpmsa2 m_str2 (""), m_initialValuesOfDisabledParameters (0), @@ -133,6 +134,7 @@ MLSamplingLevelOptions::MLSamplingLevelOptions( m_option_initialPosition_dataInputFileType (m_prefix + "initialPosition_dataInputFileType" ), m_option_initialProposalCovMatrix_dataInputFileName(m_prefix + "initialProposalCovMatrix_dataInputFileName"), m_option_initialProposalCovMatrix_dataInputFileType(m_prefix + "initialProposalCovMatrix_dataInputFileType"), + m_option_initialPositionUsePreviousLevelLikelihood (m_prefix + "initialPositionUsePreviousLevelLikelihood" ), // ml_likelihood_caching m_option_listOfDisabledParameters (m_prefix + "listOfDisabledParameters" ), // gpmsa2 m_option_initialValuesOfDisabledParameters (m_prefix + "initialValuesOfDisabledParameters" ), // gpmsa2 m_option_rawChain_dataInputFileName (m_prefix + "rawChain_dataInputFileName" ), @@ -175,7 +177,8 @@ MLSamplingLevelOptions::MLSamplingLevelOptions( m_option_am_adaptedMatrices_dataOutputAllowAll (m_prefix + "amAdaptedMatrices_dataOutputAllowAll" ), m_option_am_adaptedMatrices_dataOutputAllowedSet (m_prefix + "amAdaptedMatrices_dataOutputAllowedSet" ), m_option_am_eta (m_prefix + "am_eta" ), - m_option_am_epsilon (m_prefix + "am_epsilon" ) + m_option_am_epsilon (m_prefix + "am_epsilon" ), + m_option_doLogitTransform (m_prefix + "doLogitTransform" ) { } @@ -205,6 +208,7 @@ MLSamplingLevelOptions::copyOptionsValues(const MLSamplingLevelOptions& srcOptio m_initialPositionDataInputFileType = srcOptions.m_initialPositionDataInputFileType; m_initialProposalCovMatrixDataInputFileName = srcOptions.m_initialProposalCovMatrixDataInputFileName; m_initialProposalCovMatrixDataInputFileType = srcOptions.m_initialProposalCovMatrixDataInputFileType; + m_initialPositionUsePreviousLevelLikelihood = srcOptions.m_initialPositionUsePreviousLevelLikelihood; m_parameterDisabledSet = srcOptions.m_parameterDisabledSet; // gpmsa2 m_str2 = srcOptions.m_str2; // gpmsa2 m_initialValuesOfDisabledParameters = srcOptions.m_initialValuesOfDisabledParameters; @@ -259,6 +263,7 @@ MLSamplingLevelOptions::copyOptionsValues(const MLSamplingLevelOptions& srcOptio m_str7 = srcOptions.m_str7; m_amEta = srcOptions.m_amEta; m_amEpsilon = srcOptions.m_amEpsilon; + m_doLogitTransform = srcOptions.m_doLogitTransform; return; } @@ -336,6 +341,7 @@ MLSamplingLevelOptions::defineMyOptions(po::options_description& optionsDesc) co (m_option_initialPosition_dataInputFileType.c_str(), po::value()->default_value(m_initialPositionDataInputFileType ), "type of input file for initial position" ) (m_option_initialProposalCovMatrix_dataInputFileName.c_str(), po::value()->default_value(m_initialProposalCovMatrixDataInputFileName), "name of input file for initial proposal covariance matrix" ) (m_option_initialProposalCovMatrix_dataInputFileType.c_str(), po::value()->default_value(m_initialProposalCovMatrixDataInputFileType), "type of input file for initial proposal covariance matrix" ) + (m_option_initialPositionUsePreviousLevelLikelihood.c_str(), po::value()->default_value(m_initialPositionUsePreviousLevelLikelihood), "use previous level likelihood for initial chain position instead of re-computing from target pdf") (m_option_listOfDisabledParameters.c_str(), po::value()->default_value(m_str2 ), "list of disabled parameters" ) // gpmsa2 (m_option_initialValuesOfDisabledParameters.c_str(), po::value()->default_value(m_str3 ), "initial values of disabled parameters" ) // gpmsa2 (m_option_rawChain_dataInputFileName.c_str(), po::value()->default_value(m_rawChainDataInputFileName ), "name of input file for raw chain " ) @@ -379,6 +385,7 @@ MLSamplingLevelOptions::defineMyOptions(po::options_description& optionsDesc) co (m_option_am_adaptedMatrices_dataOutputAllowedSet.c_str(), po::value()->default_value(m_str7 ), "type of output file for 'am' adapted matrices" ) (m_option_am_eta.c_str(), po::value()->default_value(m_amEta ), "'am' eta" ) (m_option_am_epsilon.c_str(), po::value()->default_value(m_amEpsilon ), "'am' epsilon" ) + (m_option_doLogitTransform.c_str(), po::value()->default_value(UQ_ML_SAMPLING_L_DO_LOGIT_TRANSFORM ), "flag for doing logit transform for bounded domains" ) ; return; @@ -558,6 +565,10 @@ MLSamplingLevelOptions::getMyOptionValues(po::options_description& optionsDesc) m_initialProposalCovMatrixDataInputFileType = ((const po::variable_value&) m_env.allOptionsMap()[m_option_initialProposalCovMatrix_dataInputFileType.c_str()]).as(); } + if (m_env.allOptionsMap().count(m_option_initialPositionUsePreviousLevelLikelihood.c_str())) { // ml_likelihood_caching + m_initialPositionUsePreviousLevelLikelihood = ((const po::variable_value&) m_env.allOptionsMap()[m_option_initialPositionUsePreviousLevelLikelihood.c_str()]).as(); + } + if (m_env.allOptionsMap().count(m_option_listOfDisabledParameters)) { // gpmsa2 m_parameterDisabledSet.clear(); std::vector tmpAllow(0,0.); @@ -847,7 +858,9 @@ MLSamplingLevelOptions::getMyOptionValues(po::options_description& optionsDesc) m_amEpsilon = ((const po::variable_value&) m_env.allOptionsMap()[m_option_am_epsilon.c_str()]).as(); } - return; + if (m_env.allOptionsMap().count(m_option_doLogitTransform.c_str())) { + m_doLogitTransform = ((const po::variable_value&) m_env.allOptionsMap()[m_option_doLogitTransform.c_str()]).as(); + } } void @@ -879,6 +892,7 @@ MLSamplingLevelOptions::print(std::ostream& os) const << "\n" << m_option_initialPosition_dataInputFileType << " = " << m_initialPositionDataInputFileType << "\n" << m_option_initialProposalCovMatrix_dataInputFileName << " = " << m_initialProposalCovMatrixDataInputFileName << "\n" << m_option_initialProposalCovMatrix_dataInputFileType << " = " << m_initialProposalCovMatrixDataInputFileType + << "\n" << m_option_initialPositionUsePreviousLevelLikelihood << " = " << m_initialPositionUsePreviousLevelLikelihood << "\n" << m_option_listOfDisabledParameters << " = "; // gpmsa2 for (std::set::iterator setIt = m_parameterDisabledSet.begin(); setIt != m_parameterDisabledSet.end(); ++setIt) { os << *setIt << " "; @@ -942,6 +956,7 @@ MLSamplingLevelOptions::print(std::ostream& os) const } os << "\n" << m_option_am_eta << " = " << m_amEta << "\n" << m_option_am_epsilon << " = " << m_amEpsilon + << "\n" << m_option_doLogitTransform << " = " << m_doLogitTransform << std::endl; return; diff --git a/src/stats/src/MetropolisHastingsSG.C b/src/stats/src/MetropolisHastingsSG.C index 1ba05b3cf..9f42c2c6c 100644 --- a/src/stats/src/MetropolisHastingsSG.C +++ b/src/stats/src/MetropolisHastingsSG.C @@ -22,12 +22,18 @@ // //-----------------------------------------------------------------------el- +#include #include #include #include #include #include +#include + +#include + +#include namespace QUESO { @@ -158,7 +164,10 @@ MetropolisHastingsSG::MetropolisHastingsSG( #else m_alternativeOptionsValues (), #endif - m_optionsObj (NULL) + m_optionsObj (NULL), + m_computeInitialPriorAndLikelihoodValues(true), + m_initialLogPriorValue (0.), + m_initialLogLikelihoodValue (0.) { if (inputProposalCovMatrix != NULL) { m_initialProposalCovMatrix = *inputProposalCovMatrix; @@ -206,6 +215,91 @@ MetropolisHastingsSG::MetropolisHastingsSG( << std::endl; } } +template +MetropolisHastingsSG::MetropolisHastingsSG( + /*! Prefix */ const char* prefix, + /*! Options (if no input file) */ const MhOptionsValues* alternativeOptionsValues, // dakota + /*! The source RV */ const BaseVectorRV& sourceRv, + /*! Initial chain position */ const P_V& initialPosition, + /*! Initial chain prior */ double initialLogPrior, + /*! Initial chain likelihood */ double initialLogLikelihood, + /*! Proposal cov. matrix */ const P_M* inputProposalCovMatrix) + : + m_env (sourceRv.env()), + m_vectorSpace (sourceRv.imageSet().vectorSpace()), + m_targetPdf (sourceRv.pdf()), + m_initialPosition (initialPosition), + m_initialProposalCovMatrix (m_vectorSpace.zeroVector()), + m_nullInputProposalCovMatrix(inputProposalCovMatrix == NULL), + m_numDisabledParameters (0), // gpmsa2 + m_parameterEnabledStatus (m_vectorSpace.dimLocal(),true), // gpmsa2 + m_targetPdfSynchronizer (new ScalarFunctionSynchronizer(m_targetPdf,m_initialPosition)), + m_tk (NULL), + m_positionIdForDebugging (0), + m_stageIdForDebugging (0), + m_idsOfUniquePositions (0),//0.), + m_logTargets (0),//0.), + m_alphaQuotients (0),//0.), + m_lastChainSize (0), + m_lastMean (NULL), + m_lastAdaptedCovMatrix (NULL), + m_numPositionsNotSubWritten (0), +#ifdef QUESO_USES_SEQUENCE_STATISTICAL_OPTIONS + m_alternativeOptionsValues (NULL,NULL), +#else + m_alternativeOptionsValues (), +#endif + m_optionsObj (NULL), + m_computeInitialPriorAndLikelihoodValues(false), + m_initialLogPriorValue (initialLogPrior), + m_initialLogLikelihoodValue (initialLogLikelihood) +{ + if (inputProposalCovMatrix != NULL) { + m_initialProposalCovMatrix = *inputProposalCovMatrix; + } + if (alternativeOptionsValues) m_alternativeOptionsValues = *alternativeOptionsValues; + if (m_env.optionsInputFileName() == "") { + m_optionsObj = new MetropolisHastingsSGOptions(m_env,prefix,m_alternativeOptionsValues); + } + else { + m_optionsObj = new MetropolisHastingsSGOptions(m_env,prefix); + m_optionsObj->scanOptionsValues(); + } + + if ((m_env.subDisplayFile() ) && + (m_optionsObj->m_ov.m_totallyMute == false)) { + *m_env.subDisplayFile() << "Entering MetropolisHastingsSG::constructor(2)" + << ": prefix = " << prefix + << ", alternativeOptionsValues = " << alternativeOptionsValues + << ", m_env.optionsInputFileName() = " << m_env.optionsInputFileName() + << ", m_initialProposalCovMatrix = " << m_initialProposalCovMatrix + << std::endl; + } + + UQ_FATAL_TEST_MACRO(sourceRv.imageSet().vectorSpace().dimLocal() != initialPosition.sizeLocal(), + m_env.worldRank(), + "MetropolisHastingsSG::constructor(2)", + "'sourceRv' and 'initialPosition' should have equal dimensions"); + + if (inputProposalCovMatrix) { + UQ_FATAL_TEST_MACRO(sourceRv.imageSet().vectorSpace().dimLocal() != inputProposalCovMatrix->numRowsLocal(), + m_env.worldRank(), + "MetropolisHastingsSG::constructor(2)", + "'sourceRv' and 'inputProposalCovMatrix' should have equal dimensions"); + UQ_FATAL_TEST_MACRO(inputProposalCovMatrix->numCols() != inputProposalCovMatrix->numRowsGlobal(), + m_env.worldRank(), + "MetropolisHastingsSG::constructor(2)", + "'inputProposalCovMatrix' should be a square matrix"); + } + + commonConstructor(); + + if ((m_env.subDisplayFile() ) && + (m_optionsObj->m_ov.m_totallyMute == false)) { + *m_env.subDisplayFile() << "Leaving MetropolisHastingsSG::constructor(2)" + << std::endl; + } +} // Constructor ------------------------------------- template MetropolisHastingsSG::MetropolisHastingsSG( @@ -237,20 +331,23 @@ MetropolisHastingsSG::MetropolisHastingsSG( #else m_alternativeOptionsValues (), #endif - m_optionsObj (new MetropolisHastingsSGOptions(mlOptions)) + m_optionsObj (new MetropolisHastingsSGOptions(mlOptions)), + m_computeInitialPriorAndLikelihoodValues(true), + m_initialLogPriorValue (0.), + m_initialLogLikelihoodValue (0.) { if (inputProposalCovMatrix != NULL) { m_initialProposalCovMatrix = *inputProposalCovMatrix; if ((m_env.subDisplayFile() ) && (m_optionsObj->m_ov.m_totallyMute == false)) { - *m_env.subDisplayFile() << "In MetropolisHastingsSG::constructor(2)" + *m_env.subDisplayFile() << "In MetropolisHastingsSG::constructor(3)" << ": just set m_initialProposalCovMatrix = " << m_initialProposalCovMatrix << std::endl; } } if ((m_env.subDisplayFile() ) && (m_optionsObj->m_ov.m_totallyMute == false)) { - *m_env.subDisplayFile() << "Entering MetropolisHastingsSG::constructor(2)" + *m_env.subDisplayFile() << "Entering MetropolisHastingsSG::constructor(3)" << std::endl; } @@ -258,7 +355,68 @@ MetropolisHastingsSG::MetropolisHastingsSG( if ((m_env.subDisplayFile() ) && (m_optionsObj->m_ov.m_totallyMute == false)) { - *m_env.subDisplayFile() << "Leaving MetropolisHastingsSG::constructor(2)" + *m_env.subDisplayFile() << "Leaving MetropolisHastingsSG::constructor(3)" + << std::endl; + } +} +// Constructor ------------------------------------- +template +MetropolisHastingsSG::MetropolisHastingsSG( + const MLSamplingLevelOptions& mlOptions, + const BaseVectorRV& sourceRv, + const P_V& initialPosition, // KEY + double initialLogPrior, + double initialLogLikelihood, + const P_M* inputProposalCovMatrix) + : + m_env (sourceRv.env()), + m_vectorSpace (sourceRv.imageSet().vectorSpace()), + m_targetPdf (sourceRv.pdf()), + m_initialPosition (initialPosition), + m_initialProposalCovMatrix (m_vectorSpace.zeroVector()), + m_nullInputProposalCovMatrix(inputProposalCovMatrix == NULL), + m_numDisabledParameters (0), // gpmsa2 + m_parameterEnabledStatus (m_vectorSpace.dimLocal(),true), // gpmsa2 + m_targetPdfSynchronizer (new ScalarFunctionSynchronizer(m_targetPdf,m_initialPosition)), + m_tk (NULL), + m_positionIdForDebugging (0), + m_stageIdForDebugging (0), + m_idsOfUniquePositions (0),//0.), + m_logTargets (0),//0.), + m_alphaQuotients (0),//0.), + m_lastChainSize (0), + m_lastMean (NULL), + m_lastAdaptedCovMatrix (NULL), +#ifdef QUESO_USES_SEQUENCE_STATISTICAL_OPTIONS + m_alternativeOptionsValues (NULL,NULL), +#else + m_alternativeOptionsValues (), +#endif + m_optionsObj (new MetropolisHastingsSGOptions(mlOptions)), + m_computeInitialPriorAndLikelihoodValues(false), + m_initialLogPriorValue (initialLogPrior), + m_initialLogLikelihoodValue (initialLogLikelihood) +{ + if (inputProposalCovMatrix != NULL) { + m_initialProposalCovMatrix = *inputProposalCovMatrix; + if ((m_env.subDisplayFile() ) && + (m_optionsObj->m_ov.m_totallyMute == false)) { + *m_env.subDisplayFile() << "In MetropolisHastingsSG::constructor(4)" + << ": just set m_initialProposalCovMatrix = " << m_initialProposalCovMatrix + << std::endl; + } + } + if ((m_env.subDisplayFile() ) && + (m_optionsObj->m_ov.m_totallyMute == false)) { + *m_env.subDisplayFile() << "Entering MetropolisHastingsSG::constructor(4)" + << std::endl; + } + + commonConstructor(); + + if ((m_env.subDisplayFile() ) && + (m_optionsObj->m_ov.m_totallyMute == false)) { + *m_env.subDisplayFile() << "Leaving MetropolisHastingsSG::constructor(4)" << std::endl; } } @@ -368,10 +526,25 @@ MetropolisHastingsSG::commonConstructor() "proposal cov matrix should have been passed by user, since, according to the input algorithm options, local Hessians will not be used in the proposal"); } - m_tk = new ScaledCovMatrixTKGroup(m_optionsObj->m_prefix.c_str(), - m_vectorSpace, - drScalesAll, - m_initialProposalCovMatrix); + // Decide whether or not to do logit transform + if (m_optionsObj->m_ov.m_doLogitTransform) { + // Variable transform initial proposal cov matrix + transformInitialCovMatrixToGaussianSpace( + dynamic_cast & >(m_targetPdf.domainSet())); + + // We need this dynamic_cast to BoxSubset so that m_tk can inspect the + // domain bounds and do the necessary transform + m_tk = new TransformedScaledCovMatrixTKGroup( + m_optionsObj->m_prefix.c_str(), + dynamic_cast & >(m_targetPdf.domainSet()), + drScalesAll, m_initialProposalCovMatrix); + } + else { + m_tk = new ScaledCovMatrixTKGroup( + m_optionsObj->m_prefix.c_str(), m_vectorSpace, drScalesAll, + m_initialProposalCovMatrix); + } + if ((m_env.subDisplayFile() ) && (m_optionsObj->m_ov.m_totallyMute == false)) { *m_env.subDisplayFile() << "In MetropolisHastingsSG::commonConstructor()" @@ -434,8 +607,10 @@ MetropolisHastingsSG::alpha( } else { double yLogTargetToUse = y.logTarget(); + if (m_tk->symmetric()) { alphaQuotient = std::exp(yLogTargetToUse - x.logTarget()); + if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 3 ) && (m_optionsObj->m_ov.m_totallyMute == false)) { @@ -450,30 +625,22 @@ MetropolisHastingsSG::alpha( } } else { -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN double qyx = m_tk->rv(yStageId).pdf().lnValue(x.vecValues(),NULL,NULL,NULL,NULL); -#else - double qyx = -.5 * m_tk->rv(yStageId).pdf().lnValue(x.vecValues(),NULL,NULL,NULL,NULL); -#endif if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 10 ) && (m_optionsObj->m_ov.m_totallyMute == false)) { - const GaussianJointPdf* pdfYX = dynamic_cast< const GaussianJointPdf* >(&(m_tk->rv(yStageId).pdf())); + const InvLogitGaussianJointPdf* pdfYX = dynamic_cast< const InvLogitGaussianJointPdf* >(&(m_tk->rv(yStageId).pdf())); *m_env.subDisplayFile() << "In MetropolisHastingsSG::alpha(x,y)" << ", rvYX.lawExpVector = " << pdfYX->lawExpVector() << ", rvYX.lawVarVector = " << pdfYX->lawVarVector() << ", rvYX.lawCovMatrix = " << pdfYX->lawCovMatrix() << std::endl; } -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN double qxy = m_tk->rv(xStageId).pdf().lnValue(y.vecValues(),NULL,NULL,NULL,NULL); -#else - double qxy = -.5 * m_tk->rv(xStageId).pdf().lnValue(y.vecValues(),NULL,NULL,NULL,NULL); -#endif if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 10 ) && (m_optionsObj->m_ov.m_totallyMute == false)) { - const GaussianJointPdf* pdfXY = dynamic_cast< const GaussianJointPdf* >(&(m_tk->rv(xStageId).pdf())); + const InvLogitGaussianJointPdf* pdfXY = dynamic_cast< const InvLogitGaussianJointPdf* >(&(m_tk->rv(xStageId).pdf())); *m_env.subDisplayFile() << "In MetropolisHastingsSG::alpha(x,y)" << ", rvXY.lawExpVector = " << pdfXY->lawExpVector() << ", rvXY.lawVarVector = " << pdfXY->lawVarVector() @@ -618,13 +785,8 @@ MetropolisHastingsSG::alpha( const P_V& _lastTKPosition = m_tk->preComputingPosition( tkStageIds[inputSize-1]); const P_V& _lastBackwardTKPosition = m_tk->preComputingPosition(backwardTKStageIds[inputSize-1]); -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN double numContrib = m_tk->rv(backwardTKStageIdsLess1).pdf().lnValue(_lastBackwardTKPosition,NULL,NULL,NULL,NULL); - double denContrib = m_tk->rv( tkStageIdsLess1).pdf().lnValue(_lastTKPosition ,NULL,NULL,NULL,NULL); -#else - double numContrib = -.5 * m_tk->rv(backwardTKStageIdsLess1).pdf().lnValue(_lastBackwardTKPosition,NULL,NULL,NULL,NULL); - double denContrib = -.5 * m_tk->rv( tkStageIdsLess1).pdf().lnValue(_lastTKPosition ,NULL,NULL,NULL,NULL); -#endif + double denContrib = m_tk->rv(tkStageIdsLess1).pdf().lnValue(_lastTKPosition,NULL,NULL,NULL,NULL); if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 10 ) && (m_optionsObj->m_ov.m_totallyMute == false)) { @@ -651,13 +813,8 @@ MetropolisHastingsSG::alpha( tkStageIdsLess1.pop_back(); backwardTKStageIdsLess1.pop_back(); -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN numContrib = m_tk->rv(backwardTKStageIdsLess1).pdf().lnValue(lastBackwardTKPosition,NULL,NULL,NULL,NULL); - denContrib = m_tk->rv( tkStageIdsLess1).pdf().lnValue(lastTKPosition ,NULL,NULL,NULL,NULL); -#else - numContrib = -.5 * m_tk->rv(backwardTKStageIdsLess1).pdf().lnValue(lastBackwardTKPosition,NULL,NULL,NULL,NULL); - denContrib = -.5 * m_tk->rv( tkStageIdsLess1).pdf().lnValue(lastTKPosition ,NULL,NULL,NULL,NULL); -#endif + denContrib = m_tk->rv(tkStageIdsLess1).pdf().lnValue(lastTKPosition,NULL,NULL,NULL,NULL); if ((m_env.subDisplayFile() ) && (m_env.displayVerbosity() >= 10 ) && (m_optionsObj->m_ov.m_totallyMute == false)) { @@ -799,6 +956,13 @@ MetropolisHastingsSG::writeInfo( return iRC; } +template +const BaseTKGroup & +MetropolisHastingsSG::transitionKernel() const +{ + return *m_tk; +} + // Statistical methods ----------------------------- /* This operation currently implements the DRAM algorithm (Heikki Haario, Marko * Laine, Antonietta Mira and Eero Saksman, "DRAM: Efficient Adaptive MCMC", @@ -820,12 +984,33 @@ MetropolisHastingsSG::generateSequence( << std::endl; } - UQ_FATAL_TEST_MACRO(m_vectorSpace.dimLocal() != workingChain.vectorSizeLocal(), - m_env.worldRank(), - "MetropolisHastingsSG::generateSequence()", - "'m_vectorSpace' and 'workingChain' are related to vector spaces of different dimensions"); + if (m_vectorSpace.dimLocal() != workingChain.vectorSizeLocal()) { + std::cerr << "'m_vectorSpace' and 'workingChain' are related to vector" + << "spaces of different dimensions" + << std::endl; + queso_error(); + } + + // Set a flag to write out log likelihood or not + bool writeLogLikelihood; + if ((workingLogLikelihoodValues != NULL) && + (m_optionsObj->m_ov.m_outputLogLikelihood)) { + writeLogLikelihood = true; + } + else { + writeLogLikelihood = false; + } + + // Set a flag to write out log target or not + bool writeLogTarget; + if ((workingLogTargetValues != NULL) && + (m_optionsObj->m_ov.m_outputLogTarget)) { + writeLogTarget = true; + } + else { + writeLogTarget = false; + } - //m_env.syncPrintDebugMsg("Entering MetropolisHastingsSG::generateSequence()",2,3000000,m_env.fullComm()); // Dangerous to barrier on fullComm ... // KAUST MiscCheckTheParallelEnvironment(m_initialPosition, m_initialPosition); @@ -922,18 +1107,18 @@ MetropolisHastingsSG::generateSequence( << std::endl; } - if (workingLogLikelihoodValues) { + if (writeLogLikelihood) { workingLogLikelihoodValues->subWriteContents(m_optionsObj->m_ov.m_rawChainSize - m_numPositionsNotSubWritten, m_numPositionsNotSubWritten, - m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_likelihood", + m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_loglikelihood", m_optionsObj->m_ov.m_rawChainDataOutputFileType, m_optionsObj->m_ov.m_rawChainDataOutputAllowedSet); } - if (workingLogTargetValues) { + if (writeLogTarget) { workingLogTargetValues->subWriteContents(m_optionsObj->m_ov.m_rawChainSize - m_numPositionsNotSubWritten, m_numPositionsNotSubWritten, - m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_target", + m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_logtarget", m_optionsObj->m_ov.m_rawChainDataOutputFileType, m_optionsObj->m_ov.m_rawChainDataOutputAllowedSet); } @@ -1024,13 +1209,13 @@ MetropolisHastingsSG::generateSequence( << std::endl; } - if (workingLogLikelihoodValues) { - workingLogLikelihoodValues->unifiedWriteContents(m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_likelihood", + if (writeLogLikelihood) { + workingLogLikelihoodValues->unifiedWriteContents(m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_loglikelihood", m_optionsObj->m_ov.m_rawChainDataOutputFileType); } - if (workingLogTargetValues) { - workingLogTargetValues->unifiedWriteContents(m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_target", + if (writeLogTarget) { + workingLogTargetValues->unifiedWriteContents(m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_logtarget", m_optionsObj->m_ov.m_rawChainDataOutputFileType); } @@ -1155,18 +1340,18 @@ MetropolisHastingsSG::generateSequence( << std::endl; } - if (workingLogLikelihoodValues) { + if (writeLogLikelihood) { workingLogLikelihoodValues->subWriteContents(0, workingChain.subSequenceSize(), - m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_likelihood", + m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_loglikelihood", m_optionsObj->m_ov.m_filteredChainDataOutputFileType, m_optionsObj->m_ov.m_filteredChainDataOutputAllowedSet); } - if (workingLogTargetValues) { + if (writeLogTarget) { workingLogTargetValues->subWriteContents(0, workingChain.subSequenceSize(), - m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_target", + m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_logtarget", m_optionsObj->m_ov.m_filteredChainDataOutputFileType, m_optionsObj->m_ov.m_filteredChainDataOutputAllowedSet); } @@ -1188,13 +1373,13 @@ MetropolisHastingsSG::generateSequence( << std::endl; } - if (workingLogLikelihoodValues) { - workingLogLikelihoodValues->unifiedWriteContents(m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_likelihood", + if (writeLogLikelihood) { + workingLogLikelihoodValues->unifiedWriteContents(m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_loglikelihood", m_optionsObj->m_ov.m_filteredChainDataOutputFileType); } - if (workingLogTargetValues) { - workingLogTargetValues->unifiedWriteContents(m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_target", + if (writeLogTarget) { + workingLogTargetValues->unifiedWriteContents(m_optionsObj->m_ov.m_filteredChainDataOutputFileName + "_logtarget", m_optionsObj->m_ov.m_filteredChainDataOutputFileType); } } @@ -1311,6 +1496,26 @@ MetropolisHastingsSG::generateFullChain( *m_env.subDisplayFile() << std::endl; } + // Set a flag to write out log likelihood or not + bool writeLogLikelihood; + if ((workingLogLikelihoodValues != NULL) && + (m_optionsObj->m_ov.m_outputLogLikelihood)) { + writeLogLikelihood = true; + } + else { + writeLogLikelihood = false; + } + + // Set a flag to write out log target or not + bool writeLogTarget; + if ((workingLogTargetValues != NULL) && + (m_optionsObj->m_ov.m_outputLogTarget)) { + writeLogTarget = true; + } + else { + writeLogTarget = false; + } + bool outOfTargetSupport = !m_targetPdf.domainSet().contains(valuesOf1stPosition); if ((m_env.subDisplayFile()) && (outOfTargetSupport )) { @@ -1326,26 +1531,44 @@ MetropolisHastingsSG::generateFullChain( m_env.worldRank(), "MetropolisHastingsSG::generateFullChain()", "initial position should not be out of target pdf support"); - if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) iRC = gettimeofday(&timevalTarget, NULL); + double logPrior = 0.; double logLikelihood = 0.; -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN - double logTarget = m_targetPdfSynchronizer->callFunction(&valuesOf1stPosition,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment // KEY -#else - double logTarget = -0.5 * m_targetPdfSynchronizer->callFunction(&valuesOf1stPosition,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment -#endif - if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) m_rawChainInfo.targetRunTime += MiscGetEllapsedSeconds(&timevalTarget); - m_rawChainInfo.numTargetCalls++; - if ((m_env.subDisplayFile() ) && - (m_env.displayVerbosity() >= 3 ) && - (m_optionsObj->m_ov.m_totallyMute == false)) { - *m_env.subDisplayFile() << "In MetropolisHastingsSG::generateFullChain()" - << ": just returned from likelihood() for initial chain position" - << ", m_rawChainInfo.numTargetCalls = " << m_rawChainInfo.numTargetCalls - << ", logPrior = " << logPrior - << ", logLikelihood = " << logLikelihood - << ", logTarget = " << logTarget - << std::endl; + double logTarget = 0.; + if (m_computeInitialPriorAndLikelihoodValues) { + if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) iRC = gettimeofday(&timevalTarget, NULL); + logTarget = m_targetPdfSynchronizer->callFunction(&valuesOf1stPosition,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment // KEY + if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) { + m_rawChainInfo.targetRunTime += MiscGetEllapsedSeconds(&timevalTarget); + } + m_rawChainInfo.numTargetCalls++; + if ((m_env.subDisplayFile() ) && + (m_env.displayVerbosity() >= 3 ) && + (m_optionsObj->m_ov.m_totallyMute == false)) { + *m_env.subDisplayFile() << "In MetropolisHastingsSG::generateFullChain()" + << ": just returned from likelihood() for initial chain position" + << ", m_rawChainInfo.numTargetCalls = " << m_rawChainInfo.numTargetCalls + << ", logPrior = " << logPrior + << ", logLikelihood = " << logLikelihood + << ", logTarget = " << logTarget + << std::endl; + } + } + else { + logPrior = m_initialLogPriorValue; + logLikelihood = m_initialLogLikelihoodValue; + logTarget = logPrior + logLikelihood; + if ((m_env.subDisplayFile() ) && + (m_env.displayVerbosity() >= 3 ) && + (m_optionsObj->m_ov.m_totallyMute == false)) { + *m_env.subDisplayFile() << "In MetropolisHastingsSG::generateFullChain()" + << ": used input prior and likelihood for initial chain position" + << ", m_rawChainInfo.numTargetCalls = " << m_rawChainInfo.numTargetCalls + << ", logPrior = " << logPrior + << ", logLikelihood = " << logLikelihood + << ", logTarget = " << logTarget + << std::endl; + } } //*m_env.subDisplayFile() << "AQUI 001" << std::endl; @@ -1391,18 +1614,18 @@ MetropolisHastingsSG::generateFullChain( << std::endl; } - if (workingLogLikelihoodValues) { + if (writeLogLikelihood) { workingLogLikelihoodValues->subWriteContents(0 + 1 - m_optionsObj->m_ov.m_rawChainDataOutputPeriod, m_optionsObj->m_ov.m_rawChainDataOutputPeriod, - m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_likelihood", + m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_loglikelihood", m_optionsObj->m_ov.m_rawChainDataOutputFileType, m_optionsObj->m_ov.m_rawChainDataOutputAllowedSet); } - if (workingLogTargetValues) { + if (writeLogTarget) { workingLogTargetValues->subWriteContents(0 + 1 - m_optionsObj->m_ov.m_rawChainDataOutputPeriod, m_optionsObj->m_ov.m_rawChainDataOutputPeriod, - m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_target", + m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_logtarget", m_optionsObj->m_ov.m_rawChainDataOutputFileType, m_optionsObj->m_ov.m_rawChainDataOutputAllowedSet); } @@ -1499,11 +1722,14 @@ MetropolisHastingsSG::generateFullChain( // Point 2/6 of logic for new position // Loop: generate new position //**************************************************** - // sep2011 bool keepGeneratingCandidates = true; while (keepGeneratingCandidates) { - if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) iRC = gettimeofday(&timevalCandidate, NULL); + if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) { + iRC = gettimeofday(&timevalCandidate, NULL); + } + m_tk->rv(0).realizer().realization(tmpVecValues); + if (m_numDisabledParameters > 0) { // gpmsa2 for (unsigned int paramId = 0; paramId < m_vectorSpace.dimLocal(); ++paramId) { if (m_parameterEnabledStatus[paramId] == false) { @@ -1557,11 +1783,7 @@ MetropolisHastingsSG::generateFullChain( } else { if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) iRC = gettimeofday(&timevalTarget, NULL); -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN - logTarget = m_targetPdfSynchronizer->callFunction(&tmpVecValues,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment -#else - logTarget = -0.5 * m_targetPdfSynchronizer->callFunction(&tmpVecValues,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment -#endif + logTarget = m_targetPdfSynchronizer->callFunction(&tmpVecValues,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) m_rawChainInfo.targetRunTime += MiscGetEllapsedSeconds(&timevalTarget); m_rawChainInfo.numTargetCalls++; if ((m_env.subDisplayFile() ) && @@ -1737,11 +1959,7 @@ MetropolisHastingsSG::generateFullChain( } else { if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) iRC = gettimeofday(&timevalTarget, NULL); -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN - logTarget = m_targetPdfSynchronizer->callFunction(&tmpVecValues,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment -#else - logTarget = -0.5 * m_targetPdfSynchronizer->callFunction(&tmpVecValues,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment -#endif + logTarget = m_targetPdfSynchronizer->callFunction(&tmpVecValues,NULL,NULL,NULL,NULL,&logPrior,&logLikelihood); // Might demand parallel environment if (m_optionsObj->m_ov.m_rawChainMeasureRunTimes) m_rawChainInfo.targetRunTime += MiscGetEllapsedSeconds(&timevalTarget); m_rawChainInfo.numTargetCalls++; if ((m_env.subDisplayFile() ) && @@ -1837,18 +2055,18 @@ MetropolisHastingsSG::generateFullChain( << std::endl; } - if (workingLogLikelihoodValues) { + if (writeLogLikelihood) { workingLogLikelihoodValues->subWriteContents(0 + 1 - m_optionsObj->m_ov.m_rawChainDataOutputPeriod, m_optionsObj->m_ov.m_rawChainDataOutputPeriod, - m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_likelihood", + m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_loglikelihood", m_optionsObj->m_ov.m_rawChainDataOutputFileType, m_optionsObj->m_ov.m_rawChainDataOutputAllowedSet); } - if (workingLogTargetValues) { + if (writeLogTarget) { workingLogTargetValues->subWriteContents(0 + 1 - m_optionsObj->m_ov.m_rawChainDataOutputPeriod, m_optionsObj->m_ov.m_rawChainDataOutputPeriod, - m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_target", + m_optionsObj->m_ov.m_rawChainDataOutputFileName + "_logtarget", m_optionsObj->m_ov.m_rawChainDataOutputFileType, m_optionsObj->m_ov.m_rawChainDataOutputAllowedSet); } @@ -1924,7 +2142,22 @@ MetropolisHastingsSG::generateFullChain( P_V transporterVec(m_vectorSpace.zeroVector()); for (unsigned int i = 0; i < partialChain.subSequenceSize(); ++i) { workingChain.getPositionValues(idOfFirstPositionInSubChain+i,transporterVec); - partialChain.setPositionValues(i,transporterVec); + + // Transform to the space without boundaries. This is the space + // where the proposal distribution is Gaussian + if (this->m_optionsObj->m_ov.m_doLogitTransform == true) { + // Only do this when we don't use the Hessian (this may change in + // future, but transformToGaussianSpace() is only implemented in + // TransformedScaledCovMatrixTKGroup + P_V transformedTransporterVec(m_vectorSpace.zeroVector()); + dynamic_cast* >( + m_tk)->transformToGaussianSpace(transporterVec, + transformedTransporterVec); + partialChain.setPositionValues(i, transformedTransporterVec); + } + else { + partialChain.setPositionValues(i, transporterVec); + } } updateAdaptedCovMatrix(partialChain, idOfFirstPositionInSubChain, @@ -2062,7 +2295,6 @@ MetropolisHastingsSG::generateFullChain( tmpCholIsPositiveDefinite = true; } if (tmpCholIsPositiveDefinite) { - ScaledCovMatrixTKGroup* tempTK = dynamic_cast* >(m_tk); P_M tmpMatrix(m_optionsObj->m_ov.m_amEta*attemptedMatrix); if (m_numDisabledParameters > 0) { // gpmsa2 for (unsigned int paramId = 0; paramId < m_vectorSpace.dimLocal(); ++paramId) { @@ -2077,7 +2309,14 @@ MetropolisHastingsSG::generateFullChain( } } } - tempTK->updateLawCovMatrix(tmpMatrix); + if (this->m_optionsObj->m_ov.m_doLogitTransform) { + (dynamic_cast* >(m_tk)) + ->updateLawCovMatrix(tmpMatrix); + } + else{ + (dynamic_cast* >(m_tk)) + ->updateLawCovMatrix(tmpMatrix); + } #ifdef UQ_DRAM_MCG_REQUIRES_INVERTED_COV_MATRICES UQ_FATAL_RC_MACRO(UQ_INCOMPLETE_IMPLEMENTATION_RC, @@ -2280,6 +2519,57 @@ MetropolisHastingsSG::updateAdaptedCovMatrix( return; } +template +void +MetropolisHastingsSG::transformInitialCovMatrixToGaussianSpace( + const BoxSubset & boxSubset) +{ + P_V min_domain_bounds(boxSubset.minValues()); + P_V max_domain_bounds(boxSubset.maxValues()); + + for (unsigned int i = 0; i < min_domain_bounds.sizeLocal(); i++) { + double min_val = min_domain_bounds[i]; + double max_val = max_domain_bounds[i]; + + if (boost::math::isfinite(min_val) && boost::math::isfinite(max_val)) { + if (m_initialProposalCovMatrix(i, i) >= max_val - min_val) { + // User is trying to specify a uniform proposal distribution, which + // is unsupported. Throw an error for now. + std::cerr << "Proposal variance element " + << i + << " is " + << m_initialProposalCovMatrix(i, i) + << " but domain is of size " + << max_val - min_val + << std::endl; + std::cerr << "QUESO does not support uniform-like proposal " + << "distributions. Try making the proposal variance smaller" + << std::endl; + } + + // The mean is the midpoint + double midpoint = (max_val + min_val) / 2.0; + double derivative = (1.0 / (midpoint - min_val)) + + (1.0 / (max_val - midpoint)); + + // User specified the covariance matrix on a bounded domain, so we + // need to *divide* by the derivative of the transform + double transformJacobian = 1.0 / derivative; + + // Just do the multiplication by hand for now. There's no method in + // Gsl(Vector|Matrix) to do this for me. + for (unsigned int j = 0; j < min_domain_bounds.sizeLocal(); j++) { + // Multiply column j by element j + m_initialProposalCovMatrix(j, i) *= transformJacobian; + } + for (unsigned int j = 0; j < min_domain_bounds.sizeLocal(); j++) { + // Multiply row j by element j + m_initialProposalCovMatrix(i, j) *= transformJacobian; + } + } + } +} + } // End namespace QUESO template class QUESO::MetropolisHastingsSG; diff --git a/src/stats/src/MetropolisHastingsSGOptions.C b/src/stats/src/MetropolisHastingsSGOptions.C index 397bcfcc9..9437cf080 100644 --- a/src/stats/src/MetropolisHastingsSGOptions.C +++ b/src/stats/src/MetropolisHastingsSGOptions.C @@ -90,7 +90,10 @@ MhOptionsValues::MhOptionsValues( m_amEta (UQ_MH_SG_AM_ETA_ODV), m_amEpsilon (UQ_MH_SG_AM_EPSILON_ODV), m_enableBrooksGelmanConvMonitor (UQ_MH_SG_ENABLE_BROOKS_GELMAN_CONV_MONITOR), - m_BrooksGelmanLag (UQ_MH_SG_BROOKS_GELMAN_LAG) + m_BrooksGelmanLag (UQ_MH_SG_BROOKS_GELMAN_LAG), + m_outputLogLikelihood (UQ_MH_SG_OUTPUT_LOG_LIKELIHOOD), + m_outputLogTarget (UQ_MH_SG_OUTPUT_LOG_TARGET), + m_doLogitTransform (UQ_MH_SG_DO_LOGIT_TRANSFORM) #ifdef QUESO_USES_SEQUENCE_STATISTICAL_OPTIONS , m_alternativeRawSsOptionsValues (), @@ -178,6 +181,9 @@ MhOptionsValues::copy(const MhOptionsValues& src) m_amEpsilon = src.m_amEpsilon; m_enableBrooksGelmanConvMonitor = src.m_enableBrooksGelmanConvMonitor; m_BrooksGelmanLag = src.m_BrooksGelmanLag; + m_outputLogLikelihood = src.m_outputLogLikelihood; + m_outputLogTarget = src.m_outputLogTarget; + m_doLogitTransform = src.m_doLogitTransform; #ifdef QUESO_USES_SEQUENCE_STATISTICAL_OPTIONS m_alternativeRawSsOptionsValues = src.m_alternativeRawSsOptionsValues; @@ -259,7 +265,10 @@ MetropolisHastingsSGOptions::MetropolisHastingsSGOptions( m_option_am_eta (m_prefix + "am_eta" ), m_option_am_epsilon (m_prefix + "am_epsilon" ), m_option_enableBrooksGelmanConvMonitor (m_prefix + "enableBrooksGelmanConvMonitor" ), - m_option_BrooksGelmanLag (m_prefix + "BrooksGelmanLag" ) + m_option_BrooksGelmanLag (m_prefix + "BrooksGelmanLag" ), + m_option_outputLogLikelihood (m_prefix + "outputLogLikelihood" ), + m_option_outputLogTarget (m_prefix + "outputLogTarget" ), + m_option_doLogitTransform (m_prefix + "doLogitTransform" ) { UQ_FATAL_TEST_MACRO(m_env.optionsInputFileName() == "", m_env.worldRank(), @@ -334,7 +343,10 @@ MetropolisHastingsSGOptions::MetropolisHastingsSGOptions( m_option_am_eta (m_prefix + "am_eta" ), m_option_am_epsilon (m_prefix + "am_epsilon" ), m_option_enableBrooksGelmanConvMonitor (m_prefix + "enableBrooksGelmanConvMonitor" ), - m_option_BrooksGelmanLag (m_prefix + "BrooksGelmanLag" ) + m_option_BrooksGelmanLag (m_prefix + "BrooksGelmanLag" ), + m_option_outputLogLikelihood (m_prefix + "outputLogLikelihood" ), + m_option_outputLogTarget (m_prefix + "outputLogTarget" ), + m_option_doLogitTransform (m_prefix + "doLogitTransform" ) { UQ_FATAL_TEST_MACRO(m_env.optionsInputFileName() != "", m_env.worldRank(), @@ -429,7 +441,10 @@ MetropolisHastingsSGOptions::MetropolisHastingsSGOptions( m_option_am_eta (m_prefix + "am_eta" ), m_option_am_epsilon (m_prefix + "am_epsilon" ), m_option_enableBrooksGelmanConvMonitor (m_prefix + "enableBrooksGelmanConvMonitor" ), - m_option_BrooksGelmanLag (m_prefix + "BrooksGelmanLag" ) + m_option_BrooksGelmanLag (m_prefix + "BrooksGelmanLag" ), + m_option_outputLogLikelihood (m_prefix + "outputLogLikelihood" ), + m_option_outputLogTarget (m_prefix + "outputLogTarget" ), + m_option_doLogitTransform (m_prefix + "doLogitTransform" ) { m_ov.m_dataOutputFileName = mlOptions.m_dataOutputFileName; m_ov.m_dataOutputAllowAll = mlOptions.m_dataOutputAllowAll; @@ -483,6 +498,9 @@ MetropolisHastingsSGOptions::MetropolisHastingsSGOptions( m_ov.m_amEpsilon = mlOptions.m_amEpsilon; m_ov.m_enableBrooksGelmanConvMonitor = UQ_MH_SG_ENABLE_BROOKS_GELMAN_CONV_MONITOR; m_ov.m_BrooksGelmanLag = UQ_MH_SG_BROOKS_GELMAN_LAG; + m_ov.m_outputLogLikelihood = UQ_MH_SG_OUTPUT_LOG_LIKELIHOOD; + m_ov.m_outputLogTarget = UQ_MH_SG_OUTPUT_LOG_TARGET; + m_ov.m_doLogitTransform = mlOptions.m_doLogitTransform; #ifdef QUESO_USES_SEQUENCE_STATISTICAL_OPTIONS //m_ov.m_alternativeRawSsOptionsValues = mlOptions.; // dakota @@ -625,6 +643,9 @@ MetropolisHastingsSGOptions::print(std::ostream& os) const << "\n" << m_option_am_epsilon << " = " << m_ov.m_amEpsilon << "\n" << m_option_enableBrooksGelmanConvMonitor << " = " << m_ov.m_enableBrooksGelmanConvMonitor << "\n" << m_option_BrooksGelmanLag << " = " << m_ov.m_BrooksGelmanLag + << "\n" << m_option_outputLogLikelihood << " = " << m_ov.m_outputLogLikelihood + << "\n" << m_option_outputLogTarget << " = " << m_ov.m_outputLogTarget + << "\n" << m_option_doLogitTransform << " = " << m_ov.m_doLogitTransform << std::endl; return; @@ -679,7 +700,7 @@ MetropolisHastingsSGOptions::defineMyOptions(po::options_description& optionsDes (m_option_am_keepInitialMatrix.c_str(), po::value()->default_value(UQ_MH_SG_AM_KEEP_INITIAL_MATRIX_ODV ), "'am' keep initial (given) matrix" ) (m_option_am_initialNonAdaptInterval.c_str(), po::value()->default_value(UQ_MH_SG_AM_INIT_NON_ADAPT_INT_ODV ), "'am' initial non adaptation interval" ) (m_option_am_adaptInterval.c_str(), po::value()->default_value(UQ_MH_SG_AM_ADAPT_INTERVAL_ODV ), "'am' adaptation interval" ) - (m_option_am_adaptedMatrices_dataOutputPeriod.c_str(), po::value()->default_value(UQ_MH_SG_AM_ADAPTED_MATRICES_DATA_OUTPUT_PERIOD_ODV ), "period for outputting 'am' adapted matrices" ) + (m_option_am_adaptedMatrices_dataOutputPeriod.c_str(), po::value()->default_value(UQ_MH_SG_AM_ADAPTED_MATRICES_DATA_OUTPUT_PERIOD_ODV ), "period for outputting 'am' adapted matrices" ) (m_option_am_adaptedMatrices_dataOutputFileName.c_str(), po::value()->default_value(UQ_MH_SG_AM_ADAPTED_MATRICES_DATA_OUTPUT_FILE_NAME_ODV ), "name of output file for 'am' adapted matrices" ) (m_option_am_adaptedMatrices_dataOutputFileType.c_str(), po::value()->default_value(UQ_MH_SG_AM_ADAPTED_MATRICES_DATA_OUTPUT_FILE_TYPE_ODV ), "type of output file for 'am' adapted matrices" ) (m_option_am_adaptedMatrices_dataOutputAllowAll.c_str(), po::value()->default_value(UQ_MH_SG_AM_ADAPTED_MATRICES_DATA_OUTPUT_ALLOW_ALL_ODV ), "type of output file for 'am' adapted matrices" ) @@ -688,6 +709,9 @@ MetropolisHastingsSGOptions::defineMyOptions(po::options_description& optionsDes (m_option_am_epsilon.c_str(), po::value()->default_value(UQ_MH_SG_AM_EPSILON_ODV ), "'am' epsilon" ) (m_option_enableBrooksGelmanConvMonitor.c_str(), po::value()->default_value(UQ_MH_SG_ENABLE_BROOKS_GELMAN_CONV_MONITOR ), "assess convergence using Brooks-Gelman metric" ) (m_option_BrooksGelmanLag.c_str(), po::value()->default_value(UQ_MH_SG_BROOKS_GELMAN_LAG ), "number of chain positions before starting to compute metric") + (m_option_outputLogLikelihood.c_str(), po::value()->default_value(UQ_MH_SG_OUTPUT_LOG_LIKELIHOOD ), "flag to toggle output of log likelihood values" ) + (m_option_outputLogTarget.c_str(), po::value()->default_value(UQ_MH_SG_OUTPUT_LOG_TARGET ), "flag to toggle output of log target values" ) + (m_option_doLogitTransform.c_str(), po::value()->default_value(UQ_MH_SG_DO_LOGIT_TRANSFORM ), "flag to toggle logit transform for bounded domains" ) ; return; @@ -990,7 +1014,17 @@ MetropolisHastingsSGOptions::getMyOptionValues(po::options_description& optionsD m_ov.m_BrooksGelmanLag = ((const po::variable_value&) m_env.allOptionsMap()[m_option_BrooksGelmanLag]).as(); } - return; + if (m_env.allOptionsMap().count(m_option_outputLogLikelihood)) { + m_ov.m_outputLogLikelihood = ((const po::variable_value&) m_env.allOptionsMap()[m_option_outputLogLikelihood]).as(); + } + + if (m_env.allOptionsMap().count(m_option_outputLogTarget)) { + m_ov.m_outputLogTarget = ((const po::variable_value&) m_env.allOptionsMap()[m_option_outputLogTarget]).as(); + } + + if (m_env.allOptionsMap().count(m_option_doLogitTransform)) { + m_ov.m_doLogitTransform = ((const po::variable_value&) m_env.allOptionsMap()[m_option_doLogitTransform]).as(); + } } // -------------------------------------------------- diff --git a/src/stats/src/ScaledCovMatrixTKGroup.C b/src/stats/src/ScaledCovMatrixTKGroup.C index 5846033a8..0327eac83 100644 --- a/src/stats/src/ScaledCovMatrixTKGroup.C +++ b/src/stats/src/ScaledCovMatrixTKGroup.C @@ -25,6 +25,7 @@ #include #include #include +#include namespace QUESO { @@ -75,7 +76,7 @@ ScaledCovMatrixTKGroup::symmetric() const //--------------------------------------------------- template const GaussianVectorRV& -ScaledCovMatrixTKGroup::rv(unsigned int stageId) +ScaledCovMatrixTKGroup::rv(unsigned int stageId) const { UQ_FATAL_TEST_MACRO(m_rvs.size() == 0, m_env.worldRank(), @@ -105,9 +106,12 @@ ScaledCovMatrixTKGroup::rv(unsigned int stageId) << ", vector = " << *m_preComputingPositions[stageId] // FIX ME: might demand parallelism << std::endl; } - m_rvs[0]->updateLawExpVector(*m_preComputingPositions[stageId]); - return (*m_rvs[0]); + GaussianVectorRV * gaussian_rv = dynamic_cast * >(m_rvs[0]); + + gaussian_rv->updateLawExpVector(*m_preComputingPositions[stageId]); + + return (*gaussian_rv); } //--------------------------------------------------- template @@ -143,9 +147,12 @@ ScaledCovMatrixTKGroup::rv(const std::vector& stageIds) << ", vector = " << *m_preComputingPositions[stageIds[0]] // FIX ME: might demand parallelism << std::endl; } - m_rvs[stageIds.size()-1]->updateLawExpVector(*m_preComputingPositions[stageIds[0]]); - return (*m_rvs[stageIds.size()-1]); + GaussianVectorRV * gaussian_rv = dynamic_cast * >(m_rvs[stageIds.size()-1]); + + gaussian_rv->updateLawExpVector(*m_preComputingPositions[stageIds[0]]); + + return (*gaussian_rv); } //--------------------------------------------------- template @@ -165,7 +172,7 @@ ScaledCovMatrixTKGroup::updateLawCovMatrix(const M& covMatrix) << ", covMatrix = \n" << factor*covMatrix // FIX ME: might demand parallelism << std::endl; } - m_rvs[i]->updateLawCovMatrix(factor*covMatrix); + dynamic_cast * >(m_rvs[i])->updateLawCovMatrix(factor*covMatrix); } return; diff --git a/src/stats/src/StatisticalInverseProblem.C b/src/stats/src/StatisticalInverseProblem.C index 4551404c5..13a97a2b0 100644 --- a/src/stats/src/StatisticalInverseProblem.C +++ b/src/stats/src/StatisticalInverseProblem.C @@ -25,6 +25,8 @@ #include #include #include +#include +#include namespace QUESO { @@ -52,7 +54,8 @@ StatisticalInverseProblem::StatisticalInverseProblem( m_logLikelihoodValues (NULL), m_logTargetValues (NULL), m_alternativeOptionsValues(), - m_optionsObj (NULL) + m_optionsObj (NULL), + m_seedWithMAPEstimator (false) { #ifdef QUESO_MEMORY_DEBUGGING std::cout << "Entering Sip" << std::endl; @@ -96,6 +99,67 @@ StatisticalInverseProblem::StatisticalInverseProblem( return; } +template +StatisticalInverseProblem::StatisticalInverseProblem( + const char * prefix, + const SipOptionsValues * alternativeOptionsValues, + const GPMSAFactory & gpmsaFactory, + GenericVectorRV & postRv) + : + m_env (gpmsaFactory.m_totalPrior->env()), + m_priorRv (*(gpmsaFactory.m_totalPrior)), + m_likelihoodFunction (gpmsaFactory.getGPMSAEmulator()), + m_postRv (postRv), + m_solutionDomain (NULL), + m_solutionPdf (NULL), + m_subSolutionMdf (NULL), + m_subSolutionCdf (NULL), + m_solutionRealizer (NULL), + m_mhSeqGenerator (NULL), + m_mlSampler (NULL), + m_chain (NULL), + m_logLikelihoodValues (NULL), + m_logTargetValues (NULL), + m_alternativeOptionsValues(), + m_optionsObj (NULL), + m_seedWithMAPEstimator (false) +{ + if (m_env.subDisplayFile()) { + *m_env.subDisplayFile() << "Entering StatisticalInverseProblem::constructor()" + << ": prefix = " << prefix + << ", alternativeOptionsValues = " << alternativeOptionsValues + << ", m_env.optionsInputFileName() = " << m_env.optionsInputFileName() + << std::endl; + } + + if (alternativeOptionsValues) m_alternativeOptionsValues = *alternativeOptionsValues; + if (m_env.optionsInputFileName() == "") { + m_optionsObj = new StatisticalInverseProblemOptions(m_env,prefix,m_alternativeOptionsValues); + } + else { + m_optionsObj = new StatisticalInverseProblemOptions(m_env,prefix); + m_optionsObj->scanOptionsValues(); + } + + UQ_FATAL_TEST_MACRO(m_priorRv.imageSet().vectorSpace().dimLocal() != m_likelihoodFunction.domainSet().vectorSpace().dimLocal(), + m_env.worldRank(), + "StatisticalInverseProblem::constructor()", + "'priorRv' and 'likelihoodFunction' are related to vector spaces of different dimensions"); + + UQ_FATAL_TEST_MACRO(m_priorRv.imageSet().vectorSpace().dimLocal() != postRv.imageSet().vectorSpace().dimLocal(), + m_env.worldRank(), + "StatisticalInverseProblem::constructor()", + "'priorRv' and 'postRv' are related to vector spaces of different dimensions"); + + if (m_env.subDisplayFile()) { + *m_env.subDisplayFile() << "Leaving StatisticalInverseProblem::constructor()" + << ": prefix = " << m_optionsObj->m_prefix + << std::endl; + } + + return; +} + // Destructor -------------------------------------- template StatisticalInverseProblem::~StatisticalInverseProblem() @@ -186,20 +250,42 @@ StatisticalInverseProblem::solveWithBayesMetropolisHastings( *m_solutionDomain); m_postRv.setPdf(*m_solutionPdf); + m_chain = new SequenceOfVectors(m_postRv.imageSet().vectorSpace(),0,m_optionsObj->m_prefix+"chain"); + + // Decide whether or not to create a MetropolisHastingsSG instance from the + // user-provided initial seed, or use the user-provided seed for a + // deterministic optimisation instead and seed the chain with the result of + // the optimisation + if (this->m_seedWithMAPEstimator) { + // Do optimisation before sampling + GslOptimizer optimizer(*m_solutionPdf); + optimizer.setInitialPoint(dynamic_cast(initialValues)); + optimizer.minimize(); + + // Compute output realizer: Metropolis-Hastings approach + m_mhSeqGenerator = new MetropolisHastingsSG( + m_optionsObj->m_prefix.c_str(), alternativeOptionsValues, + m_postRv, optimizer.minimizer(), initialProposalCovMatrix); + } + else { + // Compute output realizer: Metropolis-Hastings approach + m_mhSeqGenerator = new MetropolisHastingsSG( + m_optionsObj->m_prefix.c_str(), alternativeOptionsValues, m_postRv, + initialValues, initialProposalCovMatrix); + } - // Compute output realizer: Metropolis-Hastings approach - m_chain = new SequenceOfVectors(m_postRv.imageSet().vectorSpace(),0,m_optionsObj->m_prefix+"chain"); - m_logLikelihoodValues = new ScalarSequence (m_env,0,m_optionsObj->m_prefix+"logLike" ); - m_logTargetValues = new ScalarSequence (m_env,0,m_optionsObj->m_prefix+"logTarget"); - m_mhSeqGenerator = new MetropolisHastingsSG(m_optionsObj->m_prefix.c_str(), // dakota - alternativeOptionsValues, - m_postRv, - initialValues, - initialProposalCovMatrix); - m_mhSeqGenerator->generateSequence(*m_chain, - NULL, //m_logLikelihoodValues, - NULL);//m_logTargetValues); + m_logLikelihoodValues = new ScalarSequence(m_env, 0, + m_optionsObj->m_prefix + + "logLike"); + + m_logTargetValues = new ScalarSequence(m_env, 0, + m_optionsObj->m_prefix + + "logTarget"); + + // m_logLikelihoodValues and m_logTargetValues may be NULL + m_mhSeqGenerator->generateSequence(*m_chain, m_logLikelihoodValues, + m_logTargetValues); m_solutionRealizer = new SequentialVectorRealizer(m_optionsObj->m_prefix.c_str(), *m_chain); @@ -266,7 +352,14 @@ StatisticalInverseProblem::solveWithBayesMetropolisHastings( // grvy_timer_end("BayesMetropolisHastings"); TODO: revisit timers return; } -//-------------------------------------------------- + +template +void +StatisticalInverseProblem::seedWithMAPEstimator() +{ + this->m_seedWithMAPEstimator = true; +} + template void StatisticalInverseProblem::solveWithBayesMLSampling() @@ -337,16 +430,24 @@ StatisticalInverseProblem::solveWithBayesMLSampling() return; } + +template +const MetropolisHastingsSG & +StatisticalInverseProblem::sequenceGenerator() const +{ + return *m_mhSeqGenerator; +} + //-------------------------------------------------- template -const BaseVectorRV& +const BaseVectorRV& StatisticalInverseProblem::priorRv() const { return m_priorRv; } //-------------------------------------------------- template -const GenericVectorRV& +const GenericVectorRV& StatisticalInverseProblem::postRv() const { return m_postRv; diff --git a/src/stats/src/TransformedScaledCovMatrixTKGroup.C b/src/stats/src/TransformedScaledCovMatrixTKGroup.C new file mode 100644 index 000000000..53574280b --- /dev/null +++ b/src/stats/src/TransformedScaledCovMatrixTKGroup.C @@ -0,0 +1,320 @@ +//-----------------------------------------------------------------------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 + +namespace QUESO { + +template +TransformedScaledCovMatrixTKGroup::TransformedScaledCovMatrixTKGroup( + const char * prefix, + const BoxSubset & boxSubset, + const std::vector & scales, + const M & covMatrix) + : BaseTKGroup(prefix, boxSubset.vectorSpace(), scales), + m_boxSubset(boxSubset), + m_originalCovMatrix(covMatrix) +{ + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "Entering TransformedScaledCovMatrixTKGroup::constructor()" + << std::endl; + } + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "In TransformedScaledCovMatrixTKGroup::constructor()" + << ": m_scales.size() = " << m_scales.size() + << ", m_preComputingPositions.size() = " << m_preComputingPositions.size() + << ", m_rvs.size() = " << m_rvs.size() + << ", m_originalCovMatrix = " << m_originalCovMatrix + << std::endl; + } + + // Set RVs to have zero mean in the Gaussian space + setRVsWithZeroMean(); + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "Leaving TransformedScaledCovMatrixTKGroup::constructor()" + << std::endl; + } +} +// Destructor --------------------------------------- +template +TransformedScaledCovMatrixTKGroup::~TransformedScaledCovMatrixTKGroup() +{ +} +// Math/Stats methods-------------------------------- +template +bool +TransformedScaledCovMatrixTKGroup::symmetric() const +{ + return false; +} +//--------------------------------------------------- +template +const InvLogitGaussianVectorRV& +TransformedScaledCovMatrixTKGroup::rv(unsigned int stageId) const +{ + UQ_FATAL_TEST_MACRO(m_rvs.size() == 0, + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv1()", + "m_rvs.size() = 0"); + + UQ_FATAL_TEST_MACRO(m_rvs[0] == NULL, // Yes, '0', because that is the id used below + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv1()", + "m_rvs[0] == NULL"); + + UQ_FATAL_TEST_MACRO(m_preComputingPositions.size() <= stageId, + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv1()", + "m_preComputingPositions.size() <= stageId"); + + UQ_FATAL_TEST_MACRO(m_preComputingPositions[stageId] == NULL, + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv1()", + "m_preComputingPositions[stageId] == NULL"); + + if ((m_env.subDisplayFile() ) && + (m_env.displayVerbosity() >= 10)) { + *m_env.subDisplayFile() << "In TransformedScaledCovMatrixTKGroup::rv1()" + << ", stageId = " << stageId + << ": about to call m_rvs[0]->updateLawExpVector()" + << ", vector = " << *m_preComputingPositions[stageId] // FIX ME: might demand parallelism + << std::endl; + } + + InvLogitGaussianVectorRV * invlogit_gaussian = + dynamic_cast * >(m_rvs[0]); + + V transformedPreComputingPositions(*m_preComputingPositions[stageId]); + transformToGaussianSpace(*m_preComputingPositions[stageId], + transformedPreComputingPositions); + + invlogit_gaussian->updateLawExpVector(transformedPreComputingPositions); + + return (*invlogit_gaussian); +} +//--------------------------------------------------- +template +const InvLogitGaussianVectorRV& +TransformedScaledCovMatrixTKGroup::rv(const std::vector& stageIds) +{ + UQ_FATAL_TEST_MACRO(m_rvs.size() < stageIds.size(), + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv2()", + "m_rvs.size() < stageIds.size()"); + + UQ_FATAL_TEST_MACRO(m_rvs[stageIds.size()-1] == NULL, + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv2()", + "m_rvs[stageIds.size()-1] == NULL"); + + UQ_FATAL_TEST_MACRO(m_preComputingPositions.size() <= stageIds[0], + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv2()", + "m_preComputingPositions.size() <= stageIds[0]"); + + UQ_FATAL_TEST_MACRO(m_preComputingPositions[stageIds[0]] == NULL, + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::rv2()", + "m_preComputingPositions[stageIds[0]] == NULL"); + + if ((m_env.subDisplayFile() ) && + (m_env.displayVerbosity() >= 10)) { + *m_env.subDisplayFile() << "In TransformedScaledCovMatrixTKGroup::rv2()" + << ", stageIds.size() = " << stageIds.size() + << ", stageIds[0] = " << stageIds[0] + << ": about to call m_rvs[stageIds.size()-1]->updateLawExpVector()" + << ", vector = " << *m_preComputingPositions[stageIds[0]] // FIX ME: might demand parallelism + << std::endl; + } + + InvLogitGaussianVectorRV * invlogit_gaussian = + dynamic_cast * >(m_rvs[stageIds.size()-1]); + + V transformedPreComputingPositions(*m_preComputingPositions[stageIds[0]]); + transformToGaussianSpace(*m_preComputingPositions[stageIds[0]], + transformedPreComputingPositions); + + invlogit_gaussian->updateLawExpVector(transformedPreComputingPositions); + + return (*invlogit_gaussian); +} +//--------------------------------------------------- +template +void +TransformedScaledCovMatrixTKGroup::updateLawCovMatrix(const M& covMatrix) +{ + for (unsigned int i = 0; i < m_scales.size(); ++i) { + double factor = 1./m_scales[i]/m_scales[i]; + if ((m_env.subDisplayFile() ) && + (m_env.displayVerbosity() >= 10)) { + *m_env.subDisplayFile() << "In TransformedScaledCovMatrixTKGroup::updateLawCovMatrix()" + << ", m_scales.size() = " << m_scales.size() + << ", i = " << i + << ", m_scales[i] = " << m_scales[i] + << ", factor = " << factor + << ": about to call m_rvs[i]->updateLawCovMatrix()" + << ", covMatrix = \n" << factor*covMatrix // FIX ME: might demand parallelism + << std::endl; + } + + InvLogitGaussianVectorRV * invlogit_gaussian = + dynamic_cast * >(m_rvs[i]); + + invlogit_gaussian->updateLawCovMatrix(factor*covMatrix); + } + + return; +} + +// Misc methods ------------------------------------- +template +bool +TransformedScaledCovMatrixTKGroup::setPreComputingPosition(const V& position, unsigned int stageId) +{ + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "Entering TransformedScaledCovMatrixTKGroup::setPreComputingPosition()" + << ": position = " << position + << ", stageId = " << stageId + << std::endl; + } + + BaseTKGroup::setPreComputingPosition(position,stageId); + //setRVsWithZeroMean(); + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "In TransformedScaledCovMatrixTKGroup::setPreComputingPosition()" + << ", position = " << position + << ", stageId = " << stageId + << ": preComputingPos = " << *m_preComputingPositions[stageId]; + if (stageId < m_scales.size()) { + *m_env.subDisplayFile() << ", factor = " << 1./m_scales[stageId]/m_scales[stageId]; + } + if (stageId < m_rvs.size()) { + const InvLogitGaussianJointPdf* pdfPtr = dynamic_cast< const InvLogitGaussianJointPdf* >(&(m_rvs[stageId]->pdf())); + *m_env.subDisplayFile() << ", rvCov = " << pdfPtr->lawCovMatrix(); // FIX ME: might demand parallelism + } + *m_env.subDisplayFile() << std::endl; + } + + if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 5)) { + *m_env.subDisplayFile() << "Leaving TransformedScaledCovMatrixTKGroup::setPreComputingPosition()" + << ": position = " << position + << ", stageId = " << stageId + << std::endl; + } + + return true; +} +//--------------------------------------------------- +template +void +TransformedScaledCovMatrixTKGroup::clearPreComputingPositions() +{ + BaseTKGroup::clearPreComputingPositions(); + return; +} + + +// Private methods------------------------------------ +template +void +TransformedScaledCovMatrixTKGroup::setRVsWithZeroMean() +{ + UQ_FATAL_TEST_MACRO(m_rvs.size() == 0, + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::setRVsWithZeroMean()", + "m_rvs.size() = 0"); + + UQ_FATAL_TEST_MACRO(m_rvs.size() != m_scales.size(), + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::setRVsWithZeroMean()", + "m_rvs.size() != m_scales.size()"); + + for (unsigned int i = 0; i < m_scales.size(); ++i) { + double factor = 1./m_scales[i]/m_scales[i]; + UQ_FATAL_TEST_MACRO(m_rvs[i] != NULL, + m_env.worldRank(), + "TransformedScaledCovMatrixTKGroup::setRVsWithZeroMean()", + "m_rvs[i] != NULL"); + m_rvs[i] = new InvLogitGaussianVectorRV(m_prefix.c_str(), + m_boxSubset, m_vectorSpace->zeroVector(), + factor*m_originalCovMatrix); + } + + return; +} + +template +void +TransformedScaledCovMatrixTKGroup::print(std::ostream& os) const +{ + BaseTKGroup::print(os); + return; +} + +template +void +TransformedScaledCovMatrixTKGroup::transformToGaussianSpace( + const V & physicalPoint, V & transformedPoint) const +{ + V min_domain_bounds(this->m_boxSubset.minValues()); + V max_domain_bounds(this->m_boxSubset.maxValues()); + + for (unsigned int i = 0; i < transformedPoint.sizeLocal(); i++) { + double min_val = min_domain_bounds[i]; + double max_val = max_domain_bounds[i]; + + if (boost::math::isfinite(min_val) && + boost::math::isfinite(max_val)) { + // Left- and right-hand sides are finite. Do full transform. + transformedPoint[i] = std::log(physicalPoint[i] - min_val) - + std::log(max_val - physicalPoint[i]); + } + else if (boost::math::isfinite(min_val) && + !boost::math::isfinite(max_val)) { + // Left-hand side finite, but right-hand side is not. + // Do only left-hand transform. + transformedPoint[i] = std::log(physicalPoint[i] - min_val); + } + else if (!boost::math::isfinite(min_val) && + boost::math::isfinite(max_val)) { + // Right-hand side is finite, but left-hand side is not. + // Do only right-hand transform. + transformedPoint[i] = -std::log(max_val - physicalPoint[i]); + } + else { + // No transform. + transformedPoint[i] = physicalPoint[i]; + } + } +} + +} // End namespace QUESO + +template class QUESO::TransformedScaledCovMatrixTKGroup; diff --git a/src/stats/src/UniformVectorRV.C b/src/stats/src/UniformVectorRV.C index 27d68016d..4fd8cd5ff 100644 --- a/src/stats/src/UniformVectorRV.C +++ b/src/stats/src/UniformVectorRV.C @@ -23,6 +23,8 @@ //-----------------------------------------------------------------------el- #include +#include +#include #include #include diff --git a/src/stats/src/WignerVectorRV.C b/src/stats/src/WignerVectorRV.C index c7c9c3990..be9b63e9f 100644 --- a/src/stats/src/WignerVectorRV.C +++ b/src/stats/src/WignerVectorRV.C @@ -23,6 +23,8 @@ //-----------------------------------------------------------------------el- #include +#include +#include #include #include diff --git a/src/stats/test/Makefile.old b/src/stats/test/Makefile.old deleted file mode 100644 index a350d9f53..000000000 --- a/src/stats/test/Makefile.old +++ /dev/null @@ -1,85 +0,0 @@ -include ../../../Make.common - -INC_PATHS = \ - -I../../basic/inc \ - -I../inc \ - $(QUESO_TOOL_INCLUDE) - -LIBS = \ - -L../../basic/lib \ - -luq_basic_$(UQBT) \ - -luq_basic_any \ - $(QUESO_TOOL_LIBS) \ - -l$(BOOST_UNIT_TEST_LIB_FOR_QUESO_TOOL) - - -OBJS_ANY = - -OBJS_gsl = uqGslVectorSpace.o - -OBJS_tri = uqTrilinosParamSpace.o \ - uqTrilinosFinDimLinearSpace.o - -UNIT_TESTS = uqTestExample \ - uqTestVectorPdf_gsl \ - uqTestVectorRealizer_gsl \ - uqTestVectorRV_gsl - -all: check - -echo_num_proc: - @echo 'UQBT = ' $(UQBT) - -clean: clean_all - -clean_lib: echo_num_proc clean_obj clean_docs - rm -f ../lib/libuq_queso_any.a - rm -f ../lib/libuq_queso_$(UQBT).a - rm -f $(UNIT_TESTS) - -clean_obj: - rm -f ../inc/*~ - rm -rf ../inc/html - rm -f *~ - rm -f *.o - -clean_exec: - -clean_docs: - rm -rf html - -%.o: %.C - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< - -# Unit testing -# -# For new unit test, add executable name to UNIT_TESTS variable above, and -# create target and rule for building exectuable. Use 'make check' to -# build and run all unit tests. - -check: $(UNIT_TESTS) - @for mytest in $^; do echo "Running ./$${mytest}..."; ./$${mytest}; done - -uqTestExample: uqTestExample.o - $(CXX) -L$(BOOST_LIB_PATH_FOR_QUESO_TOOL) $< -o $@ -l$(BOOST_UNIT_TEST_LIB_FOR_QUESO_TOOL) - -uqTestVectorPdf_gsl: uqTestVectorPdf_gsl.o libs_basic - $(CXX) $< -o $@ $(LIBS) - -uqTestVectorPdf_gsl.o: uqTestVectorPdf_gsl.C ../inc/uqVectorPdf.h - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< - -uqTestVectorRealizer_gsl: uqTestVectorRealizer_gsl.o libs_basic - $(CXX) $< -o $@ $(LIBS) - -uqTestVectorRealizer_gsl.o: uqTestVectorRealizer_gsl.C ../inc/uqVectorRealizer.h - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< - -uqTestVectorRV_gsl: uqTestVectorRV_gsl.o libs_basic - $(CXX) $< -o $@ $(LIBS) - -uqTestVectorRV_gsl.o: uqTestVectorRV_gsl.C ../inc/uqVectorRV.h - $(CXX) $(DEFINES) $(INC_PATHS) $(CXXFLAGS) $< - -libs_basic: - cd ../../basic/src; make libuq_basic_all UQBT=$(UQBT) diff --git a/test/Makefile.am b/test/Makefile.am index e6c9a61e1..89e31702d 100644 --- a/test/Makefile.am +++ b/test/Makefile.am @@ -27,57 +27,74 @@ check_PROGRAMS += test_operator check_PROGRAMS += test_inf_gaussian check_PROGRAMS += test_inf_options check_PROGRAMS += test_SequenceOfVectorsErase +check_PROGRAMS += test_GaussianMean1DRegression +check_PROGRAMS += test_gpmsa_cobra +check_PROGRAMS += test_LlhdTargetOutput +check_PROGRAMS += test_jeffreys +check_PROGRAMS += test_gsloptimizer +check_PROGRAMS += test_seedwithmap +check_PROGRAMS += test_seedwithmap_fd +check_PROGRAMS += test_logitadaptedcov -LIBS = -L$(top_builddir)/src/ -lqueso +LDADD = $(top_builddir)/src/libqueso.la AM_CPPFLAGS = AM_CPPFLAGS += -I. AM_CPPFLAGS += -I$(top_builddir)/inc AM_CPPFLAGS += $(BOOST_CPPFLAGS) $(GSL_CFLAGS) $(ANN_CFLAGS) -if GRVY_ENABLED +if GRVY_ENABLED AM_CPPFLAGS += $(GRVY_CFLAGS) endif if TRILINOS_ENABLED - AM_CPPFLAGS += -I$(TRILINOS_INCLUDE) + AM_CPPFLAGS += -I$(TRILINOS_INCLUDE) LIBS += -lteuchoscore -lteuchoscomm -lteuchosnumerics -lteuchosparameterlist -lteuchosremainder -lepetra endif -if GLPK_ENABLED +if GLPK_ENABLED AM_CPPFLAGS += $(GLPK_CFLAGS) endif -if HDF5_ENABLED - AM_CPPFLAGS += $(HDF5_CFLAGS) +if HDF5_ENABLED + AM_CPPFLAGS += $(HDF5_CFLAGS) endif if LIBMESH_ENABLED AM_CPPFLAGS += $(LIBMESH_CPPFLAGS) endif -test_uqEnvironmentCopy_SOURCES = $(top_srcdir)/test/test_Environment/test_uqEnvironmentCopy.C -test_uqEnvironmentEquals_SOURCES = $(top_srcdir)/test/test_Environment/test_uqEnvironmentEquals.C -test_uqEnvironmentNonFatal_SOURCES = $(top_srcdir)/test/test_Environment/test_uqEnvironmentNonFatal.C -test_uqEnvironmentOptionsPrint_SOURCES = $(top_srcdir)/test/test_uqEnvironmentOptions/test_uqEnvironmentOptionsPrint.C -test_uqGslVectorConstructorFatal_SOURCES = $(top_srcdir)/test/test_GslVector/test_uqGslVectorConstructorFatal.C -test_uqGslVector_SOURCES = $(top_srcdir)/test/test_GslVector/test_uqGslVector.C -test_uqGaussianVectorRVClass_SOURCES = $(top_srcdir)/test/test_GaussianVectorRVClass/test_uqGaussianVectorRVClass.C -test_uqGslMatrixConstructorFatal_SOURCES = $(top_srcdir)/test/test_GslMatrix/test_uqGslMatrixConstructorFatal.C -test_uqGslMatrix_SOURCES = $(top_srcdir)/test/test_GslMatrix/test_uqGslMatrix.C -test_uqTeuchosVector_SOURCES = $(top_srcdir)/test/test_TeuchosVector/test_uqTeuchosVector.C -test_uqexception_SOURCES = $(top_srcdir)/test/test_exception/test_exception.C -test_DistArrayCtor_SOURCES = $(top_srcdir)/test/test_DistArray/test_DistArrayCtor.C -test_DistArrayCopy_SOURCES = $(top_srcdir)/test/test_DistArray/test_DistArrayCopy.C -test_DistArrayEquals_SOURCES = $(top_srcdir)/test/test_DistArray/test_DistArrayEquals.C -test_DistArrayMisc_SOURCES = $(top_srcdir)/test/test_DistArray/test_DistArrayMisc.C -test_GslVectorSpaceCtor_SOURCES = $(top_srcdir)/test/test_GslVectorSpace/test_GslVectorSpaceCtor.C -test_GslVectorSpaceMisc_SOURCES = $(top_srcdir)/test/test_GslVectorSpace/test_GslVectorSpaceMisc.C -test_IntersectionSubsetContains_SOURCES = $(top_srcdir)/test/test_IntersectionSubset/test_IntersectionSubsetContains.C -test_operator_SOURCES = $(top_srcdir)/test/test_infinite/test_operator.C -test_inf_gaussian_SOURCES = $(top_srcdir)/test/test_infinite/test_inf_gaussian.C -test_inf_options_SOURCES = $(top_srcdir)/test/test_infinite/test_inf_options.C -test_SequenceOfVectorsErase_SOURCES = $(top_srcdir)/test/test_SequenceOfVectors/test_SequenceOfVectorsErase.C +test_uqEnvironmentCopy_SOURCES = test_Environment/test_uqEnvironmentCopy.C +test_uqEnvironmentEquals_SOURCES = test_Environment/test_uqEnvironmentEquals.C +test_uqEnvironmentNonFatal_SOURCES = test_Environment/test_uqEnvironmentNonFatal.C +test_uqEnvironmentOptionsPrint_SOURCES = test_uqEnvironmentOptions/test_uqEnvironmentOptionsPrint.C +test_uqGslVectorConstructorFatal_SOURCES = test_GslVector/test_uqGslVectorConstructorFatal.C +test_uqGslVector_SOURCES = test_GslVector/test_uqGslVector.C +test_uqGaussianVectorRVClass_SOURCES = test_GaussianVectorRVClass/test_uqGaussianVectorRVClass.C +test_uqGslMatrixConstructorFatal_SOURCES = test_GslMatrix/test_uqGslMatrixConstructorFatal.C +test_uqGslMatrix_SOURCES = test_GslMatrix/test_uqGslMatrix.C +test_uqTeuchosVector_SOURCES = test_TeuchosVector/test_uqTeuchosVector.C +test_uqexception_SOURCES = test_exception/test_exception.C +test_DistArrayCtor_SOURCES = test_DistArray/test_DistArrayCtor.C +test_DistArrayCopy_SOURCES = test_DistArray/test_DistArrayCopy.C +test_DistArrayEquals_SOURCES = test_DistArray/test_DistArrayEquals.C +test_DistArrayMisc_SOURCES = test_DistArray/test_DistArrayMisc.C +test_GslVectorSpaceCtor_SOURCES = test_GslVectorSpace/test_GslVectorSpaceCtor.C +test_GslVectorSpaceMisc_SOURCES = test_GslVectorSpace/test_GslVectorSpaceMisc.C +test_IntersectionSubsetContains_SOURCES = test_IntersectionSubset/test_IntersectionSubsetContains.C +test_operator_SOURCES = test_infinite/test_operator.C +test_inf_gaussian_SOURCES = test_infinite/test_inf_gaussian.C +test_inf_options_SOURCES = test_infinite/test_inf_options.C +test_SequenceOfVectorsErase_SOURCES = test_SequenceOfVectors/test_SequenceOfVectorsErase.C +test_GaussianMean1DRegression_SOURCES = test_Regression/test_GaussianMean1DRegression.C +test_GaussianMean1DRegression_LDFLAGS = $(BOOST_PROGRAM_OPTIONS_LDFLAGS) $(BOOST_PROGRAM_OPTIONS_LIBS) +test_gpmsa_cobra_SOURCES = test_Regression/test_gpmsa_cobra.C +test_LlhdTargetOutput_SOURCES = test_StatisticalInverseProblem/test_LlhdTargetOutput.C +test_jeffreys_SOURCES = test_Regression/test_jeffreys.C +test_gsloptimizer_SOURCES = test_optimizer/test_gsloptimizer.C +test_seedwithmap_SOURCES = test_optimizer/test_seedwithmap.C +test_seedwithmap_fd_SOURCES = test_optimizer/test_seedwithmap_fd.C +test_logitadaptedcov_SOURCES = test_Regression/test_logitadaptedcov.C # Files to freedom stamp srcstamp = @@ -102,6 +119,14 @@ srcstamp += $(test_IntersectionSubsetContains_SOURCES) srcstamp += $(test_operator_SOURCES) srcstamp += $(test_inf_gaussian_SOURCES) srcstamp += $(test_inf_options_SOURCES) +srcstamp += $(test_GaussianMean1DRegression_SOURCES) +srcstamp += $(test_gpmsa_cobra_SOURCES) +srcstamp += $(test_LlhdTargetOutput_SOURCES) +srcstamp += $(test_jeffreys_SOURCES) +srcstamp += $(test_gsloptimizer_SOURCES) +srcstamp += $(test_seedwithmap_SOURCES) +srcstamp += $(test_seedwithmap_fd_SOURCES) +srcstamp += $(test_logitadaptedcov_SOURCES) TESTS = TESTS += $(top_builddir)/test/test_uqEnvironmentCopy @@ -126,6 +151,14 @@ TESTS += $(top_builddir)/test/test_operator TESTS += $(top_builddir)/test/test_inf_gaussian TESTS += $(top_builddir)/test/test_inf_options TESTS += $(top_builddir)/test/test_SequenceOfVectorsErase +TESTS += $(top_builddir)/test/test_GaussianMean1DRegression +TESTS += $(top_builddir)/test/test_Regression/test_cobra_samples_diff.sh +TESTS += $(top_builddir)/test/test_StatisticalInverseProblem/test_LlhdTargetOutput.sh +TESTS += $(top_builddir)/test/test_Regression/test_jeffreys_samples_diff.sh +TESTS += $(top_builddir)/test/test_gsloptimizer +TESTS += $(top_builddir)/test/test_seedwithmap +TESTS += $(top_builddir)/test/test_seedwithmap_fd +TESTS += $(top_builddir)/test/test_logitadaptedcov XFAIL_TESTS = $(top_builddir)/test/test_SequenceOfVectorsErase @@ -135,6 +168,31 @@ EXTRA_DIST += common/verify.sh EXTRA_DIST += test_uqEnvironmentOptions/test.inp EXTRA_DIST += test_Environment/copy_env EXTRA_DIST += test_infinite/inf_options +EXTRA_DIST += test_Regression/GaussianMean1DRegression_options.in +EXTRA_DIST += test_Regression/gpmsa_cobra_input.txt.in +EXTRA_DIST += test_Regression/test_cobra_samples_diff.sh.in +EXTRA_DIST += test_Regression/test_gpmsa_cobra_samples.m.in +EXTRA_DIST += test_Regression/ctf_dat.txt.in +EXTRA_DIST += test_Regression/dakota_pstudy.dat.in +EXTRA_DIST += test_StatisticalInverseProblem/both_input.txt.in +EXTRA_DIST += test_StatisticalInverseProblem/llhdout_input.txt.in +EXTRA_DIST += test_StatisticalInverseProblem/targetout_input.txt.in +EXTRA_DIST += test_StatisticalInverseProblem/neither_input.txt.in +EXTRA_DIST += test_StatisticalInverseProblem/test_LlhdTargetOutput.sh.in +EXTRA_DIST += test_Regression/jeffreys_input.txt.in +EXTRA_DIST += test_Regression/test_jeffreys_samples_diff.sh.in +EXTRA_DIST += test_Regression/test_jeffreys_samples.m.in +EXTRA_DIST += test_Regression/adaptedcov_input.txt.in + +DISTCLEANFILES = +DISTCLEANFILES += test_gpmsa_cobra_output/display_sub0.txt +DISTCLEANFILES += test_gpmsa_cobra_output/ip_raw_chain.m +DISTCLEANFILES += test_gpmsa_cobra_output/ip_raw_chain_loglikelihood.m +DISTCLEANFILES += test_gpmsa_cobra_output/ip_raw_chain_loglikelihood_sub0.m +DISTCLEANFILES += test_gpmsa_cobra_output/ip_raw_chain_logtarget.m +DISTCLEANFILES += test_gpmsa_cobra_output/ip_raw_chain_logtarget_sub0.m +DISTCLEANFILES += test_gpmsa_cobra_output/ip_raw_chain_sub0.m +DISTCLEANFILES += test_gpmsa_cobra_output/sipOutput_sub0.m CLEANFILES = CLEANFILES += $(top_srcdir)/test/test_Environment/debug_output_sub0.txt @@ -150,7 +208,7 @@ endif if GIT_CLONE .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(srcstamp) + @$(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/test/common/compare.pl b/test/common/compare.pl index 191cbdc05..fb81431eb 100755 --- a/test/common/compare.pl +++ b/test/common/compare.pl @@ -1,7 +1,7 @@ #!/usr/bin/perl # #------------------------------------------------------------------------------ -# Simple solution comparison tool for QUESO generated output (Matlab format). +# Simple solution comparison tool for QUESO generated output (Matlab format). #------------------------------------------------------------------------------ use warnings; @@ -12,7 +12,7 @@ if (@ARGV >= 2) { $file1 = shift @ARGV; $file2 = shift @ARGV; - + } else { print "\nUsage: compare [solfile1] [solfile2] \n\n"; exit 0; @@ -47,7 +47,7 @@ sub parse_queso_matlab_output { while ($line = <$IN1>) { - + # Terminate at the end of the array definition if($line =~ m/\];/) { @@ -55,29 +55,29 @@ sub parse_queso_matlab_output { } # 2nd line of output is special: defines the array and has 1st value (2 col format) - + if($line =~ m/\S+ = \[(\S+) (\d+)\b/) { push(@sola,$1); push(@solb,$2); $two_cols = 1; } - + # 2nd line of output is special: defines the array and has 1st value (1 col format) - + elsif($line =~ m/\S+ = \[(\S+)\b/) { push(@sola,$1); } # Normal lines: two column format... - + elsif($line =~ m/(\S+) (\S+)\b/ ) { push(@sola,$1); push(@solb,$2); $two_cols = 1; } - + # Normal lines: one column format... - + elsif($line =~ m/(\S+)\b/ ) { push(@sola,$1); } @@ -99,14 +99,14 @@ sub parse_queso_matlab_output { #------------------------------------------------------------- # Call routine to Store groovy contents of each solution file # -# Note: slight bit of perl trickery here to get two array +# Note: slight bit of perl trickery here to get two array # references back from the parsing routine #------------------------------------------------------------- my ($sol1a_ref,$sol1b_ref) = parse_queso_matlab_output ( $file1 ); my ($sol2a_ref,$sol2b_ref) = parse_queso_matlab_output ( $file2 ); -my @sol1a = @$sol1a_ref; +my @sol1a = @$sol1a_ref; my @sol1b = @$sol1b_ref; my @sol2a = @$sol2a_ref; @@ -141,13 +141,13 @@ sub parse_queso_matlab_output { for($count = 0;$count < $num_sol1; $count++) { if( $sol1a[$count] != $sol2a[$count] ) { - my $delta = abs($sol1a[$count] - $sol2a[$count]); + my $delta = abs(($sol1a[$count] - $sol2a[$count]) / $sol2a[$count]); if ( $delta <= $TOL ) { - print " --> Within tol. ($sol1a[$count] - $sol2a[$count]) -> [delta = $delta] -> " . + print " --> Within tol. ($sol1a[$count] - $sol2a[$count]) / $sol2a[$count] -> [delta = $delta] -> " . "Col 1 -> Index = $count\n"; } else { - print " --> Different ($sol1a[$count] - $sol2a[$count]) -> [delta = $delta] -> " . - "Col 1 -> Index =$count\n"; + print " --> Different ($sol1a[$count] - $sol2a[$count]) / $sol2a[$count] -> [delta = $delta] -> " . + "Col 1 -> Index =$count\n"; $success=0; } } @@ -155,12 +155,12 @@ sub parse_queso_matlab_output { if($two_cols == 1) { if( $sol1b[$count] != $sol2b[$count] ) { - my $delta = abs($sol1b[$count] - $sol2b[$count]); + my $delta = abs(($sol1b[$count] - $sol2b[$count]) / $sol2b[$count]); if ( $delta <= $TOL ) { - print " --> Within tol. ($sol1b[$count] - $sol2b[$count]) -> [delta = $delta] -> " . + print " --> Within tol. ($sol1b[$count] - $sol2b[$count]) / $sol2b[$count] -> [delta = $delta] -> " . "Col 2 -> Index = $count\n"; } else { - print " --> Different ($sol1b[$count] - $sol2b[$count]) -> [delta = $delta] -> " . + print " --> Different ($sol1b[$count] - $sol2b[$count]) / $sol2b[$count] -> [delta = $delta] -> " . "Col 2 -> Index = $count\n"; $success=0; } @@ -176,6 +176,3 @@ sub parse_queso_matlab_output { print " --> FAILED\n"; exit -1; } - - - diff --git a/test/gsl_tests/Makefile.am b/test/gsl_tests/Makefile.am index 7ba49b8d2..e7ba2eb14 100644 --- a/test/gsl_tests/Makefile.am +++ b/test/gsl_tests/Makefile.am @@ -62,7 +62,7 @@ endif if GIT_CLONE .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(srcdir)/*.C $(srcdir)/*.h + $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/test/t01_valid_cycle/Makefile.am b/test/t01_valid_cycle/Makefile.am index 782332f5f..c74875cb8 100644 --- a/test/t01_valid_cycle/Makefile.am +++ b/test/t01_valid_cycle/Makefile.am @@ -10,30 +10,30 @@ AM_CPPFLAGS += $(BOOST_CPPFLAGS) $(GSL_CFLAGS) $(ANN_CFLAGS) LIBS = -L$(top_builddir)/src/ -lqueso $(GSL_LIBS) if TRILINOS_ENABLED - AM_CPPFLAGS += -I$(TRILINOS_INCLUDE) + AM_CPPFLAGS += -I$(TRILINOS_INCLUDE) LIBS += -lteuchoscore -lteuchoscomm -lteuchosnumerics -lteuchosparameterlist -lteuchosremainder -lepetra endif -if GLPK_ENABLED +if GLPK_ENABLED AM_CPPFLAGS += $(GLPK_CFLAGS) LIBS += $(GLPK_LIBS) endif -if GRVY_ENABLED +if GRVY_ENABLED AM_CPPFLAGS += $(GRVY_CFLAGS) endif -if HDF5_ENABLED - AM_CPPFLAGS += $(HDF5_CFLAGS) +if HDF5_ENABLED + AM_CPPFLAGS += $(HDF5_CFLAGS) LIBS += $(HDF5_LIBS) endif EXTRA_DIST = inputData regression rtest01.sh -TESTS = rtest01.sh +TESTS = rtest01.sh check_PROGRAMS = TgaValidationCycle_gsl TgaValidationCycle_gsl_SOURCES = -TgaValidationCycle_gsl_SOURCES += TgaValidationCycle_gsl.C TgaValidationCycle_appl.h +TgaValidationCycle_gsl_SOURCES += TgaValidationCycle_gsl.C TgaValidationCycle_appl.h TgaValidationCycle_gsl_SOURCES += TgaValidationCycle_likelihood.h TgaValidationCycle_qoi.h TgaValidationCycle_gsl_SOURCES += tgaCycle.inp @@ -43,7 +43,7 @@ endif if GIT_CLONE .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(srcdir)/*.C $(srcdir)/*.h + $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/test/t01_valid_cycle/TgaValidationCycle_appl.h b/test/t01_valid_cycle/TgaValidationCycle_appl.h index 5952ef4d6..53d6fcf10 100644 --- a/test/t01_valid_cycle/TgaValidationCycle_appl.h +++ b/test/t01_valid_cycle/TgaValidationCycle_appl.h @@ -39,11 +39,11 @@ //Just declaration: actual code is below template -void +void uqAppl_LocalComparisonStage(QUESO::ValidationCycle& cycle); template -void +void uqAppl_UnifiedComparisonStage(QUESO::ValidationCycle& cycle); //******************************************************** @@ -57,7 +57,7 @@ uqAppl_UnifiedComparisonStage(QUESO::ValidationCycle& cycle); // Stages 2, 3 and 4 constitute the actual validation cycle. //******************************************************** template -void +void uqAppl(const QUESO::BaseEnvironment& env) { if (env.fullRank() == 0) { @@ -276,7 +276,7 @@ uqAppl(const QUESO::BaseEnvironment& env) // The 'local comparison stage' of the driving routine "uqAppl()" //******************************************************** template -void +void uqAppl_LocalComparisonStage(QUESO::ValidationCycle& cycle) { if (cycle.calFP().computeSolutionFlag() && @@ -366,7 +366,7 @@ uqAppl_LocalComparisonStage(QUESO::ValidationCycle& cycle) // The 'unified comparison stage' of the driving routine "uqAppl()" //******************************************************** template -void +void uqAppl_UnifiedComparisonStage(QUESO::ValidationCycle& cycle) { if (cycle.calFP().computeSolutionFlag() && diff --git a/test/t01_valid_cycle/TgaValidationCycle_likelihood.h b/test/t01_valid_cycle/TgaValidationCycle_likelihood.h index ab502dee0..46592b44b 100644 --- a/test/t01_valid_cycle/TgaValidationCycle_likelihood.h +++ b/test/t01_valid_cycle/TgaValidationCycle_likelihood.h @@ -420,11 +420,7 @@ likelihoodRoutine( env.subComm().Barrier(); //env.syncPrintDebugMsg("Leaving likelihoodRoutine()",1,env.fullComm()); -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN return -.5*resultValue; -#else - return resultValue; -#endif } #endif // EX_TGA_VALIDATION_CYCLE_LIKELIHOOD_H diff --git a/test/t01_valid_cycle/regression/09-15-2014/file_cal_fp_qoi2.m b/test/t01_valid_cycle/regression/09-15-2014/file_cal_fp_qoi2.m new file mode 100644 index 000000000..0a6402ff1 --- /dev/null +++ b/test/t01_valid_cycle/regression/09-15-2014/file_cal_fp_qoi2.m @@ -0,0 +1,822 @@ +cycle_cal_fp_mc_QoiSeq_unified = zeros(820,1); +cycle_cal_fp_mc_QoiSeq_unified = [9.4165978475076684e-01 +4.9417188198651946e-01 +3.2670124962553360e-01 +4.5953983155055333e-01 +3.4184971280858933e-01 +3.3902378902775371e-01 +3.1825379768451129e-01 +3.4910346272181758e-01 +1.8878190201360634e-01 +2.6278439939475029e-01 +2.9278582457381880e-01 +3.5677278521238370e-01 +2.2819377095333596e-01 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+2.4000000000000000e+11 1.8000000000000000e+05 +2.4000000000000000e+11 1.8000000000000000e+05 +]; diff --git a/test/t01_valid_cycle/regression/README b/test/t01_valid_cycle/regression/README index 1d52f1c39..b0652862b 100644 --- a/test/t01_valid_cycle/regression/README +++ b/test/t01_valid_cycle/regression/README @@ -9,3 +9,5 @@ Notes: automatic regression testing. -> July 2010: new reference solution checked in (basically, it is printed out to the output file with more precision) +-> September 2014: new samples are drawn as a result of a change in transition + kernel, so the reference solution is being updated diff --git a/test/t01_valid_cycle/rtest01.sh b/test/t01_valid_cycle/rtest01.sh index 97336e345..a2a93ae15 100755 --- a/test/t01_valid_cycle/rtest01.sh +++ b/test/t01_valid_cycle/rtest01.sh @@ -1,14 +1,17 @@ #!/bin/bash #---------------------------------------------------------- # PECOS Regression tests for QUESO -# +# # 1. Validation cycle using TGA example. # # Originally: 5-19-09 #---------------------------------------------------------- +set -eu +set -o pipefail + TOLERANCE="1e-10" # solution diff tolerance (absolute) -SAVELOG=0 # Log model output? +SAVELOG=1 # Log model output? COMMONDIR="$srcdir/../common" #---------------- @@ -22,9 +25,9 @@ RUNDIR=`pwd` #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # -# Regresion Test #1: Validation cycle with TGA example +# Regresion Test #1: Validation cycle with TGA example -VERIFY_DATE="07-30-2010" +VERIFY_DATE="11-19-2014" SOLDIR="outputData" EXE="./TgaValidationCycle_gsl" # executable name SOLREFS="$srcdir/regression/$VERIFY_DATE" @@ -57,26 +60,12 @@ fi verify_file_exists $SOLDIR/file_val_ip_raw2.m verify_file_exists $SOLREFS/file_val_ip_raw2.m -igot=1 - # Compare solutions from 4 output files. for file in file_cal_ip_raw2.m file_val_ip_raw2.m file_cal_fp_qoi2.m file_val_fp_qoi2.m ; do - - $RUNDIR/$COMMONDIR/compare.pl $SOLDIR/$file $SOLREFS/$file - let igot="$igot * $?" + if ! $RUNDIR/$COMMONDIR/compare.pl $SOLDIR/$file $SOLREFS/$file; then + message_fail "$TESTNAME failed verification" + fi done -cd - >& /dev/null - -if [ $igot -eq 0 ];then - message_passed "$TESTNAME" -else - message_fail "$TESTNAME failed verification" -fi - -#------------------------------------------------------------------------------ -#------------------------------------------------------------------------------ - - - +message_passed "$TESTNAME" diff --git a/test/t02_sip_sfp/Makefile.am b/test/t02_sip_sfp/Makefile.am index 550fbd4c2..688062a18 100644 --- a/test/t02_sip_sfp/Makefile.am +++ b/test/t02_sip_sfp/Makefile.am @@ -43,7 +43,7 @@ endif if GIT_CLONE .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(srcdir)/*.C $(srcdir)/*.h + $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/test/t02_sip_sfp/example_likelihood.C b/test/t02_sip_sfp/example_likelihood.C index ee6f6b583..827370b0d 100644 --- a/test/t02_sip_sfp/example_likelihood.C +++ b/test/t02_sip_sfp/example_likelihood.C @@ -77,9 +77,5 @@ double likelihoodRoutine( // Do nothing; } -#ifdef QUESO_EXPECTS_LN_LIKELIHOOD_INSTEAD_OF_MINUS_2_LN return -.5*result; -#else - return result; -#endif } diff --git a/test/t02_sip_sfp/regression/06-11-2014/display_sub0.txt b/test/t02_sip_sfp/regression/06-11-2014/display_sub0.txt new file mode 100644 index 000000000..4c572983b --- /dev/null +++ b/test/t02_sip_sfp/regression/06-11-2014/display_sub0.txt @@ -0,0 +1,213 @@ +-------------------------------------------------------------------------------------------------------------- +QUESO Library: Version = 0.50.1 (5001) + +Development Build + +Build Date = 2014-06-11 14:44 +Build Host = zoidberg.ices.utexas.edu +Build User = damon +Build Arch = x86_64-unknown-linux-gnu +Build Rev = 9ad7de3 + +C++ Config = /opt/apps/ossw/libraries/mpich2/mpich2-1.4.1p1/sl6/gcc-4.7/bin/mpicxx -g -O2 -Wall + +Trilinos DIR = +GSL Libs = -L/opt/apps/ossw/libraries/gsl/gsl-1.15/sl6/gcc-4.7/lib -lgsl -lgslcblas -lm +GRVY DIR = /opt/apps/ossw/libraries/grvy/grvy-0.32.0/sl6/gcc-4.7/boost-1.51.0/hdf5-1.8.9 +GLPK DIR = +HDF5 DIR = /opt/apps/ossw/libraries/hdf5/hdf5-1.8.9/sl6/gcc-4.7 +-------------------------------------------------------------------------------------------------------------- +Beginning run at Wed Jun 11 14:48:10 2014 + +Entering StatisticalInverseProblem::constructor(): prefix = , alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp +In StatisticalInverseProblemOptions::scanOptionsValues(): after reading values of options with prefix 'ip_', state of object is: + +ip_computeSolution = 1 +ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +Leaving StatisticalInverseProblem::constructor(): prefix = ip_ +In StatisticalInverseProblem::solveWithBayesMetropolisHastings(): computing solution, as requested by user +In MetropolisHastingsSGOptions::scanOptionsValues(): after reading values of options with prefix 'ip_mh_', state of object is: +ip_mh_dataOutputFileName = outputData/sipOutput +ip_mh_dataOutputAllowAll = 0 +ip_mh_dataOutputAllowedSet = 0 1 +ip_mh_totallyMute = 0 +ip_mh_initialPosition_dataInputFileName = . +ip_mh_initialPosition_dataInputFileType = m +ip_mh_initialProposalCovMatrix_dataInputFileName = . +ip_mh_initialProposalCovMatrix_dataInputFileType = m +ip_mh_listOfDisabledParameters = +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_dataInputFileType = m +ip_mh_rawChain_size = 32768 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 50000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputPeriod = 0 +ip_mh_rawChain_dataOutputFileName = outputData/ip_raw_chain +ip_mh_rawChain_dataOutputFileType = m +ip_mh_rawChain_dataOutputAllowAll = 0 +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_filteredChain_generate = 1 +ip_mh_filteredChain_discardedPortion = 0 +ip_mh_filteredChain_lag = 16 +ip_mh_filteredChain_dataOutputFileName = outputData/ip_filt_chain +ip_mh_filteredChain_dataOutputFileType = m +ip_mh_filteredChain_dataOutputAllowAll = 0 +ip_mh_filteredChain_dataOutputAllowedSet = 0 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_dr_duringAmNonAdaptiveInt = 1 +ip_mh_am_keepInitialMatrix = 0 +ip_mh_am_initialNonAdaptInterval = 0 +ip_mh_am_adaptInterval = 100 +ip_mh_am_adaptedMatrices_dataOutputPeriod = 0 +ip_mh_am_adaptedMatrices_dataOutputFileName = . +ip_mh_am_adaptedMatrices_dataOutputFileType = m +ip_mh_am_adaptedMatrices_dataOutputAllowAll = 0 +ip_mh_am_adaptedMatrices_dataOutputAllowedSet = +ip_mh_am_eta = 1.92 +ip_mh_am_epsilon = 1e-05 +ip_mh_enableBrooksGelmanConvMonitor = 0 +ip_mh_BrooksGelmanLag = 100 +ip_mh_outputLogLikelihood = 0 +ip_mh_outputLogTarget = 0 + +Entering MetropolisHastingsSG::constructor(1): prefix = ip_, alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp, m_initialProposalCovMatrix = 8 4 ; 4 16 +Entering MetropolisHastingsSG::commonConstructor() +In MetropolisHastingsSG::commonConstructor(): just instantiated a 'ScaledCovMatrix' TK class +Leaving MetropolisHastingsSG::commonConstructor() +Leaving MetropolisHastingsSG::constructor(1) +Starting the generation of Markov chain ip_mh_rawChain, with 32768 positions... + +In MetropolisHastingsSG::generateFullChain(): contents of initial position are:0.1 -1.4 +In MetropolisHastingsSG::generateFullChain(): targetPdf.domaintSet() info is:In BoxSubset::print(): m_minValues = -inf -inf , m_maxValues = inf inf , m_volume = inf + +Finished the generation of Markov chain ip_mh_rawChain, with sub 32768 positions +Some information about this chain: + Chain run time = 0.860662 seconds + + Breaking of the chain run time: + + Candidate run time = 0.227863 seconds (26.4753%) + Num target calls = 58468 + Target d. run time = 0.114551 seconds (13.3096%) + Avg target run time = 1.95921e-06 seconds + Mh alpha run time = 0.005854 seconds (0.680174%) + Dr alpha run time = 0.338052 seconds (39.2781%) +---------------------- -------------- + Sum = 0.68632 seconds (79.7433%) + + Other run times: + DR run time = 0.552945 seconds (64.2465%) + AM run time = 0 seconds (0%) + Number of DRs = 25700(num_DRs/chain_size = 0.784302) + Out of target support in DR = 0 + Rejection percentage = 23.8525 % + Out of target support percentage = 0 % +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, chain name = ip_mh_rawChain: about to try to open generic output file 'outputData/sipOutput.m', subId = 0, subenv is allowed to write (1/true or 0/false) = 1... +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: returned from opening generic output file 'outputData/sipOutput.m', subId = 0 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: about to try to write raw sub chain output file 'outputData/ip_raw_chain.m', subId = 0, subenv is allowed to write 1/true or 0/false) = 1... +In MetropolisHastingsSG::generateSequence(): just wrote (per period request) remaining 32768 chain positions , 0 <= pos <= 32767 +Entering BaseVectorSequence::subPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::subPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MLE, rawSubMLEvalue = -3.41481e-06, rawSubMLEpositions.subSequenceSize() = 1, rawSubMLEpositions[0] = -0.994878 1.99948 +Entering BaseVectorSequence::subPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::subPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MAP, rawSubMAPvalue = -3.41481e-06, rawSubMAPpositions.subSequenceSize() = 1, rawSubMAPpositions[0] = -0.994878 1.99948 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: returned from writing raw sub chain output file 'outputData/ip_raw_chain.m', subId = 0 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: about to try to write raw unified chain output file 'outputData/ip_raw_chain.m', subId = 0... +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: returned from writing raw unified chain output file 'outputData/ip_raw_chain.m', subId = 0 +Entering BaseVectorSequence::unifiedPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::unifiedPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MLE, rawUnifiedMLEvalue = -3.41481e-06, rawUnifiedMLEpositions.subSequenceSize() = 1, rawUnifiedMLEpositions[0] = -0.994878 1.99948 +Entering BaseVectorSequence::unifiedPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::unifiedPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MAP, rawUnifiedMAPvalue = -3.41481e-06, rawUnifiedMAPpositions.subSequenceSize() = 1, rawUnifiedMAPpositions[0] = -0.994878 1.99948 + + +----------------------------------------------------- + Writing more information about the Markov chain ip_mh_rawChain to output file ... +----------------------------------------------------- + + +----------------------------------------------------- + Finished writing more information about the Markov chain ip_mh_rawChain +----------------------------------------------------- + +Entering SequenceOfVectors::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768 +Leaving SequenceOfVectors::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048 +Entering ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768 +Leaving ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048 +Entering ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768 +Leaving ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: checking necessity of opening output files for filtered chain ip_mh_filtChain... +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: closed sub output file 'outputData/ip_filt_chain' for filtered chain ip_mh_filtChain +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: closed unified output file 'outputData/ip_filt_chain' for filtered chain ip_mh_filtChain +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: closed generic output file 'outputData/sipOutput' (chain name is ip_mh_filtChain) + +In SequentialVectorRealizer::constructor(): m_chain.subSequenceSize() = 2048 + +Entering