Superclass of supervised learning algorithms. More...

Inheritance diagram for shark::AbstractTrainer< Model, LabelTypeT >:

Public Types
typedef Model	ModelType

typedef ModelType::InputType	InputType

typedef LabelTypeT	LabelType

typedef LabeledData< InputType, LabelType >	DatasetType

Public Member Functions
virtual void	train (ModelType &model, DatasetType const &dataset)=0
	Core of the Trainer interface.

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

virtual std::string	name () const
	returns the name of the object

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor.

virtual void	read (InArchive &archive)
	Read the component from the supplied archive.

virtual void	write (OutArchive &archive) const
	Write the component to the supplied archive.

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...).

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...).

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Detailed Description

template<class Model, class LabelTypeT = typename Model::OutputType>
class shark::AbstractTrainer< Model, LabelTypeT >

Superclass of supervised learning algorithms.

: AbstractTrainer is the super class of all trainers, i.e., procedures for training or learning model parameters. It provides a single virtual function to train the model.

: Note: Most learning algorithms of this type operate on a special model type, such as a linear model, a kernel expansion, etc. Thus, these algorithms should provide a specialized train method accepting only this model type. The virtual train method should be overriden with a method that checks the type of the model and calls the specialized train method.

Definition at line 75 of file AbstractTrainer.h.