int main(int argc, char ** argv) {
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"space_", 3, NULL);
QUESO::GslVector minBound(paramSpace.zeroVector());
minBound[0] = -10.0;
minBound[1] = -10.0;
minBound[2] = -10.0;
QUESO::GslVector maxBound(paramSpace.zeroVector());
maxBound[0] = 10.0;
maxBound[1] = 10.0;
maxBound[2] = 10.0;
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> domain("", paramSpace,
minBound, maxBound);
ObjectiveFunction<QUESO::GslVector, QUESO::GslMatrix> objectiveFunction(
"", domain);
QUESO::GslVector initialPoint(paramSpace.zeroVector());
initialPoint[0] = 9.0;
initialPoint[1] = -9.0;
initialPoint[1] = -1.0;
QUESO::GslOptimizer optimizer(objectiveFunction);
double tol = 1.0e-10;
optimizer.setTolerance(tol);
optimizer.set_solver_type(QUESO::GslOptimizer::STEEPEST_DESCENT);
QUESO::OptimizerMonitor monitor(env);
monitor.set_display_output(true,true);
std::cout << "Solving with Steepest Decent" << std::endl;
optimizer.minimize(&monitor);
if (std::abs( optimizer.minimizer()[0] - 1.0) > tol) {
std::cerr << "GslOptimize failed. Found minimizer at: " << optimizer.minimizer()[0]
<< std::endl;
std::cerr << "Actual minimizer is 1.0" << std::endl;
queso_error();
}
std::string nm = "nelder_mead2";
optimizer.set_solver_type(nm);
monitor.reset();
monitor.set_display_output(true,true);
std::cout << std::endl << "Solving with Nelder Mead" << std::endl;
optimizer.minimize(&monitor);
monitor.print(std::cout,false);
return 0;
}
/* Separated this out into a function because we want
the destructor for paramSpace to be called before
we delete env in main. */
int actualChecking(const uqFullEnvironmentClass* env)
{
int return_flag = 0;
const double fp_tol = 1.0e-14;
// Instantiate the parameter space
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass>
paramSpace( (*env), "param_", 2, NULL);
// Populate Matrix.
uqGslMatrixClass* Matrix = paramSpace.newMatrix();
(*Matrix)(0,0) = 4.; (*Matrix)(0,1) = 3.;
(*Matrix)(1,0) = 5.; (*Matrix)(1,1) = 7.;
// Conduct test.
uqGslMatrixClass Result( (*Matrix) );
Matrix->invertMultiply( (*Matrix), Result );
if( fabs(Result(0,0) - 1.0) > fp_tol ||
fabs(Result(1,1) - 1.0) > fp_tol ||
fabs(Result(1,0)) > fp_tol ||
fabs(Result(0,1)) > fp_tol )
{
return_flag = 1;
}
// Deallocate pointers we created.
delete Matrix;
return return_flag;
}
/* Separated this out into a function because we want
the destructor for paramSpace to be called before
we delete env in main. */
int actualChecking(const QUESO::FullEnvironment* env)
{
int return_flag = 0;
// Instantiate the parameter space
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix>
paramSpace( (*env), "param_", 2, NULL);
// Instantiate the parameter domain
QUESO::GslVector Vector( paramSpace.zeroVector() );
Vector[0] = -4.;
Vector[1] = 3.;
// Conduct tests.
double value;
int index;
Vector.getMinValueAndIndex( value, index );
if( index != 0 || value != Vector[0] )
{
return_flag = 1;
}
Vector.getMaxValueAndIndex( value, index );
if( index != 1 || value != Vector[1] )
{
return_flag = 1;
}
return return_flag;
}
/* Separated this out into a function because we want
the destructor for paramSpace to be called before
we delete env in main. */
int actualChecking(const uqFullEnvironmentClass* env)
{
int return_flag = 0;
// Instantiate the parameter space
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass>
paramSpace( (*env), "param_", 2, NULL);
// Instantiate the parameter domain
uqGslVectorClass Vector( paramSpace.zeroVector() );
Vector[0] = -4.;
Vector[1] = 3.;
// Conduct tests.
double value;
int index;
Vector.getMinValueAndIndex( value, index );
if( index != 0 || value != Vector[0] )
{
return_flag = 1;
}
Vector.getMaxValueAndIndex( value, index );
if( index != 1 || value != Vector[1] )
{
return_flag = 1;
}
return return_flag;
}
int main(int argc, char ** argv)
{
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL);
#else
QUESO::FullEnvironment env("", "", NULL);
#endif
QUESO::VectorSpace<> paramSpace1(env, "param1_", 4, NULL);
QUESO::VectorSpace<> paramSpace2(env, "param2_", 2, NULL);
QUESO::GslVector paramMins1(paramSpace1.zeroVector());
paramMins1[0] = 1e2;
paramMins1[1] = -1e5;
paramMins1[2] = 4e-3;
paramMins1[3] = 1;
QUESO::GslVector paramMaxs1(paramSpace1.zeroVector());
paramMaxs1[0] = 2e2;
paramMaxs1[1] = 1e5;
paramMaxs1[2] = 6e-3;
paramMaxs1[3] = 11;
QUESO::BoxSubset<> paramDomain1("", paramSpace1, paramMins1, paramMaxs1);
QUESO::GslVector paramMins2(paramSpace2.zeroVector());
paramMins2[0] = -1e5;
paramMins2[1] = 2e-3;
QUESO::GslVector paramMaxs2(paramSpace2.zeroVector());
paramMaxs2[0] = 1e5;
paramMaxs2[1] = 4e-3;
QUESO::BoxSubset<> paramDomain2("", paramSpace2, paramMins2, paramMaxs2);
QUESO::VectorSpace<> paramSpace(env, "param_", 6, NULL);
QUESO::ConcatenationSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomain("",paramSpace,paramDomain1,paramDomain2);
QUESO::GslVector centroid(paramSpace.zeroVector());
paramDomain.centroid(centroid);
const char *msg = "ConcatenationSubset centroid is incorrect";
queso_require_less_equal_msg(std::abs(centroid[0]-1.5e2), TOL, msg);
queso_require_less_equal_msg(std::abs(centroid[1]), TOL, msg);
queso_require_less_equal_msg(std::abs(centroid[2]-5e-3), TOL, msg);
queso_require_less_equal_msg(std::abs(centroid[3]-6), TOL, msg);
queso_require_less_equal_msg(std::abs(centroid[4]), TOL, msg);
queso_require_less_equal_msg(std::abs(centroid[5]-3e-3), TOL, msg);
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
int main(int argc, char **argv) {
MPI_Init(&argc, &argv);
QUESO::EnvOptionsValues options;
options.m_numSubEnvironments = 1;
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", &options);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 3, NULL);
// Example RHS
QUESO::GslVector b(paramSpace.zeroVector());
b[0] = 1.0;
b[1] = 2.0;
b[2] = 3.0;
// Set up block sizes for observation covariance matrix
std::vector<unsigned int> blockSizes(2);
blockSizes[0] = 1; // First block is 1x1 (scalar)
blockSizes[1] = 2; // Second block is 2x2
// Set up block (identity) matrix with specified block sizes
QUESO::GslBlockMatrix covariance(blockSizes, b, 1.0);
// The matrix [[1, 0, 0], [0, 1, 2], [0, 2, 8]]
// has inverse 0.25 * [[1, 0, 0], [0, 2, -0.5], [0, -0.5, 0.25]]
covariance.getBlock(0)(0, 0) = 1.0;
covariance.getBlock(1)(0, 0) = 1.0;
covariance.getBlock(1)(0, 1) = 2.0;
covariance.getBlock(1)(1, 0) = 2.0;
covariance.getBlock(1)(1, 1) = 8.0;
// Compute solution
QUESO::GslVector x(paramSpace.zeroVector());
covariance.invertMultiply(b, x);
// This is the analytical solution
QUESO::GslVector sol(paramSpace.zeroVector());
sol[0] = 1.0;
sol[1] = 2.5;
sol[2] = -0.25;
// So if the solve worked, this sucker should be the zero vector
sol -= x;
// So its norm should be zero
if (sol.norm2() > TOL) {
std::cerr << "Computed solution:" << std::endl;
std::cerr << b << std::endl;
std::cerr << "Actual solution:" << std::endl;
std::cerr << sol << std::endl;
queso_error_msg("TEST: GslBlockMatrix::invertMultiply failed.");
}
MPI_Finalize();
return 0;
}
void compute(const QUESO::FullEnvironment& env) {
// Step 1 of 6: Instantiate the parameter space
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix>
paramSpace(env, "param_", 2, NULL);
// Step 2 of 6: Instantiate the parameter domain
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(-INFINITY);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet( INFINITY);
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomain("param_",paramSpace,paramMins,paramMaxs);
// Step 3 of 6: Instantiate the qoi space
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix>
qoiSpace(env, "qoi_", 1, NULL);
// Step 4 of 6: Instantiate the qoi function object
qoiRoutine_DataType qoiRoutine_Data;
qoiRoutine_Data.coef1 = 1.;
qoiRoutine_Data.coef2 = 1.;
QUESO::GenericVectorFunction<QUESO::GslVector,QUESO::GslMatrix,
QUESO::GslVector,QUESO::GslMatrix>
qoiFunctionObj("qoi_",
paramDomain,
qoiSpace,
qoiRoutine,
(void *) &qoiRoutine_Data);
// Step 5 of 6: Instantiate the forward problem
// Parameters are Gaussian RV
QUESO::GslVector meanVector( paramSpace.zeroVector() );
meanVector[0] = -1;
meanVector[1] = 2;
QUESO::GslMatrix covMatrix = QUESO::GslMatrix(paramSpace.zeroVector());
covMatrix(0,0) = 4.;
covMatrix(0,1) = 0.;
covMatrix(1,0) = 0.;
covMatrix(1,1) = 1.;
QUESO::GaussianVectorRV<QUESO::GslVector,QUESO::GslMatrix>
paramRv("param_",paramDomain,meanVector,covMatrix);
QUESO::GenericVectorRV<QUESO::GslVector,QUESO::GslMatrix>
qoiRv("qoi_", qoiSpace);
QUESO::StatisticalForwardProblem<QUESO::GslVector,QUESO::GslMatrix,
QUESO::GslVector,QUESO::GslMatrix>
fp("", NULL, paramRv, qoiFunctionObj, qoiRv);
// Step 6 of 6: Solve the forward problem
fp.solveWithMonteCarlo(NULL);
return;
}
void compute(const QUESO::FullEnvironment& env) {
// Step 1 of 5: Instantiate the parameter space
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix>
paramSpace(env, "param_", 2, NULL);
// Step 2 of 5: Instantiate the parameter domain
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(-INFINITY);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet( INFINITY);
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomain("param_",paramSpace,paramMins,paramMaxs);
// Step 3 of 5: Instantiate the likelihood function object
QUESO::GslVector meanVector(paramSpace.zeroVector());
meanVector[0] = -1;
meanVector[1] = 2;
QUESO::GslMatrix covMatrix(paramSpace.zeroVector());
covMatrix(0,0) = 4.; covMatrix(0,1) = 0.;
covMatrix(1,0) = 0.; covMatrix(1,1) = 1.;
likelihoodRoutine_DataType likelihoodRoutine_Data;
likelihoodRoutine_Data.meanVector = &meanVector;
likelihoodRoutine_Data.covMatrix = &covMatrix;
QUESO::GenericScalarFunction<QUESO::GslVector,QUESO::GslMatrix>
likelihoodFunctionObj("like_",
paramDomain,
likelihoodRoutine,
(void *) &likelihoodRoutine_Data,
true); // routine computes [ln(function)]
// Step 4 of 5: Instantiate the inverse problem
QUESO::UniformVectorRV<QUESO::GslVector,QUESO::GslMatrix>
priorRv("prior_", paramDomain);
QUESO::GenericVectorRV<QUESO::GslVector,QUESO::GslMatrix>
postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<QUESO::GslVector,QUESO::GslMatrix>
ip("", NULL, priorRv, likelihoodFunctionObj, postRv);
// Step 5 of 5: Solve the inverse problem
QUESO::GslVector paramInitials(paramSpace.zeroVector());
paramInitials[0] = 0.1;
paramInitials[1] = -1.4;
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
proposalCovMatrix(0,0) = 8.; proposalCovMatrix(0,1) = 4.;
proposalCovMatrix(1,0) = 4.; proposalCovMatrix(1,1) = 16.;
ip.solveWithBayesMetropolisHastings(NULL,paramInitials, &proposalCovMatrix);
return;
}
int main(int argc, char ** argv) {
MPI_Init(&argc, &argv);
// Step 0 of 5: Set up environment
QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL);
// Step 1 of 5: Instantiate the parameter space
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 1, NULL);
// Step 2 of 5: Set up the prior
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(min_val);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(max_val);
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> paramDomain("param_",
paramSpace, paramMins, paramMaxs);
// Uniform prior here. Could be a different prior.