StatisticalForwardProblem::constructor(): prefix = , alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp +In StatisticalForwardProblemOptions::scanOptionsValues(): after reading values of options with prefix 'fp_', state of object is: +fp_computeSolution = 1 +fp_computeCovariances = 1 +fp_computeCorrelations = 1 +fp_dataOutputFileName = outputData/sfpOutput +fp_dataOutputAllowedSet = 0 1 + +Leaving StatisticalForwardProblem::constructor(): prefix = fp_ +In StatisticalForwardProblem::solveWithMonteCarlo(): computing solution, as requested by user +Entering MonteCarloSG::constructor(): prefix = fp_, alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp +In MonteCarloSGOptions::scanOptionsValues(): after reading values of options with prefix 'fp_mc_', state of object is: +fp_mc_dataOutputFileName = outputData/sfpOutput +fp_mc_dataOutputAllowedSet = 0 1 +fp_mc_pseq_dataOutputPeriod = 0 +fp_mc_pseq_dataOutputFileName = outputData/fp_p_seq +fp_mc_pseq_dataOutputFileType = m +fp_mc_pseq_dataOutputAllowedSet = 0 1 +fp_mc_qseq_dataInputFileName = . +fp_mc_qseq_dataInputFileType = m +fp_mc_qseq_size = 1048576 +fp_mc_qseq_displayPeriod = 20000 +fp_mc_qseq_measureRunTimes = 1 +fp_mc_qseq_dataOutputPeriod = 0 +fp_mc_qseq_dataOutputFileName = outputData/fp_q_seq +fp_mc_qseq_dataOutputFileType = m +fp_mc_qseq_dataOutputAllowedSet = 0 1 +Leaving MonteCarloSG::constructor() +In MonteCarloSG::internGenerateSequence(): m_optionsObj->m_ov.m_qseqSize = 1048576, paramRv.realizer().subPeriod() = 2048, about to call actualGenerateSequence() with subActualSize = 2048 +Starting the generation of qoi sequence fp_mc_QoiSeq, with 2048 samples... +Finished the generation of qoi sequence fp_mc_QoiSeq, with sub 2048 samples +Some information about this sequence: + Sequence run time = 0.0025 seconds + + Breaking of the seq run time: + + QoI function run time = 0.000355 seconds (14.2%) +In MonteCarloSG::internGenerateSequence(): returned from call to actualGenerateSequence() with subActualSize = 2048 +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening generic output file (qseq name is fp_mc_QoiSeq) ... +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening output files for pseq fp_mc_ParamSeq... +In MonteCarloG::internGenerateSequence(): just wrote remaining pseq positions (per period request) +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: closed unified data output file 'outputData/fp_p_seq' for pseq fp_mc_ParamSeq +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening output files for qseq fp_mc_QoiSeq... +In MonteCarloG::internGenerateSequence(): just wrote remaining qseq positions (per period request) +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: closed unified data output file 'outputData/fp_q_seq' for qseq fp_mc_QoiSeq +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: closed generic data output file 'outputData/sfpOutput' for QoI sequence fp_mc_QoiSeq + +In SequentialVectorRealizer::constructor(): m_chain.subSequenceSize() = 2048 +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: instantiating cov and corr matrices +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: contents of covariance matrix are +4.02364 ; 1.01851 +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: contents of correlation matrix are +0.897592 ; 0.45814 +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: checking necessity of opening data output file 'outputData/sfpOutput' +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: closed data output file 'outputData/sfpOutput' + +Ending run at Wed Jun 11 14:48:11 2014 +Total run time = 1 seconds diff --git a/test/t02_sip_sfp/regression/09-15-2014/display_sub0.txt b/test/t02_sip_sfp/regression/09-15-2014/display_sub0.txt new file mode 100644 index 000000000..acc83a886 --- /dev/null +++ b/test/t02_sip_sfp/regression/09-15-2014/display_sub0.txt @@ -0,0 +1,213 @@ +-------------------------------------------------------------------------------------------------------------- +QUESO Library: Version = 0.50.1 (5001) + +Development Build + +Build Date = 2014-11-18 18:08 +Build Host = zoidberg.ices.utexas.edu +Build User = damon +Build Arch = x86_64-unknown-linux-gnu +Build Rev = 73f936c + +C++ Config = /opt/apps/ossw/libraries/mpich2/mpich2-1.4.1p1/sl6/gcc-4.7/bin/mpicxx -g -O2 -Wall + +Trilinos DIR = +GSL Libs = -L/opt/apps/ossw/libraries/gsl/gsl-1.15/sl6/gcc-4.7/lib -lgsl -lgslcblas -lm +GRVY DIR = +GLPK DIR = +HDF5 DIR = /opt/apps/ossw/libraries/hdf5/hdf5-1.8.9/sl6/gcc-4.7 +-------------------------------------------------------------------------------------------------------------- +Beginning run at Tue Nov 18 18:26:23 2014 + +Entering StatisticalInverseProblem::constructor(): prefix = , alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp +In StatisticalInverseProblemOptions::scanOptionsValues(): after reading values of options with prefix 'ip_', state of object is: + +ip_computeSolution = 1 +ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +Leaving StatisticalInverseProblem::constructor(): prefix = ip_ +In StatisticalInverseProblem::solveWithBayesMetropolisHastings(): computing solution, as requested by user +In MetropolisHastingsSGOptions::scanOptionsValues(): after reading values of options with prefix 'ip_mh_', state of object is: +ip_mh_dataOutputFileName = outputData/sipOutput +ip_mh_dataOutputAllowAll = 0 +ip_mh_dataOutputAllowedSet = 0 1 +ip_mh_totallyMute = 0 +ip_mh_initialPosition_dataInputFileName = . +ip_mh_initialPosition_dataInputFileType = m +ip_mh_initialProposalCovMatrix_dataInputFileName = . +ip_mh_initialProposalCovMatrix_dataInputFileType = m +ip_mh_listOfDisabledParameters = +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_dataInputFileType = m +ip_mh_rawChain_size = 32768 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 50000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputPeriod = 0 +ip_mh_rawChain_dataOutputFileName = outputData/ip_raw_chain +ip_mh_rawChain_dataOutputFileType = m +ip_mh_rawChain_dataOutputAllowAll = 0 +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_filteredChain_generate = 1 +ip_mh_filteredChain_discardedPortion = 0 +ip_mh_filteredChain_lag = 16 +ip_mh_filteredChain_dataOutputFileName = outputData/ip_filt_chain +ip_mh_filteredChain_dataOutputFileType = m +ip_mh_filteredChain_dataOutputAllowAll = 0 +ip_mh_filteredChain_dataOutputAllowedSet = 0 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_dr_duringAmNonAdaptiveInt = 1 +ip_mh_am_keepInitialMatrix = 0 +ip_mh_am_initialNonAdaptInterval = 0 +ip_mh_am_adaptInterval = 100 +ip_mh_am_adaptedMatrices_dataOutputPeriod = 0 +ip_mh_am_adaptedMatrices_dataOutputFileName = . +ip_mh_am_adaptedMatrices_dataOutputFileType = m +ip_mh_am_adaptedMatrices_dataOutputAllowAll = 0 +ip_mh_am_adaptedMatrices_dataOutputAllowedSet = +ip_mh_am_eta = 1.92 +ip_mh_am_epsilon = 1e-05 +ip_mh_enableBrooksGelmanConvMonitor = 0 +ip_mh_BrooksGelmanLag = 100 +ip_mh_outputLogLikelihood = 1 +ip_mh_outputLogTarget = 1 + +Entering MetropolisHastingsSG::constructor(1): prefix = ip_, alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp, m_initialProposalCovMatrix = 8 4 ; 4 16 +Entering MetropolisHastingsSG::commonConstructor() +In MetropolisHastingsSG::commonConstructor(): just instantiated a 'ScaledCovMatrix' TK class +Leaving MetropolisHastingsSG::commonConstructor() +Leaving MetropolisHastingsSG::constructor(1) +Starting the generation of Markov chain ip_mh_rawChain, with 32768 positions... + +In MetropolisHastingsSG::generateFullChain(): contents of initial position are:0.1 -1.4 +In MetropolisHastingsSG::generateFullChain(): targetPdf.domaintSet() info is:In BoxSubset::print(): m_minValues = -inf -inf , m_maxValues = inf inf , m_volume = inf + +Finished the generation of Markov chain ip_mh_rawChain, with sub 32768 positions +Some information about this chain: + Chain run time = 0.413144 seconds + + Breaking of the chain run time: + + Candidate run time = 0.119816 seconds (29.001%) + Num target calls = 58468 + Target d. run time = 0.055445 seconds (13.4203%) + Avg target run time = 9.48297e-07 seconds + Mh alpha run time = 0.004881 seconds (1.18143%) + Dr alpha run time = 0.137105 seconds (33.1858%) +---------------------- -------------- + Sum = 0.317247 seconds (76.7885%) + + Other run times: + DR run time = 0.249725 seconds (60.445%) + AM run time = 0 seconds (0%) + Number of DRs = 25700(num_DRs/chain_size = 0.784302) + Out of target support in DR = 0 + Rejection percentage = 23.8525 % + Out of target support percentage = 0 % +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, chain name = ip_mh_rawChain: about to try to open generic output file 'outputData/sipOutput.m', subId = 0, subenv is allowed to write (1/true or 0/false) = 1... +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: returned from opening generic output file 'outputData/sipOutput.m', subId = 0 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: about to try to write raw sub chain output file 'outputData/ip_raw_chain.m', subId = 0, subenv is allowed to write 1/true or 0/false) = 1... +In MetropolisHastingsSG::generateSequence(): just wrote (per period request) remaining 32768 chain positions , 0 <= pos <= 32767 +Entering BaseVectorSequence::subPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::subPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MLE, rawSubMLEvalue = -3.41481e-06, rawSubMLEpositions.subSequenceSize() = 1, rawSubMLEpositions[0] = -0.994878 1.99948 +Entering BaseVectorSequence::subPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::subPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MAP, rawSubMAPvalue = -3.41481e-06, rawSubMAPpositions.subSequenceSize() = 1, rawSubMAPpositions[0] = -0.994878 1.99948 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: returned from writing raw sub chain output file 'outputData/ip_raw_chain.m', subId = 0 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: about to try to write raw unified chain output file 'outputData/ip_raw_chain.m', subId = 0... +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: returned from writing raw unified chain output file 'outputData/ip_raw_chain.m', subId = 0 +Entering BaseVectorSequence::unifiedPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::unifiedPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MLE, rawUnifiedMLEvalue = -3.41481e-06, rawUnifiedMLEpositions.subSequenceSize() = 1, rawUnifiedMLEpositions[0] = -0.994878 1.99948 +Entering BaseVectorSequence::unifiedPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768 +Leaving BaseVectorSequence::unifiedPositionsOfMaximum() +In MetropolisHastingsSG::generateSequence(): just computed MAP, rawUnifiedMAPvalue = -3.41481e-06, rawUnifiedMAPpositions.subSequenceSize() = 1, rawUnifiedMAPpositions[0] = -0.994878 1.99948 + + +----------------------------------------------------- + Writing more information about the Markov chain ip_mh_rawChain to output file ... +----------------------------------------------------- + + +----------------------------------------------------- + Finished writing more information about the Markov chain ip_mh_rawChain +----------------------------------------------------- + +Entering SequenceOfVectors::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768 +Leaving SequenceOfVectors::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048 +Entering ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768 +Leaving ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048 +Entering ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768 +Leaving ScalarSequence::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048 +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: checking necessity of opening output files for filtered chain ip_mh_filtChain... +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: closed sub output file 'outputData/ip_filt_chain' for filtered chain ip_mh_filtChain +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: closed unified output file 'outputData/ip_filt_chain' for filtered chain ip_mh_filtChain +In MetropolisHastingsSG::generateSequence(), prefix = ip_mh_: closed generic output file 'outputData/sipOutput' (chain name is ip_mh_filtChain) + +In SequentialVectorRealizer::constructor(): m_chain.subSequenceSize() = 2048 + +Entering StatisticalForwardProblem::constructor(): prefix = , alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp +In StatisticalForwardProblemOptions::scanOptionsValues(): after reading values of options with prefix 'fp_', state of object is: +fp_computeSolution = 1 +fp_computeCovariances = 1 +fp_computeCorrelations = 1 +fp_dataOutputFileName = outputData/sfpOutput +fp_dataOutputAllowedSet = 0 1 + +Leaving StatisticalForwardProblem::constructor(): prefix = fp_ +In StatisticalForwardProblem::solveWithMonteCarlo(): computing solution, as requested by user +Entering MonteCarloSG::constructor(): prefix = fp_, alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp +In MonteCarloSGOptions::scanOptionsValues(): after reading values of options with prefix 'fp_mc_', state of object is: +fp_mc_dataOutputFileName = outputData/sfpOutput +fp_mc_dataOutputAllowedSet = 0 1 +fp_mc_pseq_dataOutputPeriod = 0 +fp_mc_pseq_dataOutputFileName = outputData/fp_p_seq +fp_mc_pseq_dataOutputFileType = m +fp_mc_pseq_dataOutputAllowedSet = 0 1 +fp_mc_qseq_dataInputFileName = . +fp_mc_qseq_dataInputFileType = m +fp_mc_qseq_size = 1048576 +fp_mc_qseq_displayPeriod = 20000 +fp_mc_qseq_measureRunTimes = 1 +fp_mc_qseq_dataOutputPeriod = 0 +fp_mc_qseq_dataOutputFileName = outputData/fp_q_seq +fp_mc_qseq_dataOutputFileType = m +fp_mc_qseq_dataOutputAllowedSet = 0 1 +Leaving MonteCarloSG::constructor() +In MonteCarloSG::internGenerateSequence(): m_optionsObj->m_ov.m_qseqSize = 1048576, paramRv.realizer().subPeriod() = 2048, about to call actualGenerateSequence() with subActualSize = 2048 +Starting the generation of qoi sequence fp_mc_QoiSeq, with 2048 samples... +Finished the generation of qoi sequence fp_mc_QoiSeq, with sub 2048 samples +Some information about this sequence: + Sequence run time = 0.001256 seconds + + Breaking of the seq run time: + + QoI function run time = 0.000192 seconds (15.2866%) +In MonteCarloSG::internGenerateSequence(): returned from call to actualGenerateSequence() with subActualSize = 2048 +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening generic output file (qseq name is fp_mc_QoiSeq) ... +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening output files for pseq fp_mc_ParamSeq... +In MonteCarloG::internGenerateSequence(): just wrote remaining pseq positions (per period request) +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: closed unified data output file 'outputData/fp_p_seq' for pseq fp_mc_ParamSeq +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening output files for qseq fp_mc_QoiSeq... +In MonteCarloG::internGenerateSequence(): just wrote remaining qseq positions (per period request) +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: closed unified data output file 'outputData/fp_q_seq' for qseq fp_mc_QoiSeq +In MonteCarloSG::internGenerateSequence(), prefix = fp_mc_: closed generic data output file 'outputData/sfpOutput' for QoI sequence fp_mc_QoiSeq + +In SequentialVectorRealizer::constructor(): m_chain.subSequenceSize() = 2048 +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: instantiating cov and corr matrices +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: contents of covariance matrix are +4.02364 ; 1.01851 +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: contents of correlation matrix are +0.897592 ; 0.45814 +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: checking necessity of opening data output file 'outputData/sfpOutput' +In StatisticalForwardProblem::solveWithMonteCarlo(), prefix = fp_: closed data output file 'outputData/sfpOutput' + +Ending run at Tue Nov 18 18:26:24 2014 +Total run time = 1 seconds diff --git a/test/t02_sip_sfp/rtest02.sh b/test/t02_sip_sfp/rtest02.sh index 02c50bfbe..4aed6cf6f 100755 --- a/test/t02_sip_sfp/rtest02.sh +++ b/test/t02_sip_sfp/rtest02.sh @@ -1,13 +1,13 @@ #!/bin/bash #---------------------------------------------------------- # PECOS Regression tests for QUESO -# +# # 1. Validation cycle using TGA example. # # Originally: 5-19-09 #---------------------------------------------------------- -TOLERANCE="1e-10" # solution diff tolerance (absolute) +TOLERANCE="1e-10" # solution diff tolerance (relative) SAVELOG=1 # Log model output? COMMONDIR="$srcdir/../common" @@ -22,13 +22,15 @@ RUNDIR=`pwd` #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # -# Regresion Test #1: Validation cycle with TGA example +# Regresion Test #1: Validation cycle with TGA example #VERIFY_DATE="07-12-2009" #VERIFY_DATE="09-06-2009" #VERIFY_DATE="07-09-2012" #VERIFY_DATE="09-03-2012" -VERIFY_DATE="09-04-2012" +#VERIFY_DATE="09-04-2012" +# VERIFY_DATE="06-11-2014" +VERIFY_DATE="09-15-2014" SOLDIR="outputData" EXE="./SipSfpExample_gsl" # executable name SOLREFS="$srcdir/regression/$VERIFY_DATE" @@ -66,14 +68,10 @@ igot=0 # Compare outputs from 1 output file. for file in display_sub0.txt ; do - -# pwd -# echo $SOLREFS/$file grep "e-[0-9]\|e+[0-9]" $SOLREFS/$file | grep -v sec | grep -v Arch > $SOLDIR/nada0 grep "e-[0-9]\|e+[0-9]" $SOLDIR/$file | grep -v sec | grep -v Arch > $SOLDIR/nada1 diff $SOLDIR/nada0 $SOLDIR/nada1 let igot="$igot + $?" - done cd - >& /dev/null @@ -83,9 +81,3 @@ if [ $igot -eq 0 ];then else message_fail "$TESTNAME failed verification" fi - -#------------------------------------------------------------------------------ -#------------------------------------------------------------------------------ - - - diff --git a/test/t03_sequence/Makefile.am b/test/t03_sequence/Makefile.am index 6decb7b68..ec5c5010b 100644 --- a/test/t03_sequence/Makefile.am +++ b/test/t03_sequence/Makefile.am @@ -43,7 +43,7 @@ endif if GIT_CLONE .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(srcdir)/*.C $(srcdir)/*.h + $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/test/t03_sequence/sequence/Makefile.am b/test/t03_sequence/sequence/Makefile.am index 9e0be2469..b36fa8f2b 100644 --- a/test/t03_sequence/sequence/Makefile.am +++ b/test/t03_sequence/sequence/Makefile.am @@ -17,7 +17,7 @@ SequenceExample_gsl_SOURCES = example_main.C example_compute.C if SVN_CHECKOUT .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE *.C *.h + $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/test/t04_bimodal/Makefile.am b/test/t04_bimodal/Makefile.am index 1cf0b9523..de4f1a78e 100644 --- a/test/t04_bimodal/Makefile.am +++ b/test/t04_bimodal/Makefile.am @@ -9,28 +9,28 @@ AM_CPPFLAGS += $(BOOST_CPPFLAGS) $(GSL_CFLAGS) $(ANN_CFLAGS) LIBS = -L$(top_builddir)/src/ -lqueso -if GRVY_ENABLED +if GRVY_ENABLED AM_CPPFLAGS += $(GRVY_CFLAGS) endif if TRILINOS_ENABLED - AM_CPPFLAGS += -I$(TRILINOS_INCLUDE) + AM_CPPFLAGS += -I$(TRILINOS_INCLUDE) LIBS += -lteuchoscore -lteuchoscomm -lteuchosnumerics -lteuchosparameterlist -lteuchosremainder -lepetra endif -if GLPK_ENABLED +if GLPK_ENABLED AM_CPPFLAGS += $(GLPK_CFLAGS) LIBS += $(GLPK_LIBS) endif -if HDF5_ENABLED - AM_CPPFLAGS += $(HDF5_CFLAGS) +if HDF5_ENABLED + AM_CPPFLAGS += $(HDF5_CFLAGS) LIBS += $(HDF5_LIBS) endif EXTRA_DIST = regression rtest04.sh check_PROGRAMS = BimodalExample_gsl -TESTS = rtest04.sh +TESTS = rtest04.sh BimodalExample_gsl_SOURCES = BimodalExample_gsl_SOURCES += example_main.C example_compute.C example_likelihood.C @@ -43,7 +43,7 @@ endif if GIT_CLONE .license.stamp: $(top_srcdir)/LICENSE - $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(srcdir)/*.C $(srcdir)/*.h + $(top_srcdir)/src/common/lic_utils/update_license.pl $(top_srcdir)/LICENSE $(QUESO_STAMPED_FILES) echo 'updated source license headers' >$@ endif diff --git a/test/t04_bimodal/regression/09-04-2014/display_sub0.txt b/test/t04_bimodal/regression/09-04-2014/display_sub0.txt new file mode 100644 index 000000000..c9d817f73 --- /dev/null +++ b/test/t04_bimodal/regression/09-04-2014/display_sub0.txt @@ -0,0 +1,1133 @@ +-------------------------------------------------------------------------------------------------------------- +QUESO Library: Version = 0.50.1 (5001) + +Development Build + +Build Date = 2014-11-26 23:37 +Build Host = quagmire +Build User = damon +Build Arch = x86_64-apple-darwin14.0.0 +Build Rev = d5b812c + +C++ Config = mpic++ -g -O2 -Wall + +Trilinos DIR = +GSL Libs = -L/Users/damon/ossw/libraries/gsl/gsl-1.16/gcc-4.8.3/lib -lgsl -lgslcblas +GRVY DIR = +GLPK DIR = +HDF5 DIR = +-------------------------------------------------------------------------------------------------------------- +Beginning run at Thu Nov 27 21:16:51 2014 + +Entering StatisticalInverseProblem::constructor(): prefix = , alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example_1chain.inp +In StatisticalInverseProblemOptions::scanOptionsValues(): after reading values of options with prefix 'ip_', state of object is: + +ip_computeSolution = 1 +ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +Leaving StatisticalInverseProblem::constructor(): prefix = ip_ +In StatisticalInverseProblem::solveWithBayesMLSampling(): computing solution, as requested by user +Entering MLSampling::constructor() +In MLSamplingOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_', state of object is: +ip_ml_restartOutput_levelPeriod = 0 +ip_ml_restartOutput_baseNameForFiles = . +ip_ml_restartOutput_fileType = m +ip_ml_restartInput_baseNameForFiles = . +ip_ml_restartInput_fileType = m +ip_ml_dataOutputFileName = outputData/sipOutput_ml +ip_ml_dataOutputAllowedSet = 0 1 + +Leaving MLSampling::constructor() +Entering MLSampling::generateSequence(), at Thu Nov 27 21:16:51 2014 +, after 0 seconds from queso environment instatiation... +In MLSamplingLevelOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_default_', state of object is: +m_prefix = ip_ml_default_ +ip_ml_default_stopAtEnd = 0 +ip_ml_default_dataOutputFileName = . +ip_ml_default_dataOutputAllowAll = 0 +ip_ml_default_dataOutputAllowedSet = +ip_ml_default_loadBalanceAlgorithmId = 2 +ip_ml_default_loadBalanceTreshold = 1 +ip_ml_default_minEffectiveSizeRatio = 0.49 +ip_ml_default_maxEffectiveSizeRatio = 0.51 +ip_ml_default_scaleCovMatrix = 1 +ip_ml_default_minRejectionRate = 0.24 +ip_ml_default_maxRejectionRate = 0.4 +ip_ml_default_covRejectionRate = 0.25 +ip_ml_default_minAcceptableEta = 0 +ip_ml_default_totallyMute = 1 +ip_ml_default_initialPosition_dataInputFileName = . +ip_ml_default_initialPosition_dataInputFileType = m +ip_ml_default_initialProposalCovMatrix_dataInputFileName = . +ip_ml_default_initialProposalCovMatrix_dataInputFileType = m +ip_ml_default_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_default_listOfDisabledParameters = +ip_ml_default_initialValuesOfDisabledParameters = +ip_ml_default_rawChain_dataInputFileName = . +ip_ml_default_rawChain_dataInputFileType = m +ip_ml_default_rawChain_size = 5000 +ip_ml_default_rawChain_generateExtra = 0 +ip_ml_default_rawChain_displayPeriod = 500 +ip_ml_default_rawChain_measureRunTimes = 1 +ip_ml_default_rawChain_dataOutputPeriod = 0 +ip_ml_default_rawChain_dataOutputFileName = . +ip_ml_default_rawChain_dataOutputFileType = m +ip_ml_default_rawChain_dataOutputAllowAll = 0 +ip_ml_default_rawChain_dataOutputAllowedSet = +ip_ml_default_filteredChain_generate = 0 +ip_ml_default_filteredChain_discardedPortion = 0 +ip_ml_default_filteredChain_lag = 1 +ip_ml_default_filteredChain_dataOutputFileName = . +ip_ml_default_filteredChain_dataOutputFileType = m +ip_ml_default_filteredChain_dataOutputAllowAll = 0 +ip_ml_default_filteredChain_dataOutputAllowedSet = +ip_ml_default_displayCandidates = 0 +ip_ml_default_putOutOfBoundsInChain = 0 +ip_ml_default_tk_useLocalHessian = 0 +ip_ml_default_tk_useNewtonComponent = 1 +ip_ml_default_dr_maxNumExtraStages = 0 +ip_ml_default_dr_listOfScalesForExtraStages = +ip_ml_default_dr_duringAmNonAdaptiveInt = 1 +ip_ml_default_am_keepInitialMatrix = 0 +ip_ml_default_am_initialNonAdaptInterval = 0 +ip_ml_default_am_adaptInterval = 0 +ip_ml_default_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_default_amAdaptedMatrices_dataOutputFileName = . +ip_ml_default_amAdaptedMatrices_dataOutputFileType = m +ip_ml_default_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_default_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_default_am_eta = 1 +ip_ml_default_am_epsilon = 1e-05 +ip_ml_default_doLogitTransform = 0 + +In MLSamplingLevelOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_last_', state of object is: +m_prefix = ip_ml_last_ +ip_ml_last_stopAtEnd = 0 +ip_ml_last_dataOutputFileName = outputData/sipOutput_ml +ip_ml_last_dataOutputAllowAll = 0 +ip_ml_last_dataOutputAllowedSet = 0 1 +ip_ml_last_loadBalanceAlgorithmId = 2 +ip_ml_last_loadBalanceTreshold = 1 +ip_ml_last_minEffectiveSizeRatio = 0.49 +ip_ml_last_maxEffectiveSizeRatio = 0.51 +ip_ml_last_scaleCovMatrix = 1 +ip_ml_last_minRejectionRate = 0.24 +ip_ml_last_maxRejectionRate = 0.4 +ip_ml_last_covRejectionRate = 0.25 +ip_ml_last_minAcceptableEta = 0 +ip_ml_last_totallyMute = 1 +ip_ml_last_initialPosition_dataInputFileName = . +ip_ml_last_initialPosition_dataInputFileType = m +ip_ml_last_initialProposalCovMatrix_dataInputFileName = . +ip_ml_last_initialProposalCovMatrix_dataInputFileType = m +ip_ml_last_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_last_listOfDisabledParameters = +ip_ml_last_initialValuesOfDisabledParameters = +ip_ml_last_rawChain_dataInputFileName = . +ip_ml_last_rawChain_dataInputFileType = m +ip_ml_last_rawChain_size = 10000 +ip_ml_last_rawChain_generateExtra = 0 +ip_ml_last_rawChain_displayPeriod = 500 +ip_ml_last_rawChain_measureRunTimes = 1 +ip_ml_last_rawChain_dataOutputPeriod = 0 +ip_ml_last_rawChain_dataOutputFileName = outputData/rawChain_ml +ip_ml_last_rawChain_dataOutputFileType = m +ip_ml_last_rawChain_dataOutputAllowAll = 0 +ip_ml_last_rawChain_dataOutputAllowedSet = 0 +ip_ml_last_filteredChain_generate = 1 +ip_ml_last_filteredChain_discardedPortion = 0 +ip_ml_last_filteredChain_lag = 2 +ip_ml_last_filteredChain_dataOutputFileName = outputData/filtChain_ml +ip_ml_last_filteredChain_dataOutputFileType = m +ip_ml_last_filteredChain_dataOutputAllowAll = 0 +ip_ml_last_filteredChain_dataOutputAllowedSet = 0 +ip_ml_last_displayCandidates = 0 +ip_ml_last_putOutOfBoundsInChain = 0 +ip_ml_last_tk_useLocalHessian = 0 +ip_ml_last_tk_useNewtonComponent = 1 +ip_ml_last_dr_maxNumExtraStages = 1 +ip_ml_last_dr_listOfScalesForExtraStages = 5 +ip_ml_last_dr_duringAmNonAdaptiveInt = 1 +ip_ml_last_am_keepInitialMatrix = 0 +ip_ml_last_am_initialNonAdaptInterval = 0 +ip_ml_last_am_adaptInterval = 0 +ip_ml_last_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_last_amAdaptedMatrices_dataOutputFileName = . +ip_ml_last_amAdaptedMatrices_dataOutputFileType = m +ip_ml_last_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_last_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_last_am_eta = 1 +ip_ml_last_am_epsilon = 1e-05 +ip_ml_last_doLogitTransform = 0 + +In MLSamplingLevelOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_0_', state of object is: +m_prefix = ip_ml_0_ +ip_ml_0_stopAtEnd = 0 +ip_ml_0_dataOutputFileName = . +ip_ml_0_dataOutputAllowAll = 0 +ip_ml_0_dataOutputAllowedSet = +ip_ml_0_loadBalanceAlgorithmId = 2 +ip_ml_0_loadBalanceTreshold = 1 +ip_ml_0_minEffectiveSizeRatio = 0.49 +ip_ml_0_maxEffectiveSizeRatio = 0.51 +ip_ml_0_scaleCovMatrix = 1 +ip_ml_0_minRejectionRate = 0.24 +ip_ml_0_maxRejectionRate = 0.4 +ip_ml_0_covRejectionRate = 0.25 +ip_ml_0_minAcceptableEta = 0 +ip_ml_0_totallyMute = 1 +ip_ml_0_initialPosition_dataInputFileName = . +ip_ml_0_initialPosition_dataInputFileType = m +ip_ml_0_initialProposalCovMatrix_dataInputFileName = . +ip_ml_0_initialProposalCovMatrix_dataInputFileType = m +ip_ml_0_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_0_listOfDisabledParameters = +ip_ml_0_initialValuesOfDisabledParameters = +ip_ml_0_rawChain_dataInputFileName = . +ip_ml_0_rawChain_dataInputFileType = m +ip_ml_0_rawChain_size = 5000 +ip_ml_0_rawChain_generateExtra = 0 +ip_ml_0_rawChain_displayPeriod = 500 +ip_ml_0_rawChain_measureRunTimes = 1 +ip_ml_0_rawChain_dataOutputPeriod = 0 +ip_ml_0_rawChain_dataOutputFileName = . +ip_ml_0_rawChain_dataOutputFileType = m +ip_ml_0_rawChain_dataOutputAllowAll = 0 +ip_ml_0_rawChain_dataOutputAllowedSet = +ip_ml_0_filteredChain_generate = 0 +ip_ml_0_filteredChain_discardedPortion = 0 +ip_ml_0_filteredChain_lag = 1 +ip_ml_0_filteredChain_dataOutputFileName = . +ip_ml_0_filteredChain_dataOutputFileType = m +ip_ml_0_filteredChain_dataOutputAllowAll = 0 +ip_ml_0_filteredChain_dataOutputAllowedSet = +ip_ml_0_displayCandidates = 0 +ip_ml_0_putOutOfBoundsInChain = 0 +ip_ml_0_tk_useLocalHessian = 0 +ip_ml_0_tk_useNewtonComponent = 1 +ip_ml_0_dr_maxNumExtraStages = 0 +ip_ml_0_dr_listOfScalesForExtraStages = +ip_ml_0_dr_duringAmNonAdaptiveInt = 1 +ip_ml_0_am_keepInitialMatrix = 0 +ip_ml_0_am_initialNonAdaptInterval = 0 +ip_ml_0_am_adaptInterval = 0 +ip_ml_0_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_0_amAdaptedMatrices_dataOutputFileName = . +ip_ml_0_amAdaptedMatrices_dataOutputFileType = m +ip_ml_0_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_0_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_0_am_eta = 1 +ip_ml_0_am_epsilon = 1e-05 +ip_ml_0_doLogitTransform = 0 + +KEY In MLSampling::generateSequence(): beginning level 0, currOptions.m_rawChainSize = 5000 +In MLSampling::generateSequence(), level 0: finished generating 5000 chain positions +In MLSampling::generateSequence(): ending level 0, total level time = 0.005796 seconds +In MLSampling::generateSequence(): at end of level 0, sub minLogLike = -2453.13, sub maxLogLike = -3.16228 +In MLSampling::generateSequence(): at end of level 0, unified minLogLike = -2453.13, unified maxLogLike = -3.16228 +In MLSampling::generateSequence(): beginning level 1, at Thu Nov 27 21:16:51 2014 +, after 0 seconds from entering the routine, after 0 seconds from queso environment instatiation +In IMLSampling::generateSequence(), level 1, beginning 'do-while(tryExponentEta): failedExponent = 0, failedEta = 0 +In MLSamplingLevelOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_1_', state of object is: +m_prefix = ip_ml_1_ +ip_ml_1_stopAtEnd = 0 +ip_ml_1_dataOutputFileName = . +ip_ml_1_dataOutputAllowAll = 0 +ip_ml_1_dataOutputAllowedSet = +ip_ml_1_loadBalanceAlgorithmId = 2 +ip_ml_1_loadBalanceTreshold = 1 +ip_ml_1_minEffectiveSizeRatio = 0.49 +ip_ml_1_maxEffectiveSizeRatio = 0.51 +ip_ml_1_scaleCovMatrix = 1 +ip_ml_1_minRejectionRate = 0.24 +ip_ml_1_maxRejectionRate = 0.4 +ip_ml_1_covRejectionRate = 0.25 +ip_ml_1_minAcceptableEta = 0 +ip_ml_1_totallyMute = 1 +ip_ml_1_initialPosition_dataInputFileName = . +ip_ml_1_initialPosition_dataInputFileType = m +ip_ml_1_initialProposalCovMatrix_dataInputFileName = . +ip_ml_1_initialProposalCovMatrix_dataInputFileType = m +ip_ml_1_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_1_listOfDisabledParameters = +ip_ml_1_initialValuesOfDisabledParameters = +ip_ml_1_rawChain_dataInputFileName = . +ip_ml_1_rawChain_dataInputFileType = m +ip_ml_1_rawChain_size = 5000 +ip_ml_1_rawChain_generateExtra = 0 +ip_ml_1_rawChain_displayPeriod = 500 +ip_ml_1_rawChain_measureRunTimes = 1 +ip_ml_1_rawChain_dataOutputPeriod = 0 +ip_ml_1_rawChain_dataOutputFileName = . +ip_ml_1_rawChain_dataOutputFileType = m +ip_ml_1_rawChain_dataOutputAllowAll = 0 +ip_ml_1_rawChain_dataOutputAllowedSet = +ip_ml_1_filteredChain_generate = 0 +ip_ml_1_filteredChain_discardedPortion = 0 +ip_ml_1_filteredChain_lag = 1 +ip_ml_1_filteredChain_dataOutputFileName = . +ip_ml_1_filteredChain_dataOutputFileType = m +ip_ml_1_filteredChain_dataOutputAllowAll = 0 +ip_ml_1_filteredChain_dataOutputAllowedSet = +ip_ml_1_displayCandidates = 0 +ip_ml_1_putOutOfBoundsInChain = 0 +ip_ml_1_tk_useLocalHessian = 0 +ip_ml_1_tk_useNewtonComponent = 1 +ip_ml_1_dr_maxNumExtraStages = 0 +ip_ml_1_dr_listOfScalesForExtraStages = +ip_ml_1_dr_duringAmNonAdaptiveInt = 1 +ip_ml_1_am_keepInitialMatrix = 0 +ip_ml_1_am_initialNonAdaptInterval = 0 +ip_ml_1_am_adaptInterval = 0 +ip_ml_1_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_1_amAdaptedMatrices_dataOutputFileName = . +ip_ml_1_amAdaptedMatrices_dataOutputFileType = m +ip_ml_1_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_1_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_1_am_eta = 1 +ip_ml_1_am_epsilon = 1e-05 +ip_ml_1_doLogitTransform = 0 + +In MLSampling::generateSequence(), level 1, step 1: beginning step 1 of 11 +KEY In MLSampling::generateSequence(), level 1, step 1, currOptions->m_rawChainSize = 5000 +Leaving MLSampling::generateSequence_Step(), level 1, step 1, after 7e-06 seconds +In MLSampling::generateSequence(), level 1, step 2: beginning step 2 of 11 +In MLSampling::generateSequence_Step(), level 1, step 2, prevLogLikelihoodValues[0] = -1943.14 +In MLSampling::generateSequence(), level 1, step 2: prevChain.unifiedSequenceSize() = 5000, currChain.unifiedSequenceSize() = 0, prevLogLikelihoodValues.unifiedSequenceSize() = 5000, currLogLikelihoodValues.unifiedSequenceSize() = 0, prevLogTargetValues.unifiedSequenceSize() = 5000, currLogTargetValues.unifiedSequenceSize() = 0 +Leaving MLSampling::generateSequence_Step(), level 1, step 2, after 0.002724 seconds +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: beginning step 3 of 11 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 0, prevExponent = 0, exponents[0] = 0, nowExponent = 1, exponents[1] = 1, subWeightRatioSum = 74.9557, unifiedWeightRatioSum = 74.9557, unifiedOmegaLnMax = -3.16228, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -7.36258, effectiveSampleSize = 0.0049713 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 0, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 1, exponents[1] = 1, effectiveSampleSize = 201.155, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.0402309, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 1 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 1, prevExponent = 0, exponents[0] = 0, nowExponent = 0.5, exponents[1] = 1, subWeightRatioSum = 150.885, unifiedWeightRatioSum = 150.885, unifiedOmegaLnMax = -1.58114, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -5.08182, effectiveSampleSize = 0.00329241 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 1, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.5, exponents[1] = 1, effectiveSampleSize = 303.729, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.0607458, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 2 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 2, prevExponent = 0, exponents[0] = 0, nowExponent = 0.25, exponents[1] = 0.5, subWeightRatioSum = 254.679, unifiedWeightRatioSum = 254.679, unifiedOmegaLnMax = -0.79057, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -3.76776, effectiveSampleSize = 0.00232626 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 2, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.25, exponents[1] = 0.5, effectiveSampleSize = 429.875, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.085975, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 3 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 3, prevExponent = 0, exponents[0] = 0, nowExponent = 0.125, exponents[1] = 0.25, subWeightRatioSum = 394.684, unifiedWeightRatioSum = 394.684, unifiedOmegaLnMax = -0.395285, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -2.93439, effectiveSampleSize = 0.00163491 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 3, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.125, exponents[1] = 0.25, effectiveSampleSize = 611.653, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.122331, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 4 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 4, prevExponent = 0, exponents[0] = 0, nowExponent = 0.0625, exponents[1] = 0.125, subWeightRatioSum = 586.715, unifiedWeightRatioSum = 586.715, unifiedOmegaLnMax = -0.197643, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -2.3403, effectiveSampleSize = 0.00114655 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 4, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.0625, exponents[1] = 0.125, effectiveSampleSize = 872.178, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.174436, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 5 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 5, prevExponent = 0, exponents[0] = 0, nowExponent = 0.03125, exponents[1] = 0.0625, subWeightRatioSum = 849.164, unifiedWeightRatioSum = 849.164, unifiedOmegaLnMax = -0.0988213, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.87176, effectiveSampleSize = 0.000813662 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 5, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.03125, exponents[1] = 0.0625, effectiveSampleSize = 1229.01, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.245802, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 6 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 6, prevExponent = 0, exponents[0] = 0, nowExponent = 0.015625, exponents[1] = 0.03125, subWeightRatioSum = 1185.54, unifiedWeightRatioSum = 1185.54, unifiedOmegaLnMax = -0.0494106, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.48865, effectiveSampleSize = 0.000604174 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 6, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.015625, exponents[1] = 0.03125, effectiveSampleSize = 1655.15, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.331031, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 7 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 7, prevExponent = 0, exponents[0] = 0, nowExponent = 0.0078125, exponents[1] = 0.015625, subWeightRatioSum = 1592.03, unifiedWeightRatioSum = 1592.03, unifiedOmegaLnMax = -0.0247053, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.16913, effectiveSampleSize = 0.000467748 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 7, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.0078125, exponents[1] = 0.015625, effectiveSampleSize = 2137.9, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.427581, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 8 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 8, prevExponent = 0, exponents[0] = 0, nowExponent = 0.00390625, exponents[1] = 0.0078125, subWeightRatioSum = 2082.84, unifiedWeightRatioSum = 2082.84, unifiedOmegaLnMax = -0.0123527, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -0.888057, effectiveSampleSize = 0.000366977 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 8, prevExponent = 0, failedExponent = 0, exponents[0] = 0, nowExponent = 0.00390625, exponents[1] = 0.0078125, effectiveSampleSize = 2724.97, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.544993, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 9 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 9, prevExponent = 0, exponents[0] = 0.00390625, nowExponent = 0.00585938, exponents[1] = 0.0078125, subWeightRatioSum = 1784.87, unifiedWeightRatioSum = 1784.87, unifiedOmegaLnMax = -0.018529, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.04862, effectiveSampleSize = 0.000422209 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 9, prevExponent = 0, failedExponent = 0, exponents[0] = 0.00390625, nowExponent = 0.00585938, exponents[1] = 0.0078125, effectiveSampleSize = 2368.49, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.473699, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 10 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 10, prevExponent = 0, exponents[0] = 0.00390625, nowExponent = 0.00488281, exponents[1] = 0.00585938, subWeightRatioSum = 1915.05, unifiedWeightRatioSum = 1915.05, unifiedOmegaLnMax = -0.0154408, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -0.975136, effectiveSampleSize = 0.000396111 +In MLSampling::generateSequence(), level 1, step 3: nowAttempt = 10, prevExponent = 0, failedExponent = 0, exponents[0] = 0.00390625, nowExponent = 0.00488281, exponents[1] = 0.00585938, effectiveSampleSize = 2524.55, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.504909, maxEffectiveSizeRatio = 0.51, testResult = 1 +In MLSampling::generateSequence(), level 1, step 3: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, failedExponent = 0, currExponent = 0.00488281, effective ratio = 0.504909, log(evidence factor) = -0.975136, evidence factor = 0.377141 +Leaving MLSampling::generateSequence_Step(), level 1, step 3, failedExponent = 0, after 0.005078 seconds +In MLSampling::generateSequence(), level 1, step 4: beginning step 4 of 11 +In MLSampling::generateSequence(), level 1, step 4: unifiedCovMatrix = 4704.98 +Leaving MLSampling::generateSequence_Step(), level 1, step 4, after 0.006492 seconds +In MLSampling::generateSequence(), level 1, step 5: beginning step 5 of 11 +Entering MLSampling::sampleIndexes_proc0(), level 1, step 5: unifiedRequestedNumSamples = 5000, unifiedWeightStdVectorAtProc0Only.size() = 5000 +Leaving MLSampling::generateSequence_Step(), level 1, step 5, after 0.001984 seconds +Entering MLSampling::decideOnBalancedChains_all(), level 1, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 1, step 6: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 1, step 6, Np = 1, totalNumberOfChains = 2271 + KEY, level 1, step 6, origNumChainsPerNode[0] = 2271, origNumPositionsPerNode[0] = 5000 + KEY, level 1, step 6, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 1, step 6: result = 0 +Leaving MLSampling::generateSequence_Step(), level 1, step 6, after 8.8e-05 seconds +In MLSampling::generateSequence(), level 1, step 7: beginning step 7 of 11 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 7: subNumSamples = 5000, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 7: minModifiedSubNumSamples = 5000, avgModifiedSubNumSamples = 5000, maxModifiedSubNumSamples = 5000 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 7: numberOfPositionsToGuaranteeForNode = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 7: unbalancedLinkControl.unbLinkedChains.size() = 2271 +In MLSampling::generateSequence(), level 1, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 2271 +Leaving MLSampling::generateSequence_Step(), level 1, step 7, after 8.8e-05 seconds +In MLSampling::generateSequence(), level 1, step 8: beginning step 8 of 11 +Leaving MLSampling::generateSequence_Step(), level 1, step 8, after 3e-06 seconds +In MLSampling::generateSequence_Step09_all(), level 1, step 9: beginning step 9 of 11 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 1, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 1, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 1, step 9, Np = 1, totalNumberOfChains = 33 + KEY, level 1, step 9, origNumChainsPerNode[0] = 33, origNumPositionsPerNode[0] = 35 + KEY, level 1, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 1, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: unbalancedLinkControl.unbLinkedChains.size() = 33 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 33, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 1, step 9: chainIdMax = 33, numberOfPositions = 35, at Thu Nov 27 21:16:51 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 1, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 1, step 9: ended chain loop after 0.002754 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:51 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 1, step 9: after 4.3e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:51 2014 + +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, nowAttempt = 0, beforeEta = 1, etas[0] = 1, nowEta = 1, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.171429, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.171429 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, with nowAttempt = 1, useMiddlePointLogicForEta = false, nowEta just updated to value (to be tested) 4 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 1, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 1, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 1, step 9, Np = 1, totalNumberOfChains = 34 + KEY, level 1, step 9, origNumChainsPerNode[0] = 34, origNumPositionsPerNode[0] = 35 + KEY, level 1, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 1, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 1, step 9: unbalancedLinkControl.unbLinkedChains.size() = 34 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 34, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 1, step 9: chainIdMax = 34, numberOfPositions = 35, at Thu Nov 27 21:16:51 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 1, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 1, step 9: ended chain loop after 0.001883 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:51 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 1, step 9: after 1.4e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:51 2014 + +In MLSampling::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, nowAttempt = 1, beforeEta = 1, etas[0] = 1, nowEta = 4, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.342857, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 1, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 4, assessed rejection rate = 0.342857 +Leaving MLSampling::generateSequence_Step(), level 1, step 9, after 0.007106 seconds +In MLSampling::generateSequence(), level 1, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0 +In MLSampling::generateSequence(), level 1, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050 +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 2271, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 1, step 10: chainIdMax = 2271, numberOfPositions = 5000, at Thu Nov 27 21:16:51 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 1, step 10: minNumberOfPositions = 5000, avgNumberOfPositions = 5000, maxNumberOfPositions = 5000 +In MLSampling::generateUnbLinkedChains_all(), level 1, step 10: ended chain loop after 0.115156 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 1, step 10: after 4.1e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step(), level 1, step 10, after chain generatrion, currLogLikelihoodValues[0] = -0.0438532 +Leaving MLSampling::generateSequence_Step(), level 1, step 10, after 0.115216 seconds +In MLSampling::generateSequence(), level 1, step 11: beginning step 11 of 11 +Leaving MLSampling::generateSequence_Step(), level 1, step 11, after 4e-06 seconds +In MLSampling::generateSequence(): at end of level 1, sub minLogLike = -5.60322, sub maxLogLike = -0.0154408 +In MLSampling::generateSequence(): at end of level 1, unified minLogLike = -5.60322, unified maxLogLike = -0.0154408 +In MLSampling::generateSequence(): ending level 1, having generated 5000 chain positions, cumulativeRawChainRunTime = 0.036321 seconds, total level time = 0.139906 seconds, cumulativeRawChainRejections = 1874 (37.48% at this processor) (37.48% over all processors), stopAtEndOfLevel = 0 +In MLSampling::generateSequence(), level 1: min cumul seconds = 0.036321, avg cumul seconds = 0.036321, max cumul seconds = 0.036321, min level seconds = 0.139906, avg level seconds = 0.139906, max level seconds = 0.139906 +Getting at the end of level 1, as part of a 'while' on levels, at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In MLSampling::generateSequence(): beginning level 2, at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In IMLSampling::generateSequence(), level 2, beginning 'do-while(tryExponentEta): failedExponent = 0, failedEta = 0 +In MLSamplingLevelOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_2_', state of object is: +m_prefix = ip_ml_2_ +ip_ml_2_stopAtEnd = 0 +ip_ml_2_dataOutputFileName = . +ip_ml_2_dataOutputAllowAll = 0 +ip_ml_2_dataOutputAllowedSet = +ip_ml_2_loadBalanceAlgorithmId = 2 +ip_ml_2_loadBalanceTreshold = 1 +ip_ml_2_minEffectiveSizeRatio = 0.49 +ip_ml_2_maxEffectiveSizeRatio = 0.51 +ip_ml_2_scaleCovMatrix = 1 +ip_ml_2_minRejectionRate = 0.24 +ip_ml_2_maxRejectionRate = 0.4 +ip_ml_2_covRejectionRate = 0.25 +ip_ml_2_minAcceptableEta = 0 +ip_ml_2_totallyMute = 1 +ip_ml_2_initialPosition_dataInputFileName = . +ip_ml_2_initialPosition_dataInputFileType = m +ip_ml_2_initialProposalCovMatrix_dataInputFileName = . +ip_ml_2_initialProposalCovMatrix_dataInputFileType = m +ip_ml_2_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_2_listOfDisabledParameters = +ip_ml_2_initialValuesOfDisabledParameters = +ip_ml_2_rawChain_dataInputFileName = . +ip_ml_2_rawChain_dataInputFileType = m +ip_ml_2_rawChain_size = 5000 +ip_ml_2_rawChain_generateExtra = 0 +ip_ml_2_rawChain_displayPeriod = 500 +ip_ml_2_rawChain_measureRunTimes = 1 +ip_ml_2_rawChain_dataOutputPeriod = 0 +ip_ml_2_rawChain_dataOutputFileName = . +ip_ml_2_rawChain_dataOutputFileType = m +ip_ml_2_rawChain_dataOutputAllowAll = 0 +ip_ml_2_rawChain_dataOutputAllowedSet = +ip_ml_2_filteredChain_generate = 0 +ip_ml_2_filteredChain_discardedPortion = 0 +ip_ml_2_filteredChain_lag = 1 +ip_ml_2_filteredChain_dataOutputFileName = . +ip_ml_2_filteredChain_dataOutputFileType = m +ip_ml_2_filteredChain_dataOutputAllowAll = 0 +ip_ml_2_filteredChain_dataOutputAllowedSet = +ip_ml_2_displayCandidates = 0 +ip_ml_2_putOutOfBoundsInChain = 0 +ip_ml_2_tk_useLocalHessian = 0 +ip_ml_2_tk_useNewtonComponent = 1 +ip_ml_2_dr_maxNumExtraStages = 0 +ip_ml_2_dr_listOfScalesForExtraStages = +ip_ml_2_dr_duringAmNonAdaptiveInt = 1 +ip_ml_2_am_keepInitialMatrix = 0 +ip_ml_2_am_initialNonAdaptInterval = 0 +ip_ml_2_am_adaptInterval = 0 +ip_ml_2_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_2_amAdaptedMatrices_dataOutputFileName = . +ip_ml_2_amAdaptedMatrices_dataOutputFileType = m +ip_ml_2_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_2_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_2_am_eta = 1 +ip_ml_2_am_epsilon = 1e-05 +ip_ml_2_doLogitTransform = 0 + +In MLSampling::generateSequence(), level 2, step 1: beginning step 1 of 11 +KEY In MLSampling::generateSequence(), level 2, step 1, currOptions->m_rawChainSize = 5000 +Leaving MLSampling::generateSequence_Step(), level 2, step 1, after 6e-06 seconds +In MLSampling::generateSequence(), level 2, step 2: beginning step 2 of 11 +In MLSampling::generateSequence_Step(), level 2, step 2, prevLogLikelihoodValues[0] = -0.0438532 +In MLSampling::generateSequence(), level 2, step 2: prevChain.unifiedSequenceSize() = 5000, currChain.unifiedSequenceSize() = 0, prevLogLikelihoodValues.unifiedSequenceSize() = 5000, currLogLikelihoodValues.unifiedSequenceSize() = 0, prevLogTargetValues.unifiedSequenceSize() = 5000, currLogTargetValues.unifiedSequenceSize() = 0 +Leaving MLSampling::generateSequence_Step(), level 2, step 2, after 0.003129 seconds +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: beginning step 3 of 11 +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 0, prevExponent = 0.00488281, exponents[0] = 0.00488281, nowExponent = 1, exponents[1] = 1, subWeightRatioSum = 191.743, unifiedWeightRatioSum = 191.743, unifiedOmegaLnMax = -3.14684, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -6.40788, effectiveSampleSize = 0.00214483 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 0, prevExponent = 0.00488281, failedExponent = 0, exponents[0] = 0.00488281, nowExponent = 1, exponents[1] = 1, effectiveSampleSize = 466.237, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.0932474, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 1 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 1, prevExponent = 0.00488281, exponents[0] = 0.00488281, nowExponent = 0.502441, exponents[1] = 1, subWeightRatioSum = 383.332, unifiedWeightRatioSum = 383.332, unifiedOmegaLnMax = -1.57342, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -4.14171, effectiveSampleSize = 0.00130488 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 1, prevExponent = 0.00488281, failedExponent = 0, exponents[0] = 0.00488281, nowExponent = 0.502441, exponents[1] = 1, effectiveSampleSize = 766.357, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.153271, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 2 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 2, prevExponent = 0.00488281, exponents[0] = 0.00488281, nowExponent = 0.253662, exponents[1] = 0.502441, subWeightRatioSum = 654.55, unifiedWeightRatioSum = 654.55, unifiedOmegaLnMax = -0.78671, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -2.81996, effectiveSampleSize = 0.000894725 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 2, prevExponent = 0.00488281, failedExponent = 0, exponents[0] = 0.00488281, nowExponent = 0.253662, exponents[1] = 0.502441, effectiveSampleSize = 1117.66, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.223532, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 3 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 3, prevExponent = 0.00488281, exponents[0] = 0.00488281, nowExponent = 0.129272, exponents[1] = 0.253662, subWeightRatioSum = 1017.28, unifiedWeightRatioSum = 1017.28, unifiedOmegaLnMax = -0.393355, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.98566, effectiveSampleSize = 0.0006325 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 3, prevExponent = 0.00488281, failedExponent = 0, exponents[0] = 0.00488281, nowExponent = 0.129272, exponents[1] = 0.253662, effectiveSampleSize = 1581.03, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.316206, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 4 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 4, prevExponent = 0.00488281, exponents[0] = 0.00488281, nowExponent = 0.0670776, exponents[1] = 0.129272, subWeightRatioSum = 1488.73, unifiedWeightRatioSum = 1488.73, unifiedOmegaLnMax = -0.196678, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.4082, effectiveSampleSize = 0.000458999 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 4, prevExponent = 0.00488281, failedExponent = 0, exponents[0] = 0.00488281, nowExponent = 0.0670776, exponents[1] = 0.129272, effectiveSampleSize = 2178.65, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.435731, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 5 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 5, prevExponent = 0.00488281, exponents[0] = 0.00488281, nowExponent = 0.0359802, exponents[1] = 0.0670776, subWeightRatioSum = 2078.53, unifiedWeightRatioSum = 2078.53, unifiedOmegaLnMax = -0.0983388, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -0.976114, effectiveSampleSize = 0.000344588 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 5, prevExponent = 0.00488281, failedExponent = 0, exponents[0] = 0.00488281, nowExponent = 0.0359802, exponents[1] = 0.0670776, effectiveSampleSize = 2902.01, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.580403, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 6 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 6, prevExponent = 0.00488281, exponents[0] = 0.0359802, nowExponent = 0.0515289, exponents[1] = 0.0670776, subWeightRatioSum = 1719.55, unifiedWeightRatioSum = 1719.55, unifiedOmegaLnMax = -0.147508, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.21488, effectiveSampleSize = 0.000405431 +In MLSampling::generateSequence(), level 2, step 3: nowAttempt = 6, prevExponent = 0.00488281, failedExponent = 0, exponents[0] = 0.0359802, nowExponent = 0.0515289, exponents[1] = 0.0670776, effectiveSampleSize = 2466.51, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.493303, maxEffectiveSizeRatio = 0.51, testResult = 1 +In MLSampling::generateSequence(), level 2, step 3: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, failedExponent = 0, currExponent = 0.0515289, effective ratio = 0.493303, log(evidence factor) = -1.21488, evidence factor = 0.296745 +Leaving MLSampling::generateSequence_Step(), level 2, step 3, failedExponent = 0, after 0.003167 seconds +In MLSampling::generateSequence(), level 2, step 4: beginning step 4 of 11 +In MLSampling::generateSequence(), level 2, step 4: unifiedCovMatrix = 1591.43 +Leaving MLSampling::generateSequence_Step(), level 2, step 4, after 0.007171 seconds +In MLSampling::generateSequence(), level 2, step 5: beginning step 5 of 11 +Entering MLSampling::sampleIndexes_proc0(), level 2, step 5: unifiedRequestedNumSamples = 5000, unifiedWeightStdVectorAtProc0Only.size() = 5000 +Leaving MLSampling::generateSequence_Step(), level 2, step 5, after 0.001823 seconds +Entering MLSampling::decideOnBalancedChains_all(), level 2, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 2, step 6: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 2, step 6, Np = 1, totalNumberOfChains = 2258 + KEY, level 2, step 6, origNumChainsPerNode[0] = 2258, origNumPositionsPerNode[0] = 5000 + KEY, level 2, step 6, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 2, step 6: result = 0 +Leaving MLSampling::generateSequence_Step(), level 2, step 6, after 5.8e-05 seconds +In MLSampling::generateSequence(), level 2, step 7: beginning step 7 of 11 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 7: subNumSamples = 5000, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 7: minModifiedSubNumSamples = 5000, avgModifiedSubNumSamples = 5000, maxModifiedSubNumSamples = 5000 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 7: numberOfPositionsToGuaranteeForNode = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 7: unbalancedLinkControl.unbLinkedChains.size() = 2258 +In MLSampling::generateSequence(), level 2, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 2258 +Leaving MLSampling::generateSequence_Step(), level 2, step 7, after 7e-05 seconds +In MLSampling::generateSequence(), level 2, step 8: beginning step 8 of 11 +Leaving MLSampling::generateSequence_Step(), level 2, step 8, after 2e-06 seconds +In MLSampling::generateSequence_Step09_all(), level 2, step 9: beginning step 9 of 11 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 2, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 2, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 2, step 9, Np = 1, totalNumberOfChains = 35 + KEY, level 2, step 9, origNumChainsPerNode[0] = 35, origNumPositionsPerNode[0] = 35 + KEY, level 2, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 2, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: ended chain loop after 0.001444 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 2, step 9: after 1.7e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, nowAttempt = 0, beforeEta = 4, etas[0] = 4, nowEta = 4, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.6, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.6 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, with nowAttempt = 1, useMiddlePointLogicForEta = false, nowEta just updated to value (to be tested) 1 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 2, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 2, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 2, step 9, Np = 1, totalNumberOfChains = 35 + KEY, level 2, step 9, origNumChainsPerNode[0] = 35, origNumPositionsPerNode[0] = 35 + KEY, level 2, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 2, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: ended chain loop after 0.001476 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 2, step 9: after 1.7e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, nowAttempt = 1, beforeEta = 4, etas[0] = 4, nowEta = 1, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.514286, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: entering loop for assessing rejection rate, with nowAttempt = 2, nowRejectionRate = 0.514286 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, with nowAttempt = 2, useMiddlePointLogicForEta = false, nowEta just updated to value (to be tested) 0.25 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 2, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 2, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 2, step 9, Np = 1, totalNumberOfChains = 35 + KEY, level 2, step 9, origNumChainsPerNode[0] = 35, origNumPositionsPerNode[0] = 35 + KEY, level 2, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 2, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 2, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 2, step 9: ended chain loop after 0.001402 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 2, step 9: after 1.2e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, nowAttempt = 2, beforeEta = 1, etas[0] = 4, nowEta = 0.25, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.257143, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 2, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 0.25, assessed rejection rate = 0.257143 +Leaving MLSampling::generateSequence_Step(), level 2, step 9, after 0.007112 seconds +In MLSampling::generateSequence(), level 2, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0 +In MLSampling::generateSequence(), level 2, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050 +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 2258, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 10: chainIdMax = 2258, numberOfPositions = 5000, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 2, step 10: minNumberOfPositions = 5000, avgNumberOfPositions = 5000, maxNumberOfPositions = 5000 +In MLSampling::generateUnbLinkedChains_all(), level 2, step 10: ended chain loop after 0.106492 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 2, step 10: after 4e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step(), level 2, step 10, after chain generatrion, currLogLikelihoodValues[0] = -0.263406 +Leaving MLSampling::generateSequence_Step(), level 2, step 10, after 0.10655 seconds +In MLSampling::generateSequence(), level 2, step 11: beginning step 11 of 11 +Leaving MLSampling::generateSequence_Step(), level 2, step 11, after 4e-06 seconds +In MLSampling::generateSequence(): at