aGrUM 3.2.0
a C++ library for (probabilistic) graphical models
gum::learning::BNDatabaseGenerator< GUM_SCALAR > Member List

This is the complete list of members for gum::learning::BNDatabaseGenerator< GUM_SCALAR >, including all inherited members.

_bn_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_database_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_discretizedLabelMode_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_drawnSamples_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_label_(const std::vector< Idx > &row, const DiscreteVariable &v, Idx i) constgum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_log2likelihood_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_names2ids_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_nbVars_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_varOrder_gum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_varOrderFromCSV_(std::string_view csvFileURL, std::string_view csvSeparator=",") constgum::learning::BNDatabaseGenerator< GUM_SCALAR >private
_varOrderFromCSV_(std::ifstream &csvFile, std::string_view csvSeparator=",") constgum::learning::BNDatabaseGenerator< GUM_SCALAR >private
bn(void)gum::learning::BNDatabaseGenerator< GUM_SCALAR >
BNDatabaseGenerator(const BayesNet< GUM_SCALAR > &bn)gum::learning::BNDatabaseGenerator< GUM_SCALAR >explicit
BNDatabaseGenerator(const BNDatabaseGenerator &)=deletegum::learning::BNDatabaseGenerator< GUM_SCALAR >private
BNDatabaseGenerator(BNDatabaseGenerator &&)=deletegum::learning::BNDatabaseGenerator< GUM_SCALAR >private
database() constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
DiscretizedLabelMode enum namegum::learning::BNDatabaseGenerator< GUM_SCALAR >
drawSamples(Size nbSamples)gum::learning::BNDatabaseGenerator< GUM_SCALAR >
drawSamples(Size nbSamples, const gum::Instantiation &evs, int timeout=300)gum::learning::BNDatabaseGenerator< GUM_SCALAR >
log2likelihood() constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
onProgressgum::ProgressNotifier
onStopgum::ProgressNotifier
operator=(const BNDatabaseGenerator &)=deletegum::learning::BNDatabaseGenerator< GUM_SCALAR >private
operator=(BNDatabaseGenerator &&)=deletegum::learning::BNDatabaseGenerator< GUM_SCALAR >private
samplesAt(Idx row, Idx col) constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
samplesLabelAt(Idx row, Idx col) constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
samplesNbCols() constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
samplesNbRows() constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
setAntiTopologicalVarOrder()gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setDiscretizedLabelModeInterval()gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setDiscretizedLabelModeMedian()gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setDiscretizedLabelModeRandom()gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setRandomVarOrder()gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setTopologicalVarOrder()gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setVarOrder(const std::vector< Idx > &varOrder)gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setVarOrder(const std::vector< std::string > &varOrder)gum::learning::BNDatabaseGenerator< GUM_SCALAR >
setVarOrderFromCSV(std::string_view csvFileURL, std::string_view csvSeparator=",")gum::learning::BNDatabaseGenerator< GUM_SCALAR >
toCSV(std::string_view csvFileURL, bool useLabels=true, bool append=false, std::string csvSeparator=",", bool checkOnAppend=false) constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
toDatabaseTable(bool useLabels=true) constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
varOrder() constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
varOrderNames() constgum::learning::BNDatabaseGenerator< GUM_SCALAR >
~BNDatabaseGenerator()gum::learning::BNDatabaseGenerator< GUM_SCALAR >