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, 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;
}
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;
}
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;
}
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;
}
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) {
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 infer_slope(const QUESO::FullEnvironment & env) {
// Statistical Inverse Problem: Compute posterior pdf for slope 'm' and y-intercept c
// Step 1: Instantiate the parameter space
QUESO::VectorSpace<> paramSpace(env, "param_", 2, NULL); // 2 since we have a 2D problem
// Step 2: Parameter domain
QUESO::GslVector paramMinValues(paramSpace.zeroVector());
QUESO::GslVector paramMaxValues(paramSpace.zeroVector());
paramMinValues[0] = 2.;
paramMaxValues[0] = 5.;
paramMinValues[1] = 3.;
paramMaxValues[1] = 7.;
QUESO::BoxSubset<> paramDomain("param_", paramSpace, paramMinValues, paramMaxValues);
// Step 3: Instantiate likelihood
Likelihood<> lhood("like_", paramDomain);
// Step 4: Define the prior RV
QUESO::UniformVectorRV<> priorRv("prior_", paramDomain);
// Step 5: Instantiate the inverse problem
QUESO::GenericVectorRV<> postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<> ip("", NULL, priorRv, lhood, postRv);
// Step 6: Solve the inverse problem
// Randomly sample for the initial state?
QUESO::GslVector paramInitials(paramSpace.zeroVector());
priorRv.realizer().realization(paramInitials);
// Initialize the Cov matrix:
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
proposalCovMatrix(0,0) = std::pow(std::abs(paramInitials[0]) / 20.0, 2.0);
proposalCovMatrix(1,1) = std::pow(std::abs(paramInitials[1]) / 20.0, 2.0);
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
// Using the posterior pdfs for m and c, compute 'y' at a given 'x'
// Step 1: Instantiate the qoi space
QUESO::VectorSpace<> qoiSpace(env, "qoi_", 1, NULL);
// Step 2: Instantiate the parameter domain
// Not necessary here because the posterior from SIP is used as the RV for SFP
// Step 3: Instantiate the qoi object to be used by QUESO
Qoi<> qoi("qoi_", paramDomain, qoiSpace);
// Step 4: Define the input RV
// Not required because we use the posterior as RV
// Step 5: Instantiate the forward problem
QUESO::GenericVectorRV<> qoiRv("qoi_", qoiSpace);
QUESO::StatisticalForwardProblem<> fp("", NULL, postRv, qoi, qoiRv);
// Step 6: Solve the forward problem
std::cout << "Solving the SFP with Monte Carlo" << std::endl << std::endl;
fp.solveWithMonteCarlo(NULL);
system("mv outputData/sfp_lineSlope_qoi_seq.txt outputData/sfp_lineSlope_qoi_seq_post.txt");
// SENSITIVITY ANALYSIS
// For m
Qoi_m<> qoi_m("qoi_", paramDomain, qoiSpace);
// Step 4: Define the input RV
// Not required because we use the prior as RV for sensitivity analysis
// Step 5: Instantiate the forward problem
QUESO::StatisticalForwardProblem<> fp_m("", NULL, priorRv, qoi_m, qoiRv);
// Step 6: Solve the forward problem
fp_m.solveWithMonteCarlo(NULL);
system("mv outputData/sfp_lineSlope_qoi_seq.txt outputData/sense_m.txt");
// For c
Qoi_c<> qoi_c("qoi_", paramDomain, qoiSpace);
// Step 4: Define the input RV
// Not required because we use the prior as RV for sensitivity analysis
// Step 5: Instantiate the forward problem
QUESO::StatisticalForwardProblem<> fp_c("", NULL, priorRv, qoi_c, qoiRv);
// Step 6: Solve the forward problem
fp_c.solveWithMonteCarlo(NULL);
system("mv outputData/sfp_lineSlope_qoi_seq.txt outputData/sense_c.txt");
// For both, m and c
Qoi_mc<> qoi_mc("qoi_", paramDomain, qoiSpace);
// Step 4: Define the input RV
// Not required because we use the prior as RV for sensitivity analysis
// Step 5: Instantiate the forward problem
QUESO::StatisticalForwardProblem<> fp_mc("", NULL, priorRv, qoi_mc, qoiRv);
// Step 6: Solve the forward problem
fp_mc.solveWithMonteCarlo(NULL);
system("mv outputData/sfp_lineSlope_qoi_seq.txt outputData/sense_mc.txt");
}
int main(int argc, char* argv[])
{
#ifndef QUESO_HAS_MPI
// Skip this test if we're not in parallel
return 77;
#else
MPI_Init(&argc, &argv);
std::string inputFileName = argv[1];
const char * test_srcdir = std::getenv("srcdir");
if (test_srcdir)
inputFileName = test_srcdir + ('/' + inputFileName);
// Initialize QUESO environment
QUESO::FullEnvironment env(MPI_COMM_WORLD, inputFileName, "", NULL);
//================================================================
// Statistical inverse problem (SIP): find posterior PDF for 'g'
//================================================================
//------------------------------------------------------
// SIP Step 1 of 6: Instantiate the parameter space
//------------------------------------------------------
QUESO::VectorSpace<> 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<> paramDomain("param_", paramSpace, paramMinValues,
paramMaxValues);
//------------------------------------------------------
// SIP Step 3 of 6: Instantiate the likelihood function
// object to be used by QUESO.