QUESO::UniformVectorRV<QUESO::GslVector, QUESO::GslMatrix> priorRv("prior_",
paramDomain);
// Step 3 of 5: Set up the likelihood using the class above
Likelihood<QUESO::GslVector, QUESO::GslMatrix> lhood("llhd_", paramDomain);
// Step 4 of 5: Instantiate the inverse problem
QUESO::GenericVectorRV<QUESO::GslVector, QUESO::GslMatrix>
postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<QUESO::GslVector, QUESO::GslMatrix>
ip("", NULL, priorRv, lhood, postRv);
// Step 5 of 5: Solve the inverse problem
QUESO::GslVector paramInitials(paramSpace.zeroVector());
// Initial condition of the chain
paramInitials[0] = 0.0;
paramInitials[1] = 0.0;
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
for (unsigned int i = 0; i < 2; i++) {
// Might need to tweak this
proposalCovMatrix(i, i) = 0.1;
}
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
MPI_Finalize();
return 0;
}
int main(int argc, char ** argv) {
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"space_", 1, NULL);
QUESO::GslVector minBound(paramSpace.zeroVector());
minBound[0] = -10.0;
QUESO::GslVector maxBound(paramSpace.zeroVector());
maxBound[0] = 10.0;
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> domain("", paramSpace,
minBound, maxBound);
QUESO::UniformVectorRV<QUESO::GslVector, QUESO::GslMatrix> prior("", domain);
Likelihood<QUESO::GslVector, QUESO::GslMatrix> likelihood("", domain);
QUESO::GenericVectorRV<QUESO::GslVector, QUESO::GslMatrix> posterior("",
domain);
QUESO::StatisticalInverseProblem<QUESO::GslVector, QUESO::GslMatrix> ip("",
NULL, prior, likelihood, posterior);
QUESO::GslVector initialValues(paramSpace.zeroVector());
initialValues[0] = 9.0;
QUESO::GslMatrix proposalCovarianceMatrix(paramSpace.zeroVector());
proposalCovarianceMatrix(0, 0) = 1.0;
ip.seedWithMAPEstimator();
ip.solveWithBayesMetropolisHastings(NULL, initialValues,
&proposalCovarianceMatrix);
// The first sample should be the seed
QUESO::GslVector first_sample(paramSpace.zeroVector());
posterior.realizer().realization(first_sample);
// Looser tolerance for the derivative calculated by using a finite
// difference
if (std::abs(first_sample[0]) > 1e-5) {
std::cerr << "seedWithMAPEstimator failed. Seed was: " << first_sample[0]
<< std::endl;
std::cerr << "Actual seed should be 0.0" << std::endl;
queso_error();
}
return 0;
}
int main(int argc, char ** argv)
{
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL);
#else
QUESO::FullEnvironment env("", "", NULL);
#endif
QUESO::VectorSpace<> paramSpace(env, "param_", 1, NULL);
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(0.0);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(1.0);
QUESO::BoxSubset<> paramDomain("param_", paramSpace, paramMins, paramMaxs);
// We should test the other cases of alpha and beta
QUESO::GslVector alpha(paramSpace.zeroVector());
alpha[0] = 2.0;
QUESO::GslVector beta(paramSpace.zeroVector());
beta[0] = 3.0;
QUESO::BetaJointPdf<> pdf("", paramDomain, alpha, beta);
QUESO::GslVector mean(paramSpace.zeroVector());
pdf.distributionMean(mean);
const char *msg = "BetaJointPdf mean is incorrect";
double real_mean = alpha[0] / (alpha[0] + beta[0]);
queso_require_less_equal_msg(std::abs(mean[0]-real_mean), TOL, msg);
QUESO::GslMatrix var(paramSpace.zeroVector());
pdf.distributionVariance(var);
const char *msgv = "BetaJointPdf variance is incorrect";
double real_var = alpha[0] * beta[0] / (alpha[0] + beta[0]) /
(alpha[0] + beta[0]) / (alpha[0] + beta[0] + 1);
queso_require_less_equal_msg(std::abs(var(0,0)-real_var), TOL, msgv);
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
int main(int argc, char ** argv)
{
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL);
#else
QUESO::FullEnvironment env("", "", NULL);
#endif
QUESO::VectorSpace<> paramSpace(env, "param_", 2, NULL);
QUESO::GslVector lawexp(paramSpace.zeroVector());
lawexp[0] = 2.4;
lawexp[1] = 0.4;
QUESO::GslVector lawvar(paramSpace.zeroVector());
lawvar[0] = 1.2;
lawvar[1] = 0.9;
QUESO::LogNormalJointPdf<> pdf("", paramSpace, lawexp, lawvar);
QUESO::GslVector mean(paramSpace.zeroVector());
pdf.distributionMean(mean);
double realmean0 = std::exp(lawexp[0] + lawvar[0]/2);
double realmean1 = std::exp(lawexp[1] + lawvar[1]/2);
const char *msg = "LogNormalJointPdf mean is incorrect";
queso_require_less_equal_msg(std::abs(mean[0]-realmean0), TOL, msg);
queso_require_less_equal_msg(std::abs(mean[1]-realmean1), TOL, msg);
QUESO::GslMatrix var(paramSpace.zeroVector());
pdf.distributionVariance(var);
double realvar0 = (std::exp(lawvar[0])-1) * std::exp(2*lawexp[0] + lawvar[0]);
double realvar1 = (std::exp(lawvar[1])-1) * std::exp(2*lawexp[1] + lawvar[1]);
const char *msgv = "LogNormalJointPdf variance is incorrect";
queso_require_less_equal_msg(std::abs(var(0,0)-realvar0), TOL, msgv);
queso_require_less_equal_msg(std::abs(var(0,1)), TOL, msgv);
queso_require_less_equal_msg(std::abs(var(1,0)), TOL, msgv);
queso_require_less_equal_msg(std::abs(var(1,1)-realvar1), TOL, msgv);
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
void compute(const QUESO::FullEnvironment& env) {
//step 1: instatiate parameter space
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix>
paramSpace(env, "param_", 1, NULL);
//step 2: instantiate the parameter domain
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(0.001);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(100.); //TODO: this is not working with gsl sampling right now in FP
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomain("param_", paramSpace, paramMins, paramMaxs);
//step 3: instantiate the qoi space
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix>
qoiSpace(env, "qoi_", 1, NULL);
//step 4: instantiate the qoi function object
qoiRoutine_DataType qoiRoutine_Data;
qoiRoutine_Data.coef1 = 1.;
QUESO::GenericVectorFunction<QUESO::GslVector,QUESO::GslMatrix,
QUESO::GslVector,QUESO::GslMatrix>
qoiFunctionObj("qoi_",
paramDomain,
qoiSpace,
qoiRoutine,
(void *) &qoiRoutine_Data);
//step 5: instantiate the forward problem
//parameter is Jeffreys RV
QUESO::JeffreysVectorRV<QUESO::GslVector,QUESO::GslMatrix>
paramRv("param_", paramDomain);
QUESO::GenericVectorRV<QUESO::GslVector,QUESO::GslMatrix>
qoiRv("qoi_",qoiSpace);
QUESO::StatisticalForwardProblem<QUESO::GslVector,QUESO::GslMatrix,
QUESO::GslVector,QUESO::GslMatrix>
fp("", NULL, paramRv, qoiFunctionObj, qoiRv);
//step 6: solve the forward problem
fp.solveWithMonteCarlo(NULL);
}
int main(int argc, char ** argv) {
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL);
QUESO::VectorSpace<> paramSpace(env, "param_", 1, NULL);
QUESO::GslVector paramMins(paramSpace.zeroVector());
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
double min_val = 0.0;
double max_val = 1.0;
paramMins.cwSet(min_val);
paramMaxs.cwSet(max_val);
QUESO::BoxSubset<> paramDomain("param_", paramSpace, paramMins, paramMaxs);
QUESO::UniformVectorRV<> priorRv("prior_", paramDomain);
Likelihood<> lhood("llhd_", paramDomain);
QUESO::GenericVectorRV<> postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<> ip("", NULL, priorRv, lhood, postRv);
QUESO::GslVector paramInitials(paramSpace.zeroVector());
paramInitials[0] = 0.0;
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
proposalCovMatrix(0, 0) = 0.1;
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
MPI_Finalize();
return 0;
}
void solveSip(const uqFullEnvironmentClass& env)
{
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Entering solveSip()..."
