47#ifndef GUM_LEARNING_PRIOR_K2_H
48#define GUM_LEARNING_PRIOR_K2_H
the internal prior for the K2 score = Laplace Prior
The class representing a tabular database as used by learning tasks.
virtual ~K2Prior()
destructor
K2Prior(K2Prior &&from)
move constructor
K2Prior & operator=(K2Prior &&from)
move operator
K2Prior & operator=(const K2Prior &from)
copy operator
virtual void setWeight(const double weight) final
dummy set weight function: in K2, weights are always equal to 1
virtual K2Prior * clone() const
virtual copy constructor
K2Prior(const DatabaseTable &database, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
K2Prior(const K2Prior &from)
copy constructor
double weight() const
returns the weight assigned to the prior
SmoothingPrior(const DatabaseTable &database, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
include the inlined functions if necessary
gum is the global namespace for all aGrUM entities
the smooth a priori: adds a weight w to all the counts