double likelihoodRoutine(
const QUESO::GslVector& paramValues,
const QUESO::GslVector* paramDirection,
const void* functionDataPtr,
QUESO::GslVector* gradVector,
QUESO::GslMatrix* hessianMatrix,
QUESO::GslVector* hessianEffect)
{
likelihoodCounter++;
if (paramDirection ||
functionDataPtr ||
gradVector ||
hessianMatrix ||
hessianEffect) {}; // just to remove compiler warning
double returnValue = 0.;
double x = paramValues[0];
double mean1 = 10.;
double sigma1 = 1.;
double y1 = (x-mean1)*(x-mean1)/(2.*sigma1*sigma1);
double z1 = (1./sigma1/sqrt(2*M_PI))*exp(-y1);
double mean2 = 100.;
double sigma2 = 5.;
double y2 = (x-mean2)*(x-mean2)/(2.*sigma2*sigma2);
double z2 = (1./sigma2/sqrt(2*M_PI))*exp(-y2);
double resultValue = -2*log((z1+2.*z2)/3.);
if (resultValue == INFINITY) {
//std::cerr << "WARNING In likelihoodRoutine"
// << ", fullRank " << paramValues.env().fullRank()
// << ", subEnvironment " << paramValues.env().subId()
// << ", subRank " << paramValues.env().subRank()
// << ", inter0Rank " << paramValues.env().inter0Rank()
// << ": x = " << x
// << ", z1 = " << z1
// << ", z2 = " << z2
// << ", resultValue = " << resultValue
// << std::endl;
resultValue = 1040.;
}
returnValue = -.5*resultValue;
if (paramValues.env().exceptionalCircumstance()) {
if ((paramValues.env().subDisplayFile() ) &&
(paramValues.env().displayVerbosity() > 0)) { // detailed output debug
*paramValues.env().subDisplayFile() << "Leaving likelihood function"
<< ": paramValues = " << paramValues
<< ", returnValue = " << returnValue
<< std::endl;
}
}
return returnValue;
}
void
qoiRoutine(
const QUESO::GslVector& paramValues,
const QUESO::GslVector* paramDirection,
const void* functionDataPtr,
QUESO::GslVector& qoiValues,
QUESO::DistArray<QUESO::GslVector*>* gradVectors,
QUESO::DistArray<QUESO::GslMatrix*>* hessianMatrices,
QUESO::DistArray<QUESO::GslVector*>* hessianEffects)
{
// Logic just to avoid warnings from INTEL compiler
const QUESO::GslVector* aux1 = paramDirection;
if (aux1) {};
QUESO::DistArray<QUESO::GslVector*>* aux2 = gradVectors;
if (aux2) {};
aux2 = hessianEffects;
QUESO::DistArray<QUESO::GslMatrix*>* aux3 = hessianMatrices;
if (aux3) {};
// Just checking: the user, at the application level, expects
// vector 'paramValues' to have size 2 and
// vector 'qoiValues' to have size 1.
UQ_FATAL_TEST_MACRO(paramValues.sizeGlobal() != 2,
QUESO::UQ_UNAVAILABLE_RANK,
"qoiRoutine()",
"paramValues vector does not have size 2");
UQ_FATAL_TEST_MACRO(qoiValues.sizeGlobal() != 1,
QUESO::UQ_UNAVAILABLE_RANK,
"qoiRoutine()",
"qoiValues vector does not have size 1");
// Actual code
//
// This code exemplifies multiple Monte Carlo solvers, each calling this qoi routine.
// In this simple example, only node 0 in each sub-environment does the job even though
// there might be more than one node per sub-environment.