<< std::endl;
}
////////////////////////////////////////////////////////
// Step 1 of 5: Instantiate the parameter space
////////////////////////////////////////////////////////
unsigned int p = 2;
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass> paramSpace(env, "param_", p, NULL);
uqGslVectorClass aVec(paramSpace.zeroVector());
aVec[0] = 2.;
aVec[1] = 5.;
uqGslVectorClass xGiven(paramSpace.zeroVector());
xGiven[0] = -1.;
xGiven[1] = 7.;
////////////////////////////////////////////////////////
// Step 2 of 5: Instantiate the parameter domain
////////////////////////////////////////////////////////
//uqGslVectorClass paramMins (paramSpace.zeroVector());
//uqGslVectorClass paramMaxs (paramSpace.zeroVector());
//paramMins [0] = -1.e+16;
//paramMaxs [0] = 1.e+16;
//paramMins [1] = -1.e+16;
//paramMaxs [1] = 1.e+16;
//uqBoxSubsetClass<uqGslVectorClass,uqGslMatrixClass> paramDomain("param_",paramSpace,paramMins,paramMaxs);
uqVectorSetClass<uqGslVectorClass,uqGslMatrixClass>* paramDomain = ¶mSpace;
////////////////////////////////////////////////////////
// Step 3 of 5: Instantiate the likelihood function object
////////////////////////////////////////////////////////
unsigned int n = 5;
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass> dataSpace(env, "data_", n, NULL);
uqGslVectorClass yMeanVec(dataSpace.zeroVector());
double tmp = scalarProduct(aVec,xGiven);
for (unsigned int i = 0; i < n; ++i) {
yMeanVec[i] = tmp;
}
double sigmaEps = 2.1;
uqGslMatrixClass yCovMat(dataSpace.zeroVector());
tmp = sigmaEps*sigmaEps;
for (unsigned int i = 0; i < n; ++i) {
yCovMat(i,i) = tmp;
}
uqGslVectorClass ySamples(dataSpace.zeroVector());
uqGaussianVectorRVClass<uqGslVectorClass,uqGslMatrixClass> yRv("y_", dataSpace, yMeanVec, yCovMat);
yRv.realizer().realization(ySamples);
double ySampleMean = 0.;
for (unsigned int i = 0; i < n; ++i) {
ySampleMean += ySamples[i];
}
ySampleMean /= ((double) n);
struct likelihoodDataStruct likelihoodData;
likelihoodData.aVec = &aVec;
likelihoodData.sigmaEps = sigmaEps;
likelihoodData.ySamples = &ySamples;
uqGenericScalarFunctionClass<uqGslVectorClass,uqGslMatrixClass>
likelihoodFunctionObj("like_",
*paramDomain,
likelihoodRoutine,
(void *) &likelihoodData,
true); // routine computes [ln(function)]
////////////////////////////////////////////////////////
// Step 4 of 5: Instantiate the inverse problem
////////////////////////////////////////////////////////
uqGslVectorClass xPriorMeanVec(paramSpace.zeroVector());
xPriorMeanVec[0] = 0.;
xPriorMeanVec[1] = 0.;
uqGslMatrixClass sigma0Mat(paramSpace.zeroVector());
sigma0Mat(0,0) = 1.e-3;
sigma0Mat(0,1) = 0.;
sigma0Mat(1,0) = 0.;
sigma0Mat(1,1) = 1.e-3;
uqGslMatrixClass sigma0MatInverse(paramSpace.zeroVector());
sigma0MatInverse = sigma0Mat.inverse();
uqGaussianVectorRVClass<uqGslVectorClass,uqGslMatrixClass> priorRv("prior_", *paramDomain, xPriorMeanVec, sigma0MatInverse);
uqGenericVectorRVClass <uqGslVectorClass,uqGslMatrixClass> postRv ("post_", paramSpace);
uqStatisticalInverseProblemClass<uqGslVectorClass,uqGslMatrixClass> sip("sip_", NULL, priorRv, likelihoodFunctionObj, postRv);
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "In solveSip():"
<< "\n p = " << p
<< "\n xGiven = " << xGiven
<< "\n sigma0Mat = " << sigma0Mat
<< "\n sigma0MatInverse = " << sigma0MatInverse
<< "\n aVec = " << aVec
<< "\n n = " << n
<< "\n sigmaEps = " << sigmaEps
<< "\n yMeanVec = " << yMeanVec
<< "\n yCovMat = " << yCovMat
<< "\n ySamples = " << ySamples
<< "\n ySampleMean = " << ySampleMean
<< std::endl;
}
uqGslMatrixClass sigmaMatInverse(paramSpace.zeroVector());
sigmaMatInverse = matrixProduct(aVec,aVec);
sigmaMatInverse *= (((double) n)/sigmaEps/sigmaEps);
sigmaMatInverse += sigma0Mat;
uqGslMatrixClass sigmaMat(paramSpace.zeroVector());
sigmaMat = sigmaMatInverse.inverse();
uqGslVectorClass muVec(paramSpace.zeroVector());
muVec = sigmaMat * aVec;
muVec *= (((double) n) * ySampleMean)/sigmaEps/sigmaEps;
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "In solveSip():"
<< "\n muVec = " << muVec
<< "\n sigmaMat = " << sigmaMat
<< "\n sigmaMatInverse = " << sigmaMatInverse
<< std::endl;
}
////////////////////////////////////////////////////////
// Step 5 of 5: Solve the inverse problem
////////////////////////////////////////////////////////
uqGslVectorClass initialValues(paramSpace.zeroVector());
initialValues[0] = 25.;
initialValues[1] = 25.;
uqGslMatrixClass proposalCovMat(paramSpace.zeroVector());
proposalCovMat(0,0) = 10.;
proposalCovMat(0,1) = 0.;
proposalCovMat(1,0) = 0.;
proposalCovMat(1,1) = 10.;
sip.solveWithBayesMetropolisHastings(NULL,initialValues,&proposalCovMat);
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Leaving solveSip()"
<< std::endl;
}
return;
}
int main(int argc, char ** argv)
{
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", NULL);
#else
QUESO::FullEnvironment env("", "", NULL);
#endif
unsigned int dim = 3;
QUESO::VectorSpace<> paramSpace(env, "param_", dim, NULL);
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(-INFINITY);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(INFINITY);
QUESO::BoxSubset<> paramDomain("param_", paramSpace, paramMins, paramMaxs);
QUESO::GslVector mean(paramSpace.zeroVector());
QUESO::GslMatrix var(paramSpace.zeroVector());
mean[0] = 2.0;
mean[1] = 3.0;
mean[2] = 4.0;
var(0,0) = 5.0;
var(1,1) = 6.0;
var(2,2) = 7.0;
// Construct a Gaussian PDF
QUESO::GaussianJointPdf<> pdf("", paramDomain, mean, var);
// Vectors to store gradient calculations
QUESO::GslVector lnGradVector(paramSpace.zeroVector());
QUESO::GslVector gradVector(paramSpace.zeroVector());
// Where to evaluate the gradient. Evaluating at the mean (the mode for a
// Gaussian) should give a gradient consisting of a vector of zeros.
QUESO::GslVector point(mean);
// We are testing that the gradient of log of the pdf is all zeros
pdf.lnValue(point, NULL, &lnGradVector, NULL, NULL);
queso_require_less_equal_msg(std::abs(lnGradVector[0]), TOL, "grad log gaussian pdf values are incorrect");
queso_require_less_equal_msg(std::abs(lnGradVector[1]), TOL, "grad log gaussian pdf values are incorrect");
queso_require_less_equal_msg(std::abs(lnGradVector[2]), TOL, "grad log gaussian pdf values are incorrect");
// We are testing that the of the pdf is all zeros
pdf.actualValue(point, NULL, &gradVector, NULL, NULL);
queso_require_less_equal_msg(std::abs(gradVector[0]), TOL, "grad guassian pdf values are incorrect");
queso_require_less_equal_msg(std::abs(gradVector[1]), TOL, "grad guassian pdf values are incorrect");
queso_require_less_equal_msg(std::abs(gradVector[2]), TOL, "grad guassian pdf values are incorrect");
// Now construct another Gaussian. This time we're constructing a Gaussian
// that we know will have a gradient consisting entirely of ones (in log
// space).
mean[0] = 0.0;
mean[1] = 0.0;
mean[2] = 0.0;
var(0,0) = 1.0;
var(0,1) = 0.8;
var(0,2) = 0.7;
var(1,0) = 0.8;
var(1,1) = 2.0;
var(1,2) = 0.6;
var(2,0) = 0.7;
var(2,1) = 0.6;
var(2,2) = 3.0;
point[0] = -2.5;
point[1] = -3.4;
point[2] = -4.3;
QUESO::GaussianJointPdf<> pdf2("", paramDomain, mean, var);
pdf2.lnValue(point, NULL, &lnGradVector, NULL, NULL);
queso_require_less_equal_msg(std::abs(lnGradVector[0] - 1.0), TOL, "grad log gaussian pdf2 values are incorrect");
queso_require_less_equal_msg(std::abs(lnGradVector[1] - 1.0), TOL, "grad log gaussian pdf2 values are incorrect");
queso_require_less_equal_msg(std::abs(lnGradVector[2] - 1.0), TOL, "grad log gaussian pdf2 values are incorrect");
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
int main(int argc, char ** argv) {
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "test_gaussian_likelihoods/queso_input.txt", "", NULL);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 1, NULL);
double min_val = -INFINITY;
double max_val = INFINITY;
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(min_val);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(max_val);
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> paramDomain("param_",
paramSpace, paramMins, paramMaxs);
// Set up observation space
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> obsSpace(env,
"obs_", 2, NULL);
// Fill up observation vector
