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 |
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
| typedef base_type::DatasetType shark::AbstractWeightedUnsupervisedTrainer< Model >::DatasetType |
Definition at line 100 of file AbstractWeightedTrainer.h.
◆ InputType
| typedef base_type::InputType shark::AbstractWeightedUnsupervisedTrainer< Model >::InputType |
Definition at line 99 of file AbstractWeightedTrainer.h.
◆ ModelType
| typedef base_type::ModelType shark::AbstractWeightedUnsupervisedTrainer< Model >::ModelType |
Definition at line 98 of file AbstractWeightedTrainer.h.
◆ WeightedDatasetType
| typedef WeightedUnlabeledData<InputType> shark::AbstractWeightedUnsupervisedTrainer< Model >::WeightedDatasetType |
Definition at line 101 of file AbstractWeightedTrainer.h.
Member Function Documentation
◆ train() [1/2]
|
pure virtual |
Excecutes the algorithm and trains a model on the given weighted data.
◆ train() [2/2]
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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:
- include/shark/Algorithms/Trainers/AbstractWeightedTrainer.h