// In a more realistic situation, if the user is asking for multiple nodes per sub-
// environment, then the model code in the qoi routine might really demand more than one
// node. Here we use 'env.subRank()' only. A realistic application might want to use
// either 'env.subComm()' or 'env.subComm().Comm()'.
const QUESO::BaseEnvironment& env = paramValues.env();
if (env.subRank() == 0) {
double coef1 = ((qoiRoutine_DataType *) functionDataPtr)->coef1;
double coef2 = ((qoiRoutine_DataType *) functionDataPtr)->coef2;
qoiValues[0] = (coef1*paramValues[0] + coef2*paramValues[1]);
}
else {
qoiValues[0] = 0.;
}
return;
}
void qoiRoutine(
const QUESO::GslVector& paramValues,
const QUESO::GslVector* paramDirection,
const void* functionDataPtr,
QUESO::GslVector& qoiValues,
QUESO::DistArray<QUESO::GslVector*>* gradVectors,
QUESO::DistArray<QUESO::GslMatrix*>* hessianMatrices,
QUESO::DistArray<QUESO::GslVector*>* hessianEffects)
{
//logic to avoid warnings from intel compiler
const QUESO::GslVector* aux1 = paramDirection;
if (aux1) {};
QUESO::DistArray<QUESO::GslVector*>* aux2 = gradVectors;
if (aux2) {};
aux2 = hessianEffects;
QUESO::DistArray<QUESO::GslMatrix*>* aux3 = hessianMatrices;
if (aux3) {};
//checking size of paramValues and qoiValues
//both should be size 1
UQ_FATAL_TEST_MACRO(paramValues.sizeGlobal() != 1,
QUESO::UQ_UNAVAILABLE_RANK,
"qoiRoutine()",
"paramValues vector does not have size 1");
UQ_FATAL_TEST_MACRO(qoiValues.sizeGlobal() != 1,
QUESO::UQ_UNAVAILABLE_RANK,
"qoiRoutine()",
"qoiValues vector does not have size 1");
//compute qoi
const QUESO::BaseEnvironment& env = paramValues.env();
if(env.subRank() == 0) {
double coef1 = ((qoiRoutine_DataType *) functionDataPtr)->coef1;
qoiValues[0] = coef1*paramValues[0];
}
else {
qoiValues[0] = 0.;
}
return;
}
// The actual (user defined) qoi routine
void qoiRoutine(const QUESO::GslVector& paramValues,
const QUESO::GslVector* paramDirection,
const void* functionDataPtr,
QUESO::GslVector& qoiValues,
QUESO::DistArray<QUESO::GslVector*>* gradVectors,
QUESO::DistArray<QUESO::GslMatrix*>* hessianMatrices,
QUESO::DistArray<QUESO::GslVector*>* hessianEffects)
{
if (paramDirection &&
functionDataPtr &&
gradVectors &&
hessianMatrices &&
hessianEffects) {
// Just to eliminate INTEL compiler warnings
}
double A = paramValues[0];
double E = paramValues[1];
double beta = ((qoiRoutine_Data *) functionDataPtr)->m_beta;
double criticalMass = ((qoiRoutine_Data *) functionDataPtr)->m_criticalMass;
double criticalTime = ((qoiRoutine_Data *) functionDataPtr)->m_criticalTime;
double params[]={A,E,beta};
// integration
const gsl_odeiv_step_type *T = gsl_odeiv_step_rkf45; //rkf45; //gear1;
gsl_odeiv_step *s = gsl_odeiv_step_alloc(T,1);
gsl_odeiv_control *c = gsl_odeiv_control_y_new(1e-6,0.0);
gsl_odeiv_evolve *e = gsl_odeiv_evolve_alloc(1);
gsl_odeiv_system sys = {func, NULL, 1, (void *)params};
double temperature = 0.1;
double h = 1e-3;
double Mass[1];
Mass[0]=1.;
double temperature_old = 0.;
double M_old[1];
M_old[0]=1.;
double crossingTemperature = 0.;
//unsigned int loopSize = 0;
while ((temperature < criticalTime*beta) &&