QUESO::GslVector observations(obsSpace.zeroVector());
observations[0] = 1.0;
observations[1] = 1.0;
// Fill up covariance 'matrix'
QUESO::GslVector covariance(obsSpace.zeroVector());
covariance[0] = 1.0;
covariance[1] = 2.0;
// Pass in observations to Gaussian likelihood object
Likelihood<QUESO::GslVector, QUESO::GslMatrix> lhood("llhd_", paramDomain,
observations, covariance);
double lhood_value;
double truth_value;
QUESO::GslVector point(paramSpace.zeroVector());
point[0] = 0.0;
lhood_value = lhood.actualValue(point, NULL, NULL, NULL, NULL);
truth_value = std::exp(-3.0);
if (std::abs(lhood_value - truth_value) > TOL) {
std::cerr << "Scalar Gaussian test case failure." << std::endl;
std::cerr << "Computed likelihood value is: " << lhood_value << std::endl;
std::cerr << "Likelihood value should be: " << truth_value << std::endl;
queso_error();
}
point[0] = -2.0;
lhood_value = lhood.actualValue(point, NULL, NULL, NULL, NULL);
truth_value = 1.0;
if (std::abs(lhood_value - truth_value) > TOL) {
std::cerr << "Scalar Gaussian test case failure." << std::endl;
std::cerr << "Computed likelihood value is: " << lhood_value << std::endl;
std::cerr << "Likelihood value should be: " << truth_value << std::endl;
queso_error();
}
MPI_Finalize();
return 0;
}
void GaussianMean1DRegressionCompute(const QUESO::BaseEnvironment& env,
double priorMean, double priorVar, const likelihoodData& dat)
{
// parameter space: 1-D on (-infinity, infinity)
QUESO::VectorSpace<P_V, P_M> paramSpace(
env, // queso environment
"param_", // name prefix
1, // dimensions
NULL); // names
P_V paramMin(paramSpace.zeroVector());
P_V paramMax(paramSpace.zeroVector());
paramMin[0] = -INFINITY;
paramMax[0] = INFINITY;
QUESO::BoxSubset<P_V, P_M> paramDomain(
"paramBox_", // name prefix
paramSpace, // vector space
paramMin, // min values
paramMax); // max values
// gaussian prior with user supplied mean and variance
P_V priorMeanVec(paramSpace.zeroVector());
P_V priorVarVec(paramSpace.zeroVector());
priorMeanVec[0] = priorMean;
priorVarVec[0] = priorVar;
QUESO::GaussianVectorRV<P_V, P_M> priorRv("prior_", paramDomain, priorMeanVec,
priorVarVec);
// likelihood is important
QUESO::GenericScalarFunction<P_V, P_M> likelihoodFunctionObj(
"like_", // name prefix
paramDomain, // image set
LikelihoodFunc<P_V, P_M>, // routine
(void *) &dat, // routine data ptr
true); // routineIsForLn
QUESO::GenericVectorRV<P_V, P_M> postRv(
"post_", // name prefix
paramSpace); // image set
// Initialize and solve the Inverse Problem with Bayes multi-level sampling
QUESO::StatisticalInverseProblem<P_V, P_M> invProb(
"", // name prefix
NULL, // alt options
priorRv, // prior RV
likelihoodFunctionObj, // likelihood fcn
postRv); // posterior RV
invProb.solveWithBayesMLSampling();
// compute mean and second moment of samples on each proc via Knuth online mean/variance algorithm
int N = invProb.postRv().realizer().subPeriod();
double subMean = 0.0;
double subM2 = 0.0;
double delta;
P_V sample(paramSpace.zeroVector());
for (int n = 1; n <= N; n++) {
invProb.postRv().realizer().realization(sample);
delta = sample[0] - subMean;
subMean += delta / n;
subM2 += delta * (sample[0] - subMean);
}
// gather all Ns, means, and M2s to proc 0
std::vector<int> unifiedNs(env.inter0Comm().NumProc());
std::vector<double> unifiedMeans(env.inter0Comm().NumProc());
std::vector<double> unifiedM2s(env.inter0Comm().NumProc());
MPI_Gather(&N, 1, MPI_INT, &(unifiedNs[0]), 1, MPI_INT, 0,
env.inter0Comm().Comm());
MPI_Gather(&subMean, 1, MPI_DOUBLE, &(unifiedMeans[0]), 1, MPI_DOUBLE, 0,
env.inter0Comm().Comm());
MPI_Gather(&subM2, 1, MPI_DOUBLE, &(unifiedM2s[0]), 1, MPI_DOUBLE, 0,
env.inter0Comm().Comm());
// get the total number of likelihood calls at proc 0
unsigned long totalLikelihoodCalls = 0;
MPI_Reduce(&likelihoodCalls, &totalLikelihoodCalls, 1, MPI_UNSIGNED_LONG,
MPI_SUM, 0, env.inter0Comm().Comm());
// compute global posterior mean and std via Chan algorithm, output results on proc 0
if (env.inter0Rank() == 0) {
int postN = unifiedNs[0];
double postMean = unifiedMeans[0];
double postVar = unifiedM2s[0];
for (unsigned int i = 1; i < unifiedNs.size(); i++) {
delta = unifiedMeans[i] - postMean;
postMean = (postN * postMean + unifiedNs[i] * unifiedMeans[i]) /
(postN + unifiedNs[i]);
postVar += unifiedM2s[i] + delta * delta *
(((double)postN * unifiedNs[i]) / (postN + unifiedNs[i]));
postN += unifiedNs[i];
}
postVar /= postN;
//compute exact answer - available in this case since the exact posterior is a gaussian
N = dat.dataSet.size();
double dataSum = 0.0;
for (int i = 0; i < N; i++)
dataSum += dat.dataSet[i];
double datMean = dataSum / N;
double postMeanExact = (N * priorVar / (N * priorVar + dat.samplingVar)) *
datMean + (dat.samplingVar / (N * priorVar + dat.samplingVar)) * priorMean;
double postVarExact = 1.0 / (N / dat.samplingVar + 1.0 / priorVar);
std::cout << "Number of posterior samples: " << postN << std::endl;
std::cout << "Estimated posterior mean: " << postMean << " +/- "
<< std::sqrt(postVar) << std::endl;
std::cout << "Likelihood function calls: " << totalLikelihoodCalls
<< std::endl;
std::cout << "\nExact posterior: Gaussian with mean " << postMeanExact
<< ", standard deviation " << std::sqrt(postVarExact) << std::endl;
}
}
int main(int argc, char ** argv) {
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
#endif
QUESO::EnvOptionsValues envOptions;
envOptions.m_numSubEnvironments = 1;
envOptions.m_subDisplayFileName = "test_outputNoInputFile/display";
envOptions.m_subDisplayAllowAll = 1;
envOptions.m_displayVerbosity = 2;
envOptions.m_syncVerbosity = 0;
envOptions.m_seed = 0;
#ifdef QUESO_HAS_MPI
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", &envOptions);
#else
QUESO::FullEnvironment env("", "", &envOptions);
#endif
unsigned int dim = 2;
QUESO::VectorSpace<> paramSpace(env, "param_", dim, NULL);
QUESO::GslVector paramMins(paramSpace.zeroVector());
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
double min_val = -10.0;
double max_val = 10.0;
paramMins.cwSet(min_val);
paramMaxs.cwSet(max_val);
QUESO::BoxSubset<> paramDomain("param_", paramSpace, paramMins, paramMaxs);
QUESO::UniformVectorRV<> priorRv("prior_", paramDomain);
Likelihood<> lhood("llhd_", paramDomain);
QUESO::GenericVectorRV<> postRv("post_", paramSpace);
QUESO::SipOptionsValues sipOptions;
sipOptions.m_computeSolution = 1;
sipOptions.m_dataOutputFileName = "test_outputNoInputFile/sipOutput";
sipOptions.m_dataOutputAllowedSet.clear();
sipOptions.m_dataOutputAllowedSet.insert(0);
QUESO::StatisticalInverseProblem<> ip("", &sipOptions, priorRv, lhood,
postRv);
QUESO::GslVector paramInitials(paramSpace.zeroVector());
paramInitials[0] = 0.0;
paramInitials[1] = 0.0;
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
proposalCovMatrix(0, 0) = 1.0;
proposalCovMatrix(0, 1) = 0.0;
proposalCovMatrix(1, 0) = 0.0;
proposalCovMatrix(1, 1) = 1.0;
QUESO::MhOptionsValues mhOptions;
mhOptions.m_dataOutputFileName = "test_outputNoInputFile/sipOutput";
mhOptions.m_dataOutputAllowAll = 1;
mhOptions.m_rawChainGenerateExtra = 0;
mhOptions.m_rawChainDisplayPeriod = 50000;
mhOptions.m_rawChainMeasureRunTimes = 1;
mhOptions.m_rawChainDataOutputFileName = "test_outputNoInputFile/ip_raw_chain";
mhOptions.m_rawChainDataOutputAllowAll = 1;
mhOptions.m_displayCandidates = 0;
mhOptions.m_tkUseLocalHessian = 0;
mhOptions.m_tkUseNewtonComponent = 1;
mhOptions.m_filteredChainGenerate = 0;
mhOptions.m_rawChainSize = 1000;
mhOptions.m_putOutOfBoundsInChain = false;
mhOptions.m_drMaxNumExtraStages = 1;
mhOptions.m_drScalesForExtraStages.resize(1);
mhOptions.m_drScalesForExtraStages[0] = 5.0;
mhOptions.m_amInitialNonAdaptInterval = 100;
mhOptions.m_amAdaptInterval = 100;
mhOptions.m_amEta = (double) 2.4 * 2.4 / dim; // From Gelman 95
mhOptions.m_amEpsilon = 1.e-8;
mhOptions.m_doLogitTransform = false;
mhOptions.m_algorithm = "random_walk";
mhOptions.m_tk = "random_walk";
ip.solveWithBayesMetropolisHastings(&mhOptions, paramInitials,
&proposalCovMatrix);
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
void solveSip(const uqFullEnvironmentClass& env, bool useML)
{
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Entering solveSip()..."
