void TaylorApproximation::build()
{
// base class implementation checks data set against min required
Approximation::build();
// No computations needed. Just do sanity checking on approxData.
// Check number of data points
if (!approxData.anchor() || approxData.size()) {
Cerr << "Error: wrong number of data points in TaylorApproximation::"
<< "build()." << std::endl;
abort_handler(-1);
}
// Check gradient
short bdo = sharedDataRep->buildDataOrder;
size_t num_v = sharedDataRep->numVars;
if ( (bdo & 2) && approxData.anchor_gradient().length() != num_v) {
Cerr << "Error: gradient vector required in TaylorApproximation::build()."
<< std::endl;
abort_handler(-1);
}
// Check Hessian
if ( (bdo & 4) && approxData.anchor_hessian().numRows() != num_v) {
Cerr << "Error: Hessian matrix required in TaylorApproximation::build()."
<< std::endl;
abort_handler(-1);
}
}
PythonInterface::PythonInterface(const ProblemDescDB& problem_db)
: DirectApplicInterface(problem_db),
userNumpyFlag(problem_db.get_bool("interface.python.numpy"))
{
Py_Initialize();
if (Py_IsInitialized()) {
if (outputLevel >= NORMAL_OUTPUT)
Cout << "Python interpreter initialized for direct function evaluation."
<< std::endl;
}
else {
Cerr << "Error: Could not initialize Python for direct function "
<< "evaluation." << std::endl;
abort_handler(-1);
}
if (userNumpyFlag) {
#ifdef DAKOTA_PYTHON_NUMPY
import_array();
#else
Cerr << "\nError: Direct Python interface 'numpy' option requested, but "
<< "not available." << std::endl;
abort_handler(-1);
#endif
}
// prepend sys.path (env PYTHONPATH) with empty string to find module in pwd
// This assumes any directory changing in the driver is reversed
// between function evaluations
PyRun_SimpleString("import sys\nsys.path.insert(0,\"\")");
}
/** Executes the sysCommand by passing it to system(). Appends an
"&" if asynchFlag is set (background system call) and echos the
sysCommand to Cout if suppressOutputFlag is not set. */
CommandShell& CommandShell::flush()
{
if (asynchFlag)
#if !defined(_MSC_VER)
sysCommand += " &";
#else
sysCommand = "start \"SystemInterface-Evaluation\" " + sysCommand;
#endif
if (!suppressOutputFlag)
Cout << sysCommand << std::endl; // output the cmd string for verification
if ( !workDir.empty() )
WorkdirHelper::change_cwd(workDir);
#ifdef HAVE_SYSTEM
std::system(sysCommand.c_str());
#else
Cout << "ERROR: attempting to use a system call on a system that does"
<< " NOT support system calls" << std::endl;
abort_handler(-1);
#endif
if ( !workDir.empty() )
WorkdirHelper::reset();
sysCommand.clear();
return *this;
}
/// On-the-fly constructor which uses mostly Surfpack model defaults
SharedSurfpackApproxData::
SharedSurfpackApproxData(const String& approx_type,
const UShortArray& approx_order, size_t num_vars,
short data_order, short output_level):
SharedApproxData(NoDBBaseConstructor(), approx_type, num_vars, data_order,
output_level),
crossValidateFlag(false), numFolds(0), percentFold(0.0), pressFlag(false)
{
approxType = approx_type;
if (approx_order.empty())
approxOrder = 2;
else {
approxOrder = approx_order[0];
if (approx_order.size() != num_vars) {
Cerr << "Error: bad size of " << approx_order.size()
<< " for approx_order in SharedSurfpackApproxData lightweight "
<< "constructor. Expected " << num_vars << "." << std::endl;
abort_handler(-1);
}
for (size_t i=1; i<num_vars; ++i)
if (approx_order[i] != approxOrder) {
Cerr << "Warning: SharedSurfpackApproxData lightweight constructor "
<< "requires homogeneous approximation order. Promoting to max "
<< "value." << std::endl;
approxOrder = std::max(approx_order[i], approxOrder);
}
}
}
void Approximation::
add(const Variables& vars, bool anchor_flag, bool deep_copy)
{
if (approxRep)
approxRep->add(vars, anchor_flag, deep_copy);
else { // not virtual: all derived classes use following definition
// Approximation does not know about view mappings; therefore, take the
// simple approach of matching up active or all counts with numVars.
