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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     = 
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Beginning run at Thu Nov 27 21:16:51 2014

Entering StatisticalInverseProblem<P_V,P_M>::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<P_V,P_M>::constructor(): prefix = ip_
In StatisticalInverseProblem<P_V,P_M>::solveWithBayesMLSampling(): computing solution, as requested by user
Entering MLSampling<P_V,P_M>::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<P_V,P_M>::constructor()
Entering MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence(): beginning level 0, currOptions.m_rawChainSize = 5000
In MLSampling<P_V,P_M>::generateSequence(), level 0: finished generating 5000 chain positions
In MLSampling<P_V,P_M>::generateSequence(): ending level 0, total level time = 0.005796 seconds
In MLSampling<P_V,P_M>::generateSequence(): at end of level 0, sub minLogLike = -2453.13, sub maxLogLike = -3.16228
In MLSampling<P_V,P_M>::generateSequence(): at end of level 0, unified minLogLike = -2453.13, unified maxLogLike = -3.16228
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 1: beginning step 1 of 11
KEY In MLSampling<P_V,P_M>::generateSequence(), level 1, step 1, currOptions->m_rawChainSize = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 1, after 7e-06 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 2: beginning step 2 of 11
In MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 2, prevLogLikelihoodValues[0] = -1943.14
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 1, step 2, after 0.002724 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: beginning step 3 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 1
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 2
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 3
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 4
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 5
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 6
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 7
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 8
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 9
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 1, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 10
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 1, step 3, failedExponent = 0, after 0.005078 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 4: beginning step 4 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 4: unifiedCovMatrix = 4704.98 
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 4, after 0.006492 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 5: beginning step 5 of 11
Entering MLSampling<P_V,P_M>::sampleIndexes_proc0(), level 1, step 5: unifiedRequestedNumSamples = 5000, unifiedWeightStdVectorAtProc0Only.size() = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 5, after 0.001984 seconds
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 1, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 1, step 6: result = 0
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 6, after 8.8e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 7: beginning step 7 of 11
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 7: subNumSamples = 5000, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 7: minModifiedSubNumSamples = 5000, avgModifiedSubNumSamples = 5000, maxModifiedSubNumSamples = 5000
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 7: numberOfPositionsToGuaranteeForNode = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 7: unbalancedLinkControl.unbLinkedChains.size() = 2271
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 2271
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 7, after 8.8e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 8: beginning step 8 of 11
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 8, after 3e-06 seconds
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: beginning step 9 of 11
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 1, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 1, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: unbalancedLinkControl.unbLinkedChains.size() = 33
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 33, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 9: chainIdMax = 33, numberOfPositions = 35, at Thu Nov 27 21:16:51 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 9: after 4.3e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:51 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.171429
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 1, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 1, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 1, step 9: unbalancedLinkControl.unbLinkedChains.size() = 34
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 34, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 9: chainIdMax = 34, numberOfPositions = 35, at Thu Nov 27 21:16:51 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 9: after 1.4e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:51 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 1, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 4, assessed rejection rate = 0.342857
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 9, after 0.007106 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 2271, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 10: chainIdMax = 2271, numberOfPositions = 5000, at Thu Nov 27 21:16:51 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 10: minNumberOfPositions = 5000, avgNumberOfPositions = 5000, maxNumberOfPositions = 5000
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 1, step 10: after 4.1e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 10, after chain generatrion, currLogLikelihoodValues[0] = -0.0438532
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 10, after 0.115216 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 1, step 11: beginning step 11 of 11
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 1, step 11, after 4e-06 seconds
In MLSampling<P_V,P_M>::generateSequence(): at end of level 1, sub minLogLike = -5.60322, sub maxLogLike = -0.0154408
In MLSampling<P_V,P_M>::generateSequence(): at end of level 1, unified minLogLike = -5.60322, unified maxLogLike = -0.0154408
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 2, step 1: beginning step 1 of 11
KEY In MLSampling<P_V,P_M>::generateSequence(), level 2, step 1, currOptions->m_rawChainSize = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 1, after 6e-06 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 2: beginning step 2 of 11
In MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 2, prevLogLikelihoodValues[0] = -0.0438532
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 2, step 2, after 0.003129 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: beginning step 3 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 1
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 2
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 3
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 4
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 5
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 2, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 6
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 2, step 3, failedExponent = 0, after 0.003167 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 4: beginning step 4 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 4: unifiedCovMatrix = 1591.43 
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 4, after 0.007171 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 5: beginning step 5 of 11
