gum::learning::genericBNLearner::Database Class Reference

a helper to easily read databases More...

#include <genericBNLearner.h>

Collaboration diagram for gum::learning::genericBNLearner::Database:

Public Member Functions
template<typename GUM_SCALAR >
	Database (const std::string &filename, const BayesNet< GUM_SCALAR > &bn, const std::vector< std::string > &missing_symbols)
Constructors / Destructors
	Database (const std::string &file, const std::vector< std::string > &missing_symbols)
	default constructor More...
	Database (const DatabaseTable<> &db)
	default constructor More...
	Database (const std::string &filename, Database &score_database, const std::vector< std::string > &missing_symbols)
	constructor for the aprioris More...
template<typename GUM_SCALAR >
	Database (const std::string &filename, const gum::BayesNet< GUM_SCALAR > &bn, const std::vector< std::string > &missing_symbols)
	constructor with a BN providing the variables of interest More...
	Database (const Database &from)
	copy constructor More...
	Database (Database &&from)
	move constructor More...
	~Database ()
	destructor More...
Operators
Database &	operator= (const Database &from)
	copy operator More...
Database &	operator= (Database &&from)
	move operator More...
Accessors / Modifiers
DBRowGeneratorParser &	parser ()
	returns the parser for the database More...
const std::vector< std::size_t > &	domainSizes () const
	returns the domain sizes of the variables More...
const std::vector< std::string > &	names () const
	returns the names of the variables in the database More...
NodeId	idFromName (const std::string &var_name) const
	returns the node id corresponding to a variable name More...
const std::string &	nameFromId (NodeId id) const
	returns the variable name corresponding to a given node id More...
const DatabaseTable &	databaseTable () const
	returns the internal database table More...
void	setDatabaseWeight (const double new_weight)
	assign a weight to all the rows of the database so that the sum of their weights is equal to new_weight More...
const Bijection< NodeId, std::size_t > &	nodeId2Columns () const
	returns the mapping between node ids and their columns in the database More...
const std::vector< std::string > &	missingSymbols () const
	returns the set of missing symbols taken into account More...
std::size_t	nbRows () const
	returns the number of records in the database More...
std::size_t	size () const
	returns the number of records in the database More...
void	setWeight (const std::size_t i, const double weight)
	sets the weight of the ith record More...
double	weight (const std::size_t i) const
	returns the weight of the ith record More...
double	weight () const
	returns the weight of the whole database More...

Protected Attributes
DatabaseTable	database__
	the database itself More...
DBRowGeneratorParser *	parser__ {nullptr}
	the parser used for reading the database More...
std::vector< std::size_t >	domain_sizes__
	the domain sizes of the variables (useful to speed-up computations) More...
Bijection< NodeId, std::size_t >	nodeId2cols__
	a bijection assigning to each variable name its NodeId More...
Size	max_threads_number__ {1}
	the max number of threads authorized More...
Size	min_nb_rows_per_thread__ {100}
	the minimal number of rows to parse (on average) by thread More...

Detailed Description

a helper to easily read databases

Definition at line 145 of file genericBNLearner.h.

Constructor & Destructor Documentation

Database() [1/7]

gum::learning::genericBNLearner::Database::Database ( const std::string &  file, const std::vector< std::string > &  missing_symbols  )

explicit

default constructor

Parameters

file	the name of the CSV file containing the data
missing_symbols	the set of symbols in the CSV file that correspond to missing data

Definition at line 69 of file genericBNLearner.cpp.

                                                      :
        Database(genericBNLearner::readFile__(filename, missing_symbols)) {}

Database() [2/7]

gum::learning::genericBNLearner::Database::Database ( const DatabaseTable<> &  db )

explicit

default constructor

Parameters

db	an already initialized database table that is used to fill the Database

Definition at line 52 of file genericBNLearner.cpp.

                                                                :
        database__(db) {
      // get the variables names
      const auto&       var_names = database__.variableNames();
      const std::size_t nb_vars   = var_names.size();
      for (auto dom: database__.domainSizes())
        domain_sizes__.push_back(dom);
      for (std::size_t i = 0; i < nb_vars; ++i) {
        nodeId2cols__.insert(NodeId(i), i);
      }

      // create the parser
      parser__
         = new DBRowGeneratorParser<>(database__.handler(), DBRowGeneratorSet<>());
    }

Database() [3/7]

gum::learning::genericBNLearner::Database::Database	(	const std::string &	filename,
		Database &	score_database,
		const std::vector< std::string > &	missing_symbols
	)

constructor for the aprioris

We must ensure that the variables of the Database are identical to those of the score database (else the countings used by the scores might be erroneous). However, we allow the variables to be ordered differently in the two databases: variables with the same name in both databases are supposed to be the same.

