aGrUM  0.13.2
Tools for learning
+ Collaboration diagram for Tools for learning:

Detailed Description

Modules

 Database Manipulations
 
 Scores and Independence Tests
 
 Scores A Prioris
 
 Structural Constraints
 

Classes

class  gum::learning::Apriori< IdSetAlloc, CountAlloc >
 the base class for all apriori More...
 
class  gum::learning::AprioriDirichletFromDatabase< IdSetAlloc, CountAlloc >
 A dirichlet priori: computes its N'_ijk from a database. More...
 
class  gum::learning::AprioriNoApriori< IdSetAlloc, CountAlloc >
 the no a priori class: corresponds to 0 weight-sample More...
 
class  gum::learning::AprioriSmoothing< IdSetAlloc, CountAlloc >
 the smooth a priori: adds a weight w to all the countings More...
 
class  gum::learning::BNLearner< GUM_SCALAR >
 A pack of learning algorithms that can easily be used. More...
 
class  gum::learning::BNLearnerListener
 A class that redirects gum_signal from algorithms to the listeners of BNLearn. More...
 
class  gum::learning::genericBNLearner
 A pack of learning algorithms that can easily be used. More...
 
class  gum::learning::StructuralConstraintEmpty
 the base class for all structural constraints More...
 
class  gum::learning::StructuralConstraintDAG
 The base class for structural constraints imposed by DAGs. More...
 
class  gum::learning::StructuralConstraintDiGraph
 The base class for structural constraints used by learning algorithms that learn a directed graph structure. More...
 
class  gum::learning::StructuralConstraintForbiddenArcs
 the structural constraint for forbidding the creation of some arcs during structure learning More...
 
class  gum::learning::StructuralConstraintIndegree
 the class for structural constraints limiting the number of parents of nodes in a directed graph More...
 
class  gum::learning::StructuralConstraintMandatoryArcs
 the structural constraint indicating that some arcs shall never be removed or reversed More...
 
class  gum::learning::StructuralConstraintSetStatic< CONSTRAINT1, OTHER_CONSTRAINTS >
 the "meta-programming" class for storing structural constraintsIn aGrUM, there are two ways to store sets of structural constraints: the first one is to put them into a StructuralConstraintSetDynamic. More...
 
class  gum::learning::StructuralConstraintSliceOrder
 the structural constraint imposing a partial order over nodes More...
 
class  gum::learning::StructuralConstraintTabuList
 The class imposing a N-sized tabu list as a structural constraints for learning algorithms. More...
 
class  gum::learning::StructuralConstraintUndiGraph
 The base class for structural constraints used by learning algorithms that learn an undirected graph structure. More...
 
class  gum::learning::GreedyHillClimbing
 The greedy hill climbing learning algorithm (for directed graphs) More...
 
class  gum::learning::K2
 The K2 algorithm. More...
 
class  gum::learning::LocalSearchWithTabuList
 The local search with tabu list learning algorithm (for directed graphs) More...
 
class  gum::learning::Miic
 The miic learning algorithm. More...
 
class  gum::learning::DAG2BNLearner
 A class that, given a structure and a parameter estimator returns a full Bayes net. More...
 
class  gum::learning::ParamEstimator< IdSetAlloc, CountAlloc >
 The base class for estimating parameters of CPTsThe class should be used as follows: first, to speed-up computations, you should consider computing all the parameters you need in one pass. More...
 
class  gum::learning::ParamEstimatorML< IdSetAlloc, CountAlloc >
 The class for estimating parameters of CPTs using Maximum LikelihoodThe class should be used as follows: first, to speed-up computations, you should consider computing all the parameters you need in one pass. More...
 
class  gum::learning::Cache4Score
 a cache for caching 3 points mutual information in 3off2Caching previously computed scores is very important for learning algorithms because computing independence tests requires parsing the database and this is the most time consuming operation in learning. More...
 
class  gum::learning::Cache4PartEntropy
 a cache for caching partial entropy in PartEntropy classCaching previously computed scores is very important for learning algorithms because computing a score requires parsing the database and this is the most time consuming operation in learning. More...
 
class  gum::learning::CorrectedMutualInformation< IdSetAlloc, CountAlloc >
 class CorrectedMutualInformation, used in the 3off2 algorithm More...
 
class  gum::learning::Counter< IdSetAlloc, CountAlloc >
 The counting class for all the scores used for learning (BIC, BDeu, etc) as well as for all the independence tests. More...
 
class  gum::learning::IdSet< Alloc >
 transforms an unordered set of ids into an (increasingly) ordered one More...
 