(Mass[0] > criticalMass )) {
int status = gsl_odeiv_evolve_apply(e, c, s, &sys, &temperature, criticalTime*beta, &h, Mass);
UQ_FATAL_TEST_MACRO((status != GSL_SUCCESS),
paramValues.env().fullRank(),
"qoiRoutine()",
"gsl_odeiv_evolve_apply() failed");
//printf("t = %6.1lf, mass = %10.4lf\n",t,Mass[0]);
//loopSize++;
if (Mass[0] <= criticalMass) {
crossingTemperature = temperature_old + (temperature - temperature_old) * (M_old[0]-criticalMass)/(M_old[0]-Mass[0]);
}
temperature_old=temperature;
M_old[0]=Mass[0];
}
if (criticalMass > 0.) qoiValues[0] = crossingTemperature/beta; // QoI = time to achieve critical mass
if (criticalTime > 0.) qoiValues[0] = Mass[0]; // QoI = mass fraction remaining at critical time
//printf("loopSize = %d\n",loopSize);
if ((paramValues.env().displayVerbosity() >= 3) && (paramValues.env().fullRank() == 0)) {
printf("In qoiRoutine(), A = %g, E = %g, beta = %.3lf, criticalTime = %.3lf, criticalMass = %.3lf: qoi = %lf.\n",A,E,beta,criticalTime,criticalMass,qoiValues[0]);
}
gsl_odeiv_evolve_free (e);
gsl_odeiv_control_free(c);
gsl_odeiv_step_free (s);
return;
}
double
Likelihood<V, M>::lnValue(const QUESO::GslVector & paramValues) const
{
double resultValue = 0.;
m_env.subComm().Barrier();
//env.syncPrintDebugMsg("Entering likelihoodRoutine()",1,env.fullComm());
// Compute likelihood for scenario 1
double betaTest = m_beta1;
if (betaTest) {
double A = paramValues[0];
double E = paramValues[1];
double beta = m_beta1;
double variance = m_variance1;
const std::vector<double>& Te = m_Te1;
const std::vector<double>& Me = m_Me1;
std::vector<double> Mt(Me.size(),0.);
double params[]={A,E,beta};
// integration
const gsl_odeiv_step_type *T = gsl_odeiv_step_rkf45; //rkf45; //gear1;
gsl_odeiv_step *s = gsl_odeiv_step_alloc(T,1);
gsl_odeiv_control *c = gsl_odeiv_control_y_new(1e-6,0.0);
gsl_odeiv_evolve *e = gsl_odeiv_evolve_alloc(1);
gsl_odeiv_system sys = {func, NULL, 1, (void *)params};
double t = 0.1, t_final = 1900.;
double h = 1e-3;
double Mass[1];
Mass[0]=1.;
unsigned int i = 0;
double t_old = 0.;
double M_old[1];
M_old[0]=1.;
double misfit=0.;
//unsigned int loopSize = 0;
while ((t < t_final) && (i < Me.size())) {
int status = gsl_odeiv_evolve_apply(e, c, s, &sys, &t, t_final, &h, Mass);
UQ_FATAL_TEST_MACRO((status != GSL_SUCCESS),
paramValues.env().fullRank(),
"likelihoodRoutine()",
"gsl_odeiv_evolve_apply() failed");
//printf("t = %6.1lf, mass = %10.4lf\n",t,Mass[0]);
//loopSize++;
while ( (i < Me.size()) && (t_old <= Te[i]) && (Te[i] <= t) ) {
Mt[i] = (Te[i]-t_old)*(Mass[0]-M_old[0])/(t-t_old) + M_old[0];
misfit += (Me[i]-Mt[i])*(Me[i]-Mt[i]);
//printf("%i %lf %lf %lf %lf\n",i,Te[i],Me[i],Mt[i],misfit);
i++;
}
t_old=t;
M_old[0]=Mass[0];
}
resultValue += misfit/variance;
//printf("loopSize = %d\n",loopSize);
if ((paramValues.env().displayVerbosity() >= 10) && (paramValues.env().fullRank() == 0)) {
printf("In likelihoodRoutine(), A = %g, E = %g, beta = %.3lf: misfit = %lf, likelihood = %lf.\n",A,E,beta,misfit,resultValue);
}
gsl_odeiv_evolve_free (e);
gsl_odeiv_control_free(c);
gsl_odeiv_step_free (s);
}
// Compute likelihood for scenario 2
