shark::AbstractWeightedUnsupervisedTrainer< Model > Class Template Referenceabstract

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< InputTypeWeightedDatasetType
 
- Public Types inherited from shark::AbstractUnsupervisedTrainer< Model >
typedef Model ModelType
 
typedef Model::InputType InputType
 

Public Member Functions

virtual void train (ModelType &model, WeightedDatasetType const &dataset)=0
 Excecutes the algorithm and trains a model on the given weighted data. More...
 
virtual void train (ModelType &model, DatasetType const &dataset)
 Excecutes the algorithm and trains a model on the given undata. More...
 
- Public Member Functions inherited from shark::AbstractUnsupervisedTrainer< Model >
virtual void train (ModelType &model, const UnlabeledData< InputType > &inputset)=0
 Core of the Trainer interface. More...
 
- Public Member Functions inherited from shark::INameable
virtual ~INameable ()
 
virtual std::string name () const
 returns the name of the object More...
 
- Public Member Functions inherited from shark::ISerializable
virtual ~ISerializable ()
 Virtual d'tor. More...
 
virtual void read (InArchive &archive)
 Read the component from the supplied archive. More...
 
virtual void write (OutArchive &archive) const
 Write the component to the supplied archive. More...
 
void load (InArchive &archive, unsigned int version)
 Versioned loading of components, calls read(...). More...
 
void save (OutArchive &archive, unsigned int version) const
 Versioned storing of components, calls write(...). More...
 
 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 93 of file AbstractWeightedTrainer.h.

Member Typedef Documentation

◆ DatasetType

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

Definition at line 100 of file AbstractWeightedTrainer.h.

◆ InputType

Definition at line 99 of file AbstractWeightedTrainer.h.

◆ ModelType

Definition at line 98 of file AbstractWeightedTrainer.h.

◆ WeightedDatasetType

Member Function Documentation

◆ train() [1/2]

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.

◆ train() [2/2]

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.

Definition at line 111 of file AbstractWeightedTrainer.h.

References shark::AbstractWeightedTrainer< Model, LabelTypeT >::train().


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