//------------------------------------------------------
Likelihood<> lhood("like_", paramDomain);
//------------------------------------------------------
// SIP Step 4 of 6: Define the prior RV
//------------------------------------------------------
QUESO::UniformVectorRV<> priorRv("prior_", paramDomain);
//------------------------------------------------------
// SIP Step 5 of 6: Instantiate the inverse problem
//------------------------------------------------------
// Extra prefix before the default "rv_" prefix
QUESO::GenericVectorRV<> postRv("post_", paramSpace);
// No extra prefix before the default "ip_" prefix
QUESO::StatisticalInverseProblem<> ip("", NULL, priorRv, lhood, postRv);
//------------------------------------------------------
// SIP Step 6 of 6: Solve the inverse problem, that is,
// set the 'pdf' and the 'realizer' of the posterior RV
//------------------------------------------------------
QUESO::GslVector paramInitials(paramSpace.zeroVector());
priorRv.realizer().realization(paramInitials);
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
proposalCovMatrix(0,0) = std::pow(std::abs(paramInitials[0]) / 20.0, 2.0);
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.
//================================================================
//------------------------------------------------------
// 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<> 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 object
// to be used by QUESO.
//------------------------------------------------------
Qoi<> qoi("qoi_", paramDomain, qoiSpace);
//------------------------------------------------------
// 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<> qoiRv("qoi_", qoiSpace);
QUESO::StatisticalForwardProblem<> fp("", NULL, postRv, qoi, qoiRv);
//------------------------------------------------------
// SFP Step 6 of 6: Solve the forward problem
//------------------------------------------------------
fp.solveWithMonteCarlo(NULL);
MPI_Finalize();
return 0;
#endif // QUESO_HAS_MPI
}
void
somatic_snv_caller_strand_grid::
position_somatic_snv_call(const extended_pos_info& normal_epi,
const extended_pos_info& tumor_epi,
const extended_pos_info* normal_epi_t2_ptr,
const extended_pos_info* tumor_epi_t2_ptr,
somatic_snv_genotype_grid& sgt) const {
static const bool is_always_test(false);
{
const snp_pos_info& normal_pi(normal_epi.pi);
const snp_pos_info& tumor_pi(tumor_epi.pi);
if(normal_pi.ref_base=='N') return;
sgt.ref_gt=base_to_id(normal_pi.ref_base);
// check that a non-reference call meeting quality criteria even
// exists:
if(not is_always_test) {
if(is_spi_allref(normal_pi,sgt.ref_gt) and is_spi_allref(tumor_pi,sgt.ref_gt)) return;
}
}
// strawman model treats normal and tumor as independent, so
// calculate separate lhoods:
blt_float_t normal_lhood[DIGT_SGRID::SIZE];
blt_float_t tumor_lhood[DIGT_SGRID::SIZE];
const bool is_tier2(NULL != normal_epi_t2_ptr);
static const unsigned n_tier(2);
result_set tier_rs[n_tier];
for(unsigned i(0); i<n_tier; ++i) {
const bool is_include_tier2(i==1);
if(is_include_tier2) {
if(! is_tier2) continue;
if(tier_rs[0].snv_qphred==0) {
tier_rs[1].snv_qphred=0;
continue;
}
}
// get likelihood of each genotype
//
static const bool is_normal_het_bias(false);
static const blt_float_t normal_het_bias(0.0);
static const bool is_tumor_het_bias(false);
static const blt_float_t tumor_het_bias(0.0);
const extended_pos_info& nepi(is_include_tier2 ? *normal_epi_t2_ptr : normal_epi );
const extended_pos_info& tepi(is_include_tier2 ? *tumor_epi_t2_ptr : tumor_epi );