Entering MLSampling<P_V,P_M>::sampleIndexes_proc0(), level 2, step 5: unifiedRequestedNumSamples = 5000, unifiedWeightStdVectorAtProc0Only.size() = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 5, after 0.001823 seconds
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 6: result = 0
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 6, after 5.8e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 7: beginning step 7 of 11
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 7: subNumSamples = 5000, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 7: minModifiedSubNumSamples = 5000, avgModifiedSubNumSamples = 5000, maxModifiedSubNumSamples = 5000
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 7: numberOfPositionsToGuaranteeForNode = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 7: unbalancedLinkControl.unbLinkedChains.size() = 2258
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 2258
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 7, after 7e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 8: beginning step 8 of 11
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 8, after 2e-06 seconds
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: beginning step 9 of 11
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 2, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: after 1.7e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.6
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 2, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: after 1.7e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: entering loop for assessing rejection rate, with nowAttempt = 2, nowRejectionRate = 0.514286
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 2, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 2, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 2, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 9: after 1.2e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 2, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 0.25, assessed rejection rate = 0.257143
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 9, after 0.007112 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 2258, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 10: chainIdMax = 2258, numberOfPositions = 5000, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 10: minNumberOfPositions = 5000, avgNumberOfPositions = 5000, maxNumberOfPositions = 5000
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 2, step 10: after 4e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 10, after chain generatrion, currLogLikelihoodValues[0] = -0.263406
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 10, after 0.10655 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 2, step 11: beginning step 11 of 11
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 2, step 11, after 4e-06 seconds
In MLSampling<P_V,P_M>::generateSequence(): at end of level 2, sub minLogLike = -8.68106, sub maxLogLike = -0.162949
In MLSampling<P_V,P_M>::generateSequence(): at end of level 2, unified minLogLike = -8.68106, unified maxLogLike = -0.162949
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 3, step 1: beginning step 1 of 11
KEY In MLSampling<P_V,P_M>::generateSequence(), level 3, step 1, currOptions->m_rawChainSize = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 1, after 8e-06 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 2: beginning step 2 of 11
In MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 2, prevLogLikelihoodValues[0] = -0.263406
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 3, step 2, after 0.003197 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 3, failedExponent = 0: beginning step 3 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 1
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 2
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 3
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 3, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 4
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 3, step 3, failedExponent = 0, after 0.002374 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 4: beginning step 4 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 4: unifiedCovMatrix = 1436.48 
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 4, after 0.00736 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 5: beginning step 5 of 11
Entering MLSampling<P_V,P_M>::sampleIndexes_proc0(), level 3, step 5: unifiedRequestedNumSamples = 5000, unifiedWeightStdVectorAtProc0Only.size() = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 5, after 0.001772 seconds
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 6: result = 0
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 6, after 6e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 7: beginning step 7 of 11
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 7: subNumSamples = 5000, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 7: minModifiedSubNumSamples = 5000, avgModifiedSubNumSamples = 5000, maxModifiedSubNumSamples = 5000
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 7: numberOfPositionsToGuaranteeForNode = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 7: unbalancedLinkControl.unbLinkedChains.size() = 2288
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 2288
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 7, after 7e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 8: beginning step 8 of 11
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 8, after 3e-06 seconds
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: beginning step 9 of 11
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 3, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: after 1.2e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.571429
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 3, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: after 1.1e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: entering loop for assessing rejection rate, with nowAttempt = 2, nowRejectionRate = 0.457143
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 3, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 3, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 3, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 9: after 1.5e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 3, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 0.015625, assessed rejection rate = 0.314286
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 9, after 0.007033 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 2288, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 10: chainIdMax = 2288, numberOfPositions = 5000, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 10: minNumberOfPositions = 5000, avgNumberOfPositions = 5000, maxNumberOfPositions = 5000
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 3, step 10: after 4.7e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 10, after chain generatrion, currLogLikelihoodValues[0] = -1.76423
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 10, after 0.111163 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 3, step 11: beginning step 11 of 11
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 3, step 11, after 6e-06 seconds
In MLSampling<P_V,P_M>::generateSequence(): at end of level 3, sub minLogLike = -9.67559, sub maxLogLike = -1.10024
In MLSampling<P_V,P_M>::generateSequence(): at end of level 3, unified minLogLike = -9.67559, unified maxLogLike = -1.10024
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 4, step 1: beginning step 1 of 11
KEY In MLSampling<P_V,P_M>::generateSequence(), level 4, step 1, currOptions->m_rawChainSize = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 1, after 8e-06 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 2: beginning step 2 of 11
In MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 2, prevLogLikelihoodValues[0] = -1.76423
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 4, step 2, after 0.003661 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 3, failedExponent = 0: beginning step 3 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 3, failedExponent = 0: entering loop for computing next exponent, with nowAttempt = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence_Step(), level 4, step 3, failedExponent = 0, after 0.000539 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 3: copying 'last' level options to current options
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence(), level 4, step 4: beginning step 4 of 11
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 4: unifiedCovMatrix = 1776.45 
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 4, after 0.007549 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 5: beginning step 5 of 11
Entering MLSampling<P_V,P_M>::sampleIndexes_proc0(), level 4, step 5: unifiedRequestedNumSamples = 10000, unifiedWeightStdVectorAtProc0Only.size() = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 5, after 0.002849 seconds
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 4, step 6: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 4, step 6: result = 0
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 6, after 9.1e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 7: beginning step 7 of 11
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 7: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 7: subNumSamples = 10000, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 7: minModifiedSubNumSamples = 10000, avgModifiedSubNumSamples = 10000, maxModifiedSubNumSamples = 10000
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 7: numberOfPositionsToGuaranteeForNode = 10000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 7: unbalancedLinkControl.unbLinkedChains.size() = 3633
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 7: balancedLinkControl.balLinkedChains.size() = 0, unbalancedLinkControl.unbLinkedChains.size() = 3633
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 7, after 7e-05 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 8: beginning step 8 of 11
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 8, after 3e-06 seconds
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: beginning step 9 of 11
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: entering loop for assessing rejection rate, with nowAttempt = 0, nowRejectionRate = 0
In MLSampling<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 4, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 4, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: unbalancedLinkControl.unbLinkedChains.size() = 35
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 35, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 9: chainIdMax = 35, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 9: after 2.3e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: entering loop for assessing rejection rate, with nowAttempt = 1, nowRejectionRate = 0.514286
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::sampleIndexes_proc0(), level 4, step 9: unifiedRequestedNumSamples = 35, unifiedWeightStdVectorAtProc0Only.size() = 5000
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to distribute sampled assessment indexes
Entering MLSampling<P_V,P_M>::decideOnBalancedChains_all(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
In MLSampling<P_V,P_M>::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<P_V,P_M>::decideOnBalancedChains_all(), level 4, step 9: result = 0
Entering MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: indexOfFirstWeight = 0, indexOfLastWeight = 4999
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: subNumSamples = 35, unifiedIndexCountersAtAllProcs.size() = 5000
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: minModifiedSubNumSamples = 35, avgModifiedSubNumSamples = 35, maxModifiedSubNumSamples = 35
KEY In MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: numberOfPositionsToGuaranteeForNode = 35
KEY Leaving MLSampling<P_V,P_M>::prepareUnbLinkedChains_inter0(), level 4, step 9: unbalancedLinkControl.unbLinkedChains.size() = 34
In MLSampling<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: in loop for assessing rejection rate, about to generate assessment chain
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 34, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 9: chainIdMax = 34, numberOfPositions = 35, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 9: minNumberOfPositions = 35, avgNumberOfPositions = 35, maxNumberOfPositions = 35
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 9: after 1.9e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::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<P_V,P_M>::generateSequence_Step09_all(), level 4, step 9: weightSequence.subSequenceSize() = 5000, weightSequence.unifiedSequenceSize() = 5000, currEta = 0.00390625, assessed rejection rate = 0.4
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 9, after 0.006618 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, exited 'do-while(tryExponentEta), failedExponent = 0, failedEta = 0
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 10: beginning step 10 of 11, currLogLikelihoodValues = 0x7fff5c15a050
Entering MLSampling<P_V,P_M>::generateUnbLinkedChains_all(): unbalancedLinkControl.unbLinkedChains.size() = 3633, indexOfFirstWeight = 0
KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 10: chainIdMax = 3633, numberOfPositions = 10000, at Thu Nov 27 21:16:52 2014

KEY In MLSampling<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 10: minNumberOfPositions = 10000, avgNumberOfPositions = 10000, maxNumberOfPositions = 10000
In MLSampling<P_V,P_M>::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<P_V,P_M>::generateUnbLinkedChains_all(), level 4, step 10: after 3.6e-05 seconds in fullComm().Barrier(), at Thu Nov 27 21:16:52 2014

In MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 10, after chain generatrion, currLogLikelihoodValues[0] = -5.2602
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 10, after 0.362871 seconds
In MLSampling<P_V,P_M>::generateSequence(), level 4, step 11: beginning step 11 of 11
In MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 11, before calling currLogLikelihoodValues.unifiedWriteContents(), currLogLikelihoodValues[0] = -5.2602
Entering SequenceOfVectors<V,M>::filter(): initialPos = 0, spacing = 2, subSequenceSize = 10000
Leaving SequenceOfVectors<V,M>::filter(): initialPos = 0, spacing = 2, subSequenceSize = 5000
Entering ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 2, subSequenceSize = 10000
Leaving ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 2, subSequenceSize = 5000
Entering ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 2, subSequenceSize = 10000
Leaving ScalarSequence<V,M>::filter(): initialPos = 0, spacing = 2, subSequenceSize = 5000
Leaving MLSampling<P_V,P_M>::generateSequence_Step(), level 4, step 11, after 0.143284 seconds
In MLSampling<P_V,P_M>::generateSequence(): at end of level 4, sub minLogLike = -12.2807, sub maxLogLike = -3.16228
In MLSampling<P_V,P_M>::generateSequence(): at end of level 4, unified minLogLike = -12.2807, unified maxLogLike = -3.16228
In MLSampling<P_V,P_M>::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<P_V,P_M>::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<P_V,P_M>::generateSequence(), log(evidence) = -7.41918, evidence = 0.000599641, meanLogLikelihood = -4.32658, eig = 3.0926
Leaving MLSampling<P_V,P_M>::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<V,M>::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