<< std::endl;
}
////////////////////////////////////////////////////////
// Step 1 of 5: Instantiate the parameter space
////////////////////////////////////////////////////////
unsigned int p = 1;
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass> paramSpace(env, "param_", p, NULL);
uqGslVectorClass aVec(paramSpace.zeroVector());
aVec[0] = 126831.7;
uqGslVectorClass bVec(paramSpace.zeroVector());
bVec[0] = 112136.1;
////////////////////////////////////////////////////////
// Step 2 of 5: Instantiate the parameter domain
////////////////////////////////////////////////////////
//uqGslVectorClass paramMins (paramSpace.zeroVector());
//uqGslVectorClass paramMaxs (paramSpace.zeroVector());
//paramMins [0] = -1.e+16;
//paramMaxs [0] = 1.e+16;
//paramMins [1] = -1.e+16;
//paramMaxs [1] = 1.e+16;
//uqBoxSubsetClass<uqGslVectorClass,uqGslMatrixClass> paramDomain("param_",paramSpace,paramMins,paramMaxs);
uqVectorSetClass<uqGslVectorClass,uqGslMatrixClass>* paramDomain = ¶mSpace;
////////////////////////////////////////////////////////
// Step 3 of 5: Instantiate the likelihood function object
////////////////////////////////////////////////////////
unsigned int n = 400;
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass> dataSpace(env, "data_", n, NULL);
double sigmaTotal = 4229.55;
std::set<unsigned int> tmpSet;
tmpSet.insert(env.subId());
uqGslVectorClass ySamples(dataSpace.zeroVector());
ySamples.subReadContents("input/dataPoints",
"m",
tmpSet);
struct likelihoodDataStruct likelihoodData;
likelihoodData.aVec = &aVec;
likelihoodData.bVec = &bVec;
likelihoodData.sigmaTotal = sigmaTotal;
likelihoodData.ySamples = &ySamples;
uqGenericScalarFunctionClass<uqGslVectorClass,uqGslMatrixClass>
likelihoodFunctionObj("like_",
*paramDomain,
likelihoodRoutine,
(void *) &likelihoodData,
true); // routine computes [ln(function)]
////////////////////////////////////////////////////////
// Step 4 of 5: Instantiate the inverse problem
////////////////////////////////////////////////////////
uqUniformVectorRVClass<uqGslVectorClass,uqGslMatrixClass> priorRv("prior_", *paramDomain);
uqGenericVectorRVClass<uqGslVectorClass,uqGslMatrixClass> postRv ("post_", paramSpace);
uqStatisticalInverseProblemClass<uqGslVectorClass,uqGslMatrixClass> sip("sip_", NULL, priorRv, likelihoodFunctionObj, postRv);
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "In solveSip():"
<< "\n p = " << p
<< "\n aVec = " << aVec
<< "\n bVec = " << bVec
<< "\n n = " << n
<< "\n sigmaTotal = " << sigmaTotal
<< "\n ySamples = " << ySamples
<< "\n useML = " << useML
<< std::endl;
}
////////////////////////////////////////////////////////
// Step 5 of 5: Solve the inverse problem
////////////////////////////////////////////////////////
uqGslVectorClass initialValues(paramSpace.zeroVector());
initialValues[0] = 0.;
uqGslMatrixClass proposalCovMat(paramSpace.zeroVector());
proposalCovMat(0,0) = 1.;
if (useML) {
sip.solveWithBayesMLSampling();
}
else {
sip.solveWithBayesMetropolisHastings(NULL,initialValues,&proposalCovMat);
}
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Leaving solveSip()"
<< std::endl;
}
return;
}
int main(int argc, char **argv) {
#ifndef QUESO_HAS_MPI
return 77;
#else
MPI_Init(&argc, &argv);
QUESO::EnvOptionsValues options;
options.m_numSubEnvironments = 2;
options.m_subDisplayFileName = "outputData/test_SequenceOfVectorsMax";
options.m_subDisplayAllowAll = 1;
options.m_seed = 1.0;
options.m_checkingLevel = 1;
options.m_displayVerbosity = 55;
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", &options);
// Create a vector space
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env, "", 1,
NULL);
QUESO::SequenceOfVectors<QUESO::GslVector, QUESO::GslMatrix> pretendChain(
paramSpace, 3, "pretendChain");
QUESO::GslVector v(paramSpace.zeroVector());
v[0] = 0.0;
// Create a scalar sequence on each processor
std::string name = "name";
QUESO::ScalarSequence<double> scalarSequence(env, 3, name);
pretendChain.setPositionValues(0, v);
pretendChain.setPositionValues(1, v);
pretendChain.setPositionValues(2, v);
if (env.inter0Rank() == 0) {
scalarSequence[0] = 10.0;
scalarSequence[1] = 11.0;
scalarSequence[2] = 12.0;
}
else if (env.inter0Rank() == 1) {
scalarSequence[0] = 0.0;
scalarSequence[1] = 1.0;
scalarSequence[2] = 2.0;
}
else {
queso_error_msg("Test should not get here!");
}
// Create a sequence of vectors
QUESO::SequenceOfVectors<QUESO::GslVector, QUESO::GslMatrix> maxs(paramSpace,
0, "name2");
pretendChain.unifiedPositionsOfMaximum(scalarSequence, maxs);
// Should not fail
QUESO::GslVector tmpVec(paramSpace.zeroVector());
// The loop should only actually do anything on process zero
for (unsigned int i = 0; i < maxs.subSequenceSize(); i++) {
maxs.getPositionValues(0, tmpVec);
}
MPI_Finalize();
return 0;
#endif
}
int main(int argc, char ** argv) {
std::string inputFileName = "test_gaussian_likelihoods/queso_input.txt";
const char * test_srcdir = std::getenv("srcdir");
if (test_srcdir)
inputFileName = test_srcdir + ('/' + inputFileName);
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, inputFileName, "", NULL);
#else
QUESO::FullEnvironment env(inputFileName, "", NULL);
#endif
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 1, NULL);
double min_val = -INFINITY;
double max_val = INFINITY;
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(min_val);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(max_val);
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> paramDomain("param_",
paramSpace, paramMins, paramMaxs);
// Set up observation space
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> obsSpace(env,
"obs_", 1, NULL);
// Fill up observation vector
QUESO::GslVector observations(obsSpace.zeroVector());
observations[0] = 1.0;
// Pass in observations to Gaussian likelihood object
Likelihood<QUESO::GslVector, QUESO::GslMatrix> lhood("llhd_", paramDomain,
observations, 1.0);
double lhood_value;
double truth_value;
QUESO::GslVector point(paramSpace.zeroVector());
point[0] = 0.0;
lhood_value = lhood.actualValue(point, NULL, NULL, NULL, NULL);
truth_value = std::exp(-2.0);
if (std::abs(lhood_value - truth_value) > TOL) {
std::cerr << "Scalar Gaussian test case failure." << std::endl;
std::cerr << "Computed likelihood value is: " << lhood_value << std::endl;
std::cerr << "Likelihood value should be: " << truth_value << std::endl;
queso_error();
}
point[0] = -2.0;
lhood_value = lhood.actualValue(point, NULL, NULL, NULL, NULL);
truth_value = 1.0;
if (std::abs(lhood_value - truth_value) > TOL) {
std::cerr << "Scalar Gaussian test case failure." << std::endl;
std::cerr << "Computed likelihood value is: " << lhood_value << std::endl;
std::cerr << "Likelihood value should be: " << truth_value << std::endl;
queso_error();
}
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
void
uqAppl(const QUESO::BaseEnvironment& env)
{
if (env.fullRank() == 0) {
std::cout << "Beginning run of 'uqTgaExample' example\n"
<< std::endl;
}
//int iRC;
struct timeval timevalRef;
struct timeval timevalNow;
//******************************************************
// Task 1 of 5: instantiation of basic classes
//******************************************************
// Instantiate the parameter space
std::vector<std::string> paramNames(2,"");
paramNames[0] = "A_param";
paramNames[1] = "E_param";
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix> paramSpace(env,"param_",paramNames.size(),¶mNames);
// Instantiate the parameter domain
QUESO::GslVector paramMinValues(paramSpace.zeroVector());
paramMinValues[0] = 2.40e+11;
paramMinValues[1] = 1.80e+05;
QUESO::GslVector paramMaxValues(paramSpace.zeroVector());
paramMaxValues[0] = 2.80e+11;
paramMaxValues[1] = 2.20e+05;
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix> paramDomain("param_",
paramSpace,
paramMinValues,
paramMaxValues);
// Instantiate the qoi space
std::vector<std::string> qoiNames(1,"");
qoiNames[0] = "TimeFor25PercentOfMass";
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix> qoiSpace(env,"qoi_",qoiNames.size(),&qoiNames);
// Instantiate the validation cycle
QUESO::ValidationCycle<QUESO::GslVector,QUESO::GslMatrix,QUESO::GslVector,QUESO::GslMatrix> cycle(env,
"", // No extra prefix
paramSpace,
qoiSpace);
//********************************************************
// Task 2 of 5: calibration stage
//********************************************************
/*iRC = */gettimeofday(&timevalRef, NULL);
if (env.fullRank() == 0) {
std::cout << "Beginning 'calibration stage' at " << ctime(&timevalRef.tv_sec)
<< std::endl;
}
// Inverse problem: instantiate the prior rv
QUESO::UniformVectorRV<QUESO::GslVector,QUESO::GslMatrix> calPriorRv("cal_prior_", // Extra prefix before the default "rv_" prefix
paramDomain);
// Inverse problem: instantiate the likelihood
Likelihood<> calLikelihood("cal_like_",
paramDomain,
"inputData/scenario_5_K_min.dat",
"inputData/scenario_25_K_min.dat",
"inputData/scenario_50_K_min.dat");
// Inverse problem: instantiate it (posterior rv is instantiated internally)
cycle.instantiateCalIP(NULL,
calPriorRv,
calLikelihood);
// Inverse problem: solve it, that is, set 'pdf' and 'realizer' of the posterior rv
QUESO::GslVector paramInitialValues(paramSpace.zeroVector());
if (env.numSubEnvironments() == 1) {
// For regression test purposes
paramInitialValues[0] = 2.41e+11;
paramInitialValues[1] = 2.19e+05;
}
else {
calPriorRv.realizer().realization(paramInitialValues);
}
QUESO::GslMatrix* calProposalCovMatrix = cycle.calIP().postRv().imageSet().vectorSpace().newProposalMatrix(NULL,¶mInitialValues);
cycle.calIP().solveWithBayesMetropolisHastings(NULL,
paramInitialValues,
calProposalCovMatrix);
delete calProposalCovMatrix;
// Forward problem: instantiate it (parameter rv = posterior rv of inverse problem; qoi rv is instantiated internally)
double beta_prediction = 250.;
double criticalMass_prediction = 0.;
double criticalTime_prediction = 3.9;
qoiRoutine_Data calQoiRoutine_Data;
calQoiRoutine_Data.m_beta = beta_prediction;
calQoiRoutine_Data.m_criticalMass = criticalMass_prediction;
calQoiRoutine_Data.m_criticalTime = criticalTime_prediction;
cycle.instantiateCalFP(NULL,
qoiRoutine,
(void *) &calQoiRoutine_Data);
// Forward problem: solve it, that is, set 'realizer' and 'cdf' of the qoi rv
cycle.calFP().solveWithMonteCarlo(NULL); // no extra user entities needed for Monte Carlo algorithm
/*iRC = */gettimeofday(&timevalNow, NULL);
if (env.fullRank() == 0) {
std::cout << "Ending 'calibration stage' at " << ctime(&timevalNow.tv_sec)
<< "Total 'calibration stage' run time = " << timevalNow.tv_sec - timevalRef.tv_sec
<< " seconds\n"
<< std::endl;
}
//********************************************************
// Task 3 of 5: validation stage
//********************************************************
/*iRC = */gettimeofday(&timevalRef, NULL);
if (env.fullRank() == 0) {
std::cout << "Beginning 'validation stage' at " << ctime(&timevalRef.tv_sec)
<< std::endl;
}
// Inverse problem: no need to instantiate the prior rv (= posterior rv of calibration inverse problem)
// Inverse problem: instantiate the likelihood function object
Likelihood<> valLikelihood("val_like_",
paramDomain,
"inputData/scenario_100_K_min.dat",
NULL,
NULL);
// Inverse problem: instantiate it (posterior rv is instantiated internally)
cycle.instantiateValIP(NULL,valLikelihood);
// Inverse problem: solve it, that is, set 'pdf' and 'realizer' of the posterior rv
const QUESO::SequentialVectorRealizer<QUESO::GslVector,QUESO::GslMatrix>* tmpRealizer = dynamic_cast< const QUESO::SequentialVectorRealizer<QUESO::GslVector,QUESO::GslMatrix>* >(&(cycle.calIP().postRv().realizer()));
QUESO::GslMatrix* valProposalCovMatrix = cycle.calIP().postRv().imageSet().vectorSpace().newProposalMatrix(&tmpRealizer->unifiedSampleVarVector(), // Use 'realizer()' because post. rv was computed with MH
&tmpRealizer->unifiedSampleExpVector()); // Use these values as the initial values
cycle.valIP().solveWithBayesMetropolisHastings(NULL,
tmpRealizer->unifiedSampleExpVector(),
valProposalCovMatrix);
delete valProposalCovMatrix;
// Forward problem: instantiate it (parameter rv = posterior rv of inverse problem; qoi rv is instantiated internally)
qoiRoutine_Data valQoiRoutine_Data;
valQoiRoutine_Data.m_beta = beta_prediction;
valQoiRoutine_Data.m_criticalMass = criticalMass_prediction;
valQoiRoutine_Data.m_criticalTime = criticalTime_prediction;
cycle.instantiateValFP(NULL,
qoiRoutine,
(void *) &valQoiRoutine_Data);
// Forward problem: solve it, that is, set 'realizer' and 'cdf' of the qoi rv
cycle.valFP().solveWithMonteCarlo(NULL); // no extra user entities needed for Monte Carlo algorithm
/*iRC = */gettimeofday(&timevalNow, NULL);
if (env.fullRank() == 0) {
std::cout << "Ending 'validation stage' at " << ctime(&timevalNow.tv_sec)
<< "Total 'validation stage' run time = " << timevalNow.tv_sec - timevalRef.tv_sec
<< " seconds\n"
<< std::endl;
}
//********************************************************
// Task 4 of 5: comparison stage
//********************************************************
/*iRC = */gettimeofday(&timevalRef, NULL);
if (env.fullRank() == 0) {
std::cout << "Beginning 'comparison stage' at " << ctime(&timevalRef.tv_sec)
<< std::endl;
}
uqAppl_LocalComparisonStage(cycle);
if (env.numSubEnvironments() > 1) {
uqAppl_UnifiedComparisonStage(cycle);
}
/*iRC = */gettimeofday(&timevalNow, NULL);
if (env.fullRank() == 0) {
std::cout << "Ending 'comparison stage' at " << ctime(&timevalNow.tv_sec)
<< "Total 'comparison stage' run time = " << timevalNow.tv_sec - timevalRef.tv_sec
<< " seconds\n"
<< std::endl;
}
//******************************************************
// Task 5 of 5: release memory before leaving routine.
