52#ifndef GUM_BIF_WRITER_H
53#define GUM_BIF_WRITER_H
78 template <
typename GUM_SCALAR >
146#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
Definition of abstract classes for file output manipulation of Bayesian networks.
std::string _variablesLabels_(const Sequence< const DiscreteVariable * > &varsSeq, const Instantiation &inst)
BIFWriter()
Default constructor.
BIFWriter(BIFWriter &&) noexcept=default
Default constructor.
std::string _variableCPT_(const Tensor< GUM_SCALAR > &cpt)
std::string _variableBloc_(const DiscreteVariable &var)
BIFWriter(const BIFWriter &)=default
Default constructor.
void _syntacticalCheck(const IBayesNet< GUM_SCALAR > &bn) final
Check whether the BN is syntactically correct for BIF format.
~BIFWriter() override
Destructor.
void _doWrite(std::ostream &output, const IBayesNet< GUM_SCALAR > &bn) final
Writes a Bayesian network in the output stream using the BIF format.
std::string _header_(const IBayesNet< GUM_SCALAR > &bn)
BNWriter()
Default constructor.
Base class for discrete random variable.
Class representing the minimal interface for Bayesian network with no numerical data.
Class for assigning/browsing values to tuples of discrete variables.
The generic class for storing (ordered) sequences of objects.
aGrUM's Tensor is a multi-dimensional array with tensor operators.
gum is the global namespace for all aGrUM entities