#include <sstream>
#include <memory>
#include <agrum/BN/BayesNet.h>
#include <agrum/agrum.h>
#include <agrum/core/bijection.h>
#include <agrum/core/sequence.h>
#include <agrum/graphs/DAG.h>
#include <agrum/learning/database/DBTranslator4LabelizedVariable.h>
#include <agrum/learning/database/DBRowGeneratorParser.h>
#include <agrum/learning/database/DBInitializerFromCSV.h>
#include <agrum/learning/database/databaseTable.h>
#include <agrum/learning/database/DBRowGenerator4CompleteRows.h>
#include <agrum/learning/database/DBRowGeneratorEM.h>
#include <agrum/learning/database/DBRowGeneratorSet.h>
#include <agrum/learning/scores_and_tests/scoreAIC.h>
#include <agrum/learning/scores_and_tests/scoreBD.h>
#include <agrum/learning/scores_and_tests/scoreBDeu.h>
#include <agrum/learning/scores_and_tests/scoreBIC.h>
#include <agrum/learning/scores_and_tests/scoreK2.h>
#include <agrum/learning/scores_and_tests/scoreLog2Likelihood.h>
#include <agrum/learning/aprioris/aprioriDirichletFromDatabase.h>
#include <agrum/learning/aprioris/aprioriNoApriori.h>
#include <agrum/learning/aprioris/aprioriSmoothing.h>
#include <agrum/learning/aprioris/aprioriBDeu.h>
#include <agrum/learning/constraints/structuralConstraintDAG.h>
#include <agrum/learning/constraints/structuralConstraintDiGraph.h>
#include <agrum/learning/constraints/structuralConstraintForbiddenArcs.h>
#include <agrum/learning/constraints/structuralConstraintPossibleEdges.h>
#include <agrum/learning/constraints/structuralConstraintIndegree.h>
#include <agrum/learning/constraints/structuralConstraintMandatoryArcs.h>
#include <agrum/learning/constraints/structuralConstraintSetStatic.h>
#include <agrum/learning/constraints/structuralConstraintSliceOrder.h>
#include <agrum/learning/constraints/structuralConstraintTabuList.h>
#include <agrum/learning/structureUtils/graphChange.h>
#include <agrum/learning/structureUtils/graphChangesGenerator4DiGraph.h>
#include <agrum/learning/structureUtils/graphChangesGenerator4K2.h>
#include <agrum/learning/structureUtils/graphChangesSelector4DiGraph.h>
#include <agrum/learning/paramUtils/DAG2BNLearner.h>
#include <agrum/learning/paramUtils/paramEstimatorML.h>
#include <agrum/core/approximations/IApproximationSchemeConfiguration.h>
#include <agrum/core/approximations/approximationSchemeListener.h>
#include <agrum/learning/K2.h>
#include <agrum/learning/Miic.h>
#include <agrum/learning/greedyHillClimbing.h>
#include <agrum/learning/localSearchWithTabuList.h>
#include <agrum/core/signal/signaler.h>
#include <agrum/learning/BNLearnUtils/genericBNLearner_inl.h>
#include <agrum/learning/BNLearnUtils/genericBNLearner_tpl.h>

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Classes
class	gum::learning::genericBNLearner
	A pack of learning algorithms that can easily be used. More...

class	gum::learning::genericBNLearner::Database
	a helper to easily read databases More...

Namespaces
	gum
	Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.

	gum::learning

Detailed Description

This library is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this library. If not, see http://www.gnu.org/licenses/.

A class for generic framework of learning algorithms that can easily be used.

The pack currently contains K2, GreedyHillClimbing, miic, 3off2 and LocalSearchWithTabuList

Author: Christophe GONZALES and Pierre-Henri WUILLEMIN

Definition in file genericBNLearner.h.

Classes

Namespaces

Detailed Description