//******************************************************
if (env.fullRank() == 0) {
std::cout << "Finishing run of 'uqTgaExample' example"
<< std::endl;
}
return;
}
int main(int argc, char ** argv) {
// Step 0: Set up some variables
unsigned int numExperiments = 10; // Number of experiments
unsigned int numUncertainVars = 5; // Number of things to calibrate
unsigned int numSimulations = 50; // Number of simulations
unsigned int numConfigVars = 1; // Dimension of configuration space
unsigned int numEta = 1; // Number of responses the model is returning
unsigned int experimentSize = 1; // Size of each experiment
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
// Step 1: Set up QUESO environment
QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL);
#else
QUESO::FullEnvironment env(argv[1], "", NULL);
#endif
// Step 2: Set up prior for calibration parameters
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", numUncertainVars, NULL);
// Parameter (theta) bounds:
// descriptors 'k_tmasl' 'k_xkle' 'k_xkwew' 'k_xkwlx' 'k_cd'
// upper_bounds 1.05 1.1 1.1 1.1 1.1
// lower_bounds 0.95 0.9 0.9 0.9 0.9
//
// These bounds are dealt with when reading in the data
QUESO::GslVector paramMins(paramSpace.zeroVector());
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMins.cwSet(0.0);
paramMaxs.cwSet(1.0);
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> paramDomain("param_",
paramSpace, paramMins, paramMaxs);
QUESO::UniformVectorRV<QUESO::GslVector, QUESO::GslMatrix> priorRv("prior_",
paramDomain);
// Step 3: Instantiate the 'scenario' and 'output' spaces for simulation
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> configSpace(env,
"scenario_", numConfigVars, NULL);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> nEtaSpace(env,
"output_", numEta, NULL);
// Step 4: Instantiate the 'output' space for the experiments
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> experimentSpace(env,
"experimentspace_", experimentSize, NULL);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> totalExperimentSpace(env,
"experimentspace_", experimentSize * numExperiments, NULL);
// Step 5: Instantiate the Gaussian process emulator object
//
// Regarding simulation scenario input values, the user should standardise
// them so that they exist inside a hypercube.
//
// Regarding simulation output data, the user should transform it so that the
// mean is zero and the variance is one.
//
// Regarding experimental scenario input values, the user should standardize
// them so that they exist inside a hypercube.
//
// Regarding experimental data, the user should transformed it so that it has
// zero mean and variance one.
// GPMSA stores all the information about our simulation
// data and experimental data. It also stores default information about the
// hyperparameter distributions.
QUESO::GPMSAFactory<QUESO::GslVector, QUESO::GslMatrix>
gpmsaFactory(env,
NULL,
priorRv,
configSpace,
paramSpace,
nEtaSpace,
experimentSpace,
numSimulations,
numExperiments);
// std::vector containing all the points in scenario space where we have
// simulations
std::vector<QUESO::GslVector *> simulationScenarios(numSimulations,
(QUESO::GslVector *) NULL);
// std::vector containing all the points in parameter space where we have
// simulations
std::vector<QUESO::GslVector *> paramVecs(numSimulations,
(QUESO::GslVector *) NULL);
// std::vector containing all the simulation output data
std::vector<QUESO::GslVector *> outputVecs(numSimulations,
(QUESO::GslVector *) NULL);
// std::vector containing all the points in scenario space where we have
// experiments
std::vector<QUESO::GslVector *> experimentScenarios(numExperiments,
(QUESO::GslVector *) NULL);
// std::vector containing all the experimental output data
std::vector<QUESO::GslVector *> experimentVecs(numExperiments,
(QUESO::GslVector *) NULL);
// The experimental output data observation error covariance matrix
QUESO::GslMatrix experimentMat(totalExperimentSpace.zeroVector());
// Instantiate each of the simulation points/outputs
for (unsigned int i = 0; i < numSimulations; i++) {
simulationScenarios[i] = new QUESO::GslVector(configSpace.zeroVector()); // 'x_{i+1}^*' in paper
paramVecs [i] = new QUESO::GslVector(paramSpace.zeroVector()); // 't_{i+1}^*' in paper
outputVecs [i] = new QUESO::GslVector(nEtaSpace.zeroVector()); // 'eta_{i+1}' in paper
}
for (unsigned int i = 0; i < numExperiments; i++) {
experimentScenarios[i] = new QUESO::GslVector(configSpace.zeroVector()); // 'x_{i+1}' in paper
experimentVecs[i] = new QUESO::GslVector(experimentSpace.zeroVector());
}
// Read in data and store the standard deviation of the simulation data. We
// will need the standard deviation when we pass the experiment error
// covariance matrix to QUESO.
double stdsim = readData(simulationScenarios,
paramVecs,
outputVecs,
experimentScenarios,
experimentVecs);
for (unsigned int i = 0; i < numExperiments; i++) {
// Passing in error of experiments (standardised).
experimentMat(i, i) = (0.025 / stdsim) * (0.025 / stdsim);
}
// Add simulation and experimental data
gpmsaFactory.addSimulations(simulationScenarios, paramVecs, outputVecs);
gpmsaFactory.addExperiments(experimentScenarios, experimentVecs, &experimentMat);
QUESO::GenericVectorRV<QUESO::GslVector, QUESO::GslMatrix> postRv(
"post_",
gpmsaFactory.prior().imageSet().vectorSpace());
QUESO::StatisticalInverseProblem<QUESO::GslVector, QUESO::GslMatrix> ip("",
NULL, gpmsaFactory, postRv);
QUESO::GslVector paramInitials(
gpmsaFactory.prior().imageSet().vectorSpace().zeroVector());
// Initial condition of the chain
// Have to set each of these by hand, *and* the sampler is sensitive to these
// values
paramInitials[0] = 0.5; // param 1
paramInitials[1] = 0.5; // param 2
paramInitials[2] = 0.5; // param 3
paramInitials[3] = 0.5; // param 4
paramInitials[4] = 0.5; // param 5
paramInitials[5] = 0.4; // not used. emulator mean
paramInitials[6] = 0.4; // emulator precision
paramInitials[7] = 0.97; // emulator corr str
paramInitials[8] = 0.97; // emulator corr str
paramInitials[9] = 0.97; // emulator corr str
paramInitials[10] = 0.97; // emulator corr str
paramInitials[11] = 0.20; // emulator corr str
paramInitials[12] = 0.80; // emulator corr str
paramInitials[13] = 10.0; // discrepancy precision
paramInitials[14] = 0.97; // discrepancy corr str
paramInitials[15] = 8000.0; // emulator data precision
QUESO::GslMatrix proposalCovMatrix(
gpmsaFactory.prior().imageSet().vectorSpace().zeroVector());
// Setting the proposal covariance matrix by hand. This requires great
// forethough, and can generally be referred to as a massive hack. These
// values were taken from the gpmsa matlab code and fiddled with.
double scale = 600.0;
proposalCovMatrix(0, 0) = 3.1646 / 10.0; // param 1
proposalCovMatrix(1, 1) = 3.1341 / 10.0; // param 2
proposalCovMatrix(2, 2) = 3.1508 / 10.0; // param 3
proposalCovMatrix(3, 3) = 0.3757 / 10.0; // param 4
proposalCovMatrix(4, 4) = 0.6719 / 10.0; // param 5
proposalCovMatrix(5, 5) = 0.1 / scale; // not used. emulator mean
proposalCovMatrix(6, 6) = 0.4953 / scale; // emulator precision
proposalCovMatrix(7, 7) = 0.6058 / scale; // emulator corr str
proposalCovMatrix(8, 8) = 7.6032e-04 / scale; // emulator corr str
proposalCovMatrix(9, 9) = 8.3815e-04 / scale; // emulator corr str
proposalCovMatrix(10, 10) = 7.5412e-04 / scale; // emulator corr str
proposalCovMatrix(11, 11) = 0.2682 / scale; // emulator corr str
proposalCovMatrix(12, 12) = 0.0572 / scale; // emulator corr str
proposalCovMatrix(13, 13) = 1.3417 / scale; // discrepancy precision
proposalCovMatrix(14, 14) = 0.3461 / scale; // discrepancy corr str
proposalCovMatrix(15, 15) = 495.3 / scale; // emulator data precision
// Square to get variances
for (unsigned int i = 0; i < 16; i++) {
proposalCovMatrix(i, i) = proposalCovMatrix(i, i) * proposalCovMatrix(i, i);
}
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
void solveSip(const uqFullEnvironmentClass& env, bool useML)
{
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Entering solveSip()..."
