Train a model to normalize the components of a dataset to fit into the unit inverval. More...

Inheritance diagram for shark::NormalizeComponentsUnitInterval< DataType >:

Public Member Functions
	NormalizeComponentsUnitInterval ()

std::string	name () const
	From INameable: return the class name.

void	train (Normalizer< DataType > &model, UnlabeledData< DataType > const &input)

Public Member Functions inherited from shark::AbstractUnsupervisedTrainer< Model >
virtual void	train (ModelType &model, DatasetType const &inputset)=0
	Core of the Trainer interface.

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

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 ()

Additional Inherited Members
Public Types inherited from shark::AbstractUnsupervisedTrainer< Model >
typedef Model	ModelType

typedef Model::InputType	InputType

typedef UnlabeledData< InputType >	DatasetType

Detailed Description

template<class DataType = RealVector>
class shark::NormalizeComponentsUnitInterval< DataType >

Train a model to normalize the components of a dataset to fit into the unit inverval.

: Normalizing the components of a dataset works via training a LinearMap model. This model is then applied to the dataset in order to perform the normalization. The same model can be applied to different datasets.

: The typical use case is that the AffineLinearMap model is trained on the training data. Later, as "test" data comes in, the same model is used, of course without being recalibrated. Thus, the model used for normalization must be independent of the dataset it was trained on.

: Note that the transformation represented by this trainer destroys sparsity of the data. Therefore one may prefer NormalizeComponentsUnitVariance particularly on sparse data.