end of level 2, sub minLogLike = -8.68106, sub maxLogLike = -0.162949 +In MLSampling::generateSequence(): at end of level 2, unified minLogLike = -8.68106, unified maxLogLike = -0.162949 +In MLSampling::generateSequence(): ending level 2, having generated 5000 chain positions, cumulativeRawChainRunTime = 0.031268 seconds, total level time = 0.130233 seconds, cumulativeRawChainRejections = 1744 (34.88% at this processor) (34.88% over all processors), stopAtEndOfLevel = 0 +In MLSampling::generateSequence(), level 2: min cumul seconds = 0.031268, avg cumul seconds = 0.031268, max cumul seconds = 0.031268, min level seconds = 0.130233, avg level seconds = 0.130233, max level seconds = 0.130233 +Getting at the end of level 2, as part of a 'while' on levels, at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In MLSampling::generateSequence(): beginning level 3, at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In IMLSampling::generateSequence(), level 3, beginning 'do-while(tryExponentEta): failedExponent = 0, failedEta = 0 +In MLSamplingLevelOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_3_', state of object is: +m_prefix = ip_ml_3_ +ip_ml_3_stopAtEnd = 0 +ip_ml_3_dataOutputFileName = . +ip_ml_3_dataOutputAllowAll = 0 +ip_ml_3_dataOutputAllowedSet = +ip_ml_3_loadBalanceAlgorithmId = 2 +ip_ml_3_loadBalanceTreshold = 1 +ip_ml_3_minEffectiveSizeRatio = 0.49 +ip_ml_3_maxEffectiveSizeRatio = 0.51 +ip_ml_3_scaleCovMatrix = 1 +ip_ml_3_minRejectionRate = 0.24 +ip_ml_3_maxRejectionRate = 0.4 +ip_ml_3_covRejectionRate = 0.25 +ip_ml_3_minAcceptableEta = 0 +ip_ml_3_totallyMute = 1 +ip_ml_3_initialPosition_dataInputFileName = . +ip_ml_3_initialPosition_dataInputFileType = m +ip_ml_3_initialProposalCovMatrix_dataInputFileName = . +ip_ml_3_initialProposalCovMatrix_dataInputFileType = m +ip_ml_3_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_3_listOfDisabledParameters = +ip_ml_3_initialValuesOfDisabledParameters = +ip_ml_3_rawChain_dataInputFileName = . +ip_ml_3_rawChain_dataInputFileType = m +ip_ml_3_rawChain_size = 5000 +ip_ml_3_rawChain_generateExtra = 0 +ip_ml_3_rawChain_displayPeriod = 500 +ip_ml_3_rawChain_measureRunTimes = 1 +ip_ml_3_rawChain_dataOutputPeriod = 0 +ip_ml_3_rawChain_dataOutputFileName = . +ip_ml_3_rawChain_dataOutputFileType = m +ip_ml_3_rawChain_dataOutputAllowAll = 0 +ip_ml_3_rawChain_dataOutputAllowedSet = +ip_ml_3_filteredChain_generate = 0 +ip_ml_3_filteredChain_discardedPortion = 0 +ip_ml_3_filteredChain_lag = 1 +ip_ml_3_filteredChain_dataOutputFileName = . +ip_ml_3_filteredChain_dataOutputFileType = m +ip_ml_3_filteredChain_dataOutputAllowAll = 0 +ip_ml_3_filteredChain_dataOutputAllowedSet = +ip_ml_3_displayCandidates = 0 +ip_ml_3_putOutOfBoundsInChain = 0 +ip_ml_3_tk_useLocalHessian = 0 +ip_ml_3_tk_useNewtonComponent = 1 +ip_ml_3_dr_maxNumExtraStages = 0 +ip_ml_3_dr_listOfScalesForExtraStages = +ip_ml_3_dr_duringAmNonAdaptiveInt = 1 +ip_ml_3_am_keepInitialMatrix = 0 +ip_ml_3_am_initialNonAdaptInterval = 0 +ip_ml_3_am_adaptInterval = 0 +ip_ml_3_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_3_amAdaptedMatrices_dataOutputFileName = . +ip_ml_3_amAdaptedMatrices_dataOutputFileType = m +ip_ml_3_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_3_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_3_am_eta = 1 +ip_ml_3_am_epsilon = 1e-05 +ip_ml_3_doLogitTransform = 0 + +In MLSampling::generateSequence(), level 3, step 1: beginning step 1 of 11 +KEY In MLSampling::generateSequence(), level 3, step 1, currOptions->m_rawChainSize = 5000 +Leaving MLSampling::generateSequence_Step(), level 3, step 1, after 8e-06 seconds +In MLSampling::generateSequence(), level 3, step 2: beginning step 2 of 11 +In MLSampling::generateSequence_Step(), level 3, step 2, prevLogLikelihoodValues[0] = -0.263406 +In MLSampling::generateSequence(), level 3, step 2: prevChain.unifiedSequenceSize() = 5000, currChain.unifiedSequenceSize() = 0, prevLogLikelihoodValues.unifiedSequenceSize() = 5000, currLogLikelihoodValues.unifiedSequenceSize() = 0, prevLogTargetValues.unifiedSequenceSize() = 5000, currLogTargetValues.unifiedSequenceSize() = 0 +Leaving MLSampling::generateSequence_Step(), level 3, step 2, after 0.003197 seconds +In MLSampling::generateSequence(), level 3, step 3, failedExponent = 0: beginning step 3 of 11 +In MLSampling::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 0, prevExponent = 0.0515289, exponents[0] = 0.0515289, nowExponent = 1, exponents[1] = 1, subWeightRatioSum = 555.733, unifiedWeightRatioSum = 555.733, unifiedOmegaLnMax = -2.99933, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -5.19624, effectiveSampleSize = 0.000789088 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 0, prevExponent = 0.0515289, failedExponent = 0, exponents[0] = 0.0515289, nowExponent = 1, exponents[1] = 1, effectiveSampleSize = 1267.29, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.253457, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 1 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 1, prevExponent = 0.0515289, exponents[0] = 0.0515289, nowExponent = 0.525764, exponents[1] = 1, subWeightRatioSum = 1044.8, unifiedWeightRatioSum = 1044.8, unifiedOmegaLnMax = -1.49967, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -3.06528, effectiveSampleSize = 0.000509095 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 1, prevExponent = 0.0515289, failedExponent = 0, exponents[0] = 0.0515289, nowExponent = 0.525764, exponents[1] = 1, effectiveSampleSize = 1964.27, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.392854, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 2 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 2, prevExponent = 0.0515289, exponents[0] = 0.0515289, nowExponent = 0.288647, exponents[1] = 0.525764, subWeightRatioSum = 1678.76, unifiedWeightRatioSum = 1678.76, unifiedOmegaLnMax = -0.749833, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -1.84121, effectiveSampleSize = 0.000370727 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 2, prevExponent = 0.0515289, failedExponent = 0, exponents[0] = 0.0515289, nowExponent = 0.288647, exponents[1] = 0.525764, effectiveSampleSize = 2697.4, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.53948, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 3 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 3, prevExponent = 0.0515289, exponents[0] = 0.288647, nowExponent = 0.407206, exponents[1] = 0.525764, subWeightRatioSum = 1292.21, unifiedWeightRatioSum = 1292.21, unifiedOmegaLnMax = -1.12475, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -2.47783, effectiveSampleSize = 0.00044258 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 3, prevExponent = 0.0515289, failedExponent = 0, exponents[0] = 0.288647, nowExponent = 0.407206, exponents[1] = 0.525764, effectiveSampleSize = 2259.48, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.451896, maxEffectiveSizeRatio = 0.51, testResult = 0 +In MLSampling::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 4 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 4, prevExponent = 0.0515289, exponents[0] = 0.288647, nowExponent = 0.347926, exponents[1] = 0.407206, subWeightRatioSum = 1460.9, unifiedWeightRatioSum = 1460.9, unifiedOmegaLnMax = -0.937291, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -2.16767, effectiveSampleSize = 0.000407628 +In MLSampling::generateSequence(), level 3, step 3: nowAttempt = 4, prevExponent = 0.0515289, failedExponent = 0, exponents[0] = 0.288647, nowExponent = 0.347926, exponents[1] = 0.407206, effectiveSampleSize = 2453.22, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.490643, maxEffectiveSizeRatio = 0.51, testResult = 1 +In MLSampling::generateSequence(), level 3, step 3: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, failedExponent = 0, currExponent = 0.347926, effective ratio = 0.490643, log(evidence factor) = -2.16767, evidence factor = 0.114444 +Leaving MLSampling::generateSequence_Step(), level 3, step 3, failedExponent = 0, after 0.002374 seconds +In MLSampling::generateSequence(), level 3, step 4: beginning step 4 of 11 +In MLSampling::generateSequence(), level 3, step 4: unifiedCovMatrix = 1436.48 +Leaving MLSampling::generateSequence_Step(), level 3, step 4, after 0.00736 seconds +In MLSampling::generateSequence(), level 3, step 5: beginning step 5 of 11 +Entering MLSampling::sampleIndexes_proc0(), level 3, step 5: unifiedRequestedNumSamples = 5000, unifiedWeightStdVectorAtProc0Only.size() = 5000 +Leaving MLSampling::generateSequence_Step(), level 3, step 5, after 0.001772 seconds +Entering MLSampling::decideOnBalancedChains_all(), level 3, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 3, step 6: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 3, step 6, Np = 1, totalNumberOfChains = 2288 + KEY, level 3, step 6, origNumChainsPerNode[0] = 2288, origNumPositionsPerNode[0] = 5000 + KEY, level 3, step 6, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 3, step 6: result = 0 +Leaving MLSampling::generateSequence_Step(), level 3, step 6, after 6e-05 seconds +In MLSampling::generateSequence(), level 3, step 7: beginning step 7 of 11 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 7: subNumSamples = 5000, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 7: minModifiedSubNumSamples = 5000, avgModifiedSubNumSamples = 5000, maxModifiedSubNumSamples = 5000 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 7: numberOfPositionsToGuaranteeForNode = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 7: unbalancedLinkControl.unbLinkedChains.size() = 2288 +In MLSampling::generateSequence(), level 3, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 2288 +Leaving MLSampling::generateSequence_Step(), level 3, step 7, after 7e-05 seconds +In MLSampling::generateSequence(), level 3, step 8: beginning step 8 of 11 +Leaving MLSampling::generateSequence_Step(), level 3, step 8, after 3e-06 seconds +In MLSampling::generateSequence_Step09_all(), level 3, step 9: beginning step 9 of 11 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 3, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 3, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 3, step 9, Np = 1, totalNumberOfChains = 35 + KEY, level 3, step 9, origNumChainsPerNode[0] = 35, origNumPositionsPerNode[0] = 35 + KEY, level 3, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 3, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: ended chain loop after 0.001365 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 3, step 9: after 1.2e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, nowAttempt = 0, beforeEta = 0.25, etas[0] = 0.25, nowEta = 0.25, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.571429, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.571429 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, with nowAttempt = 1, useMiddlePointLogicForEta = false, nowEta just updated to value (to be tested) 0.0625 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 3, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 3, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 3, step 9, Np = 1, totalNumberOfChains = 35 + KEY, level 3, step 9, origNumChainsPerNode[0] = 35, origNumPositionsPerNode[0] = 35 + KEY, level 3, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 3, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: ended chain loop after 0.001331 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 3, step 9: after 1.1e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, nowAttempt = 1, beforeEta = 0.25, etas[0] = 0.25, nowEta = 0.0625, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.457143, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: entering loop for assessing rejection rate, with nowAttempt = 2, nowRejectionRate = 0.457143 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, with nowAttempt = 2, useMiddlePointLogicForEta = false, nowEta just updated to value (to be tested) 0.015625 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 3, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 3, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 3, step 9, Np = 1, totalNumberOfChains = 35 + KEY, level 3, step 9, origNumChainsPerNode[0] = 35, origNumPositionsPerNode[0] = 35 + KEY, level 3, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 3, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 3, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 3, step 9: ended chain loop after 0.001534 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 3, step 9: after 1.5e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, nowAttempt = 2, beforeEta = 0.0625, etas[0] = 0.25, nowEta = 0.015625, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.314286, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 3, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 0.015625, assessed rejection rate = 0.314286 +Leaving MLSampling::generateSequence_Step(), level 3, step 9, after 0.007033 seconds +In MLSampling::generateSequence(), level 3, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0 +In MLSampling::generateSequence(), level 3, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050 +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 2288, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 10: chainIdMax = 2288, numberOfPositions = 5000, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 3, step 10: minNumberOfPositions = 5000, avgNumberOfPositions = 5000, maxNumberOfPositions = 5000 +In MLSampling::generateUnbLinkedChains_all(), level 3, step 10: ended chain loop after 0.111091 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 3, step 10: after 4.7e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step(), level 3, step 10, after chain generatrion, currLogLikelihoodValues[0] = -1.76423 +Leaving MLSampling::generateSequence_Step(), level 3, step 10, after 0.111163 seconds +In MLSampling::generateSequence(), level 3, step 11: beginning step 11 of 11 +Leaving MLSampling::generateSequence_Step(), level 3, step 11, after 6e-06 seconds +In MLSampling::generateSequence(): at end of level 3, sub minLogLike = -9.67559, sub maxLogLike = -1.10024 +In MLSampling::generateSequence(): at end of level 3, unified minLogLike = -9.67559, unified maxLogLike = -1.10024 +In MLSampling::generateSequence(): ending level 3, having generated 5000 chain positions, cumulativeRawChainRunTime = 0.032979 seconds, total level time = 0.134378 seconds, cumulativeRawChainRejections = 1277 (25.54% at this processor) (25.54% over all processors), stopAtEndOfLevel = 0 +In MLSampling::generateSequence(), level 3: min cumul seconds = 0.032979, avg cumul seconds = 0.032979, max cumul seconds = 0.032979, min level seconds = 0.134378, avg level seconds = 0.134378, max level seconds = 0.134378 +Getting at the end of level 3, as part of a 'while' on levels, at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In MLSampling::generateSequence(): beginning level 4, at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In IMLSampling::generateSequence(), level 4, beginning 'do-while(tryExponentEta): failedExponent = 0, failedEta = 0 +In MLSamplingLevelOptions::scanOptionsValues(): after getting values of options with prefix 'ip_ml_4_', state of object is: +m_prefix = ip_ml_4_ +ip_ml_4_stopAtEnd = 0 +ip_ml_4_dataOutputFileName = . +ip_ml_4_dataOutputAllowAll = 0 +ip_ml_4_dataOutputAllowedSet = +ip_ml_4_loadBalanceAlgorithmId = 2 +ip_ml_4_loadBalanceTreshold = 1 +ip_ml_4_minEffectiveSizeRatio = 0.49 +ip_ml_4_maxEffectiveSizeRatio = 0.51 +ip_ml_4_scaleCovMatrix = 1 +ip_ml_4_minRejectionRate = 0.24 +ip_ml_4_maxRejectionRate = 0.4 +ip_ml_4_covRejectionRate = 0.25 +ip_ml_4_minAcceptableEta = 0 +ip_ml_4_totallyMute = 1 +ip_ml_4_initialPosition_dataInputFileName = . +ip_ml_4_initialPosition_dataInputFileType = m +ip_ml_4_initialProposalCovMatrix_dataInputFileName = . +ip_ml_4_initialProposalCovMatrix_dataInputFileType = m +ip_ml_4_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_4_listOfDisabledParameters = +ip_ml_4_initialValuesOfDisabledParameters = +ip_ml_4_rawChain_dataInputFileName = . +ip_ml_4_rawChain_dataInputFileType = m +ip_ml_4_rawChain_size = 5000 +ip_ml_4_rawChain_generateExtra = 0 +ip_ml_4_rawChain_displayPeriod = 500 +ip_ml_4_rawChain_measureRunTimes = 1 +ip_ml_4_rawChain_dataOutputPeriod = 0 +ip_ml_4_rawChain_dataOutputFileName = . +ip_ml_4_rawChain_dataOutputFileType = m +ip_ml_4_rawChain_dataOutputAllowAll = 0 +ip_ml_4_rawChain_dataOutputAllowedSet = +ip_ml_4_filteredChain_generate = 0 +ip_ml_4_filteredChain_discardedPortion = 0 +ip_ml_4_filteredChain_lag = 1 +ip_ml_4_filteredChain_dataOutputFileName = . +ip_ml_4_filteredChain_dataOutputFileType = m +ip_ml_4_filteredChain_dataOutputAllowAll = 0 +ip_ml_4_filteredChain_dataOutputAllowedSet = +ip_ml_4_displayCandidates = 0 +ip_ml_4_putOutOfBoundsInChain = 0 +ip_ml_4_tk_useLocalHessian = 0 +ip_ml_4_tk_useNewtonComponent = 1 +ip_ml_4_dr_maxNumExtraStages = 0 +ip_ml_4_dr_listOfScalesForExtraStages = +ip_ml_4_dr_duringAmNonAdaptiveInt = 1 +ip_ml_4_am_keepInitialMatrix = 0 +ip_ml_4_am_initialNonAdaptInterval = 0 +ip_ml_4_am_adaptInterval = 0 +ip_ml_4_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_4_amAdaptedMatrices_dataOutputFileName = . +ip_ml_4_amAdaptedMatrices_dataOutputFileType = m +ip_ml_4_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_4_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_4_am_eta = 1 +ip_ml_4_am_epsilon = 1e-05 +ip_ml_4_doLogitTransform = 0 + +In MLSampling::generateSequence(), level 4, step 1: beginning step 1 of 11 +KEY In MLSampling::generateSequence(), level 4, step 1, currOptions->m_rawChainSize = 5000 +Leaving MLSampling::generateSequence_Step(), level 4, step 1, after 8e-06 seconds +In MLSampling::generateSequence(), level 4, step 2: beginning step 2 of 11 +In MLSampling::generateSequence_Step(), level 4, step 2, prevLogLikelihoodValues[0] = -1.76423 +In MLSampling::generateSequence(), level 4, step 2: prevChain.unifiedSequenceSize() = 5000, currChain.unifiedSequenceSize() = 0, prevLogLikelihoodValues.unifiedSequenceSize() = 5000, currLogLikelihoodValues.unifiedSequenceSize() = 0, prevLogTargetValues.unifiedSequenceSize() = 5000, currLogTargetValues.unifiedSequenceSize() = 0 +Leaving MLSampling::generateSequence_Step(), level 4, step 2, after 0.003661 seconds +In MLSampling::generateSequence(), level 4, step 3, failedExponent = 0: beginning step 3 of 11 +In MLSampling::generateSequence(), level 4, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0 +In MLSampling::generateSequence(), level 4, step 3: nowAttempt = 0, prevExponent = 0.347926, exponents[0] = 0.347926, nowExponent = 1, exponents[1] = 1, subWeightRatioSum = 1840.42, unifiedWeightRatioSum = 1840.42, unifiedOmegaLnMax = -2.06204, weightSequence.unifiedSequenceSize() = 5000, nowUnifiedEvidenceLnFactor = -3.06149, effectiveSampleSize = 0.000289314 +In MLSampling::generateSequence(), level 4, step 3: nowAttempt = 0, prevExponent = 0.347926, failedExponent = 0, exponents[0] = 0.347926, nowExponent = 1, exponents[1] = 1, effectiveSampleSize = 3456.45, weightSequenceSize = 5000, minEffectiveSizeRatio = 0.49, nowEffectiveSizeRatio = 0.691289, maxEffectiveSizeRatio = 0.51, testResult = 1 +In MLSampling::generateSequence(), level 4, step 3: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, failedExponent = 0, currExponent = 1, effective ratio = 0.691289, log(evidence factor) = -3.06149, evidence factor = 0.046818 +Leaving MLSampling::generateSequence_Step(), level 4, step 3, failedExponent = 0, after 0.000539 seconds +In MLSampling::generateSequence(), level 4, step 3: copying 'last' level options to current options +In MLSampling::generateSequence(), level 4, step 3: after copying 'last' level options to current options, the current options are +m_prefix = ip_ml_last_ +ip_ml_last_stopAtEnd = 0 +ip_ml_last_dataOutputFileName = outputData/sipOutput_ml +ip_ml_last_dataOutputAllowAll = 0 +ip_ml_last_dataOutputAllowedSet = 0 1 +ip_ml_last_loadBalanceAlgorithmId = 2 +ip_ml_last_loadBalanceTreshold = 1 +ip_ml_last_minEffectiveSizeRatio = 0.49 +ip_ml_last_maxEffectiveSizeRatio = 0.51 +ip_ml_last_scaleCovMatrix = 1 +ip_ml_last_minRejectionRate = 0.24 +ip_ml_last_maxRejectionRate = 0.4 +ip_ml_last_covRejectionRate = 0.25 +ip_ml_last_minAcceptableEta = 0 +ip_ml_last_totallyMute = 1 +ip_ml_last_initialPosition_dataInputFileName = . +ip_ml_last_initialPosition_dataInputFileType = m +ip_ml_last_initialProposalCovMatrix_dataInputFileName = . +ip_ml_last_initialProposalCovMatrix_dataInputFileType = m +ip_ml_last_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_last_listOfDisabledParameters = +ip_ml_last_initialValuesOfDisabledParameters = +ip_ml_last_rawChain_dataInputFileName = . +ip_ml_last_rawChain_dataInputFileType = m +ip_ml_last_rawChain_size = 10000 +ip_ml_last_rawChain_generateExtra = 0 +ip_ml_last_rawChain_displayPeriod = 500 +ip_ml_last_rawChain_measureRunTimes = 1 +ip_ml_last_rawChain_dataOutputPeriod = 0 +ip_ml_last_rawChain_dataOutputFileName = outputData/rawChain_ml +ip_ml_last_rawChain_dataOutputFileType = m +ip_ml_last_rawChain_dataOutputAllowAll = 0 +ip_ml_last_rawChain_dataOutputAllowedSet = 0 +ip_ml_last_filteredChain_generate = 1 +ip_ml_last_filteredChain_discardedPortion = 0 +ip_ml_last_filteredChain_lag = 2 +ip_ml_last_filteredChain_dataOutputFileName = outputData/filtChain_ml +ip_ml_last_filteredChain_dataOutputFileType = m +ip_ml_last_filteredChain_dataOutputAllowAll = 0 +ip_ml_last_filteredChain_dataOutputAllowedSet = 0 +ip_ml_last_displayCandidates = 0 +ip_ml_last_putOutOfBoundsInChain = 0 +ip_ml_last_tk_useLocalHessian = 0 +ip_ml_last_tk_useNewtonComponent = 1 +ip_ml_last_dr_maxNumExtraStages = 1 +ip_ml_last_dr_listOfScalesForExtraStages = 5 +ip_ml_last_dr_duringAmNonAdaptiveInt = 1 +ip_ml_last_am_keepInitialMatrix = 0 +ip_ml_last_am_initialNonAdaptInterval = 0 +ip_ml_last_am_adaptInterval = 0 +ip_ml_last_amAdaptedMatrices_dataOutputPeriod = 0 +ip_ml_last_amAdaptedMatrices_dataOutputFileName = . +ip_ml_last_amAdaptedMatrices_dataOutputFileType = m +ip_ml_last_amAdaptedMatrices_dataOutputAllowAll = 0 +ip_ml_last_amAdaptedMatrices_dataOutputAllowedSet = +ip_ml_last_am_eta = 1 +ip_ml_last_am_epsilon = 1e-05 +ip_ml_last_doLogitTransform = 0 + +In MLSampling::generateSequence(), level 4, step 4: beginning step 4 of 11 +In MLSampling::generateSequence(), level 4, step 4: unifiedCovMatrix = 1776.45 +Leaving MLSampling::generateSequence_Step(), level 4, step 4, after 0.007549 seconds +In MLSampling::generateSequence(), level 4, step 5: beginning step 5 of 11 +Entering MLSampling::sampleIndexes_proc0(), level 4, step 5: unifiedRequestedNumSamples = 10000, unifiedWeightStdVectorAtProc0Only.size() = 5000 +Leaving MLSampling::generateSequence_Step(), level 4, step 5, after 0.002849 seconds +Entering MLSampling::decideOnBalancedChains_all(), level 4, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 4, step 6: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 4, step 6, Np = 1, totalNumberOfChains = 3633 + KEY, level 4, step 6, origNumChainsPerNode[0] = 3633, origNumPositionsPerNode[0] = 10000 + KEY, level 4, step 6, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 4, step 6: result = 0 +Leaving MLSampling::generateSequence_Step(), level 4, step 6, after 9.1e-05 seconds +In MLSampling::generateSequence(), level 4, step 7: beginning step 7 of 11 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 7: subNumSamples = 10000, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 7: minModifiedSubNumSamples = 10000, avgModifiedSubNumSamples = 10000, maxModifiedSubNumSamples = 10000 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 7: numberOfPositionsToGuaranteeForNode = 10000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 7: unbalancedLinkControl.unbLinkedChains.size() = 3633 +In MLSampling::generateSequence(), level 4, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 3633 +Leaving MLSampling::generateSequence_Step(), level 4, step 7, after 7e-05 seconds +In MLSampling::generateSequence(), level 4, step 8: beginning step 8 of 11 +Leaving MLSampling::generateSequence_Step(), level 4, step 8, after 3e-06 seconds +In MLSampling::generateSequence_Step09_all(), level 4, step 9: beginning step 9 of 11 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 4, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 4, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 4, step 9, Np = 1, totalNumberOfChains = 35 + KEY, level 4, step 9, origNumChainsPerNode[0] = 35, origNumPositionsPerNode[0] = 35 + KEY, level 4, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 4, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 4, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 4, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 4, step 9: ended chain loop after 0.002393 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 4, step 9: after 2.3e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, nowAttempt = 0, beforeEta = 0.015625, etas[0] = 0.015625, nowEta = 0.015625, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.514286, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.514286 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, with nowAttempt = 1, useMiddlePointLogicForEta = false, nowEta just updated to value (to be tested) 0.00390625 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to sample 35 indexes, meanRejectionRate = 0.32, covRejectionRate = 0.25 +Entering MLSampling::sampleIndexes_proc0(), level 4, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes +Entering MLSampling::decideOnBalancedChains_all(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +In MLSampling::decideOnBalancedChains_all(), level 4, step 9: original distribution of unified indexes in 'inter0Comm' is as follows + allFirstIndexes[0] = 0 allLastIndexes[0] = 4999 + KEY, level 4, step 9, Np = 1, totalNumberOfChains = 34 + KEY, level 4, step 9, origNumChainsPerNode[0] = 34, origNumPositionsPerNode[0] = 35 + KEY, level 4, step 9, origRatioOfPosPerNode = 1, option loadBalanceTreshold = 1 +Leaving MLSampling::decideOnBalancedChains_all(), level 4, step 9: result = 0 +Entering MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35 +KEY In MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: numberOfPositionsToGuaranteeForNode = 35 +KEY Leaving MLSampling::prepareUnbLinkedChains_inter0(), level 4, step 9: unbalancedLinkControl.unbLinkedChains.size() = 34 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to generate assessment chain +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 34, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 4, step 9: chainIdMax = 34, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 4, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35 +In MLSampling::generateUnbLinkedChains_all(), level 4, step 9: ended chain loop after 0.002344 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 4, step 9: after 1.9e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, nowAttempt = 1, beforeEta = 0.015625, etas[0] = 0.015625, nowEta = 0.00390625, etas[1] = 1, minRejectionRate = 0.24, nowRejectionRate = 0.4, maxRejectionRate = 0.4 +In MLSampling::generateSequence_Step09_all(), level 4, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 0.00390625, assessed rejection rate = 0.4 +Leaving MLSampling::generateSequence_Step(), level 4, step 9, after 0.006618 seconds +In MLSampling::generateSequence(), level 4, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0 +In MLSampling::generateSequence(), level 4, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050 +Entering MLSampling::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 3633, indexOfFirstWeight = 0 +KEY In MLSampling::generateUnbLinkedChains_all(), level 4, step 10: chainIdMax = 3633, numberOfPositions = 10000, at Thu Nov 27 21:16:52 2014 + +KEY In MLSampling::generateUnbLinkedChains_all(), level 4, step 10: minNumberOfPositions = 10000, avgNumberOfPositions = 10000, maxNumberOfPositions = 10000 +In MLSampling::generateUnbLinkedChains_all(), level 4, step 10: ended chain loop after 0.362796 seconds, calling fullComm().Barrier() at Thu Nov 27 21:16:52 2014 + +Leaving MLSampling::generateUnbLinkedChains_all(), level 4, step 10: after 3.6e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014 + +In MLSampling::generateSequence_Step(), level 4, step 10, after chain generatrion, currLogLikelihoodValues[0] = -5.2602 +Leaving MLSampling::generateSequence_Step(), level 4, step 10, after 0.362871 seconds +In MLSampling::generateSequence(), level 4, step 11: beginning step 11 of 11 +In MLSampling::generateSequence_Step(), level 4, step 11, before calling currLogLikelihoodValues.unifiedWriteContents(), currLogLikelihoodValues[0] = -5.2602 +Entering SequenceOfVectors::filter(): initialPos = 0, spacing = 2, subSequenceSize = 10000 +Leaving SequenceOfVectors::filter(): initialPos = 0, spacing = 2, subSequenceSize = 5000 +Entering ScalarSequence::filter(): initialPos = 0, spacing = 2, subSequenceSize = 10000 +Leaving ScalarSequence::filter(): initialPos = 0, spacing = 2, subSequenceSize = 5000 +Entering ScalarSequence::filter(): initialPos = 0, spacing = 2, subSequenceSize = 10000 +Leaving ScalarSequence::filter(): initialPos = 0, spacing = 2, subSequenceSize = 5000 +Leaving MLSampling::generateSequence_Step(), level 4, step 11, after 0.143284 seconds +In MLSampling::generateSequence(): at end of level 4, sub minLogLike = -12.2807, sub maxLogLike = -3.16228 +In MLSampling::generateSequence(): at end of level 4, unified minLogLike = -12.2807, unified maxLogLike = -3.16228 +In MLSampling::generateSequence(): ending level 4, having generated 5000 chain positions, cumulativeRawChainRunTime = 0.120822 seconds, total level time = 0.529345 seconds, cumulativeRawChainRejections = 477 (4.77% at this processor) (4.77% over all processors), stopAtEndOfLevel = 0 +In MLSampling::generateSequence(), level 4: min cumul seconds = 0.120822, avg cumul seconds = 0.120822, max cumul seconds = 0.120822, min level seconds = 0.529345, avg level seconds = 0.529345, max level seconds = 0.529345 +Getting at the end of level 4, as part of a 'while' on levels, at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In MLSampling::generateSequence(), log(evidence) = -7.41918, evidence = 0.000599641, meanLogLikelihood = -4.32658, eig = 3.0926 +Leaving MLSampling::generateSequence(), at Thu Nov 27 21:16:52 2014 +, after 1 seconds from entering the routine, after 1 seconds from queso environment instatiation +In SequentialVectorRealizer::constructor(): m_chain.subSequenceSize() = 5000 + +numPosTotal = 5000 +numPosSmallerThan40 = 1616, ratio = 0.3232 +seq1.size() = 1616 + seq1.mean() = 10.0214 + seq1.std() = 1.05051 +seq2.size() = 3384 + seq2.mean() = 99.7669 + seq2.std() = 5.1944 +seqAll.size() = 5000 + seqAll.mean() = 70.7612 + seqAll.std() = 42.1992 +integral = 0.31831 +Ending run at Thu Nov 27 21:16:52 2014 +Total run time = 1 seconds diff --git a/test/t04_bimodal/regression/09-04-2014/rawChain_ml.m b/test/t04_bimodal/regression/09-04-2014/rawChain_ml.m new file mode 100644 index 000000000..c2be94e97 --- /dev/null +++ b/test/t04_bimodal/regression/09-04-2014/rawChain_ml.m @@ -0,0 +1,30006 @@ +ip_ml_4_rawChain_unified = zeros(10000,1); +ip_ml_4_rawChain_unified = [1.0768644700270129e+02 +9.8683837906534222e+01 +9.9723758293206402e+01 +9.9753315545811418e+01 +1.0006279050806447e+02 +9.9902561388246994e+01 +1.0011515088871468e+02 +9.4361647940434707e+01 +9.5363221171960006e+01 +9.4157847800405946e+01 +9.4302387189080406e+01 +1.0637775759621148e+02 +9.9351688912318252e+01 +9.6272961201849810e+01 +1.0080695449084718e+02 +9.9766550120407217e+01 +9.9657486927896983e+01 +1.0008397007913003e+02 +9.7412815497971565e+01 +9.7292942549817468e+01 +9.5584129413836806e+01 +9.5705128334957038e+01 +9.7990926914653869e+01 +9.9007369008174962e+01 +9.9004627236822969e+01 +9.9201468750638242e+01 +1.0329100725038825e+02 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+2.04684 +2.0014 +0.653245 +0.428975 +1.72504 +2.04615 +2.05079 +0.785986 +1.96572 +2.01506 +1.77252 +2.05016 +1.90329 +1.5533 +0.0499032 +2.10857 +2.09052 +1.57773 +1.73208 +1.7967 +1.84266 +1.60344 +2.13444 +2.0374 +2.11233 +-0.219579 +0.869438 +0.981518 +1.9912 +1.5131 +1.93154 +1.87315 +1.27231 +]; diff --git a/test/t04_bimodal/rtest04.sh b/test/t04_bimodal/rtest04.sh index de4503fc3..fedeb862a 100755 --- a/test/t04_bimodal/rtest04.sh +++ b/test/t04_bimodal/rtest04.sh @@ -1,14 +1,14 @@ #!