Parameters

file	the name of the CSV file containing the data
score_database	the main database used for the learning
missing_symbols	the set of symbols in the CSV file that correspond to missing data

Definition at line 75 of file genericBNLearner.cpp.

                                                      {
      // assign to each column name in the CSV file its column
      genericBNLearner::checkFileName__(CSV_filename);
      DBInitializerFromCSV<> initializer(CSV_filename);
      const auto&            apriori_names   = initializer.variableNames();
      std::size_t            apriori_nb_vars = apriori_names.size();
      HashTable< std::string, std::size_t > apriori_names2col(apriori_nb_vars);
      for (std::size_t i = std::size_t(0); i < apriori_nb_vars; ++i)
        apriori_names2col.insert(apriori_names[i], i);

      // check that there are at least as many variables in the a priori
      // database as those in the score_database
      if (apriori_nb_vars < score_database.database__.nbVariables()) {
        GUM_ERROR(InvalidArgument,
                  "the a apriori database has fewer variables "
                  "than the observed database");
      }

      // get the mapping from the columns of score_database to those of
      // the CSV file
      const std::vector< std::string >& score_names
         = score_database.databaseTable().variableNames();
      const std::size_t                     score_nb_vars = score_names.size();
      HashTable< std::size_t, std::size_t > mapping(score_nb_vars);
      for (std::size_t i = std::size_t(0); i < score_nb_vars; ++i) {
        try {
          mapping.insert(i, apriori_names2col[score_names[i]]);
        } catch (Exception&) {
          GUM_ERROR(MissingVariableInDatabase,
                    "Variable "
                       << score_names[i]
                       << " of the observed database does not belong to the "
                       << "apriori database");
        }
      }

      // create the translators for CSV database
      for (std::size_t i = std::size_t(0); i < score_nb_vars; ++i) {
        const Variable& var = score_database.databaseTable().variable(i);
        database__.insertTranslator(var, mapping[i], missing_symbols);
      }

      // fill the database
      initializer.fillDatabase(database__);

      // get the domain sizes of the variables
      for (auto dom: database__.domainSizes())
        domain_sizes__.push_back(dom);

      // compute the mapping from node ids to column indices
      nodeId2cols__ = score_database.nodeId2Columns();

      // create the parser
      parser__
         = new DBRowGeneratorParser<>(database__.handler(), DBRowGeneratorSet<>());
    }

Database() [4/7]

template<typename GUM_SCALAR >

gum::learning::genericBNLearner::Database::Database	(	const std::string &	filename,
		const gum::BayesNet< GUM_SCALAR > &	bn,
		const std::vector< std::string > &	missing_symbols
	)

constructor with a BN providing the variables of interest

Parameters

file	the name of the CSV file containing the data
bn	a Bayesian network indicating which variables of the CSV file are used for learning
missing_symbols	the set of symbols in the CSV file that correspond to missing data

Database() [5/7]

gum::learning::genericBNLearner::Database::Database

(

const Database &

from

)

copy constructor

Definition at line 136 of file genericBNLearner.cpp.

                                                           :
        database__(from.database__), domain_sizes__(from.domain_sizes__),
        nodeId2cols__(from.nodeId2cols__) {
      // create the parser
      parser__
         = new DBRowGeneratorParser<>(database__.handler(), DBRowGeneratorSet<>());
    }

Database() [6/7]

gum::learning::genericBNLearner::Database::Database

(

Database &&

from

)

move constructor

Definition at line 145 of file genericBNLearner.cpp.

                                                      :
        database__(std::move(from.database__)),
        domain_sizes__(std::move(from.domain_sizes__)),
        nodeId2cols__(std::move(from.nodeId2cols__)) {
      // create the parser
      parser__
         = new DBRowGeneratorParser<>(database__.handler(), DBRowGeneratorSet<>());
    }

~Database()

gum::learning::genericBNLearner::Database::~Database

(

)

destructor

Definition at line 155 of file genericBNLearner.cpp.

{ delete parser__; }

Database() [7/7]

template<typename GUM_SCALAR >

gum::learning::genericBNLearner::Database::Database	(	const std::string &	filename,
		const BayesNet< GUM_SCALAR > &	bn,
		const std::vector< std::string > &	missing_symbols
	)

Definition at line 31 of file genericBNLearner_tpl.h.