size_t num_v = sharedDataRep->numVars;
if (vars.cv() + vars.div() + vars.drv() == num_v)
add(vars.continuous_variables(), vars.discrete_int_variables(),
vars.discrete_real_variables(), anchor_flag, deep_copy);
else if (vars.acv() + vars.adiv() + vars.adrv() == num_v)
add(vars.all_continuous_variables(), vars.all_discrete_int_variables(),
vars.all_discrete_real_variables(), anchor_flag, deep_copy);
/*
else if (vars.cv() == num_v) { // compactMode does not affect vars
IntVector empty_iv; RealVector empty_rv;
add(vars.continuous_variables(), empty_iv, empty_rv,
anchor_flag, deep_copy);
}
else if (vars.acv() == num_v) { // potential conflict with cv/div/drv
IntVector empty_iv; RealVector empty_rv;
add(vars.all_continuous_variables(), empty_iv, empty_rv,
anchor_flag, deep_copy);
}
*/
else {
Cerr << "Error: variable size mismatch in Approximation::add()."
<< std::endl;
abort_handler(-1);
}
}
}
Real ExperimentData::
scalar_sigma(size_t response, size_t experiment, size_t replicate)
{
if (allExperiments[response].experimentType != SCALAR_DATA) {
Cerr << "Error (ExperimentData): invalid query of scalar data." << std::endl;
abort_handler(-1);
}
return(allExperiments[response].dataThisResponse[experiment].sigmaScalar[replicate]);
}
void GridApplicInterface::
derived_map(const Variables& vars, const ActiveSet& set, Response& response,
int fn_eval_id)
{
//
// Launch the grid solver (asynchronously)
//
ParamResponsePair prp(vars, interfaceId, response, fn_eval_id);
derived_map_asynch(prp);
//
// Call wait_local_evaluations() until our id is in the set
//
PRPQueue prp_queue;
prp_queue.push_back(prp);
if (!completionSet.empty()) {
Cerr << "derived_map - should start with an empty completion set\n";
abort_handler(-1);
}
wait_local_evaluations(prp_queue); // rebuilds completionSet
response = prp_queue.front().prp_response();
completionSet.clear();
#if 0
//
// Read the params file and handle exceptions
//
try {
if (evalCommRank == 0)
read_results_files(response, fn_eval_id);
}
catch(String& err_msg) {
// a String exception involves detection of an incomplete file/data
// set. In the synchronous case, there is no potential for an incomplete
// file resulting from a race condition -> echo the error and abort.
Cerr << err_msg << std::endl;
abort_handler(-1);
}
catch(int fail_code) {
// The approach here is to have catch(int) rethrow the exception to an
// outer catch (either the catch within manage_failure or a catch that
// calls manage_failure).
throw;
}
#endif
}
void Approximation::approximation_coefficients(const RealVector& approx_coeffs)
{
if (approxRep)
approxRep->approximation_coefficients(approx_coeffs);
else {
Cerr << "Error: approximation_coefficients() not available for this "
<< "approximation type." << std::endl;
abort_handler(-1);
}
}
Real Approximation::value(const Variables& vars)
{
if (!approxRep) {
Cerr << "Error: value() not available for this approximation type."
<< std::endl;
abort_handler(-1);
}
return approxRep->value(vars);
}
Real Approximation::prediction_variance(const Variables& vars)
{
if (!approxRep) {
Cerr << "Error: prediction_variance() not available for this approximation "
<< "type." << std::endl;
abort_handler(-1);
}
return approxRep->prediction_variance(vars);
}
void Approximation::print_coefficients(std::ostream& s, bool normalized)
{
if (approxRep)
approxRep->print_coefficients(s, normalized);
else {
Cerr << "Error: print_coefficients() not available for this approximation "
<< "type." << std::endl;
abort_handler(-1);
}
}
const RealVector& Approximation::approximation_coefficients() const
{
if (!approxRep) {
Cerr << "Error: approximation_coefficients() not available for this "
<< "approximation type." << std::endl;
abort_handler(-1);
}
return approxRep->approximation_coefficients();
}
const RealVector& Approximation::gradient(const Variables& vars)
{
if (!approxRep) {
Cerr << "Error: gradient() not available for this approximation type."
