48#ifndef GUM_LEARNING_SCORE_FNML_H
49#define GUM_LEARNING_SCORE_FNML_H
101 const std::vector< std::pair< std::size_t, std::size_t > >&
ranges,
205#ifndef DOXYGEN_SHOULD_SKIP_THIS
the class for computing the log2 of the parametric complexity of an r-ary multinomial variable
the class used to read a row in the database and to transform it into a set of DBRow instances that c...
A class for storing a pair of sets of NodeIds, the second one corresponding to a conditional set.
the no a priorclass: corresponds to 0 weight-sample
the base class for all a priori
const std::vector< std::pair< std::size_t, std::size_t > > & ranges() const
returns the current ranges
Score(const DBRowGeneratorParser &parser, const Prior &external_prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
virtual std::string isPriorCompatible() const final
indicates whether the prior is compatible (meaningful) with the score
ScorefNML & operator=(ScorefNML &&from)
move operator
ScorefNML(const DBRowGeneratorParser &parser, const Prior &prior, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
ScorefNML & operator=(const ScorefNML &from)
copy operator
virtual ScorefNML * clone() const
virtual copy constructor
ScorefNML(const DBRowGeneratorParser &parser, const Prior &prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
ScorefNML(ScorefNML &&from)
move constructor
virtual double score_(const IdCondSet &idset) final
returns the score for a given IdCondSet
virtual const Prior & internalPrior() const final
returns the internal prior of the score
ScorefNML(const ScorefNML &from)
copy constructor
virtual ~ScorefNML()
destructor
include the inlined functions if necessary
gum is the global namespace for all aGrUM entities
the no a priorclass: corresponds to 0 weight-sample
the base class for all the scores used for learning (BIC, BDeu, etc)
the class for computing fNML scores
the class for computing the log2 of the parametric complexity of an r-ary multinomial variable