References Database().

Referenced by gum::learning::Miic::addConstraints(), gum::learning::genericBNLearner::addForbiddenArc(), gum::learning::genericBNLearner::addMandatoryArc(), gum::learning::genericBNLearner::addPossibleEdge(), gum::learning::BNLearnerListener::BNLearnerListener(), BNVars__(), gum::learning::genericBNLearner::clearDatabaseRanges(), gum::learning::DAG2BNLearner< ALLOC >::clone(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner(), Database(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::database(), gum::learning::genericBNLearner::database(), gum::learning::genericBNLearner::databaseRanges(), databaseTable(), gum::learning::genericBNLearner::databaseWeight(), gum::learning::genericBNLearner::domainSize(), domainSizes(), gum::learning::genericBNLearner::domainSizes(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::drawSamples(), gum::learning::genericBNLearner::eraseForbiddenArc(), gum::learning::genericBNLearner::eraseMandatoryArc(), gum::learning::genericBNLearner::erasePossibleEdge(), gum::learning::Miic::existsDirectedPath__(), gum::learning::Miic::findBestContributor_(), gum::learning::genericBNLearner::genericBNLearner(), gum::learning::genericBNLearner::getAprioriType__(), gum::learning::Miic::getUnshieldedTriples_(), gum::learning::Miic::getUnshieldedTriplesMIIC_(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::learning::genericBNLearner::hasMissingValues(), idFromName(), gum::learning::genericBNLearner::idFromName(), gum::learning::Miic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::Miic::latentVariables(), gum::learning::genericBNLearner::latentVariables(), gum::learning::K2::learnBN(), gum::learning::GreedyHillClimbing::learnBN(), gum::learning::LocalSearchWithTabuList::learnBN(), gum::learning::Miic::learnBN(), gum::learning::Miic::learnMixedStructure(), gum::learning::K2::learnStructure(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::learning::Miic::learnStructure(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::log2likelihood(), gum::learning::Miic::Miic(), missingSymbols(), nameFromId(), gum::learning::genericBNLearner::nameFromId(), names(), gum::learning::genericBNLearner::names(), gum::learning::genericBNLearner::nbCols(), nbRows(), gum::learning::genericBNLearner::nbRows(), nodeId2Columns(), gum::learning::GreaterPairOn2nd::operator()(), gum::learning::GreaterAbsPairOn2nd::operator()(), gum::learning::GreaterTupleOnLast::operator()(), gum::learning::StructuralConstraintMandatoryArcs::operator=(), gum::learning::StructuralConstraintPossibleEdges::operator=(), gum::learning::BNLearnerListener::operator=(), gum::learning::StructuralConstraintForbiddenArcs::operator=(), gum::learning::StructuralConstraintUndiGraph::operator=(), gum::learning::StructuralConstraintDAG::operator=(), gum::learning::StructuralConstraintIndegree::operator=(), gum::learning::StructuralConstraintDiGraph::operator=(), gum::learning::GreedyHillClimbing::operator=(), gum::learning::DAG2BNLearner< ALLOC >::operator=(), gum::learning::StructuralConstraintTabuList::operator=(), gum::learning::StructuralConstraintSliceOrder::operator=(), gum::learning::Miic::operator=(), gum::learning::Miic::orientation_3off2_(), gum::learning::Miic::orientation_latents_(), gum::learning::Miic::orientation_miic_(), parser(), gum::learning::Miic::propagatesHead_(), gum::learning::genericBNLearner::recordWeight(), gum::learning::Miic::set3off2Behaviour(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::setAntiTopologicalVarOrder(), gum::learning::genericBNLearner::setAprioriWeight__(), setDatabaseWeight(), gum::learning::genericBNLearner::setDatabaseWeight(), gum::learning::genericBNLearner::setForbiddenArcs(), gum::learning::genericBNLearner::setInitialDAG(), gum::learning::genericBNLearner::setMandatoryArcs(), gum::learning::genericBNLearner::setMaxIndegree(), gum::learning::Miic::setMiicBehaviour(), gum::learning::genericBNLearner