/bin/bash #---------------------------------------------------------- # PECOS Regression tests for QUESO -# +# # 1. Validation cycle using TGA example. # # Originally: 5-19-09 #---------------------------------------------------------- TOLERANCE="1e-10" # solution diff tolerance (absolute) -SAVELOG=0 # Log model output? +SAVELOG=1 # Log model output? COMMONDIR="$srcdir/../common" #---------------- @@ -22,13 +22,14 @@ RUNDIR=`pwd` #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # -# Regresion Test #1: Validation cycle with TGA example +# Regresion Test #1: Validation cycle with TGA example #VERIFY_DATE="07-12-2009" #VERIFY_DATE="09-22-2010" #VERIFY_DATE="12-09-2010" #VERIFY_DATE="07-03-2012" -VERIFY_DATE="07-09-2012" +# VERIFY_DATE="07-09-2012" +VERIFY_DATE="09-04-2014" SOLDIR="outputData" EXE="./BimodalExample_gsl" # executable name SOLREFS="$srcdir/regression/$VERIFY_DATE" @@ -64,7 +65,7 @@ fi verify_file_exists $SOLDIR/rawChain_ml.m verify_file_exists $SOLREFS/rawChain_ml.m -igot=0 +igot=1 # Compare outputs from 1 output file. @@ -94,9 +95,3 @@ if [ $igot -eq 0 ];then else message_fail "$TESTNAME failed verification" fi - -#------------------------------------------------------------------------------ -#------------------------------------------------------------------------------ - - - diff --git a/test/test_Regression/GaussianMean1DRegression_options.in b/test/test_Regression/GaussianMean1DRegression_options.in new file mode 100644 index 000000000..cf4a76840 --- /dev/null +++ b/test/test_Regression/GaussianMean1DRegression_options.in @@ -0,0 +1,51 @@ +############################################### +# UQ Environment +############################################### +env_numSubEnvironments = 1 +env_subDisplayFileName = outputData/display +env_subDisplayAllowAll = 1 +env_displayVerbosity = 2 +env_syncVerbosity = 0 +env_seed = 0 + +############################################### +# Calibration (cal) stage: statistical inverse problem (ip) +############################################### +ip_help = anything +ip_computeSolution = 1 +ip_dataOutputFileName = outputData/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Multilevel algorithm +############################################### +#ip_ml_restartOutput_levelPeriod = 1 +#ip_ml_restartOutput_baseNameForFiles = outputData/quesoRestart +#ip_ml_restartInput_baseNameForFiles = outputData/quesoRestart +ip_ml_dataOutputFileName = outputData/sipOutput + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +# +############################################### +ip_ml_default_putOutOfBoundsInChain = 0 +ip_ml_default_initialPositionUsePreviousLevelLikelihood = 0 +ip_ml_default_totallyMute = 1 +ip_ml_default_rawChain_size = 15000 + +############################################### +# Last level +############################################### +ip_ml_last_rawChain_dataOutputFileName = outputData/sipOutput +ip_ml_last_filteredChain_generate = 1 +ip_ml_last_filteredChain_lag = 5 + +############################################### +# 'GaussianMean1DRegression_': options for gaussian regression test +# 'GaussianMean1DRegression_dataSet': samples generated via matlab cmd randn (i.e. standard normal samples) +############################################### +GaussianMean1DRegression_priorMean = 5.0 +GaussianMean1DRegression_priorVar = 1.0 +GaussianMean1DRegression_samplingVar = 1.0 +GaussianMean1DRegression_dataSet = -0.1437 -0.0982 -1.3616 -0.0171 -1.0290 0.7133 -0.5496 -0.2415 -0.0933 -0.0669 -0.2311 -1.5371 -0.7418 -0.2220 0.0209 1.6905 -0.8379 0.4233 1.7065 1.5676 0.1265 1.0728 1.8126 2.7186 -0.6680 0.5251 -0.4768 0.6027 -1.9304 -2.1159 1.8698 0.3908 -1.2513 1.2597 0.1426 -0.6527 -0.5948 -0.6698 0.3874 0.3455 -0.1988 -0.4574 0.6930 1.3184 -0.2715 -0.9510 0.3593 -0.4184 0.5243 0.2846 -0.3556 -1.4877 -0.8829 1.0829 -0.7700 -0.1138 0.6430 -0.7929 -1.6493 0.1000 + diff --git a/test/test_Regression/adaptedcov_input.txt.in b/test/test_Regression/adaptedcov_input.txt.in new file mode 100644 index 000000000..b7dadc7b5 --- /dev/null +++ b/test/test_Regression/adaptedcov_input.txt.in @@ -0,0 +1,48 @@ +############################################### +# UQ Environment +############################################### +env_help = 1 +env_numSubEnvironments = 1 +env_subDisplayFileName = test_adaptedcov_output/display +env_subDisplayAllowAll = 0 +env_subDisplayAllowedSet = 0 +env_displayVerbosity = 1000 +env_syncVerbosity = 0 +env_seed = 0 + +############################################### +# Statistical inverse problem (ip) +############################################### +ip_help = anything +ip_computeSolution = 1 +ip_dataOutputFileName = test_adaptedcov_output/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +############################################### +ip_mh_help = anything +ip_mh_dataOutputFileName = test_adaptedcov_output/sipOutput +ip_mh_dataOutputAllowedSet = 0 1 + +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_size = 100000 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 2000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputFileName = test_adaptedcov_output/ip_raw_chain +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_rawChain_computeStats = 1 + +ip_mh_displayCandidates = 0 +ip_mh_putOutOfBoundsInChain = 0 +ip_mh_tk_useLocalHessian = 0 +ip_mh_tk_useNewtonComponent = 0 +ip_mh_dr_maxNumExtraStages = 0 +ip_mh_dr_listOfScalesForExtraStages = 5. 10. 20. +ip_mh_am_initialNonAdaptInterval = 10 +ip_mh_am_adaptInterval = 1 +ip_mh_am_eta = 0.384 +ip_mh_am_epsilon = 1.e-5 + +ip_mh_filteredChain_generate = 0 diff --git a/test/test_Regression/ctf_dat.txt.in b/test/test_Regression/ctf_dat.txt.in new file mode 100644 index 000000000..dd0f94205 --- /dev/null +++ b/test/test_Regression/ctf_dat.txt.in @@ -0,0 +1,10 @@ +1.21626870795683 +1.16149729403381 +1.1866271753701 +1.17683037400566 +1.17908641397936 +1.12766758338348 +1.17093102211906 +1.2072835297607 +1.18316329384675 +1.19396014684855 diff --git a/test/test_Regression/dakota_pstudy.dat.in b/test/test_Regression/dakota_pstudy.dat.in new file mode 100644 index 000000000..2d74abcdf --- /dev/null +++ b/test/test_Regression/dakota_pstudy.dat.in @@ -0,0 +1,51 @@ +%eval_id k_tmasl k_tmoml k_tnrgl k_xkwlx k_cd TotalPressure + 1 0.9504833265 0.9565031976 0.9070661904 0.9368767201 0.9902105699 1.11491 + 2 0.9704315633 0.9673671481 1.098333711 1.042833417 0.9396811465 1.18503 + 3 0.9668456402 0.9415691043 1.088395137 1.056229445 1.082078539 1.26852 + 4 1.021933049 1.081931608 1.055263162 1.002864582 1.097385647 1.22538 + 5 0.976719735 1.045242646 0.9803728644 1.01932066 1.023714908 1.20525 + 6 1.030644258 1.000261395 1.086072538 0.9619466614 0.999087427 1.14153 + 7 1.033479868 1.00547323 0.9794220783 1.071325006 0.9826118693 1.23193 + 8 0.9803928353 1.058941195 1.007968514 1.091684579 0.9608412905 1.2387 + 9 1.017384242 1.055874904 0.9202142133 0.9106698251 1.059631639 1.12142 + 10 1.039085017 0.9903868268 1.027248507 0.902328287 1.030390783 1.10115 + 11 0.9741611498 0.9513905754 0.9946802606 0.9421607145 0.9334849739 1.09416 + 12 0.9642257071 1.028441506 1.078605675 0.9301294486 0.9586327411 1.09475 + 13 0.979482743 1.026338606 0.9197225104 0.9918824665 1.027524616 1.18198 + 14 0.9821972744 1.033900319 0.9125861151 1.004675286 0.9099149855 1.13732 + 15 0.9920868386 1.019948426 0.9665838929 1.074701427 1.062491732 1.27586 + 16 1.003825877 0.9279219027 1.039942545 0.9750344064 0.9758580042 1.14256 + 17 1.001098361 1.094175733 1.035820862 0.9691751418 0.9436327119 1.12221 + 18 0.9907264763 1.037084636 1.062263521 1.033813525 1.005303491 1.20944 + 19 0.9955255765 0.9938034285 0.9313636533 0.9543762946 0.945913695 1.11046 + 20 1.01401808 0.9223914805 0.987550559 0.9049676054 0.9293711513 1.05997 + 21 1.01169168 1.066554864 1.00159988 1.04832432 1.040735732 1.24028 + 22 0.9896416018 0.9040699829 0.9584220241 0.9864903757 1.076077307 1.19996 + 23 1.026651322 0.9629350077 0.9016065396 1.052200845 1.094490151 1.27099 + 24 0.9522009452 0.9995354981 0.9979826914 0.9234223933 1.002902883 1.10837 + 25 1.042210059 0.916662705 0.9510966253 0.9344222067 0.9079090081 1.07606 + 26 1.008565411 1.048817115 1.064460124 0.983195362 1.048049029 1.18361 + 27 0.9861303865 1.062122937 0.9431387667 0.979262433 1.086095516 1.19771 + 28 0.9682748981 0.9690028597 1.008818441 1.0977804 1.074730428 1.30385 + 29 0.997538715 1.073585489 1.074838609 1.067970087 0.9876920528 1.23136 + 30 0.960891798 0.9796612269 0.9471908791 1.02824267 1.01713938 1.21027 + 31 1.035285687 0.9282637672 0.9112777017 1.060563117 0.9926884867 1.22743 + 32 0.9857119652 0.90075304 1.056745701 0.9189727839 1.091278093 1.14313 + 33 1.018023459 1.041474636 0.9358355433 0.9649328786 0.9276575505 1.11128 + 34 1.049918142 1.091619875 0.9526624009 0.9587045741 1.033133757 1.15403 + 35 0.9983256433 0.9747358926 1.094801076 1.02551546 0.9167120121 1.15864 + 36 1.013293136 0.9143560309 1.048997465 1.037015977 0.90309727 1.16188 + 37 1.029086735 1.022171889 1.082118374 0.9488425436 1.05299279 1.15406 + 38 0.9629704313 0.9381087132 0.9755750919 1.078985522 1.037980326 1.26729 + 39 1.047678387 0.9100415862 1.01454745 1.081305261 0.9234426563 1.21039 + 40 0.973992016 1.097494297 1.041668819 0.9989493167 0.9692778694 1.16052 + 41 1.037340321 0.9855375029 1.021844299 1.087688498 1.012046642 1.26188 + 42 1.02596132 1.015994577 1.016960221 1.092127074 0.9134742555 1.21451 + 43 0.9594901969 0.9827905876 1.069914524 0.9479300522 1.009465123 1.13353 + 44 0.954635897 1.009148953 0.9703234826 1.020890506 0.9543595717 1.17295 + 45 1.004448229 0.9355603683 0.9396882 0.9950812769 0.977432089 1.1612 + 46 1.041433693 0.9555272647 1.047644115 0.9128028486 1.06796733 1.12716 + 47 0.9573383712 1.086678783 0.9266088717 1.012186879 0.9514026049 1.1637 + 48 1.044161709 1.078649595 0.9902257044 1.011338967 0.9642182413 1.16921 + 49 1.022034669 0.9441188514 0.9605187868 1.044034489 1.071108179 1.2516 + 50 1.006757408 1.070436592 1.030513925 0.9269419437 1.047784872 1.1313 diff --git a/test/test_Regression/gpmsa_cobra_input.txt.in b/test/test_Regression/gpmsa_cobra_input.txt.in new file mode 100644 index 000000000..3bf03d923 --- /dev/null +++ b/test/test_Regression/gpmsa_cobra_input.txt.in @@ -0,0 +1,59 @@ +############################################### +# UQ Environment +############################################### +#env_help = anything +env_numSubEnvironments = 1 +env_subDisplayFileName = test_gpmsa_cobra_output/display +env_subDisplayAllowAll = 0 +env_subDisplayAllowedSet = 0 +env_displayVerbosity = 2 +env_syncVerbosity = 0 +env_seed = 1 + +############################################### +# Statistical inverse problem (ip) +############################################### +#ip_help = anything +ip_computeSolution = 1 +ip_dataOutputFileName = test_gpmsa_cobra_output/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +############################################### +#ip_mh_help = anything +ip_mh_dataOutputFileName = test_gpmsa_cobra_output/sipOutput +ip_mh_dataOutputAllowedSet = 0 1 + +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_size = 1000 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 2000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputFileName = test_gpmsa_cobra_output/ip_raw_chain +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_rawChain_computeStats = 1 + +ip_mh_displayCandidates = 0 +ip_mh_putOutOfBoundsInChain = 0 +ip_mh_tk_useLocalHessian = 0 +ip_mh_tk_useNewtonComponent = 0 +ip_mh_dr_maxNumExtraStages = 0 +ip_mh_dr_listOfScalesForExtraStages = 5. 10. 20. +ip_mh_am_initialNonAdaptInterval = 100000 +ip_mh_am_adaptInterval = 100000 +ip_mh_am_eta = 0.384 +ip_mh_am_epsilon = 1.e-5 + +ip_mh_filteredChain_generate = 0 + +gpmsa_emulator_precision_shape = 5.0 +gpmsa_emulator_precision_scale = 0.2 +gpmsa_emulator_correlation_strength_alpha = 1.0 +gpmsa_emulator_correlation_strength_beta = 0.1 +gpmsa_discrepancy_precision_shape = 1.0 +gpmsa_discrepancy_precision_scale = 1e4 +gpmsa_discrepancy_correlation_strength_alpha = 1.0 +gpmsa_discrepancy_correlation_strength_beta = 0.1 +gpmsa_emulator_data_precision_shape = 3.0 +gpmsa_emulator_data_precision_scale = 333.333 diff --git a/test/test_Regression/jeffreys_input.txt.in b/test/test_Regression/jeffreys_input.txt.in new file mode 100644 index 000000000..1ef0baab9 --- /dev/null +++ b/test/test_Regression/jeffreys_input.txt.in @@ -0,0 +1,37 @@ +############################################### +# UQ Environment +############################################### +env_numSubEnvironments = 1 +env_subDisplayFileName = test_jeffreys_output/display +env_subDisplayAllowAll = 0 +env_subDisplayAllowedSet = 0 +env_displayVerbosity = 2 +env_syncVerbosity = 0 +env_seed = 0 + +############################################### +# Statistical forward problem (fp) +############################################### +fp_help = 1 +fp_computeSolution = 1 +fp_computeCovariances = 1 +fp_computeCorrelations = 1 +fp_dataOutputFileName = test_jeffreys_output/sfpOutput +fp_dataOutputAllowedSet = 0 1 + +############################################### +# 'fp_': information for Monte Carlo algorithm +############################################### +fp_mc_help = 1 +fp_mc_dataOutputFileName = test_jeffreys_output/sfpOutput +fp_mc_dataOutputAllowedSet = 0 1 + +fp_mc_pseq_dataOutputFileName = test_jeffreys_output/fp_p_seq +fp_mc_pseq_dataOutputAllowedSet = 0 1 + +fp_mc_qseq_size = 10 # 20000 +fp_mc_qseq_displayPeriod = 1 #2000 +fp_mc_qseq_measureRunTimes = 1 +fp_mc_qseq_dataOutputFileName = test_jeffreys_output/fp_q_seq +fp_mc_qseq_dataOutputAllowedSet = 0 1 + diff --git a/test/test_Regression/test_GaussianMean1DRegression.C b/test/test_Regression/test_GaussianMean1DRegression.C new file mode 100644 index 000000000..f5aa528ac --- /dev/null +++ b/test/test_Regression/test_GaussianMean1DRegression.C @@ -0,0 +1,313 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// Regression Test in One Dimension (Mean of a Gaussian Model) +// +// Model: Gaussian with known variance samplingVar, unknown mean. +// +// Objective: Use the Bayes multi-level sampler to estimate the posterior +// distribution of the unknown mean using an input data set +// +// Output: The estimated mean of the posterior distribution and the standard +// deviation about this mean. Also the number of likelihood function calls. + +// User Input +// +// Everything in this test is controlled through a queso style input file +// processed by boost::program_options. The name of this file is passed to the +// program through the command line. If no name is passed in, the program tries +// the default file name specified by inputFileODV. Options +// GaussianMean1DRegression_priorMean, GaussianMean1DRegression_priorVar, +// GaussianMean1DRegression_samplingVar, GaussianMean1DRegression_dataSet are +// added to the boost::program_options options descriptor and parsed from the +// input file. These options can also be specified on the command line, in +// which case the command line value will overwrite the input file value. This +// allows the user to supply the mean and variance of a gaussian prior, the +// known model variance, and a string of samples from the "true" model - i.e. +// the data. Running the program with the option '--help', or '-h', will show +// the proper usage of the program and list all the available options. + +#define PI 3.14159265358979323846 + +// default input file name +const std::string inputFileODV = "test_Regression/GaussianMean1DRegression_options"; + +// default option values +const double priorMeanODV = 0.0; +const double priorVarODV = 1.0; +const double samplingVarODV = 1.0; +const std::string dataSetODV = "0.0"; + +// information needed/provided by the likelihood function +struct likelihoodData { + double samplingVar; // input + std::vector dataSet; // input +}; +unsigned long likelihoodCalls = 0; // output + +// evaluate log likelihood +template +double LikelihoodFunc( + const P_V& paramValues, + const P_V* paramDirection, + const void* functionDataPtr, + P_V* gradVector, + P_M* hessianMatrix, + P_V* hessianEffect) +{ + + double samplingVar = ((likelihoodData *)functionDataPtr)->samplingVar; + const std::vector &dataSet = ((likelihoodData *)functionDataPtr)->dataSet; + + int n = dataSet.size(); + double loglh = -(n / 2.0) * std::log(2.0 * PI * samplingVar); + + double currMu = paramValues[0]; + double diff, sumsq = 0.0; + for (int i = 0; i < n; i++) { + diff = dataSet[i] - currMu; + sumsq += diff * diff; + } + loglh -= sumsq / (2.0 * samplingVar); + + likelihoodCalls++; + + return loglh; +} + +// QUESO work routine +template +void GaussianMean1DRegressionCompute(const QUESO::BaseEnvironment& env, + double priorMean, double priorVar, const likelihoodData& dat) +{ + // parameter space: 1-D on (-infinity, infinity) + QUESO::VectorSpace paramSpace( + env, // queso environment + "param_", // name prefix + 1, // dimensions + NULL); // names + + P_V paramMin(paramSpace.zeroVector()); + P_V paramMax(paramSpace.zeroVector()); + paramMin[0] = -INFINITY; + paramMax[0] = INFINITY; + QUESO::BoxSubset paramDomain( + "paramBox_", // name prefix + paramSpace, // vector space + paramMin, // min values + paramMax); // max values + + // gaussian prior with user supplied mean and variance + P_V priorMeanVec(paramSpace.zeroVector()); + P_V priorVarVec(paramSpace.zeroVector()); + priorMeanVec[0] = priorMean; + priorVarVec[0] = priorVar; + QUESO::GaussianVectorRV priorRv("prior_", paramDomain, priorMeanVec, + priorVarVec); + + // likelihood is important + QUESO::GenericScalarFunction likelihoodFunctionObj( + "like_", // name prefix + paramDomain, // image set + LikelihoodFunc, // routine + (void *) &dat, // routine data ptr + true); // routineIsForLn + + QUESO::GenericVectorRV postRv( + "post_", // name prefix + paramSpace); // image set + + + // Initialize and solve the Inverse Problem with Bayes multi-level sampling + QUESO::StatisticalInverseProblem invProb( + "", // name prefix + NULL, // alt options + priorRv, // prior RV + likelihoodFunctionObj, // likelihood fcn + postRv); // posterior RV + + invProb.solveWithBayesMLSampling(); + + // compute mean and second moment of samples on each proc via Knuth online mean/variance algorithm + int N = invProb.postRv().realizer().subPeriod(); + double subMean = 0.0; + double subM2 = 0.0; + double delta; + P_V sample(paramSpace.zeroVector()); + for (int n = 1; n <= N; n++) { + invProb.postRv().realizer().realization(sample); + delta = sample[0] - subMean; + subMean += delta / n; + subM2 += delta * (sample[0] - subMean); + } + + // gather all Ns, means, and M2s to proc 0 + std::vector unifiedNs(env.inter0Comm().NumProc()); + std::vector unifiedMeans(env.inter0Comm().NumProc()); + std::vector unifiedM2s(env.inter0Comm().NumProc()); + MPI_Gather(&N, 1, MPI_INT, &(unifiedNs[0]), 1, MPI_INT, 0, + env.inter0Comm().Comm()); + MPI_Gather(&subMean, 1, MPI_DOUBLE, &(unifiedMeans[0]), 1, MPI_DOUBLE, 0, + env.inter0Comm().Comm()); + MPI_Gather(&subM2, 1, MPI_DOUBLE, &(unifiedM2s[0]), 1, MPI_DOUBLE, 0, + env.inter0Comm().Comm()); + + // get the total number of likelihood calls at proc 0 + unsigned long totalLikelihoodCalls = 0; + MPI_Reduce(&likelihoodCalls, &totalLikelihoodCalls, 1, MPI_UNSIGNED_LONG, + MPI_SUM, 0, env.inter0Comm().Comm()); + + // compute global posterior mean and std via Chan algorithm, output results on proc 0 + if (env.inter0Rank() == 0) { + int postN = unifiedNs[0]; + double postMean = unifiedMeans[0]; + double postVar = unifiedM2s[0]; + for (unsigned int i = 1; i < unifiedNs.size(); i++) { + delta = unifiedMeans[i] - postMean; + postMean = (postN * postMean + unifiedNs[i] * unifiedMeans[i]) / + (postN + unifiedNs[i]); + postVar += unifiedM2s[i] + delta * delta * + (((double)postN * unifiedNs[i]) / (postN + unifiedNs[i])); + postN += unifiedNs[i]; + } + postVar /= postN; + + //compute exact answer - available in this case since the exact posterior is a gaussian + N = dat.dataSet.size(); + double dataSum = 0.0; + for (int i = 0; i < N; i++) + dataSum += dat.dataSet[i]; + double datMean = dataSum / N; + double postMeanExact = (N * priorVar / (N * priorVar + dat.samplingVar)) * + datMean + (dat.samplingVar / (N * priorVar + dat.samplingVar)) * priorMean; + double postVarExact = 1.0 / (N / dat.samplingVar + 1.0 / priorVar); + + std::cout << "Number of posterior samples: " << postN << std::endl; + std::cout << "Estimated posterior mean: " << postMean << " +/- " + << std::sqrt(postVar) << std::endl; + std::cout << "Likelihood function calls: " << totalLikelihoodCalls + << std::endl; + std::cout << "\nExact posterior: Gaussian with mean " << postMeanExact + << ", standard deviation " << std::sqrt(postVarExact) << std::endl; + } +} + +int main(int argc, char* argv[]) { + //************************************************ + // Initialize environments + //************************************************ + MPI_Init(&argc,&argv); + int rank; + MPI_Comm_rank(MPI_COMM_WORLD, &rank); + + // variables to be filled by command line and/or input file values + std::string inputFile; + double priorMean; + double priorVar; + std::string dataString; + likelihoodData dat; + + po::options_description generic("Generic options"); + generic.add_options() + ("help,h", "Produce help message"); + + po::options_description config("GaussianMean1DRegression specific options"); + config.add_options() + ("GaussianMean1DRegression_priorMean", po::value(&priorMean)->default_value(priorMeanODV), "Prior Mean") + ("GaussianMean1DRegression_priorVar", po::value(&priorVar)->default_value(priorVarODV), "Prior Standard Deviation") + ("GaussianMean1DRegression_samplingVar", po::value(&dat.samplingVar)->default_value(samplingVarODV), "Data Sampling Standard Deviation") + ("GaussianMean1DRegression_dataSet", po::value(&dataString)->default_value(dataSetODV), "Calibration Data"); + + po::options_description hidden("hidden options"); + hidden.add_options() + ("input_file", po::value(&inputFile)->default_value(inputFileODV), "QUESO configuration file"); + po::positional_options_description p; + p.add("input_file", -1); + + po::options_description cmdline_options; + cmdline_options.add(generic).add(config).add(hidden); + po::options_description visible("Allowed options"); + visible.add(generic).add(config); + + // parse command line for help, inputFile, and any config options present + po::variables_map vm; + try { + po::store(po::command_line_parser(argc, argv). + options(cmdline_options).positional(p).run(), vm); + } catch (std::exception& e) { + if(rank == 0) + std::cout<<"Caught exception: "<" << std::endl; + std::cout << visible << std::endl; + } + MPI_Finalize(); + return 1; + } + + // parse the input file to get GaussianMean1DRegression option values + std::ifstream config_stream(inputFile.c_str()); + try { + po::store(po::parse_config_file(config_stream, config, true), vm); + } catch (std::exception& e) { + if(rank == 0) + std::cout << "Caught exception: " << e.what() << std::endl; + MPI_Abort(MPI_COMM_WORLD, -1); + } + po::notify(vm); + config_stream.close(); + + // parse the data string into a vector of doubles and store them in dat.dataSet + std::istringstream iss(dataString); + std::copy(std::istream_iterator(iss), std::istream_iterator(), std::back_inserter(dat.dataSet)); + + // output the option values that will be used by QUESO + if (rank == 0) { + std::cout << "QUESO options file: " << inputFile << std::endl; + std::cout << "Read option GaussianMean1DRegression_priorMean: " + << priorMean << std::endl; + std::cout << "Read option GaussianMean1DRegression_priorVar: " + << priorVar << std::endl; + std::cout << "Read option GaussianMean1DRegression_samplingVar: " + << dat.samplingVar << std::endl; + std::cout << "Read option GaussianMean1DRegression_dataSet: "; + for (unsigned int i = 0; i < dat.dataSet.size(); i++) + std::cout << dat.dataSet[i] << " "; + std::cout << std::endl; + } + + // initilize environment + QUESO::FullEnvironment env(MPI_COMM_WORLD, // MPI communicator + inputFile.c_str(), // input file name + "", // name prefix + NULL ); // alt options + + // reset seed to value in input file + fullRank + env.resetSeed(env.seed() + env.fullRank()); + + // Work routine, with GSL + GaussianMean1DRegressionCompute(env, + priorMean, priorVar, dat); + + MPI_Finalize(); + + return 0; +} diff --git a/test/test_Regression/test_cobra_samples_diff.sh.in b/test/test_Regression/test_cobra_samples_diff.sh.in new file mode 100755 index 000000000..96d1c9ff6 --- /dev/null +++ b/test/test_Regression/test_cobra_samples_diff.sh.in @@ -0,0 +1,37 @@ +#!/bin/bash +set -eu +set -o pipefail + +TOLERANCE="1e-10" # solution diff tolerance (relative) +SAVELOG=0 # Log model output? +COMMONDIR="$srcdir/common" + +RUNDIR=`pwd` + +. $COMMONDIR/verify.sh + +SOLDIR="@builddir@/test_gpmsa_cobra_output" +EXE="@builddir@/test_gpmsa_cobra" # executable name +SOLREFS="@builddir@/test_Regression" +INFILE="@builddir@/test_Regression/gpmsa_cobra_input.txt" +TESTNAME='Test Cobra GPMSA' + +rm -f $SOLDIR/*.m +rm -f $SOLDIR/*.txt + +verify_file_exists $EXE +verify_file_exists $INFILE + +rm -rf ./test_gpmsa_cobra_output + +if [ $SAVELOG -eq 1 ];then + ./$EXE +else + ./$EXE >& /dev/null +fi + +verify_file_exists $SOLDIR/ip_raw_chain.m +verify_file_exists $SOLREFS/test_gpmsa_cobra_samples.m + +$COMMONDIR/compare.pl $SOLDIR/ip_raw_chain.m $SOLREFS/test_gpmsa_cobra_samples.m +exit $? diff --git a/test/test_Regression/test_gpmsa_cobra.C b/test/test_Regression/test_gpmsa_cobra.C new file mode 100644 index 000000000..4676b6801 --- /dev/null +++ b/test/test_Regression/test_gpmsa_cobra.C @@ -0,0 +1,280 @@ +#include +#include +#include +#include +#include +#include + +#include + +// Read in data files +double readData(const std::vector & simulationScenarios, + const std::vector & simulationParameters, + const std::vector & simulationOutputs, + const std::vector & experimentScenarios, + const std::vector & experimentOutputs) { + FILE * fp_in = fopen("test_Regression/dakota_pstudy.dat", "r"); + unsigned int i, id, size = 512; + double k_tmasl, k_tmoml, k_tnrgl, k_xkwlx, k_cd, pressure; + char line[size]; + + // The user should know what the bounds on the data are + double mins[] = {0.95, 0.9, 0.9, 0.9, 0.9}; + double maxs[] = {1.05, 1.1, 1.1, 1.1, 1.1}; + + double meansim = 0; + double m2sim = 0; + + // First line is a header, so we ignore it + fgets(line, size, fp_in); + + i = 0; + while (fscanf(fp_in, "%d %lf %lf %lf %lf %lf %lf\n", &id, &k_tmasl, &k_tmoml, + &k_tnrgl, &k_xkwlx, &k_cd, &pressure) != EOF) { + (*(simulationScenarios[i]))[0] = 0.5; + + (*(simulationParameters[i]))[0] = (k_tmasl - mins[0]) / (maxs[0] - mins[0]); + (*(simulationParameters[i]))[1] = (k_tmoml - mins[1]) / (maxs[1] - mins[1]); + (*(simulationParameters[i]))[2] = (k_tnrgl - mins[2]) / (maxs[2] - mins[2]); + (*(simulationParameters[i]))[3] = (k_xkwlx - mins[3]) / (maxs[3] - mins[3]); + (*(simulationParameters[i]))[4] = (k_cd - mins[4]) / (maxs[4] - mins[4]); + + (*(simulationOutputs[i]))[0] = pressure; + i++; + + double delta = pressure - meansim; + meansim += delta / i; + m2sim += delta * (pressure - meansim); + } + + double stdsim = m2sim / (double)(i - 1); + + // The user is required to standardise the experimental and simulation data + for (unsigned int j = 0; j < i; j++) { + (*(simulationOutputs[j]))[0] -= meansim; + (*(simulationOutputs[j]))[0] /= stdsim; + } + + fclose(fp_in); // Done with simulation data + + // Read in experimental data + fp_in = fopen("test_Regression/ctf_dat.txt", "r"); + i = 0; + while (fscanf(fp_in, "%lf\n", &pressure) != EOF) { + (*(experimentOutputs[i]))[0] = pressure; + i++; + } + + // Also required to standardise experimental data too + for (unsigned int j = 0; j < i; j++) { + (*(experimentOutputs[j]))[0] -= meansim; + (*(experimentOutputs[j]))[0] /= stdsim; + } + + fclose(fp_in); + + // Returning standard deviation of data (simulation data is treated as + // plentiful and the standard deviation of this data is treated as the 'true' + // standard deviation of the data. This can be argued.) + return stdsim; +} + +int main(int argc, char ** argv) { + // Step 0: Set up some variables + unsigned int numExperiments = 10; // Number of experiments + unsigned int numUncertainVars = 5; // Number of things to calibrate + unsigned int numSimulations = 50; // Number of simulations + unsigned int numConfigVars = 1; // Dimension of configuration space + unsigned int numEta = 1; // Number of responses the model is returning + unsigned int experimentSize = 1; // Size of each experiment + + MPI_Init(&argc, &argv); + + // Step 1: Set up QUESO environment + QUESO::FullEnvironment env(MPI_COMM_WORLD, + "test_Regression/gpmsa_cobra_input.txt", "", NULL); + + // Step 2: Set up prior for calibration parameters + QUESO::VectorSpace paramSpace(env, + "param_", numUncertainVars, NULL); + + // Parameter (theta) bounds: + // descriptors 'k_tmasl' 'k_xkle' 'k_xkwew' 'k_xkwlx' 'k_cd' + // upper_bounds 1.05 1.1 1.1 1.1 1.1 + // lower_bounds 0.95 0.9 0.9 0.9 0.9 + // + // These bounds are dealt with when reading in the data + QUESO::GslVector paramMins(paramSpace.zeroVector()); + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + + paramMins.cwSet(0.0); + paramMaxs.cwSet(1.0); + + QUESO::BoxSubset paramDomain("param_", + paramSpace, paramMins, paramMaxs); + + QUESO::UniformVectorRV priorRv("prior_", + paramDomain); + + // Step 3: Instantiate the 'scenario' and 'output' spaces