class  gum::learning::IndependenceTest< IdSetAlloc, CountAlloc >
 the abstract class for all the independence testsThe class should be used as follows: first, to speed-up computations, you should consider computing all the independence tests you need in one pass. More...
 
class  gum::learning::IndepTestChi2< IdSetAlloc, CountAlloc >
 the class for computing Chi2 independence test scoresThe class should be used as follows: first, to speed-up computations, you should consider computing all the independence tests you need in one pass. More...
 
class  gum::learning::IndepTestG2< IdSetAlloc, CountAlloc >
 the class for computing G2 independence test scores
 The class should be used as follows: first, to speed-up computations,
 you should consider computing all the independence tests you need in one
 pass.
More...
 
class  gum::learning::KNML< IdSetAlloc, CountAlloc >
 the class for computing Chi2 independence test scores More...
 
class  gum::learning::RecordCounter< IdSetAlloc, CountAlloc >
 The class that computes countings of observations from the database. More...
 
class  gum::learning::Score< IdSetAlloc, CountAlloc >
 The base class for all the scores used for learning (BIC, BDeu, etc)The class should be used as follows: first, to speed-up computations, you should consider computing all the scores you need in one pass. More...
 
class  gum::learning::ScoreAIC< IdSetAlloc, CountAlloc >
 the class for computing AIC scores More...
 
class  gum::learning::ScoreBD< IdSetAlloc, CountAlloc >
 The class for computing Bayesian Dirichlet (BD) log2 scores. More...
 
class  gum::learning::ScoreBDeu< IdSetAlloc, CountAlloc >
 The class for computing BDeu scores (actually their log2 value) More...
 
class  gum::learning::ScoreBIC< IdSetAlloc, CountAlloc >
 The class for computing BIC scores. More...
 
class  gum::learning::ScoreInternalApriori< IdSetAlloc, CountAlloc >
 the base class for all the score's internal apriorisSome scores include an apriori. More...
 
class  gum::learning::ScoreInternalBDeuApriori< IdSetAlloc, CountAlloc >
 the internal apriori for the BDeu score (N' / (r_i * q_i)Some scores include an apriori. More...
 
class  gum::learning::ScoreInternalK2Apriori< IdSetAlloc, CountAlloc >
 the internal apriori for the K2 score: Laplace AprioriSome scores include an apriori. More...
 
class  gum::learning::ScoreInternalNoApriori< IdSetAlloc, CountAlloc >
 the internal apriori for the scores without apriori (e.g., BD)Some scores include an apriori. More...
 
class  gum::learning::ScoreK2< IdSetAlloc, CountAlloc >
 The class for computing K2 scores (actually their log2 value) More...
 
class  gum::learning::ScoreLog2Likelihood< IdSetAlloc, CountAlloc >
 the class for computing log2-likelihood scores More...
 
class  gum::learning::GraphChange
 
class  gum::learning::ArcAddition
 The class for notifying learning algorithms of new arc additionsThis class is convenient to know at compile time which graph change we are dealing with. More...
 
class  gum::learning::ArcDeletion
 The class for notifying learning algorithms of arc removalsThis class is convenient to know at compile time which graph change we are dealing with. More...
 
class  gum::learning::ArcReversal
 The class for notifying learning algorithms of arc reversalsThis class is convenient to know at compile time which graph change we are dealing with. More...
 
class  gum::learning::EdgeAddition
 The class for notifying learning algorithms of new edge additionsThis class is convenient to know at compile time which graph change we are dealing with. More...
 
class  gum::learning::EdgeDeletion
 The class for notifying learning algorithms of edge removalsThis class is convenient to know at compile time which graph change we are dealing with. More...
 
class  gum::learning::GraphChangesGenerator4DiGraph< STRUCT_CONSTRAINT >
 The basic class for computing the next graph changes possible in a structure learning algorithm. More...
 
class  gum::learning::GraphChangesGenerator4K2< STRUCT_CONSTRAINT >
 The basic class for computing the next graph changes possible in a structure learning algorithm. More...
 
class  gum::learning::GraphChangesGenerator4UndiGraph< STRUCT_CONSTRAINT >
 The basic class for computing the next graph changes possible in an undirected structure learning algorithm. More...
 
class  gum::learning::GraphChangesGeneratorOnSubDiGraph< STRUCT_CONSTRAINT >
 The basic class for computing the next graph changes possible in a structure learning algorithm. More...
 
class  gum::learning::GraphChangesSelector4DiGraph< SCORE, STRUCTURAL_CONSTRAINT, GRAPH_CHANGES_GENERATOR >
 The mecanism to compute the next available graph changes for directed structure learning search algorithms. More...