betaTest = m_beta2;
if (betaTest > 0.) {
double A = paramValues[0];
double E = paramValues[1];
double beta = m_beta2;
double variance = m_variance2;
const std::vector<double>& Te = m_Te2;
const std::vector<double>& Me = m_Me2;
std::vector<double> Mt(Me.size(),0.);
double params[]={A,E,beta};
// integration
const gsl_odeiv_step_type *T = gsl_odeiv_step_rkf45; //rkf45; //gear1;
gsl_odeiv_step *s = gsl_odeiv_step_alloc(T,1);
gsl_odeiv_control *c = gsl_odeiv_control_y_new(1e-6,0.0);
gsl_odeiv_evolve *e = gsl_odeiv_evolve_alloc(1);
gsl_odeiv_system sys = {func, NULL, 1, (void *)params};
double t = 0.1, t_final = 1900.;
double h = 1e-3;
double Mass[1];
Mass[0]=1.;
unsigned int i = 0;
double t_old = 0.;
double M_old[1];
M_old[0]=1.;
double misfit=0.;
//unsigned int loopSize = 0;
while ((t < t_final) && (i < Me.size())) {
int status = gsl_odeiv_evolve_apply(e, c, s, &sys, &t, t_final, &h, Mass);
UQ_FATAL_TEST_MACRO((status != GSL_SUCCESS),
paramValues.env().fullRank(),
"likelihoodRoutine()",
"gsl_odeiv_evolve_apply() failed");
//printf("t = %6.1lf, mass = %10.4lf\n",t,Mass[0]);
//loopSize++;
while ( (i < Me.size()) && (t_old <= Te[i]) && (Te[i] <= t) ) {
Mt[i] = (Te[i]-t_old)*(Mass[0]-M_old[0])/(t-t_old) + M_old[0];
misfit += (Me[i]-Mt[i])*(Me[i]-Mt[i]);
//printf("%i %lf %lf %lf %lf\n",i,Te[i],Me[i],Mt[i],misfit);
i++;
}
t_old=t;
M_old[0]=Mass[0];
}
resultValue += misfit/variance;
//printf("loopSize = %d\n",loopSize);
if ((paramValues.env().displayVerbosity() >= 10) && (paramValues.env().fullRank() == 0)) {
printf("In likelihoodRoutine(), A = %g, E = %g, beta = %.3lf: misfit = %lf, likelihood = %lf.\n",A,E,beta,misfit,resultValue);
}
gsl_odeiv_evolve_free (e);
gsl_odeiv_control_free(c);
gsl_odeiv_step_free (s);
}
// Compute likelihood for scenario 3
betaTest = m_beta3;
if (betaTest > 0.) {
double A = paramValues[0];
double E = paramValues[1];
double beta = m_beta3;
double variance = m_variance3;
const std::vector<double>& Te = m_Te3;
const std::vector<double>& Me = m_Me3;
std::vector<double> Mt(Me.size(),0.);
double params[]={A,E,beta};
// integration
const gsl_odeiv_step_type *T = gsl_odeiv_step_rkf45; //rkf45; //gear1;
gsl_odeiv_step *s = gsl_odeiv_step_alloc(T,1);
gsl_odeiv_control *c = gsl_odeiv_control_y_new(1e-6,0.0);
gsl_odeiv_evolve *e = gsl_odeiv_evolve_alloc(1);
gsl_odeiv_system sys = {func, NULL, 1, (void *)params};
double t = 0.1, t_final = 1900.;
double h = 1e-3;
double Mass[1];
Mass[0]=1.;
unsigned int i = 0;
double t_old = 0.;
double M_old[1];
M_old[0]=1.;
double misfit=0.;
//unsigned int loopSize = 0;
while ((t < t_final) && (i < Me.size())) {
int status = gsl_odeiv_evolve_apply(e, c, s, &sys, &t, t_final, &h, Mass);
UQ_FATAL_TEST_MACRO((status != GSL_SUCCESS),
paramValues.env().fullRank(),
"likelihoodRoutine()",
"gsl_odeiv_evolve_apply() failed");
//printf("t = %6.1lf, mass = %10.4lf\n",t,Mass[0]);
//loopSize++;
while ( (i < Me.size()) && (t_old <= Te[i]) && (Te[i] <= t) ) {
Mt[i] = (Te[i]-t_old)*(Mass[0]-M_old[0])/(t-t_old) + M_old[0];
misfit += (Me[i]-Mt[i])*(Me[i]-Mt[i]);
//printf("%i %lf %lf %lf %lf\n",i,Te[i],Me[i],Mt[i],misfit);
