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QUESO Library: Version = 0.51.1 (5101)

Development Build

Build Date   = 2015-04-07 15:27
Build Host   = zoidberg.ices.utexas.edu
Build User   = damon
Build Arch   = x86_64-unknown-linux-gnu
Build Rev    = 128c60a

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
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Beginning run at Tue Apr  7 15:27:58 2015

Entering StatisticalInverseProblem<P_V,P_M>::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<P_V,P_M>::constructor(): prefix = ip_
In StatisticalInverseProblem<P_V,P_M>::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
ip_mh_doLogitTransform = 1

Entering MetropolisHastingsSG<P_V,P_M>::constructor(1): prefix = ip_, alternativeOptionsValues = 0, m_env.optionsInputFileName() = ./example.inp, m_initialProposalCovMatrix = 8 4 ; 4 16 
Entering MetropolisHastingsSG<P_V,P_M>::commonConstructor()
In MetropolisHastingsSG<P_V,P_M>::commonConstructor(): just instantiated a 'ScaledCovMatrix' TK class
Leaving MetropolisHastingsSG<P_V,P_M>::commonConstructor()
Leaving MetropolisHastingsSG<P_V,P_M>::constructor(1)
Starting the generation of Markov chain ip_mh_rawChain, with 32768 positions...

In MetropolisHastingsSG<P_V,P_M>::generateFullChain(): contents of initial position are:0.1 -1.4 
In MetropolisHastingsSG<P_V,P_M>::generateFullChain(): targetPdf.domaintSet() info is:In BoxSubset<V,M>::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       = 0.910827 seconds

 Breaking of the chain run time:

  Candidate run time   = 0.170257 seconds (18.6926%)
  Num target calls  = 58468
  Target d. run time   = 0.057954 seconds (6.36279%)
  Avg target run time   = 9.91209e-07 seconds
  Mh alpha run time    = 0.1401 seconds (15.3816%)
  Dr alpha run time    = 0.446783 seconds (49.0525%)
----------------------   --------------
  Sum                  = 0.815094 seconds (89.4894%)

 Other run times:
  DR run time          = 0.585539 seconds (64.2865%)
  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<P_V,P_M>::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<P_V,P_M>::generateSequence(), prefix = ip_mh_, raw chain name = ip_mh_rawChain: returned from opening generic output file 'outputData/sipOutput.m', subId = 0
In MetropolisHastingsSG<P_V,P_M>::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<P_V,P_M>::generateSequence(): just wrote (per period request) remaining 32768 chain positions , 0 <= pos <= 32767
Entering BaseVectorSequence<V,M>::subPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768
Leaving BaseVectorSequence<V,M>::subPositionsOfMaximum()
In MetropolisHastingsSG<P_V,P_M>::generateSequence(): just computed MLE, rawSubMLEvalue = -3.41481e-06, rawSubMLEpositions.subSequenceSize() = 1, rawSubMLEpositions[0] = -0.994878 1.99948 
Entering BaseVectorSequence<V,M>::subPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768
Leaving BaseVectorSequence<V,M>::subPositionsOfMaximum()
In MetropolisHastingsSG<P_V,P_M>::generateSequence(): just computed MAP, rawSubMAPvalue = -3.41481e-06, rawSubMAPpositions.subSequenceSize() = 1, rawSubMAPpositions[0] = -0.994878 1.99948 
In MetropolisHastingsSG<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<V,M>::unifiedPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768
Leaving BaseVectorSequence<V,M>::unifiedPositionsOfMaximum()
In MetropolisHastingsSG<P_V,P_M>::generateSequence(): just computed MLE, rawUnifiedMLEvalue = -3.41481e-06, rawUnifiedMLEpositions.subSequenceSize() = 1, rawUnifiedMLEpositions[0] = -0.994878 1.99948 
Entering BaseVectorSequence<V,M>::unifiedPositionsOfMaximum(): subCorrespondingScalarValues,subSequenceSize() = 32768, this->subSequenceSize = 32768
Leaving BaseVectorSequence<V,M>::unifiedPositionsOfMaximum()
In MetropolisHastingsSG<P_V,P_M>::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<V,M>::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768
Leaving SequenceOfVectors<V,M>::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048
Entering ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768
Leaving ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048
Entering ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 16, subSequenceSize = 32768
Leaving ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 16, subSequenceSize = 2048
In MetropolisHastingsSG<P_V,P_M>::generateSequence(), prefix = ip_mh_: checking necessity of opening output files for filtered chain ip_mh_filtChain...
In MetropolisHastingsSG<P_V,P_M>::generateSequence(), prefix = ip_mh_: closed sub output file 'outputData/ip_filt_chain' for filtered chain ip_mh_filtChain
In MetropolisHastingsSG<P_V,P_M>::generateSequence(), prefix = ip_mh_: closed unified output file 'outputData/ip_filt_chain' for filtered chain ip_mh_filtChain
In MetropolisHastingsSG<P_V,P_M>::generateSequence(), prefix = ip_mh_: closed generic output file 'outputData/sipOutput' (chain name is ip_mh_filtChain)

