aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
BNDatabaseGenerator.h File Reference
Include dependency graph for BNDatabaseGenerator.h:
This graph shows which files directly or indirectly include this file:

Go to the source code of this file.

Classes

class  gum::learning::BNDatabaseGenerator< GUM_SCALAR >

Namespaces

namespace  gum
 gum is the global namespace for all aGrUM entities
namespace  gum::learning
 include the inlined functions if necessary

Detailed Description

Author
Santiago CORTIJO and Pierre-Henri WUILLEMIN(_at_LIP6)

Constructor

(being "bn" a BayesNet<GUM_SCALAR>)

CSV Generation:

std::string csvFileName="foo.csv"
gum::Size nbSamples = 100;
bool useLabels = false;
bool append = false;
std::string csvSeparator(",");
dbgen.drawSamples(nbSamples);
dbgen.setRandomVarOrder();
dbgen.toCSV(csvFileName, useLabels, append, csvSeparator);
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition types.h:74

CSV append:

std::string csvFileName="foo.csv"
gum::Size nbSamples = 100;
bool useLabels = false;
bool append = true;
std::string csvSeparator(":::");
bool checkOnAppend = true;
dbgen.drawSamples(nbSamples);
dbgen.setVarOrderFromCSV(csv_file, csvSeparator);
dbgen.toCSV(csv_file, useLabels, append, csvSeparator, checkOnAppend);

DatabaseVectInRam mdoe:

gum::learning::DatabaseVectInRAM database =
dbgen.toDatabaseVectInRAM(useLabels);

Definition in file BNDatabaseGenerator.h.