<< std::endl;
}
////////////////////////////////////////////////////////
// Step 1 of 5: Instantiate the parameter space
////////////////////////////////////////////////////////
unsigned int p = 1;
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass> paramSpace(env, "param_", p, NULL);
uqGslVectorClass bVec(paramSpace.zeroVector());
bVec[0] = 0.045213;
////////////////////////////////////////////////////////
// Step 2 of 5: Instantiate the parameter domain
////////////////////////////////////////////////////////
//uqGslVectorClass paramMins (paramSpace.zeroVector());
//uqGslVectorClass paramMaxs (paramSpace.zeroVector());
//paramMins [0] = -1.e+16;
//paramMaxs [0] = 1.e+16;
//paramMins [1] = -1.e+16;
//paramMaxs [1] = 1.e+16;
//uqBoxSubsetClass<uqGslVectorClass,uqGslMatrixClass> paramDomain("param_",paramSpace,paramMins,paramMaxs);
uqVectorSetClass<uqGslVectorClass,uqGslMatrixClass>* paramDomain = ¶mSpace;
////////////////////////////////////////////////////////
// Step 3 of 5: Instantiate the likelihood function object
////////////////////////////////////////////////////////
unsigned int nAll = 100000;
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass> dataSpaceAll(env, "data_", nAll, NULL);
double sigmaTotal = bVec[0]/2.;
std::set<unsigned int> tmpSet;
tmpSet.insert(env.subId());
uqGslVectorClass ySamplesAll(dataSpaceAll.zeroVector());
ySamplesAll.subReadContents("input/dataPoints",
"m",
tmpSet);
unsigned int numCases = 5;
std::vector<unsigned int> ns(numCases,0);
ns[0] = 1;
ns[1] = 10;
ns[2] = 100;
ns[3] = 500;
ns[4] = 1000;
for (unsigned int caseId = 0; caseId < numCases; ++caseId) {
uqVectorSpaceClass<uqGslVectorClass,uqGslMatrixClass> dataSpace(env, "data_", ns[caseId], NULL);
uqGslVectorClass ySamples(dataSpace.zeroVector());
for (unsigned int i = 0; i < ns[caseId]; ++i) {
ySamples[i] = ySamplesAll[i];
}
struct likelihoodDataStruct likelihoodData;
likelihoodData.bVec = &bVec;
likelihoodData.sigmaTotal = sigmaTotal;
likelihoodData.ySamples = &ySamples;
uqGenericScalarFunctionClass<uqGslVectorClass,uqGslMatrixClass>
likelihoodFunctionObj("like_",
*paramDomain,
likelihoodRoutine,
(void *) &likelihoodData,
true); // routine computes [ln(function)]
////////////////////////////////////////////////////////
// Step 4 of 5: Instantiate the inverse problem
////////////////////////////////////////////////////////
uqUniformVectorRVClass<uqGslVectorClass,uqGslMatrixClass> priorRv("prior_", *paramDomain);
uqGenericVectorRVClass<uqGslVectorClass,uqGslMatrixClass> postRv ("post_", paramSpace);
char prefixStr[16+1];
sprintf(prefixStr,"sip%d_",caseId+1);
uqStatisticalInverseProblemClass<uqGslVectorClass,uqGslMatrixClass> sip(prefixStr, NULL, priorRv, likelihoodFunctionObj, postRv);
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "In solveSip():"
<< "\n caseId = " << caseId
<< "\n prefixStr = " << prefixStr
<< "\n p = " << p
<< "\n bVec = " << bVec
<< "\n ns[caseId] = " << ns[caseId]
<< "\n sigmaTotal = " << sigmaTotal
<< "\n ySamples = " << ySamples
<< "\n useML = " << useML
<< std::endl;
}
////////////////////////////////////////////////////////
// Step 5 of 5: Solve the inverse problem
////////////////////////////////////////////////////////
uqGslVectorClass initialValues(paramSpace.zeroVector());
initialValues[0] = 0.;
uqGslMatrixClass proposalCovMat(paramSpace.zeroVector());
proposalCovMat(0,0) = 1.;
if (useML) {
sip.solveWithBayesMLSampling();
}
else {
sip.solveWithBayesMetropolisHastings(NULL,initialValues,&proposalCovMat);
}
} // for caseId
if ((env.subDisplayFile()) && (env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Leaving solveSip()"
<< std::endl;
}
return;
}
void computeGravityAndTraveledDistance(const QUESO::FullEnvironment& env) {
struct timeval timevalNow;
gettimeofday(&timevalNow, NULL);
if (env.fullRank() == 0) {
std::cout << "\nBeginning run of 'Gravity + Projectile motion' example at "
<< ctime(&timevalNow.tv_sec)
<< "\n my fullRank = " << env.fullRank()
<< "\n my subEnvironmentId = " << env.subId()
<< "\n my subRank = " << env.subRank()
<< "\n my interRank = " << env.inter0Rank()
<< std::endl << std::endl;
}
// Just examples of possible calls
if ((env.subDisplayFile() ) &&
(env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Beginning run of 'Gravity + Projectile motion' example at "
<< ctime(&timevalNow.tv_sec)
<< std::endl;
}
env.fullComm().Barrier();
env.subComm().Barrier(); // Just an example of a possible call
//================================================================
// Statistical inverse problem (SIP): find posterior PDF for 'g'
//================================================================
gettimeofday(&timevalNow, NULL);
if (env.fullRank() == 0) {
std::cout << "Beginning 'SIP -> Gravity estimation' at "
<< ctime(&timevalNow.tv_sec)
<< std::endl;
}
//------------------------------------------------------
// SIP Step 1 of 6: Instantiate the parameter space
//------------------------------------------------------
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix> paramSpace(env, "param_", 1, NULL);
//------------------------------------------------------
// SIP Step 2 of 6: Instantiate the parameter domain
//------------------------------------------------------
QUESO::GslVector paramMinValues(paramSpace.zeroVector());
QUESO::GslVector paramMaxValues(paramSpace.zeroVector());
paramMinValues[0] = 8.;
paramMaxValues[0] = 11.;
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomain("param_",
paramSpace,
paramMinValues,
paramMaxValues);
//------------------------------------------------------
// SIP Step 3 of 6: Instantiate the likelihood function
// object to be used by QUESO.
//------------------------------------------------------
likelihoodRoutine_Data likelihoodRoutine_Data(env);
QUESO::GenericScalarFunction<QUESO::GslVector,QUESO::GslMatrix>
likelihoodFunctionObj("like_",
paramDomain,
likelihoodRoutine,
(void *) &likelihoodRoutine_Data,
true); // the routine computes [ln(function)]
//------------------------------------------------------
// SIP Step 4 of 6: Define the prior RV
//------------------------------------------------------
#ifdef PRIOR_IS_GAUSSIAN
QUESO::GslVector meanVector( paramSpace.zeroVector() );
meanVector[0] = 9;
QUESO::GslMatrix covMatrix = QUESO::GslMatrix(paramSpace.zeroVector());
covMatrix(0,0) = 1.;
// Create a Gaussian prior RV
QUESO::GaussianVectorRV<QUESO::GslVector,QUESO::GslMatrix> priorRv("prior_",paramDomain,meanVector,covMatrix);
#else
// Create an uniform prior RV
QUESO::UniformVectorRV<QUESO::GslVector,QUESO::GslMatrix> priorRv("prior_",paramDomain);
#endif
//------------------------------------------------------
// SIP Step 5 of 6: Instantiate the inverse problem
//------------------------------------------------------
QUESO::GenericVectorRV<QUESO::GslVector,QUESO::GslMatrix>
postRv("post_", // Extra prefix before the default "rv_" prefix
paramSpace);
QUESO::StatisticalInverseProblem<QUESO::GslVector,QUESO::GslMatrix>
ip("", // No extra prefix before the default "ip_" prefix
NULL,
priorRv,
likelihoodFunctionObj,
postRv);
//------------------------------------------------------
// SIP Step 6 of 6: Solve the inverse problem, that is,
// set the 'pdf' and the 'realizer' of the posterior RV
//------------------------------------------------------
std::cout << "Solving the SIP with Metropolis Hastings"
<< std::endl << std::endl;
QUESO::GslVector paramInitials(paramSpace.zeroVector());
priorRv.realizer().realization(paramInitials);
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
proposalCovMatrix(0,0) = std::pow( fabs(paramInitials[0])/20. , 2. );
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
//================================================================
// Statistical forward problem (SFP): find the max distance
// traveled by an object in projectile motion; input pdf for 'g'
// is the solution of the SIP above.
//================================================================
gettimeofday(&timevalNow, NULL);
std::cout << "Beginning 'SFP -> Projectile motion' at "
<< ctime(&timevalNow.tv_sec)
<< std::endl;
//------------------------------------------------------
// SFP Step 1 of 6: Instantiate the parameter *and* qoi spaces.
// SFP input RV = FIP posterior RV, so SFP parameter space
// has been already defined.
//------------------------------------------------------
QUESO::VectorSpace<QUESO::GslVector,QUESO::GslMatrix> qoiSpace(env, "qoi_", 1, NULL);
//------------------------------------------------------
// SFP Step 2 of 6: Instantiate the parameter domain
//------------------------------------------------------
// Not necessary because input RV of the SFP = output RV of SIP.
// Thus, the parameter domain has been already defined.
//------------------------------------------------------
// SFP Step 3 of 6: Instantiate the qoi function object
// to be used by QUESO.
//------------------------------------------------------
qoiRoutine_Data qoiRoutine_Data;
qoiRoutine_Data.m_angle = M_PI/4.0; //45 degrees (radians)
qoiRoutine_Data.m_initialVelocity= 5.; //initial speed (m/s)
qoiRoutine_Data.m_initialHeight = 0.; //initial height (m)
QUESO::GenericVectorFunction<QUESO::GslVector,QUESO::GslMatrix,QUESO::GslVector,QUESO::GslMatrix>
qoiFunctionObj("qoi_",
paramDomain,
qoiSpace,
qoiRoutine,
(void *) &qoiRoutine_Data);
//------------------------------------------------------
// SFP Step 4 of 6: Define the input RV
//------------------------------------------------------
// Not necessary because input RV of SFP = output RV of SIP
// (postRv).