<< std::endl;
abort_handler(-1);
}
return approxRep->gradient(vars);
}
const RealSymMatrix& Approximation::hessian(const Variables& vars)
{
if (!approxRep) {
Cerr << "Error: hessian() not available for this approximation type."
<< std::endl;
abort_handler(-1);
}
return approxRep->hessian(vars);
}
int Approximation::min_coefficients() const
{
if (!approxRep) { // no default implementation
Cerr << "Error: min_coefficients() not defined for this approximation type."
<< std::endl;
abort_handler(-1);
}
return approxRep->min_coefficients(); // fwd to letter
}
void Approximation::
coefficient_labels(std::vector<std::string>& coeff_labels) const
{
if (approxRep)
approxRep->coefficient_labels(coeff_labels);
else {
Cerr << "Error: coefficient_labels() not available for this approximation "
<< "type." << std::endl;
abort_handler(-1);
}
}
GridApplicInterface::
GridApplicInterface(const ProblemDescDB& problem_db):
SysCallApplicInterface(problem_db)
{
void* handle = dlopen("foo.so", RTLD_NOW);
if (!handle) {
Cerr << "Problem loading shared object file: foo.so" << std::endl;
abort_handler(-1);
}
start_grid_computing
= (start_grid_computing_t)(dlsym(handle, "start_grid_computing"));
const char* error;
if ((error = dlerror()) != NULL) {
Cerr << "Problem loading start_grid_computing function: " << error
<< std::endl;
abort_handler(-1);
}
stop_grid_computing
= (stop_grid_computing_t)dlsym(handle, "stop_grid_computing");
if ((error = dlerror()) != NULL) {
Cerr << "Problem loading stop_grid_computing function: " << error
<< std::endl;
abort_handler(-1);
}
perform_analysis = (perform_analysis_t)dlsym(handle, "perform_analysis");
if ((error = dlerror()) != NULL) {
Cerr << "Problem loading perform_analysis function: " << error << std::endl;
abort_handler(-1);
}
get_jobs_completed = (get_jobs_completed_t)dlsym(handle,"get_jobs_completed");
if ((error = dlerror()) != NULL) {
Cerr << "Problem loading get_jobs_completed function: " << error
<< std::endl;
abort_handler(-1);
}
int status = (*start_grid_computing)(programNames[0].data(),
paramsFileName.data(),
resultsFileName.data());
//fileSaveFlag=true;
}
void SharedSurfpackApproxData::
add_sd_to_surfdata(const Pecos::SurrogateDataVars& sdv,
const Pecos::SurrogateDataResp& sdr, short fail_code,
SurfData& surf_data)
{
// coarse-grained fault tolerance for now: any failure qualifies for omission
if (fail_code)
return;
// Surfpack's RealArray is std::vector<double>; use DAKOTA copy_data helpers.
// For DAKOTA's compact mode, any active discrete {int,real} variables could
// be contained within SDV's continuousVars (see Approximation::add(Real*)),
// although it depends on eval cache lookups as shown in
// ApproximationInterface::update_approximation().
RealArray x;
sdv_to_realarray(sdv, x);
Real f = sdr.response_function();
// for now only allow builds from exactly 1, 3=1+2, or 7=1+2+4; use
// different set functions so the SurfPoint data remains empty if
// not present
switch (buildDataOrder) {
case 1:
surf_data.addPoint(SurfPoint(x, f));
break;
case 3: {
RealArray gradient;
copy_data(sdr.response_gradient(), gradient);
surf_data.addPoint(SurfPoint(x, f, gradient));
break;
}
case 7: {
RealArray gradient;
copy_data(sdr.response_gradient(), gradient);
SurfpackMatrix<Real> hessian;
copy_matrix(sdr.response_hessian(), hessian);
surf_data.addPoint(SurfPoint(x, f, gradient, hessian));
break;
}
default:
Cerr << "\nError (SharedSurfpackApproxData): derivative data may only be "
<< "used if all\nlower-order information is also present. Specified "
<< "buildDataOrder is " << buildDataOrder << "." << std::endl;
abort_handler(-1);
break;
}
}
void NLPQLPOptimizer::initialize()
{
// NLPQLP does not support internal calculation of numerical derivatives
if (vendorNumericalGradFlag) {
Cerr << "\nError: vendor numerical gradients not supported by nlpql_sqp."
