void
Options::check()
{
if (static_cast<size_t>(options.rows()) != simulations.size() or
options.cols() == 0 or simulations.size() != departments.size() or
simulations.size() != years.size() or
simulations.size() != observed.size() or
not(simulations.size() == places.size() or places.empty()) or
simulations.size() != id_subdataset_reduced.size() or
subdataset.size() != simulations.size())
throw solver_error("Options are inconsistent");
}
prediction_evaluator::prediction_evaluator(std::shared_ptr<Context> context,
const Model &model,
const Options &options)
: m_context(context)
, m_model(model)
, m_options(options)
, m_globalsimulated(options.observed.size(), 0)
, simulated(options.options.rows())
, observed(options.options.rows())
, solver(context, model)
, kappa_c(model.attributes[0].scale.size())
{
if (not options.have_subdataset())
throw solver_error(
"options does not have enough data to build the training set");
}