//------------------------------------------------------
// SFP Step 5 of 6: Instantiate the forward problem
//------------------------------------------------------
QUESO::GenericVectorRV<QUESO::GslVector,QUESO::GslMatrix> qoiRv("qoi_", qoiSpace);
QUESO::StatisticalForwardProblem<QUESO::GslVector,QUESO::GslMatrix,QUESO::GslVector,QUESO::GslMatrix>
fp("",
NULL,
postRv,
qoiFunctionObj,
qoiRv);
//------------------------------------------------------
// SFP Step 6 of 6: Solve the forward problem
//------------------------------------------------------
std::cout << "Solving the SFP with Monte Carlo"
<< std::endl << std::endl;
fp.solveWithMonteCarlo(NULL);
//------------------------------------------------------
gettimeofday(&timevalNow, NULL);
if ((env.subDisplayFile() ) &&
(env.displayVerbosity() >= 2)) {
*env.subDisplayFile() << "Ending run of 'Gravity + Projectile motion' example at "
<< ctime(&timevalNow.tv_sec)
<< std::endl;
}
if (env.fullRank() == 0) {
std::cout << "Ending run of 'Gravity + Projectile motion' example at "
<< ctime(&timevalNow.tv_sec)
<< std::endl;
}
return;
}
int main(int argc, char ** argv)
{
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, "test_InterpolationSurrogate/queso_input.txt", "", NULL);
int return_flag = 0;
std::string vs_prefix = "param_";
// Filename for writing/reading surrogate data
std::string filename1 = "test_write_InterpolationSurrogateBuilder_1.dat";
std::string filename2 = "test_write_InterpolationSurrogateBuilder_2.dat";
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix>
paramSpace(env,vs_prefix.c_str(), 4, NULL);
// Point at which we will test the surrogate evaluation
QUESO::GslVector domainVector(paramSpace.zeroVector());
domainVector[0] = -0.4;
domainVector[1] = 3.0;
domainVector[2] = 1.5;
domainVector[3] = 1.65;
double exact_val_1 = four_d_fn_1(domainVector[0],domainVector[1],domainVector[2],domainVector[3]);
double exact_val_2 = four_d_fn_2(domainVector[0],domainVector[1],domainVector[2],domainVector[3]);
double tol = 2.0*std::numeric_limits<double>::epsilon();
// First test surrogate build directly from the computed values
{
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins[0] = -1;
paramMins[1] = -0.5;
paramMins[2] = 1.1;
paramMins[3] = -2.1;
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs[0] = 0.9;
paramMaxs[1] = 3.14;
paramMaxs[2] = 2.1;
paramMaxs[3] = 4.1;
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix>
paramDomain("param_", paramSpace, paramMins, paramMaxs);
std::vector<unsigned int> n_points(4);
n_points[0] = 11;
n_points[1] = 51;
n_points[2] = 31;
n_points[3] = 41;
// One dataset for each of the two functions
const unsigned int n_datasets = 2;
QUESO::InterpolationSurrogateDataSet<QUESO::GslVector, QUESO::GslMatrix>
data(paramDomain,n_points,n_datasets);
MyInterpolationBuilder<QUESO::GslVector,QUESO::GslMatrix>
builder( data );
builder.build_values();
QUESO::LinearLagrangeInterpolationSurrogate<QUESO::GslVector,QUESO::GslMatrix>
four_d_surrogate_1( data.get_dataset(0) );
QUESO::LinearLagrangeInterpolationSurrogate<QUESO::GslVector,QUESO::GslMatrix>
four_d_surrogate_2( data.get_dataset(1) );
double test_val_1 = four_d_surrogate_1.evaluate(domainVector);
double test_val_2 = four_d_surrogate_2.evaluate(domainVector);
return_flag = return_flag ||
test_val( test_val_1, exact_val_1, tol, "test_build_1" ) ||
test_val( test_val_2, exact_val_2, tol, "test_build_2" );
// Write the output to test reading next
QUESO::InterpolationSurrogateIOASCII<QUESO::GslVector,QUESO::GslMatrix>
data_writer;
data_writer.write( filename1, data.get_dataset(0) );
data_writer.write( filename2, data.get_dataset(1) );
}
// Now read the data and test
{
QUESO::InterpolationSurrogateIOASCII<QUESO::GslVector,QUESO::GslMatrix>
data_reader_1, data_reader_2;
data_reader_1.read( filename1, env, vs_prefix.c_str() );
data_reader_2.read( filename2, env, vs_prefix.c_str() );
// Build a new surrogate
QUESO::LinearLagrangeInterpolationSurrogate<QUESO::GslVector,QUESO::GslMatrix>
four_d_surrogate_1( data_reader_1.data() );
QUESO::LinearLagrangeInterpolationSurrogate<QUESO::GslVector,QUESO::GslMatrix>
four_d_surrogate_2( data_reader_2.data() );
double test_val_1 = four_d_surrogate_1.evaluate(domainVector);
double test_val_2 = four_d_surrogate_2.evaluate(domainVector);
return_flag = return_flag ||
test_val( test_val_1, exact_val_1, tol, "test_read_1" ) ||
test_val( test_val_2, exact_val_2, tol, "test_read_2" );
}
return return_flag;
}
int main(int argc, char ** argv) {
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL);
#else
QUESO::FullEnvironment env(argv[1], "", NULL);
#endif
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 1, NULL);
double min_val = 0.0;
double max_val = 1.0;
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(min_val);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(max_val);
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> paramDomain("param_",
paramSpace, paramMins, paramMaxs);
QUESO::UniformVectorRV<QUESO::GslVector, QUESO::GslMatrix> priorRv("prior_",
paramDomain);
// Set up observation space
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> obsSpace(env,
"obs_", 2, NULL);
// Fill up observation vector
QUESO::GslVector observations(obsSpace.zeroVector());
observations[0] = 1.0;
observations[1] = 1.0;
// Pass in observations to Gaussian likelihood object
Likelihood<QUESO::GslVector, QUESO::GslMatrix> lhood("llhd_", paramDomain,
observations, 1.0);
QUESO::GenericVectorRV<QUESO::GslVector, QUESO::GslMatrix>
postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<QUESO::GslVector, QUESO::GslMatrix>
ip("", NULL, priorRv, lhood, postRv);
QUESO::GslVector paramInitials(paramSpace.zeroVector());
paramInitials[0] = 0.0;
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
for (unsigned int i = 0; i < 1; i++) {
proposalCovMatrix(i, i) = 0.1;
}
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}
int main(int argc, char ** argv) {
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 2, NULL);
double min_val = 0.0;
double max_val = 1.0;
QUESO::GslVector paramMins(paramSpace.zeroVector());
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
// Model parameter between 0 and 1
paramMins[0] = 0.0;
paramMaxs[0] = 1.0;
// Hyperparameter (multiplicative coefficient of observational error
// covariance matrix) between 0.0 and \infty
paramMins[1] = 0.0;
paramMaxs[1] = INFINITY;
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> paramDomain("param_",
paramSpace, paramMins, paramMaxs);
QUESO::UniformVectorRV<QUESO::GslVector, QUESO::GslMatrix> priorRv("prior_",
paramDomain);
// Set up observation space
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> obsSpace(env,
"obs_", 2, NULL);
// Fill up observation vector
QUESO::GslVector observations(obsSpace.zeroVector());
observations[0] = 1.0;
observations[1] = 1.0;
// Fill up covariance 'matrix'
QUESO::GslMatrix covariance(obsSpace.zeroVector());
covariance(0, 0) = 1.0;
covariance(0, 1) = 0.0;
covariance(1, 0) = 0.0;
covariance(1, 1) = 1.0;
// Pass in observations to Gaussian likelihood object
Likelihood<QUESO::GslVector, QUESO::GslMatrix> lhood("llhd_", paramDomain,
observations, covariance);
QUESO::GenericVectorRV<QUESO::GslVector, QUESO::GslMatrix>
postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<QUESO::GslVector, QUESO::GslMatrix>
ip("", NULL, priorRv, lhood, postRv);
QUESO::GslVector paramInitials(paramSpace.zeroVector());
paramInitials[0] = 0.0;
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
for (unsigned int i = 0; i < 1; i++) {
proposalCovMatrix(i, i) = 0.1;
}
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
MPI_Finalize();
return 0;
}
void compute(const QUESO::FullEnvironment& env, unsigned int numModes) {
////////////////////////////////////////////////////////
// Step 1 of 5: Instantiate the parameter space
////////////////////////////////////////////////////////
#ifdef APPLS_MODAL_USES_CONCATENATION
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix>
paramSpaceA(env, "paramA_", 2, NULL);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix>
paramSpaceB(env, "paramB_", 1, NULL);
#endif
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix>
paramSpace(env, "param_", 3, NULL);
////////////////////////////////////////////////////////
// Step 2 of 5: Instantiate the parameter domain
////////////////////////////////////////////////////////
#ifdef APPLS_MODAL_USES_CONCATENATION
QUESO::GslVector paramMinsA(paramSpaceA.zeroVector());
paramMinsA[0] = 0.;
paramMinsA[1] = 0.;
QUESO::GslVector paramMaxsA(paramSpaceA.zeroVector());
paramMaxsA[0] = 3.;
paramMaxsA[1] = 3.;
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomainA("paramA_",paramSpaceA,paramMinsA,paramMaxsA);
QUESO::GslVector paramMinsB(paramSpaceB.zeroVector());
paramMinsB[0] = 0.;
QUESO::GslVector paramMaxsB(paramSpaceB.zeroVector());
paramMaxsB[0] = INFINITY;
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomainB("paramB_",paramSpaceB,paramMinsB,paramMaxsB);
QUESO::ConcatenationSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomain("",paramSpace,paramDomainA,paramDomainB);
#else
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins[0] = 0.;
paramMins[1] = 0.;
paramMins[2] = 0.;
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs[0] = 3.;
paramMaxs[1] = 3.;
paramMaxs[2] = .3;
QUESO::BoxSubset<QUESO::GslVector,QUESO::GslMatrix>
paramDomain("param_",paramSpace,paramMins,paramMaxs);
#endif
////////////////////////////////////////////////////////
// Step 3 of 5: Instantiate the likelihood function object
////////////////////////////////////////////////////////
likelihoodRoutine_DataType likelihoodRoutine_Data;
likelihoodRoutine_Data.numModes = numModes;
QUESO::GenericScalarFunction<QUESO::GslVector,QUESO::GslMatrix>
likelihoodFunctionObj("like_",
paramDomain,
likelihoodRoutine,
(void *) &likelihoodRoutine_Data,
true); // routine computes [-2.*ln(function)]
////////////////////////////////////////////////////////
// Step 4 of 5: Instantiate the inverse problem
////////////////////////////////////////////////////////
#ifdef APPLS_MODAL_USES_CONCATENATION
QUESO::UniformVectorRV<QUESO::GslVector,QUESO::GslMatrix>
priorRvA("priorA_", paramDomainA);
QUESO::GslVector alpha(paramSpaceB.zeroVector());
alpha[0] = 3.;
QUESO::GslVector beta(paramSpaceB.zeroVector());
if (numModes == 1) {
beta[0] = 0.09709133373799;
}
else {
beta[0] = 0.08335837191688;
}
QUESO::InverseGammaVectorRV<QUESO::GslVector,QUESO::GslMatrix>
priorRvB("priorB_", paramDomainB,alpha,beta);
QUESO::ConcatenatedVectorRV<QUESO::GslVector,QUESO::GslMatrix>
priorRv("prior_", priorRvA, priorRvB, paramDomain);
#else
QUESO::UniformVectorRV<QUESO::GslVector,QUESO::GslMatrix>
priorRv("prior_", paramDomain);
#endif
QUESO::GenericVectorRV<QUESO::GslVector,QUESO::GslMatrix>
postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<QUESO::GslVector,QUESO::GslMatrix>
ip("", NULL, priorRv, likelihoodFunctionObj, postRv);
////////////////////////////////////////////////////////
// Step 5 of 5: Solve the inverse problem
////////////////////////////////////////////////////////
ip.solveWithBayesMLSampling();
////////////////////////////////////////////////////////
// Print some statistics
////////////////////////////////////////////////////////
unsigned int numPosTotal = postRv.realizer().subPeriod();
if (env.subDisplayFile()) {
*env.subDisplayFile() << "numPosTotal = " << numPosTotal
<< std::endl;
}
QUESO::GslVector auxVec(paramSpace.zeroVector());
unsigned int numPosTheta1SmallerThan1dot5 = 0;
for (unsigned int i = 0; i < numPosTotal; ++i) {
postRv.realizer().realization(auxVec);
if (auxVec[0] < 1.5) numPosTheta1SmallerThan1dot5++;
}
if (env.subDisplayFile()) {
*env.subDisplayFile() << "numPosTheta1SmallerThan1dot5 = " << numPosTheta1SmallerThan1dot5
<< ", ratio = " << ((double) numPosTheta1SmallerThan1dot5)/((double) numPosTotal)
<< std::endl;
}
return;
}