::setPossibleEdges(), gum::learning::genericBNLearner::setPossibleSkeleton(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::setRandomVarOrder(), gum::learning::genericBNLearner::setRecordWeight(), gum::learning::genericBNLearner::setSliceOrder(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::setTopologicalVarOrder(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::setVarOrder(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::setVarOrderFromCSV(), setWeight(), size(), gum::learning::StructuralConstraintDAG::StructuralConstraintDAG(), gum::learning::StructuralConstraintDiGraph::StructuralConstraintDiGraph(), gum::learning::StructuralConstraintForbiddenArcs::StructuralConstraintForbiddenArcs(), gum::learning::StructuralConstraintIndegree::StructuralConstraintIndegree(), gum::learning::StructuralConstraintMandatoryArcs::StructuralConstraintMandatoryArcs(), gum::learning::StructuralConstraintPossibleEdges::StructuralConstraintPossibleEdges(), gum::learning::StructuralConstraintSliceOrder::StructuralConstraintSliceOrder(), gum::learning::StructuralConstraintTabuList::StructuralConstraintTabuList(), gum::learning::StructuralConstraintUndiGraph::StructuralConstraintUndiGraph(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::toCSV(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::toDatabaseTable(), gum::learning::GraphChange::toString(), gum::learning::ArcAddition::toString(), gum::learning::ArcDeletion::toString(), gum::learning::ArcReversal::toString(), gum::learning::EdgeAddition::toString(), gum::learning::EdgeDeletion::toString(), gum::learning::Miic::updateProbaTriples_(), gum::learning::genericBNLearner::use3off2(), gum::learning::genericBNLearner::useAprioriBDeu(), gum::learning::genericBNLearner::useAprioriDirichlet(), gum::learning::genericBNLearner::useAprioriSmoothing(), gum::learning::genericBNLearner::useDatabaseRanges(), gum::learning::genericBNLearner::useEM(), gum::learning::genericBNLearner::useGreedyHillClimbing(), gum::learning::genericBNLearner::useK2(), gum::learning::genericBNLearner::useLocalSearchWithTabuList(), gum::learning::genericBNLearner::useMDL(), gum::learning::genericBNLearner::useMIIC(), gum::learning::genericBNLearner::useNML(), gum::learning::genericBNLearner::useNoApriori(), gum::learning::genericBNLearner::useNoCorr(), gum::learning::genericBNLearner::useScoreAIC(), gum::learning::genericBNLearner::useScoreBD(), gum::learning::genericBNLearner::useScoreBDeu(), gum::learning::genericBNLearner::useScoreBIC(), gum::learning::genericBNLearner::useScoreK2(), gum::learning::genericBNLearner::useScoreLog2Likelihood(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::varOrder(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::varOrderFromCSV__(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::varOrderNames(), weight(), gum::learning::BNLearnerListener::whenProgress(), gum::learning::BNLearnerListener::whenStop(), gum::learning::BNDatabaseGenerator< GUM_SCALAR >::~BNDatabaseGenerator(), gum::learning::BNLearnerListener::~BNLearnerListener(), gum::learning::DAG2BNLearner< ALLOC >::~DAG2BNLearner(), gum::learning::GreedyHillClimbing::~GreedyHillClimbing(), gum::learning::Miic::~Miic(), gum::learning::StructuralConstraintDAG::~StructuralConstraintDAG(), gum::learning::StructuralConstraintDiGraph::~StructuralConstraintDiGraph(), gum::learning::StructuralConstraintForbiddenArcs::~StructuralConstraintForbiddenArcs(), gum::learning::StructuralConstraintIndegree::~StructuralConstraintIndegree(), gum::learning::StructuralConstraintMandatoryArcs::~StructuralConstraintMandatoryArcs(), gum::learning::StructuralConstraintPossibleEdges::~StructuralConstraintPossibleEdges(), gum::learning::StructuralConstraintSliceOrder::~StructuralConstraintSliceOrder(), gum::learning::StructuralConstraintTabuList::~StructuralConstraintTabuList(), and gum::learning::StructuralConstraintUndiGraph::~StructuralConstraintUndiGraph().

