shark::AbstractWeightedUnsupervisedTrainer< Model > Class Template Reference

Superclass of weighted unsupervised learning algorithms. More...

#include <shark/Algorithms/Trainers/AbstractWeightedTrainer.h>

Inheritance diagram for shark::AbstractWeightedUnsupervisedTrainer< Model >:

Public Types
typedef base_type::ModelType	ModelType
typedef base_type::InputType	InputType
typedef base_type::DatasetType	DatasetType
typedef WeightedUnlabeledData< InputType >	WeightedDatasetType
Public Types inherited from shark::AbstractUnsupervisedTrainer< Model >
typedef Model	ModelType
typedef Model::InputType	InputType
typedef UnlabeledData< InputType >	DatasetType

Public Member Functions
virtual void	train (ModelType &model, WeightedDatasetType const &dataset)=0
	Excecutes the algorithm and trains a model on the given weighted data.
virtual void	train (ModelType &model, DatasetType const &dataset)
	Excecutes the algorithm and trains a model on the given undata.
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 shark::AbstractWeightedUnsupervisedTrainer< Model >

Superclass of weighted unsupervised learning algorithms.

AbstractWeightedUnsupervisedTrainer is the super class of all trainers that support weighted datasets. See AbstractWeightedTrainer for more information on the weights.

See also

AbstractWeightedTrainer

Definition at line 95 of file AbstractWeightedTrainer.h.

Member Typedef Documentation

DatasetType

template<class Model >

typedef base_type::DatasetType shark::AbstractWeightedUnsupervisedTrainer< Model >::DatasetType

Definition at line 102 of file AbstractWeightedTrainer.h.

InputType

template<class Model >

typedef base_type::InputType shark::AbstractWeightedUnsupervisedTrainer< Model >::InputType

Definition at line 101 of file AbstractWeightedTrainer.h.

ModelType

template<class Model >

typedef base_type::ModelType shark::AbstractWeightedUnsupervisedTrainer< Model >::ModelType

Definition at line 100 of file AbstractWeightedTrainer.h.

WeightedDatasetType

template<class Model >

typedef WeightedUnlabeledData<InputType> shark::AbstractWeightedUnsupervisedTrainer< Model >::WeightedDatasetType

Definition at line 103 of file AbstractWeightedTrainer.h.

Member Function Documentation

train() [1/2]

template<class Model >

virtual void shark::AbstractWeightedUnsupervisedTrainer< Model >::train ( ModelType &  model, DatasetType const &  dataset  )

inlinevirtual

Excecutes the algorithm and trains a model on the given undata.

This method behaves as using train with a weighted dataset where all weights are equal. The default implementation just creates such a dataset and executes the weighted version of the algorithm.

Implements shark::AbstractUnsupervisedTrainer< Model >.

Definition at line 113 of file AbstractWeightedTrainer.h.

References shark::AbstractWeightedUnsupervisedTrainer< Model >::train().

train() [2/2]

template<class Model >

virtual void shark::AbstractWeightedUnsupervisedTrainer< Model >::train ( ModelType &  model, WeightedDatasetType const &  dataset  )

pure virtual

Excecutes the algorithm and trains a model on the given weighted data.

Referenced by shark::AbstractWeightedUnsupervisedTrainer< Model >::train().

---

The documentation for this class was generated from the following file:

• include/shark/Algorithms/Trainers/AbstractWeightedTrainer.h