for simulation + QUESO::VectorSpace configSpace(env, + "scenario_", numConfigVars, NULL); + + QUESO::VectorSpace nEtaSpace(env, + "output_", numEta, NULL); + + // Step 4: Instantiate the 'output' space for the experiments + QUESO::VectorSpace experimentSpace(env, + "experimentspace_", experimentSize, NULL); + + QUESO::VectorSpace totalExperimentSpace(env, + "experimentspace_", experimentSize * numExperiments, NULL); + + // Step 5: Instantiate the Gaussian process emulator object + // + // Regarding simulation scenario input values, the user should standardise + // them so that they exist inside a hypercube. + // + // Regarding simulation output data, the user should transform it so that the + // mean is zero and the variance is one. + // + // Regarding experimental scenario input values, the user should standardize + // them so that they exist inside a hypercube. + // + // Regarding experimental data, the user should transformed it so that it has + // zero mean and variance one. + + // GPMSA stores all the information about our simulation + // data and experimental data. It also stores default information about the + // hyperparameter distributions. + QUESO::GPMSAFactory + gpmsaFactory(env, + NULL, + priorRv, + configSpace, + paramSpace, + nEtaSpace, + experimentSpace, + numSimulations, + numExperiments); + + // std::vector containing all the points in scenario space where we have + // simulations + std::vector simulationScenarios(numSimulations, + (QUESO::GslVector *) NULL); + + // std::vector containing all the points in parameter space where we have + // simulations + std::vector paramVecs(numSimulations, + (QUESO::GslVector *) NULL); + + // std::vector containing all the simulation output data + std::vector outputVecs(numSimulations, + (QUESO::GslVector *) NULL); + + // std::vector containing all the points in scenario space where we have + // experiments + std::vector experimentScenarios(numExperiments, + (QUESO::GslVector *) NULL); + + // std::vector containing all the experimental output data + std::vector experimentVecs(numExperiments, + (QUESO::GslVector *) NULL); + + // The experimental output data observation error covariance matrix + QUESO::GslMatrix experimentMat(totalExperimentSpace.zeroVector()); + + // Instantiate each of the simulation points/outputs + for (unsigned int i = 0; i < numSimulations; i++) { + simulationScenarios[i] = new QUESO::GslVector(configSpace.zeroVector()); // 'x_{i+1}^*' in paper + paramVecs [i] = new QUESO::GslVector(paramSpace.zeroVector()); // 't_{i+1}^*' in paper + outputVecs [i] = new QUESO::GslVector(nEtaSpace.zeroVector()); // 'eta_{i+1}' in paper + } + + for (unsigned int i = 0; i < numExperiments; i++) { + experimentScenarios[i] = new QUESO::GslVector(configSpace.zeroVector()); // 'x_{i+1}' in paper + experimentVecs[i] = new QUESO::GslVector(experimentSpace.zeroVector()); + } + + // Read in data and store the standard deviation of the simulation data. We + // will need the standard deviation when we pass the experiment error + // covariance matrix to QUESO. + double stdsim = readData(simulationScenarios, + paramVecs, + outputVecs, + experimentScenarios, + experimentVecs); + + for (unsigned int i = 0; i < numExperiments; i++) { + // Passing in error of experiments (standardised). + experimentMat(i, i) = (0.025 / stdsim) * (0.025 / stdsim); + } + + // Add simulation and experimental data + gpmsaFactory.addSimulations(simulationScenarios, paramVecs, outputVecs); + gpmsaFactory.addExperiments(experimentScenarios, experimentVecs, &experimentMat); + + QUESO::GenericVectorRV postRv( + "post_", + gpmsaFactory.prior().imageSet().vectorSpace()); + QUESO::StatisticalInverseProblem ip("", + NULL, gpmsaFactory, postRv); + + QUESO::GslVector paramInitials( + gpmsaFactory.prior().imageSet().vectorSpace().zeroVector()); + + // Initial condition of the chain + // Have to set each of these by hand, *and* the sampler is sensitive to these + // values + paramInitials[0] = 0.5; // param 1 + paramInitials[1] = 0.5; // param 2 + paramInitials[2] = 0.5; // param 3 + paramInitials[3] = 0.5; // param 4 + paramInitials[4] = 0.5; // param 5 + paramInitials[5] = 0.0; // not used. emulator mean + paramInitials[6] = 1.0; // emulator precision + paramInitials[7] = 0.97; // emulator corr str + paramInitials[8] = 0.97; // emulator corr str + paramInitials[9] = 0.97; // emulator corr str + paramInitials[10] = 0.97; // emulator corr str + paramInitials[11] = 0.97; // emulator corr str + paramInitials[12] = 0.97; // emulator corr str + paramInitials[13] = 10.0; // discrepancy precision + paramInitials[14] = 0.97; // discrepancy corr str + paramInitials[15] = 8000.0; // emulator data precision + + QUESO::GslMatrix proposalCovMatrix( + gpmsaFactory.prior().imageSet().vectorSpace().zeroVector()); + + // Setting the proposal covariance matrix by hand. This requires great + // forethough, and can generally be referred to as a massive hack. These + // values were taken from the gpmsa matlab code and fiddled with. + double scale = 600.0; + proposalCovMatrix(0, 0) = 3.1646 / 10.0; // param 1 + proposalCovMatrix(1, 1) = 3.1341 / 10.0; // param 2 + proposalCovMatrix(2, 2) = 3.1508 / 10.0; // param 3 + proposalCovMatrix(3, 3) = 0.3757 / 10.0; // param 4 + proposalCovMatrix(4, 4) = 0.6719 / 10.0; // param 5 + proposalCovMatrix(5, 5) = 0.1 / scale; // not used. emulator mean + proposalCovMatrix(6, 6) = 0.4953 / scale; // emulator precision + proposalCovMatrix(7, 7) = 0.6058 / scale; // emulator corr str + proposalCovMatrix(8, 8) = 7.6032e-04 / scale; // emulator corr str + proposalCovMatrix(9, 9) = 8.3815e-04 / scale; // emulator corr str + proposalCovMatrix(10, 10) = 7.5412e-04 / scale; // emulator corr str + proposalCovMatrix(11, 11) = 0.2682 / scale; // emulator corr str + proposalCovMatrix(12, 12) = 0.0572 / scale; // emulator corr str + proposalCovMatrix(13, 13) = 1.3417e+05 / scale; // discrepancy precision + proposalCovMatrix(14, 14) = 0.3461 / scale; // discrepancy corr str + proposalCovMatrix(15, 15) = 495.3 / scale; // emulator data precision + + // Square to get variances + for (unsigned int i = 0; i < 16; i++) { + proposalCovMatrix(i, i) = proposalCovMatrix(i, i) * proposalCovMatrix(i, i); + } + + ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix); + + MPI_Finalize(); + + return 0; +} diff --git a/test/test_Regression/test_gpmsa_cobra_samples.m.in b/test/test_Regression/test_gpmsa_cobra_samples.m.in new file mode 100644 index 000000000..3066b37b1 --- /dev/null +++ b/test/test_Regression/test_gpmsa_cobra_samples.m.in @@ -0,0 +1,1002 @@ +ip_mh_rawChain_unified = zeros(1000,16); +ip_mh_rawChain_unified = [5.0000000000000000e-01 5.0000000000000000e-01 5.0000000000000000e-01 5.0000000000000000e-01 5.0000000000000000e-01 0.0000000000000000e+00 1.0000000000000000e+00 9.6999999999999997e-01 9.6999999999999997e-01 9.6999999999999997e-01 9.6999999999999997e-01 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b/test/test_Regression/test_jeffreys.C @@ -0,0 +1,123 @@ +//---------------------------------------------------------------- +//---------------------------------------------------------------- +//this is a test of the jeffreys sampling code +//---------------------------------------------------------------- +//---------------------------------------------------------------- + +#include +#include +#include +#include +#include +#include +#include + +struct qoiRoutine_DataType +{ + double coef1; +}; + +void qoiRoutine( + const QUESO::GslVector& paramValues, + const QUESO::GslVector* paramDirection, + const void* functionDataPtr, + QUESO::GslVector& qoiValues, + QUESO::DistArray* gradVectors, + QUESO::DistArray* hessianMatrices, + QUESO::DistArray* hessianEffects) +{ + //logic to avoid warnings from intel compiler + const QUESO::GslVector* aux1 = paramDirection; + if (aux1) {}; + QUESO::DistArray* aux2 = gradVectors; + if (aux2) {}; + aux2 = hessianEffects; + QUESO::DistArray* aux3 = hessianMatrices; + if (aux3) {}; + + //checking size of paramValues and qoiValues + //both should be size 1 + UQ_FATAL_TEST_MACRO(paramValues.sizeGlobal() != 1, + QUESO::UQ_UNAVAILABLE_RANK, + "qoiRoutine()", + "paramValues vector does not have size 1"); + + UQ_FATAL_TEST_MACRO(qoiValues.sizeGlobal() != 1, + QUESO::UQ_UNAVAILABLE_RANK, + "qoiRoutine()", + "qoiValues vector does not have size 1"); + + //compute qoi + const QUESO::BaseEnvironment& env = paramValues.env(); + if(env.subRank() == 0) { + double coef1 = ((qoiRoutine_DataType *) functionDataPtr)->coef1; + qoiValues[0] = coef1*paramValues[0]; + } + else { + qoiValues[0] = 0.; + } + + return; +} + +void compute(const QUESO::FullEnvironment& env) { + + //step 1: instatiate parameter space + QUESO::VectorSpace + paramSpace(env, "param_", 1, NULL); + + //step 2: instantiate the parameter domain + QUESO::GslVector paramMins(paramSpace.zeroVector()); + paramMins.cwSet(0.001); + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + paramMaxs.cwSet(100.); //TODO: this is not working with gsl sampling right now in FP + QUESO::BoxSubset + paramDomain("param_", paramSpace, paramMins, paramMaxs); + + //step 3: instantiate the qoi space + QUESO::VectorSpace + qoiSpace(env, "qoi_", 1, NULL); + + //step 4: instantiate the qoi function object + qoiRoutine_DataType qoiRoutine_Data; + qoiRoutine_Data.coef1 = 1.; + QUESO::GenericVectorFunction + qoiFunctionObj("qoi_", + paramDomain, + qoiSpace, + qoiRoutine, + (void *) &qoiRoutine_Data); + + //step 5: instantiate the forward problem + //parameter is Jeffreys RV + QUESO::JeffreysVectorRV + paramRv("param_", paramDomain); + + QUESO::GenericVectorRV + qoiRv("qoi_",qoiSpace); + + QUESO::StatisticalForwardProblem + fp("", NULL, paramRv, qoiFunctionObj, qoiRv); + + //step 6: solve the forward problem + fp.solveWithMonteCarlo(NULL); +} + +int main(int argc, char ** argv) { + //init env + MPI_Init(&argc,&argv); + // Step 1: Set up QUESO environment + QUESO::FullEnvironment* env = + new QUESO::FullEnvironment(MPI_COMM_WORLD,"test_Regression/jeffreys_input.txt","",NULL); + + //compute + compute(*env); + + //finalize enviroment + delete env; + MPI_Finalize(); + + return 0; +} diff --git a/test/test_Regression/test_jeffreys_samples.m.in b/test/test_Regression/test_jeffreys_samples.m.in new file mode 100644 index 000000000..ac75f3e68 --- /dev/null +++ b/test/test_Regression/test_jeffreys_samples.m.in @@ -0,0 +1,12 @@ +fp_mc_ParamSeq_unified = zeros(10,1); +fp_mc_ParamSeq_unified = [2.4337120696700134e-03 +3.0532280391335589e+00 +4.5213140188840812e+00 +1.0969017152512292e-02 +3.0128719500283290e+00 +4.0951226357422165e+01 +1.1458038231993228e+01 +1.3932926460222292e-01 +5.9836664970896090e+00 +8.7122112400695159e-02 +]; diff --git a/test/test_Regression/test_jeffreys_samples_diff.sh.in b/test/test_Regression/test_jeffreys_samples_diff.sh.in new file mode 100755 index 000000000..fabacca70 --- /dev/null +++ b/test/test_Regression/test_jeffreys_samples_diff.sh.in @@ -0,0 +1,7 @@ +#!/bin/bash +set -eu +set -o pipefail + +./test_jeffreys +diff test_Regression/test_jeffreys_samples.m test_jeffreys_output/fp_p_seq.m +rm -r test_jeffreys_output diff --git a/test/test_Regression/test_logitadaptedcov.C b/test/test_Regression/test_logitadaptedcov.C new file mode 100644 index 000000000..437e830a5 --- /dev/null +++ b/test/test_Regression/test_logitadaptedcov.C @@ -0,0 +1,133 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define TOL 1e-14 + +template +class Likelihood : public QUESO::BaseScalarFunction +{ +public: + + Likelihood(const char * prefix, const QUESO::VectorSet & domain, + const QUESO::GaussianJointPdf & pdf) + : QUESO::BaseScalarFunction(prefix, domain), + m_pdf(pdf) + { + } + + virtual ~Likelihood() + { + } + + virtual double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const + { + return this->m_pdf.lnValue(domainVector, NULL, NULL, NULL, NULL); + } + + virtual double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const + { + return std::exp(this->lnValue(domainVector, domainDirection, gradVector, + hessianMatrix, hessianEffect)); + } + + const QUESO::GaussianJointPdf & m_pdf; +}; + +int main(int argc, char ** argv) { + MPI_Init(&argc, &argv); + + QUESO::FullEnvironment env(MPI_COMM_WORLD, + "test_Regression/adaptedcov_input.txt", "", NULL); + + QUESO::VectorSpace paramSpace(env, + "param_", 2, NULL); + + QUESO::GslVector paramMins(paramSpace.zeroVector()); + paramMins.cwSet(0.0); + + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + paramMaxs.cwSet(1.0); + + QUESO::BoxSubset paramDomain("param_", + paramSpace, paramMins, paramMaxs); + + QUESO::UniformVectorRV prior("prior_", + paramDomain); + + QUESO::GslVector mean(paramSpace.zeroVector()); + mean.cwSet(0.5); + + QUESO::GslMatrix cov(paramSpace.zeroVector()); + cov(0, 0) = 0.05 * 0.05; + cov(0, 1) = 0.001; + cov(1, 0) = 0.001; + cov(1, 1) = 0.05 * 0.05; + + QUESO::GaussianJointPdf pdf("pdf_", + paramDomain, mean, cov); + + Likelihood likelihood("likelihood_", + paramDomain, pdf); + + QUESO::GenericVectorRV posterior( + "posterior_", paramDomain); + + QUESO::StatisticalInverseProblem ip("", + NULL, prior, likelihood, posterior); + + QUESO::GslVector paramInitials(paramSpace.zeroVector()); + paramInitials.cwSet(0.5); + + QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector()); + proposalCovMatrix(0, 0) = 0.5; + proposalCovMatrix(0, 1) = 0.0; + proposalCovMatrix(1, 0) = 0.0; + proposalCovMatrix(1, 1) = 0.5; + + ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix); + + QUESO::GslMatrix adaptedCovMatrix( + dynamic_cast &>( + ip.sequenceGenerator().transitionKernel().rv(0).pdf()).lawCovMatrix()); + + std::cout << std::setprecision(15) + << "Adapted covariance matrix is:" << std::endl + << adaptedCovMatrix(0, 0) << " " << adaptedCovMatrix(0, 1) << std::endl + << adaptedCovMatrix(1, 0) << " " << adaptedCovMatrix(1, 1) << std::endl; + + + QUESO::GslMatrix regressionTestMatrix(proposalCovMatrix); + regressionTestMatrix(0, 0) = 0.0155346059919622; + regressionTestMatrix(0, 1) = 0.00611251379952771; + regressionTestMatrix(1, 0) = 0.00611251379952771; + regressionTestMatrix(1, 1) = 0.0155897610147395; + + QUESO::GslMatrix diff(regressionTestMatrix - adaptedCovMatrix); + + + std::cout << "norm is " << diff.normFrob() / regressionTestMatrix.normFrob() << std::endl; + + unsigned int result; + if (diff.normFrob() / regressionTestMatrix.normFrob() > TOL) { + result = 1; + } + else { + result = 0; + } + + MPI_Finalize(); + + return result; +} diff --git a/test/test_StatisticalInverseProblem/both_input.txt.in b/test/test_StatisticalInverseProblem/both_input.txt.in new file mode 100644 index 000000000..058700abd --- /dev/null +++ b/test/test_StatisticalInverseProblem/both_input.txt.in @@ -0,0 +1,51 @@ +############################################### +# UQ Environment +############################################### +#env_help = anything +env_numSubEnvironments = 1 +env_subDisplayFileName = test_outputBoth/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 = test_outputBoth/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +############################################### +#ip_mh_help = anything +ip_mh_dataOutputFileName = test_outputBoth/sipOutput +ip_mh_dataOutputAllowedSet = 0 1 + +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_size = 10 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 50000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputFileName = test_outputBoth/ip_raw_chain +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_rawChain_computeStats = 1 + +ip_mh_displayCandidates = 0 +ip_mh_putOutOfBoundsInChain = 0 +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 + +ip_mh_outputLogLikelihood = 1 +ip_mh_outputLogTarget = 1 diff --git a/test/test_StatisticalInverseProblem/llhdout_input.txt.in b/test/test_StatisticalInverseProblem/llhdout_input.txt.in new file mode 100644 index 000000000..86c2434fb --- /dev/null +++ b/test/test_StatisticalInverseProblem/llhdout_input.txt.in @@ -0,0 +1,51 @@ +############################################### +# UQ Environment +############################################### +#env_help = anything +env_numSubEnvironments = 1 +env_subDisplayFileName = test_outputLlhd/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 = test_outputLlhd/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +############################################### +#ip_mh_help = anything +ip_mh_dataOutputFileName = test_outputLlhd/sipOutput +ip_mh_dataOutputAllowedSet = 0 1 + +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_size = 10 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 50000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputFileName = test_outputLlhd/ip_raw_chain +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_rawChain_computeStats = 1 + +ip_mh_displayCandidates = 0 +ip_mh_putOutOfBoundsInChain = 0 +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 + +ip_mh_outputLogLikelihood = 1 +ip_mh_outputLogTarget = 0 diff --git a/test/test_StatisticalInverseProblem/neither_input.txt.in b/test/test_StatisticalInverseProblem/neither_input.txt.in new file mode 100644 index 000000000..c8a443714 --- /dev/null +++ b/test/test_StatisticalInverseProblem/neither_input.txt.in @@ -0,0 +1,51 @@ +############################################### +# UQ Environment +############################################### +#env_help = anything +env_numSubEnvironments = 1 +env_subDisplayFileName = test_outputNeither/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 = test_outputNeither/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +############################################### +#ip_mh_help = anything +ip_mh_dataOutputFileName = test_outputNeither/sipOutput +ip_mh_dataOutputAllowedSet = 0 1 + +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_size = 10 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 50000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputFileName = test_outputNeither/ip_raw_chain +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_rawChain_computeStats = 1 + +ip_mh_displayCandidates = 0 +ip_mh_putOutOfBoundsInChain = 0 +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 + +ip_mh_outputLogLikelihood = 0 +ip_mh_outputLogTarget = 0 diff --git a/test/test_StatisticalInverseProblem/targetout_input.txt.in b/test/test_StatisticalInverseProblem/targetout_input.txt.in new file mode 100644 index 000000000..9877973a3 --- /dev/null +++ b/test/test_StatisticalInverseProblem/targetout_input.txt.in @@ -0,0 +1,51 @@ +############################################### +# UQ Environment +############################################### +#env_help = anything +env_numSubEnvironments = 1 +env_subDisplayFileName = test_outputTarget/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 = test_outputTarget/sipOutput +ip_dataOutputAllowedSet = 0 + +############################################### +# 'ip_': information for Metropolis-Hastings algorithm +############################################### +#ip_mh_help = anything +ip_mh_dataOutputFileName = test_outputTarget/sipOutput +ip_mh_dataOutputAllowedSet = 0 1 + +ip_mh_rawChain_dataInputFileName = . +ip_mh_rawChain_size = 10 +ip_mh_rawChain_generateExtra = 0 +ip_mh_rawChain_displayPeriod = 50000 +ip_mh_rawChain_measureRunTimes = 1 +ip_mh_rawChain_dataOutputFileName = test_outputTarget/ip_raw_chain +ip_mh_rawChain_dataOutputAllowedSet = 0 1 +ip_mh_rawChain_computeStats = 1 + +ip_mh_displayCandidates = 0 +ip_mh_putOutOfBoundsInChain = 0 +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 + +ip_mh_outputLogLikelihood = 0 +ip_mh_outputLogTarget = 1 diff --git a/test/test_StatisticalInverseProblem/test_LlhdTargetOutput.C b/test/test_StatisticalInverseProblem/test_LlhdTargetOutput.C new file mode 100644 index 000000000..eba4a60ef --- /dev/null +++ b/test/test_StatisticalInverseProblem/test_LlhdTargetOutput.C @@ -0,0 +1,80 @@ +#include +#include +#include +#include +#include +#include +#include + +template +class Likelihood : public QUESO::BaseScalarFunction +{ +public: + + Likelihood(const char * prefix, const QUESO::VectorSet & domain) + : QUESO::BaseScalarFunction(prefix, domain) + { + } + + virtual ~Likelihood() + { + } + + virtual double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const + { + double misfit = 1.0; + + return -0.5 * misfit; + } + + virtual double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const + { + return std::exp(this->lnValue(domainVector, domainDirection, gradVector, + hessianMatrix, hessianEffect)); + } +}; + +int main(int argc, char ** argv) { + MPI_Init(&argc, &argv); + + QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL); + + QUESO::VectorSpace paramSpace(env, + "param_", 1, NULL); + + QUESO::GslVector paramMins(paramSpace.zeroVector()); + QUESO::GslVector paramMaxs(paramSpace.zeroVector()); + + double min_val = 0.0; + double max_val = 1.0; + paramMins.cwSet(min_val); + paramMaxs.cwSet(max_val); + + QUESO::BoxSubset paramDomain("param_", + paramSpace, paramMins, paramMaxs); + + QUESO::UniformVectorRV priorRv("prior_", + paramDomain); + + Likelihood lhood("llhd_", paramDomain); + + QUESO::GenericVectorRV + postRv("post_", paramSpace); + + QUESO::StatisticalInverseProblem + ip("", NULL, priorRv, lhood, postRv); + + QUESO::GslVector paramInitials(paramSpace.zeroVector()); + paramInitials[0] = 0.0; + + QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector()); + proposalCovMatrix(0, 0) = 0.1; + + ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix); + + MPI_Finalize(); + + return 0; +} diff --git a/test/test_StatisticalInverseProblem/test_LlhdTargetOutput.sh.in b/test/test_StatisticalInverseProblem/test_LlhdTargetOutput.sh.in new file mode 100644 index 000000000..0af4f919a --- /dev/null +++ b/test/test_StatisticalInverseProblem/test_LlhdTargetOutput.sh.in @@ -0,0 +1,60 @@ +#!/bin/bash +set -eu +set -o pipefail + +PROG="./test_LlhdTargetOutput" + +INPUT_1="test_StatisticalInverseProblem/llhdout_input.txt" +INPUT_2="test_StatisticalInverseProblem/targetout_input.txt" +INPUT_3="test_StatisticalInverseProblem/both_input.txt" +INPUT_4="test_StatisticalInverseProblem/neither_input.txt" + +$PROG $INPUT_1 +$PROG $INPUT_2 +$PROG $INPUT_3 +$PROG $INPUT_4 + +if [[ -f test_outputLlhd/ip_raw_chain_loglikelihood.m ]] +then + echo "Found expected output file" +else + exit 1 +fi + +if [[ -f test_outputTarget/ip_raw_chain_logtarget.m ]] +then + echo "Found expected output file" +else + exit 1 +fi + +if [[ -f test_outputBoth/ip_raw_chain_loglikelihood.m ]] +then + echo "Found expected output file" +else + exit 1 +fi + +if [[ -f test_outputBoth/ip_raw_chain_logtarget.m ]] +then + echo "Found expected output file" +else + exit 1 +fi + +if [[ -f test_outputNeither/ip_raw_chain_loglikelihood.m ]] +then + echo "Found unexpected output file" + exit 1 +fi + +if [[ -f tetst_outputNeither/ip_raw_chain_logtarget.m ]] +then + echo "Found unexpected output file" + exit 1 +fi + +rm -r test_outputLlhd +rm -r test_outputTarget +rm -r test_outputBoth +rm -r test_outputNeither diff --git a/test/test_exception/test_exception.C b/test/test_exception/test_exception.C index 91affafa1..3288e46dc 100644 --- a/test/test_exception/test_exception.C +++ b/test/test_exception/test_exception.C @@ -23,8 +23,7 @@ int main(int argc, char **argv) // hit with exception try { - printf("Executing error\n"); - queso_error(); + queso_error_msg("Executing error"); } catch(...) { diff --git a/test/test_optimizer/test_gsloptimizer.C b/test/test_optimizer/test_gsloptimizer.C new file mode 100644 index 000000000..53cb821e4 --- /dev/null +++ b/test/test_optimizer/test_gsloptimizer.C @@ -0,0 +1,105 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +template +class ObjectiveFunction : public QUESO::BaseScalarFunction { +public: + ObjectiveFunction(const char * prefix, + const QUESO::VectorSet & domainSet) + : QUESO::BaseScalarFunction(prefix, domainSet) { + // Do nothing + } + + virtual double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const { + return std::exp(this->lnValue(domainVector, domainDirection, + gradVector, hessianMatrix, hessianEffect)); + } + + virtual double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const { + + // Need to check if NULL because QUESO will somtimes call this with a + // NULL pointer + if (gradVector != NULL) { + (*gradVector)[0] = -2.0 * (domainVector[0]-1); + (*gradVector)[1] = -2.0 * (domainVector[1]-2); + (*gradVector)[2] = -2.0 * (domainVector[2]-3); + } + + // Mean = (1,2) + return -( (domainVector[0]-1)*(domainVector[0]-1) + + (domainVector[1]-2)*(domainVector[1]-2) + + (domainVector[2]-3)*(domainVector[2]-3) ); + } +}; + +int main(int argc, char ** argv) { + MPI_Init(&argc, &argv); + + QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL); + + QUESO::VectorSpace paramSpace(env, + "space_", 3, NULL); + + QUESO::GslVector minBound(paramSpace.zeroVector()); + minBound[0] = -10.0; + minBound[1] = -10.0; + minBound[2] = -10.0; + + QUESO::GslVector maxBound(paramSpace.zeroVector()); + maxBound[0] = 10.0; + maxBound[1] = 10.0; + maxBound[2] = 10.0; + + QUESO::BoxSubset domain("", paramSpace, + minBound, maxBound); + + ObjectiveFunction objectiveFunction( + "", domain); + + QUESO::GslVector initialPoint(paramSpace.zeroVector()); + initialPoint[0] = 9.0; + initialPoint[1] = -9.0; + initialPoint[1] = -1.0; + + QUESO::GslOptimizer optimizer(objectiveFunction); + + double tol = 1.0e-10; + optimizer.setTolerance(tol); + optimizer.set_solver_type(QUESO::GslOptimizer::STEEPEST_DECENT); + + QUESO::OptimizerMonitor monitor(env); + monitor.set_display_output(true,true); + + std::cout << "Solving with Steepest Decent" << std::endl; + optimizer.minimize(&monitor); + + if (std::abs( optimizer.minimizer()[0] - 1.0) > tol) { + std::cerr << "GslOptimize failed. Found minimizer at: " << optimizer.minimizer()[0] + << std::endl; + std::cerr << "Actual minimizer is 1.0" << std::endl; + queso_error(); + } + + std::string nm = "nelder_mead2"; + optimizer.set_solver_type(nm); + monitor.reset(); + monitor.set_display_output(true,true); + + std::cout << std::endl << "Solving with Nelder Mead" << std::endl; + optimizer.minimize(&monitor); + + monitor.print(std::cout,false); + + return 0; +} diff --git a/test/test_optimizer/test_seedwithmap.C b/test/test_optimizer/test_seedwithmap.C new file mode 100644 index 000000000..9402d8380 --- /dev/null +++ b/test/test_optimizer/test_seedwithmap.C @@ -0,0 +1,92 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +template +class Likelihood : public QUESO::BaseScalarFunction { +public: + Likelihood(const char * prefix, + const QUESO::VectorSet & domainSet) + : QUESO::BaseScalarFunction(prefix, domainSet) { + // Do nothing + } + + virtual double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const { + return std::exp(this->actualValue(domainVector, domainDirection, + gradVector, hessianMatrix, hessianEffect)); + } + + virtual double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const { + + // Need to check if NULL because QUESO will somtimes call this with a + // NULL pointer + if (gradVector != NULL) { + (*gradVector)[0] = -2.0 * domainVector[0]; + } + + return -(domainVector[0] * domainVector[0]); + } +}; + +int main(int argc, char ** argv) { + MPI_Init(&argc, &argv); + + QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL); + + QUESO::VectorSpace paramSpace(env, + "space_", 1, NULL); + + QUESO::GslVector minBound(paramSpace.zeroVector()); + minBound[0] = -10.0; + + QUESO::GslVector maxBound(paramSpace.zeroVector()); + maxBound[0] = 10.0; + + QUESO::BoxSubset domain("", paramSpace, + minBound, maxBound); + + QUESO::UniformVectorRV prior("", domain); + + Likelihood likelihood("", domain); + + QUESO::GenericVectorRV posterior("", + domain); + + QUESO::StatisticalInverseProblem ip("", + NULL, prior, likelihood, posterior); + + QUESO::GslVector initialValues(paramSpace.zeroVector()); + initialValues[0] = 9.0; + + QUESO::GslMatrix proposalCovarianceMatrix(paramSpace.zeroVector()); + proposalCovarianceMatrix(0, 0) = 1.0; + + ip.seedWithMAPEstimator(); + ip.solveWithBayesMetropolisHastings(NULL, initialValues, + &proposalCovarianceMatrix); + + // The first sample should be the seed + QUESO::GslVector first_sample(paramSpace.zeroVector()); + posterior.realizer().realization(first_sample); + + // Tight tolerance for analytical derivative + if (std::abs(first_sample[0]) > 1e-10) { + std::cerr << "seedWithMAPEstimator failed. Seed was: " << first_sample[0] + << std::endl; + std::cerr << "Actual seed should be 0.0" << std::endl; + queso_error(); + } + + return 0; +} diff --git a/test/test_optimizer/test_seedwithmap_fd.C b/test/test_optimizer/test_seedwithmap_fd.C new file mode 100644 index 000000000..42a1359e5 --- /dev/null +++ b/test/test_optimizer/test_seedwithmap_fd.C @@ -0,0 +1,88 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +template +class Likelihood : public QUESO::BaseScalarFunction { +public: + Likelihood(const char * prefix, + const QUESO::VectorSet & domainSet) + : QUESO::BaseScalarFunction(prefix, domainSet) { + // Do nothing + } + + virtual double actualValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const { + return std::exp(this->actualValue(domainVector, domainDirection, + gradVector, hessianMatrix, hessianEffect)); + } + + virtual double lnValue(const V & domainVector, const V * domainDirection, + V * gradVector, M * hessianMatrix, V * hessianEffect) const { + // Note: we do not fill gradVector, so QUESO should fall back to a finite + // difference calculation for the derivative + return -(domainVector[0] * domainVector[0]); + } +}; + +int main(int argc, char ** argv) { + MPI_Init(&argc, &argv); + + QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL); + + QUESO::VectorSpace paramSpace(env, + "space_", 1, NULL); + + QUESO::GslVector minBound(paramSpace.zeroVector()); + minBound[0] = -10.0; + + QUESO::GslVector maxBound(paramSpace.zeroVector()); + maxBound[0] = 10.0; + + QUESO::BoxSubset domain("", paramSpace, + minBound, maxBound); + + QUESO::UniformVectorRV prior("", domain); + + Likelihood likelihood("", domain); + + QUESO::GenericVectorRV posterior("", + domain); + + QUESO::StatisticalInverseProblem ip("", + NULL, prior, likelihood, posterior); + + QUESO::GslVector initialValues(paramSpace.zeroVector()); + initialValues[0] = 9.0; + + QUESO::GslMatrix proposalCovarianceMatrix(paramSpace.zeroVector()); + proposalCovarianceMatrix(0, 0) = 1.0; + + ip.seedWithMAPEstimator(); + ip.solveWithBayesMetropolisHastings(NULL, initialValues, + &proposalCovarianceMatrix); + + // The first sample should be the seed + QUESO::GslVector first_sample(paramSpace.zeroVector()); + posterior.realizer().realization(first_sample); + + // Looser tolerance for the derivative calculated by using a finite + // difference + if (std::abs(first_sample[0]) > 1e-5) { + std::cerr << "seedWithMAPEstimator failed. Seed was: " << first_sample[0] + << std::endl; + std::cerr << "Actual seed should be 0.0" << std::endl; + queso_error(); + } + + return 0; +}