                                                      {
      // assign to each column name in the database its position
      genericBNLearner::checkFileName__(filename);
      DBInitializerFromCSV<> initializer(filename);
      const auto&            xvar_names = initializer.variableNames();
      std::size_t            nb_vars    = xvar_names.size();
      HashTable< std::string, std::size_t > var_names(nb_vars);
      for (std::size_t i = std::size_t(0); i < nb_vars; ++i)
        var_names.insert(xvar_names[i], i);

      // we use the bn to insert the translators into the database table
      std::vector< NodeId > nodes;
      nodes.reserve(bn.dag().sizeNodes());
      for (const auto node: bn.dag())
        nodes.push_back(node);
      std::sort(nodes.begin(), nodes.end());
      std::size_t i = std::size_t(0);
      for (auto node: nodes) {
        const Variable& var = bn.variable(node);
        try {
          database__.insertTranslator(var, var_names[var.name()], missing_symbols);
        } catch (NotFound&) {
          GUM_ERROR(MissingVariableInDatabase,
                    "Variable '" << var.name() << "' is missing");
        }
        nodeId2cols__.insert(NodeId(node), i++);
      }

      // fill the database
      initializer.fillDatabase(database__);

      // get the domain sizes of the variables
      for (auto dom: database__.domainSizes())
        domain_sizes__.push_back(dom);

      // create the parser
      parser__
         = new DBRowGeneratorParser<>(database__.handler(), DBRowGeneratorSet<>());
    }

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Member Function Documentation

BNVars__()

template<typename GUM_SCALAR >

BayesNet< GUM_SCALAR > gum::learning::genericBNLearner::Database::BNVars__ ( ) const

private

Definition at line 76 of file genericBNLearner_tpl.h.

References Database().

                                                                    {
      BayesNet< GUM_SCALAR > bn;
      const std::size_t      nb_vars = database__.nbVariables();
      for (std::size_t i = 0; i < nb_vars; ++i) {
        const DiscreteVariable& var
           = dynamic_cast< const DiscreteVariable& >(database__.variable(i));
        bn.add(var);
      }
      return bn;
    }

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databaseTable()

INLINE const DatabaseTable & gum::learning::genericBNLearner::Database::databaseTable

(

)

const

returns the internal database table

Definition at line 93 of file genericBNLearner_inl.h.

References Database().

                                                               {
      return database__;
    }

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domainSizes()

INLINE const std::vector< std::size_t > & gum::learning::genericBNLearner::Database::domainSizes

(

)

const

returns the domain sizes of the variables

Definition at line 46 of file genericBNLearner_inl.h.

References Database().

                                                             {
      return domain_sizes__;
    }

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idFromName()

INLINE NodeId gum::learning::genericBNLearner::Database::idFromName

(

const std::string &

var_name

)

const

returns the node id corresponding to a variable name

Definition at line 66 of file genericBNLearner_inl.h.

References Database().

                                                                           {
      try {
        const auto cols = database__.columnsFromVariableName(var_name);
        return nodeId2cols__.first(cols[0]);
      } catch (...) {
        GUM_ERROR(MissingVariableInDatabase,
                  "Variable " << var_name
                              << " could not be found in the database");
      }
    }

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missingSymbols()

INLINE const std::vector< std::string > & gum::learning::genericBNLearner::Database::missingSymbols

(

)

const

returns the set of missing symbols taken into account

Definition at line 100 of file genericBNLearner_inl.h.

References Database().

                                                                {
      return database__.missingSymbols();
    }

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nameFromId()

INLINE const std::string & gum::learning::genericBNLearner::Database::nameFromId

(

NodeId

id

)

const

returns the variable name corresponding to a given node id

Definition at line 80 of file genericBNLearner_inl.h.

References Database().

                                                                     {
      try {
        return database__.variableName(nodeId2cols__.second(id));
      } catch (...) {
        GUM_ERROR(MissingVariableInDatabase,
                  "Variable of Id " << id
                                    << " could not be found in the database");
      }
    }

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names()

INLINE const std::vector< std::string > & gum::learning::genericBNLearner::Database::names

(

)

const

returns the names of the variables in the database

Definition at line 52 of file genericBNLearner_inl.h.

References Database().

                                                       {
      return database__.variableNames();
    }

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nbRows()

INLINE std::size_t gum::learning::genericBNLearner::Database::nbRows

(

)

const

returns the number of records in the database

Definition at line 113 of file genericBNLearner_inl.h.

References Database().

                                                            {
      return database__.nbRows();
    }

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nodeId2Columns()

INLINE const Bijection< NodeId, std::size_t > & gum::learning::genericBNLearner::Database::nodeId2Columns

(

)

const

returns the mapping between node ids and their columns in the database

Definition at line 107 of file genericBNLearner_inl.h.

References Database().

                                                                {
      return nodeId2cols__;
    }

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operator=() [1/2]

genericBNLearner::Database & gum::learning::genericBNLearner::Database::operator=

(

const Database &

from

)

copy operator

Definition at line 158 of file genericBNLearner.cpp.

