44 SG_ERROR(
"%s::train data_lock() was called, only train_locked() is"
45 " possible. Call data_unlock if you want to call train()\n",
118 SG_ERROR(
"%s::data_lock(): Machine does not support data locking!\n",
125 SG_ERROR(
"%s::data_lock() is not possible will NULL labels!\n",
134 SG_ERROR(
"%s::data_lock() was already called. Dont lock twice!",
154 SG_DEBUG(
"entering %s::apply(%s at %p)\n",
181 SG_DEBUG(
"leaving %s::apply(%s at %p)\n",
210 SG_ERROR(
"This machine does not support apply_binary()\n")
216 SG_ERROR(
"This machine does not support apply_regression()\n")
222 SG_ERROR(
"This machine does not support apply_multiclass()\n")
228 SG_ERROR(
"This machine does not support apply_structured()\n")
234 SG_ERROR(
"This machine does not support apply_latent()\n")
240 SG_ERROR(
"apply_locked_binary(SGVector<index_t>) is not yet implemented "
247 SG_ERROR(
"apply_locked_regression(SGVector<index_t>) is not yet implemented "
254 SG_ERROR(
"apply_locked_multiclass(SGVector<index_t>) is not yet implemented "
261 SG_ERROR(
"apply_locked_structured(SGVector<index_t>) is not yet implemented "
268 SG_ERROR(
"apply_locked_latent(SGVector<index_t>) is not yet implemented "
virtual const char * get_name() const =0
void set_max_train_time(float64_t t)
Base class of the labels used in Structured Output (SO) problems.
Real Labels are real-valued labels.
virtual CLabels * apply_locked(SGVector< index_t > indices)
The class Labels models labels, i.e. class assignments of objects.
float64_t m_max_train_time
ESolverType m_solver_type
virtual CStructuredLabels * apply_locked_structured(SGVector< index_t > indices)
virtual bool train_machine(CFeatures *data=NULL)
bool m_store_model_features
virtual const char * get_name() const
Multiclass Labels for multi-class classification.
virtual CBinaryLabels * apply_binary(CFeatures *data=NULL)
virtual void set_store_model_features(bool store_model)
Class SGObject is the base class of all shogun objects.
virtual CRegressionLabels * apply_regression(CFeatures *data=NULL)
virtual void data_unlock()
virtual void data_lock(CLabels *labs, CFeatures *features)
virtual CLabels * get_labels()
float64_t get_max_train_time()
ESolverType get_solver_type()
virtual CLatentLabels * apply_latent(CFeatures *data=NULL)
virtual EMachineType get_classifier_type()
virtual EProblemType get_machine_problem_type() const
virtual CRegressionLabels * apply_locked_regression(SGVector< index_t > indices)
virtual void store_model_features()
virtual bool supports_locking() const
virtual CMulticlassLabels * apply_locked_multiclass(SGVector< index_t > indices)
virtual CStructuredLabels * apply_structured(CFeatures *data=NULL)
all of classes and functions are contained in the shogun namespace
virtual void post_lock(CLabels *labs, CFeatures *features)
virtual bool is_label_valid(CLabels *lab) const
The class Features is the base class of all feature objects.
virtual CBinaryLabels * apply_locked_binary(SGVector< index_t > indices)
virtual bool train(CFeatures *data=NULL)
Binary Labels for binary classification.
virtual CMulticlassLabels * apply_multiclass(CFeatures *data=NULL)
virtual bool train_require_labels() const
virtual CLatentLabels * apply_locked_latent(SGVector< index_t > indices)
virtual void set_labels(CLabels *lab)
abstract class for latent labels As latent labels always depends on the given application, this class only defines the API that the user has to implement for latent labels.
virtual void ensure_valid(const char *context=NULL)=0
void set_solver_type(ESolverType st)
virtual CLabels * apply(CFeatures *data=NULL)