get_diploid_gt_lhood_spi(_opt,nepi.pi,is_normal_het_bias,normal_het_bias,normal_lhood);
get_diploid_gt_lhood_spi(_opt,tepi.pi,is_tumor_het_bias,tumor_het_bias,tumor_lhood);
get_diploid_het_grid_lhood_spi(nepi.pi,normal_lhood+DIGT::SIZE);
get_diploid_het_grid_lhood_spi(tepi.pi,tumor_lhood+DIGT::SIZE);
get_diploid_strand_grid_lhood_spi(nepi.pi,sgt.ref_gt,normal_lhood+DIGT_SGRID::PRESTRAND_SIZE);
get_diploid_strand_grid_lhood_spi(tepi.pi,sgt.ref_gt,tumor_lhood+DIGT_SGRID::PRESTRAND_SIZE);
// genomic site results:
calculate_result_set_grid(normal_lhood,
tumor_lhood,
get_prior_set(sgt.ref_gt),
_ln_som_match,_ln_som_mismatch,
sgt.ref_gt,
tier_rs[i]);
#if 0
#ifdef ENABLE_POLY
// polymorphic site results:
assert(0); // still needs to be adapted for 2-tier system:
calculate_result_set(normal_lhood,tumor_lhood,
lnprior_polymorphic(sgt.ref_gt),sgt.ref_gt,sgt.poly);
#else
sgt.poly.snv_qphred = 0;
#endif
#endif
#ifdef SOMATIC_DEBUG
if((i==0) && (tier_rs[i].snv_qphred > 0)) {
const somatic_snv_caller_strand_grid::prior_set& pset(get_prior_set(sgt.ref_gt));
const blt_float_t lnmatch(_ln_som_match);
const blt_float_t lnmismatch(_ln_som_mismatch);
log_os << "DUMP ON\n";
log_os << "tier1_qphred: " << tier_rs[0].snv_qphred << "\n";
// instead of dumping the entire distribution, we sort the lhood,prior,and prob to print out the N top values of each:
std::vector<double> lhood(DDIGT_SGRID::SIZE);
std::vector<double> prior(DDIGT_SGRID::SIZE);
std::vector<double> post(DDIGT_SGRID::SIZE);
// first get raw lhood:
//
for(unsigned ngt(0); ngt<DIGT_SGRID::PRESTRAND_SIZE; ++ngt) {
for(unsigned tgt(0); tgt<DIGT_SGRID::PRESTRAND_SIZE; ++tgt) {
const unsigned dgt(DDIGT_SGRID::get_state(ngt,tgt));
// unorm takes the role of the normal prior for the somatic case:
// static const blt_float_t unorm(std::log(static_cast<blt_float_t>(DIGT_SGRID::PRESTRAND_SIZE)));
//blt_float_t prior;
//if(tgt==ngt) { prior=pset.normal[ngt]+lnmatch; }
//else { prior=pset.somatic_marginal[ngt]+lnmismatch; }
blt_float_t pr;
if(tgt==ngt) { pr=pset.normal[ngt]+lnmatch; }
else { pr=pset.somatic_marginal[ngt]+lnmismatch; }
prior[dgt] = pr;
lhood[dgt] = normal_lhood[ngt]+tumor_lhood[tgt];
post[dgt] = lhood[dgt] + prior[dgt];
}
}
for(unsigned gt(DIGT_SGRID::PRESTRAND_SIZE); gt<DIGT_SGRID::SIZE; ++gt) {
const unsigned dgt(DDIGT_SGRID::get_state(gt,gt));
lhood[dgt] = normal_lhood[gt]+tumor_lhood[gt];
prior[dgt] = pset.normal[gt]+lnmatch;
post[dgt] = lhood[dgt] + prior[dgt];
}
std::vector<double> lhood2(lhood);
sort_n_dump("lhood_prior",lhood,prior,sgt.ref_gt);
sort_n_dump("post_lhood",post,lhood2,sgt.ref_gt);
log_os << "DUMP OFF\n";
}
#endif
}
if((tier_rs[0].snv_qphred==0) ||
(is_tier2 && (tier_rs[1].snv_qphred==0))) return;
sgt.snv_tier=0;
sgt.snv_from_ntype_tier=0;
if(is_tier2) {
if(tier_rs[0].snv_qphred > tier_rs[1].snv_qphred) {
sgt.snv_tier=1;
}
if(tier_rs[0].snv_from_ntype_qphred > tier_rs[1].snv_from_ntype_qphred) {
sgt.snv_from_ntype_tier=1;
}
}
sgt.rs=tier_rs[sgt.snv_from_ntype_tier];
if(is_tier2 && (tier_rs[0].ntype != tier_rs[1].ntype)) {
// catch NTYPE conflict states:
sgt.rs.ntype = NTYPE::CONFLICT;
sgt.rs.snv_from_ntype_qphred = 0;
} else {
// classify NTYPE:
//
// convert diploid genotype into more limited ntype set:
//
if (sgt.rs.ntype==sgt.ref_gt) {
sgt.rs.ntype=NTYPE::REF;
} else if(DIGT::is_het(sgt.rs.ntype)) {
sgt.rs.ntype=NTYPE::HET;
} else {
sgt.rs.ntype=NTYPE::HOM;
}
}
sgt.rs.snv_qphred = tier_rs[sgt.snv_tier].snv_qphred;
sgt.is_snv=((sgt.rs.snv_qphred != 0));
}