                                                               {
      if (this != &from) {
        delete parser__;
        database__     = from.database__;
        domain_sizes__ = from.domain_sizes__;
        nodeId2cols__  = from.nodeId2cols__;

        // create the parser
        parser__ = new DBRowGeneratorParser<>(database__.handler(),
                                              DBRowGeneratorSet<>());
      }

      return *this;
    }

operator=() [2/2]

genericBNLearner::Database & gum::learning::genericBNLearner::Database::operator=

(

Database &&

from

)

move operator

Definition at line 174 of file genericBNLearner.cpp.

                                                          {
      if (this != &from) {
        delete parser__;
        database__     = std::move(from.database__);
        domain_sizes__ = std::move(from.domain_sizes__);
        nodeId2cols__  = std::move(from.nodeId2cols__);

        // create the parser
        parser__ = new DBRowGeneratorParser<>(database__.handler(),
                                              DBRowGeneratorSet<>());
      }

      return *this;
    }

parser()

INLINE DBRowGeneratorParser & gum::learning::genericBNLearner::Database::parser

(

)

returns the parser for the database

Definition at line 40 of file genericBNLearner_inl.h.

References Database().

                                                                    {
      return *parser__;
    }

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setDatabaseWeight()

INLINE void gum::learning::genericBNLearner::Database::setDatabaseWeight

(

const double

new_weight

)

assign a weight to all the rows of the database so that the sum of their weights is equal to new_weight

assign new weight to the rows of the learning database

Definition at line 58 of file genericBNLearner_inl.h.

References Database().

                                                                          {
      if (database__.nbRows() == std::size_t(0)) return;
      const double weight = new_weight / double(database__.nbRows());
      database__.setAllRowsWeight(weight);
    }

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setWeight()

INLINE void gum::learning::genericBNLearner::Database::setWeight	(	const std::size_t	i,
		const double	weight
	)

sets the weight of the ith record

Exceptions

OutOfBounds

if i is outside the set of indices of the records or if the weight is negative

Definition at line 125 of file genericBNLearner_inl.h.

References Database().

                                                                                {
      database__.setWeight(i, weight);
    }

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size()

INLINE std::size_t gum::learning::genericBNLearner::Database::size

(

)

const

returns the number of records in the database

Definition at line 119 of file genericBNLearner_inl.h.

References Database().

                                                          {
      return database__.size();
    }

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weight() [1/2]

INLINE double gum::learning::genericBNLearner::Database::weight

(

const std::size_t

i

)

const

returns the weight of the ith record

Exceptions

OutOfBounds

if i is outside the set of indices of the records

Definition at line 132 of file genericBNLearner_inl.h.

References Database().

                                                                          {
      return database__.weight(i);
    }

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weight() [2/2]

INLINE double gum::learning::genericBNLearner::Database::weight

(

)

const

returns the weight of the whole database

Definition at line 138 of file genericBNLearner_inl.h.

References Database().

                                                         {
      return database__.weight();
    }

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Member Data Documentation

database__

DatabaseTable gum::learning::genericBNLearner::Database::database__

protected

the database itself

Definition at line 273 of file genericBNLearner.h.

domain_sizes__

std::vector< std::size_t > gum::learning::genericBNLearner::Database::domain_sizes__

protected

the domain sizes of the variables (useful to speed-up computations)

Definition at line 279 of file genericBNLearner.h.

max_threads_number__

Size gum::learning::genericBNLearner::Database::max_threads_number__ {1}

protected

the max number of threads authorized

Definition at line 288 of file genericBNLearner.h.

min_nb_rows_per_thread__

Size gum::learning::genericBNLearner::Database::min_nb_rows_per_thread__ {100}

protected

the minimal number of rows to parse (on average) by thread

Definition at line 292 of file genericBNLearner.h.

nodeId2cols__

Bijection< NodeId, std::size_t > gum::learning::genericBNLearner::Database::nodeId2cols__

protected

a bijection assigning to each variable name its NodeId

Definition at line 282 of file genericBNLearner.h.

parser__

DBRowGeneratorParser* gum::learning::genericBNLearner::Database::parser__ {nullptr}

protected

the parser used for reading the database

Definition at line 276 of file genericBNLearner.h.

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The documentation for this class was generated from the following files:

• agrum/BN/learning/BNLearnUtils/genericBNLearner.h
• agrum/BN/learning/BNLearnUtils/genericBNLearner.cpp
• agrum/BN/learning/BNLearnUtils/genericBNLearner_inl.h
• agrum/BN/learning/BNLearnUtils/genericBNLearner_tpl.h