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check whether the model is sparse or symbolic represented

refactoring
Sebastian Junges 5 years ago
parent
commit
fbff51a096
  1. 2
      src/storage/model.cpp

2
src/storage/model.cpp

@ -121,6 +121,8 @@ void define_model(py::module& m) {
.def_property_readonly("supports_parameters", &ModelBase::supportsParameters, "Flag whether model supports parameters") .def_property_readonly("supports_parameters", &ModelBase::supportsParameters, "Flag whether model supports parameters")
.def_property_readonly("has_parameters", &ModelBase::hasParameters, "Flag whether model has parameters") .def_property_readonly("has_parameters", &ModelBase::hasParameters, "Flag whether model has parameters")
.def_property_readonly("is_exact", &ModelBase::isExact, "Flag whether model is exact") .def_property_readonly("is_exact", &ModelBase::isExact, "Flag whether model is exact")
.def_property_readonly("is_sparse_model", &ModelBase::isSparseModel, "Flag whether the model is stored as a sparse model")
.def_property_readonly("is_symbolic_model", &ModelBase::isSymbolicModel, "Flag whether the model is stored using decision diagrams")
.def("_as_sparse_dtmc", [](ModelBase &modelbase) { .def("_as_sparse_dtmc", [](ModelBase &modelbase) {
return modelbase.as<SparseDtmc<double>>(); return modelbase.as<SparseDtmc<double>>();
}, "Get model as sparse DTMC") }, "Get model as sparse DTMC")

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