<< "\n Please select dakota numerical instead." << std::endl;
abort_handler(-1);
}
// Prevent nesting of an instance of a Fortran iterator within another
// instance of the same iterator (which would result in data clashes since
// Fortran does not support object independence). Recurse through all
// sub-models and test each sub-iterator for NLPQL presence.
Iterator sub_iterator = iteratedModel.subordinate_iterator();
if (!sub_iterator.is_null() &&
( strbegins(sub_iterator.method_name(), "nlpql") ||
strbegins(sub_iterator.uses_method(), "nlpql") ) )
sub_iterator.method_recourse();
ModelList& sub_models = iteratedModel.subordinate_models();
for (ModelLIter ml_iter = sub_models.begin();
ml_iter != sub_models.end(); ml_iter++) {
sub_iterator = ml_iter->subordinate_iterator();
if (!sub_iterator.is_null() &&
( strbegins(sub_iterator.method_name(), "nlpql") ||
strbegins(sub_iterator.uses_method(), "nlpql") ) )
sub_iterator.method_recourse();
}
// Set NLPQL optimization controls
L = 1;
ACC = 1.0e-9;
ACCQP = 1.0e-11;
STPMIN = 0;
MAXFUN = 10; // max fn evals per line search
MAXIT = maxIterations;
MAX_NM = 10;
TOL_NM = 0.1;
MODE = 0;
IOUT = 6;
LQL = 1;
switch (outputLevel) {
case DEBUG_OUTPUT:
IPRINT = 4; break;
case VERBOSE_OUTPUT:
IPRINT = 2; break;
case SILENT_OUTPUT:
IPRINT = 0; break;
case NORMAL_OUTPUT: default:
IPRINT = 1; break;
}
}
/** This is the alternate envelope constructor for instantiations on
the fly. Since it does not have access to problem_db, it utilizes
the NoDBBaseConstructor constructor chain. */
Approximation::Approximation(const SharedApproxData& shared_data):
sharedDataRep(NULL), referenceCount(1)
{
#ifdef REFCOUNT_DEBUG
Cout << "Approximation::Approximation(String&) called to instantiate "
<< "envelope." << std::endl;
#endif
// Set the rep pointer to the appropriate derived type
approxRep = get_approx(shared_data);
if ( !approxRep ) // bad type or insufficient memory
abort_handler(-1);
}
/** \b Usage: "dakota_restart_util cat dakota_1.rst ... dakota_n.rst
dakota_new.rst"
Combines multiple restart files into a single restart database. */
void concatenate_restart(int argc, char** argv)
{
if (argc < 5) {
Cerr << "Usage: \"dakota_restart_util cat <restart_file_1> ... "
<< "<restart_file_n> <new_restart_file>\"." << endl;
exit(-1);
}
std::ofstream restart_output_fs(argv[argc-1], std::ios::binary);
boost::archive::binary_oarchive restart_output_archive(restart_output_fs);
cout << "Writing new restart file " << argv[argc-1] << '\n';
for (int cat_cntr=2; cat_cntr<argc-1; cat_cntr++) {
std::ifstream restart_input_fs(argv[cat_cntr], std::ios::binary);
if (!restart_input_fs.good()) {
Cerr << "Error: failed to open restart file " << argv[cat_cntr] << endl;
exit(-1);
}
boost::archive::binary_iarchive restart_input_archive(restart_input_fs);
int cntr = 0;
while (restart_input_fs.good() && !restart_input_fs.eof()) {
ParamResponsePair current_pair;
try {
restart_input_archive & current_pair;
}
catch(const boost::archive::archive_exception& e) {
Cerr << "\nError reading restart file (boost::archive exception):\n"
<< e.what() << std::endl;
abort_handler(-1);
}
catch(const std::string& err_msg) {
Cout << "\nWarning reading restart file: " << err_msg << std::endl;
break;
}
restart_output_archive & current_pair;
cntr++;
// peek to force EOF if the last restart record was read
restart_input_fs.peek();
}
cout << argv[cat_cntr] << " processing completed: " << cntr
<< " evaluations retrieved.\n";
}
restart_output_fs.close();
}
/** This is the common base class portion of the virtual fn and is
insufficient on its own; derived implementations should explicitly
invoke (or reimplement) this base class contribution. */
void Approximation::pop(bool save_data)
{
if (approxRep)
approxRep->pop(save_data);
else {
if (popCountStack.empty()) {
Cerr << "\nError: empty count stack in Approximation::pop()."