In SequentialVectorRealizer<V,M>::constructor(): m_chain.subSequenceSize() = 2048

Entering StatisticalForwardProblem<P_V,P_M,Q_V,Q_M>::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<P_V,P_M,Q_V,Q_M>::constructor(): prefix = fp_
In StatisticalForwardProblem<P_V,P_M,Q_V,Q_M>::solveWithMonteCarlo(): computing solution, as requested by user
Entering MonteCarloSG<P_V,P_M,Q_V,Q_M>::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<P_V,P_M,Q_V,Q_M>::constructor()
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::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 = 0.001181 seconds

 Breaking of the seq run time:

  QoI function run time   = 0.000254 seconds (21.5072%)
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::internGenerateSequence(): returned from call to actualGenerateSequence() with subActualSize = 2048
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening generic output file (qseq name is fp_mc_QoiSeq) ...
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening output files for pseq fp_mc_ParamSeq...
In MonteCarloG<P_V,P_M>::internGenerateSequence(): just wrote remaining pseq positions (per period request)
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::internGenerateSequence(), prefix = fp_mc_: closed unified data output file 'outputData/fp_p_seq' for pseq fp_mc_ParamSeq
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::internGenerateSequence(), prefix = fp_mc_: checking necessity of opening output files for qseq fp_mc_QoiSeq...
In MonteCarloG<P_V,P_M>::internGenerateSequence(): just wrote remaining qseq positions (per period request)
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::internGenerateSequence(), prefix = fp_mc_: closed unified data output file 'outputData/fp_q_seq' for qseq fp_mc_QoiSeq
In MonteCarloSG<P_V,P_M,Q_V,Q_M>::internGenerateSequence(), prefix = fp_mc_: closed generic data output file 'outputData/sfpOutput' for QoI sequence fp_mc_QoiSeq

In SequentialVectorRealizer<V,M>::constructor(): m_chain.subSequenceSize() = 2048
In StatisticalForwardProblem<P_V,P_M,Q_V,Q_M>::solveWithMonteCarlo(), prefix = fp_: instantiating cov and corr matrices
In StatisticalForwardProblem<P_V,P_M,Q_V,Q_M>::solveWithMonteCarlo(), prefix = fp_: contents of covariance matrix are
4.02364 ; 1.01851 
In StatisticalForwardProblem<P_V,P_M,Q_V,Q_M>::solveWithMonteCarlo(), prefix = fp_: contents of correlation matrix are
0.897592 ; 0.45814 
In StatisticalForwardProblem<P_V,P_M,Q_V,Q_M>::solveWithMonteCarlo(), prefix = fp_: checking necessity of opening data output file 'outputData/sfpOutput'
In StatisticalForwardProblem<P_V,P_M,Q_V,Q_M>::solveWithMonteCarlo(), prefix = fp_: closed data output file 'outputData/sfpOutput'

Ending run at Tue Apr  7 15:28:00 2015
Total run time = 2 seconds