i++;
}
t_old=t;
M_old[0]=Mass[0];
}
resultValue += misfit/variance;
//printf("loopSize = %d\n",loopSize);
if ((paramValues.env().displayVerbosity() >= 10) && (paramValues.env().fullRank() == 0)) {
printf("In likelihoodRoutine(), A = %g, E = %g, beta = %.3lf: misfit = %lf, likelihood = %lf.\n",A,E,beta,misfit,resultValue);
}
gsl_odeiv_evolve_free (e);
gsl_odeiv_control_free(c);
gsl_odeiv_step_free (s);
}
m_env.subComm().Barrier();
//env.syncPrintDebugMsg("Leaving likelihoodRoutine()",1,env.fullComm());
return -.5*resultValue;
}
//********************************************************
// The 'unified comparison stage' of the driving routine "uqAppl()"
//********************************************************
void
uqAppl_UnifiedComparisonStage(QUESO::ValidationCycle<QUESO::GslVector,QUESO::GslMatrix,QUESO::GslVector,QUESO::GslMatrix>& cycle)
{
if (cycle.calFP().computeSolutionFlag() &&
cycle.valFP().computeSolutionFlag()) {
#ifdef QUESO_COMPUTES_EXTRA_POST_PROCESSING_STATISTICS
QUESO::GslVector cdfDistancesVec(cycle.calFP().qoiRv().imageSet().vectorSpace().zeroVector());
QUESO::GslVector epsilonVec (cycle.calFP().qoiRv().imageSet().vectorSpace().zeroVector());
// Epsilon = 0.02
epsilonVec.cwSet(0.02);
horizontalDistances(cycle.calFP().qoiRv_unifiedCdf(),
cycle.valFP().qoiRv_unifiedCdf(),
epsilonVec,
cdfDistancesVec);
if (cycle.env().fullRank() == 0) {
std::cout << "For epsilonVec = " << epsilonVec
<< ", unifiedCdfDistancesVec = " << cdfDistancesVec
<< std::endl;
}
// Test independence of 'distance' w.r.t. order of cdfs
horizontalDistances(cycle.valFP().qoiRv_unifiedCdf(),
cycle.calFP().qoiRv_unifiedCdf(),
epsilonVec,
cdfDistancesVec);
if (cycle.env().fullRank() == 0) {
std::cout << "For epsilonVec = " << epsilonVec
<< ", unifiedCdfDistancesVec (switched order of cdfs) = " << cdfDistancesVec
<< std::endl;
}
// Epsilon = 0.04
epsilonVec.cwSet(0.04);
horizontalDistances(cycle.calFP().qoiRv_unifiedCdf(),
cycle.valFP().qoiRv_unifiedCdf(),
epsilonVec,
cdfDistancesVec);
if (cycle.env().fullRank() == 0) {
std::cout << "For epsilonVec = " << epsilonVec
<< ", unifiedCdfDistancesVec = " << cdfDistancesVec
<< std::endl;
}
// Epsilon = 0.06
epsilonVec.cwSet(0.06);
horizontalDistances(cycle.calFP().qoiRv_unifiedCdf(),
cycle.valFP().qoiRv_unifiedCdf(),
epsilonVec,
cdfDistancesVec);
if (cycle.env().fullRank() == 0) {
std::cout << "For epsilonVec = " << epsilonVec
<< ", unifiedCdfDistancesVec = " << cdfDistancesVec
<< std::endl;
}
// Epsilon = 0.08
epsilonVec.cwSet(0.08);
horizontalDistances(cycle.calFP().qoiRv_unifiedCdf(),
cycle.valFP().qoiRv_unifiedCdf(),
epsilonVec,
cdfDistancesVec);
if (cycle.env().fullRank() == 0) {
std::cout << "For epsilonVec = " << epsilonVec
<< ", unifiedCdfDistancesVec = " << cdfDistancesVec
<< std::endl;
}
// Epsilon = 0.10
epsilonVec.cwSet(0.10);
horizontalDistances(cycle.calFP().qoiRv_unifiedCdf(),
cycle.valFP().qoiRv_unifiedCdf(),
epsilonVec,
cdfDistancesVec);
if (cycle.env().fullRank() == 0) {
std::cout << "For epsilonVec = " << epsilonVec
<< ", unifiedCdfDistancesVec = " << cdfDistancesVec
<< std::endl;
}
#endif
}
return;
}