<< std::endl;
abort_handler(-1);
}
approxData.pop(popCountStack.back(), save_data);
popCountStack.pop_back();
}
}
/** This is the common base class portion of the virtual fn and is
insufficient on its own; derived implementations should explicitly
invoke (or reimplement) this base class contribution. */
void Approximation::build()
{
if (approxRep)
approxRep->build();
else {
size_t num_curr_pts = approxData.size();
int ms = min_points(true); // account for anchor point & buildDataOrder
if (num_curr_pts < ms) {
Cerr << "\nError: not enough samples to build approximation. "
<< "Construction of this approximation\n requires at least "
<< ms << " samples for " << sharedDataRep->numVars << " variables. "
<< "Only " << num_curr_pts << " samples were provided." << std::endl;
abort_handler(-1);
}
}
}
void SharedSurfpackApproxData::
sdv_to_realarray(const Pecos::SurrogateDataVars& sdv, RealArray& ra)
{
// check incoming vars for correct length (active or all views)
const RealVector& cv = sdv.continuous_variables();
const IntVector& div = sdv.discrete_int_variables();
const RealVector& drv = sdv.discrete_real_variables();
if (cv.length() + div.length() + drv.length() == numVars)
merge_variable_arrays(cv, div, drv, ra);
else {
Cerr << "Error: bad parameter set length in SharedSurfpackApproxData::"
<< "sdv_to_realarray(): " << numVars << " != " << cv.length() << " + "
<< div.length() << " + " << drv.length() << "." << std::endl;
abort_handler(-1);
}
}
void RelaxedVariables::build_active_views()
{
// Initialize active view vectors and counts. Don't bleed over any logic
// about supported view combinations; rather, keep this class general and
// encapsulated.
const SizetArray& vc_totals = sharedVarsData.components_totals();
size_t num_cdv = vc_totals[0], num_ddv = vc_totals[1] + vc_totals[2],
num_mdv = num_cdv + num_ddv, num_cauv = vc_totals[3],
num_dauv = vc_totals[4] + vc_totals[5], num_ceuv = vc_totals[6],
num_deuv = vc_totals[7] + vc_totals[8], num_mauv = num_cauv + num_dauv,
num_meuv = num_ceuv + num_deuv, num_muv = num_mauv + num_meuv,
num_csv = vc_totals[9], num_dsv = vc_totals[10] + vc_totals[11],
num_msv = num_csv + num_dsv;
// Initialize active views
size_t cv_start, num_cv;
switch (sharedVarsData.view().first) {
case EMPTY:
Cerr << "Error: active view cannot be EMPTY in RelaxedVariables."
<< std::endl; abort_handler(-1); break;
case RELAXED_ALL:
// start at the beginning
cv_start = 0; num_cv = num_mdv + num_muv + num_msv; break;
case RELAXED_DESIGN:
// start at the beginning
cv_start = 0; num_cv = num_mdv; break;
case RELAXED_ALEATORY_UNCERTAIN:
// skip over the relaxed design variables
cv_start = num_mdv; num_cv = num_mauv; break;
case RELAXED_EPISTEMIC_UNCERTAIN:
// skip over the relaxed design and aleatory variables
cv_start = num_mdv+num_mauv; num_cv = num_meuv; break;
case RELAXED_UNCERTAIN:
// skip over the relaxed design variables
cv_start = num_mdv; num_cv = num_muv; break;
case RELAXED_STATE:
// skip over the relaxed design and uncertain variables
cv_start = num_mdv + num_muv; num_cv = num_msv; break;
}
sharedVarsData.cv_start(cv_start); sharedVarsData.cv(num_cv);
sharedVarsData.div_start(0); sharedVarsData.div(0);
sharedVarsData.drv_start(0); sharedVarsData.drv(0);
sharedVarsData.initialize_active_components();
if (num_cv)
continuousVars
= RealVector(Teuchos::View, &allContinuousVars[cv_start], num_cv);
}
inline void SequentialHybridStrategy::
initialize_iterator(const VariablesArray& param_sets)
{
// Note: in current usage, we update an iterator with either:
// > 1 set from parameterSets (numIteratorJobs == parameterSets.size())
// > all of parameterSets (numIteratorJobs == 1)
size_t num_param_sets = param_sets.size();
if (num_param_sets == 1)
userDefinedModels[seqCount].active_variables(param_sets[0]);
else if (selectedIterators[seqCount].accepts_multiple_points())
selectedIterators[seqCount].initial_points(param_sets);
else {
std::cerr << "Error: bad parameter sets array in SequentialHybridStrategy::"
<< "initialize_iterator()" << std::endl;
abort_handler(-1);
}
}
void SharedSurfpackApproxData::
vars_to_realarray(const Variables& vars, RealArray& ra)
{
// check incoming vars for correct length (active or all views)
if (vars.cv() + vars.div() + vars.drv() == numVars)
merge_variable_arrays(vars.continuous_variables(),
vars.discrete_int_variables(),
vars.discrete_real_variables(), ra);
else if (vars.acv() + vars.adiv() + vars.adrv() == numVars)
merge_variable_arrays(vars.all_continuous_variables(),
vars.all_discrete_int_variables(),
vars.all_discrete_real_variables(), ra);
else {
Cerr << "Error: bad parameter set length in SharedSurfpackApproxData::"
<< "vars_to_realarray()." << std::endl;
abort_handler(-1);
}
}
void SharedSurfpackApproxData::
copy_matrix(const RealSymMatrix& rsm, SurfpackMatrix<Real>& surfpack_matrix)
{
// SymmetricMatrix = symmetric and square, but Dakota::Matrix can be general
// (e.g., functionGradients = numFns x numVars). Therefore, have to verify
// sanity of the copy. Could copy square submatrix of rsm into sm, but
// aborting with an error seems better since this should only currently be
// used for copying Hessian matrices.
size_t nr = rsm.numRows(), nc = rsm.numCols();
if (nr != nc) {
Cerr << "Error: copy_data(const Dakota::RealSymMatrix& rsm, "
<< "SurfpackMatrix<Real>& sm) called with nonsquare rsm." << std::endl;
abort_handler(-1);
}
if (surfpack_matrix.getNRows() != nr | surfpack_matrix.getNCols() != nc)
surfpack_matrix.resize(nr, nc);
for (size_t i=0; i<nr; ++i)
for (size_t j=0; j<nc; ++j)
surfpack_matrix(i,j) = rsm(i,j);
}
/// Utility function from boost/test, not available in the DAKOTA snapshot
inline void putenv_impl(const char* name_and_value)
{
if ( putenv( (char*)name_and_value) ) {
Cerr << "\nError: putenv(" << name_and_value
<< ") failed in putenv_impl()" << std::endl;
abort_handler(-1);
}
/* WJB: alternate impl IF I believe what I read at following site:
// http://stackoverflow.com/questions/5873029/questions-about-putenv-and-setenv
std::vector<std::string> var_name_and_val_tokens;
boost::split( var_name_and_val_tokens,
name_and_value, boost::is_any_of("=") );
if ( setenv(var_name_and_val_tokens[0].c_str(),
var_name_and_val_tokens[1].c_str(), true) ) {
Cerr << "\nError: setenv(" << name_and_value
<< ") failed in putenv_impl()" << std::endl;
}
*/
}
/// The main point: a python interface that passes a python object back to the interface function
NRELPythonApplicInterface::NRELPythonApplicInterface(const ProblemDescDB& problem_db, void *pData) : NRELApplicInterface(problem_db, pData)
{
if (!Py_IsInitialized())
printf ("NOT initializing python here in NRELPythonApplicInterface constructor, should have already been done\n");
Py_Initialize();
if (Py_IsInitialized()) {
if (outputLevel >= NORMAL_OUTPUT)
Cout << "Python interpreter initialized for direct function evaluation."
<< std::endl;
}
else {
Cerr << "Error: Could not initialize Python for direct function "
<< "evaluation." << std::endl;
abort_handler(-1);
}
import_array();
// userNumpyFlag = problem_db.get_bool("python_numpy");
// userNumpyFlag = true;
userNumpyFlag = true;
}
