13 #ifndef _MULTICLASSMACHINE_H___
14 #define _MULTICLASSMACHINE_H___
27 class CMulticlassLabels;
28 class CMultilabelLabels;
61 ASSERT(num<m_machines->get_num_elements() && num>=0)
141 return "MulticlassMachine";
201 void register_parameters();
CMachine * get_machine(int32_t num) const
virtual ~CMulticlassMachine()
The class Labels models labels, i.e. class assignments of objects.
virtual CMulticlassLabels * apply_multiclass(CFeatures *data=NULL)
virtual bool is_acceptable_machine(CMachine *machine)
CDynamicObjectArray * m_machines
CRejectionStrategy * get_rejection_strategy() const
virtual CMachine * get_machine_from_trained(CMachine *machine)=0
virtual void add_machine_subset(SGVector< index_t > subset)=0
virtual const char * get_name() const
CRejectionStrategy * get_rejection_strategy()
virtual float64_t get_submachine_output(int32_t i, int32_t num)
void set_prob_heuris_type(EProbHeuristicType prob_heuris)
bool set_element(CSGObject *e, int32_t idx1, int32_t idx2=0, int32_t idx3=0)
A generic learning machine interface.
Multiclass Labels for multi-class classification.
virtual bool init_machine_for_train(CFeatures *data)=0
virtual bool is_ready()=0
CMulticlassStrategy * m_multiclass_strategy
experimental abstract generic multiclass machine class
CMulticlassStrategy * get_multiclass_strategy() const
virtual CBinaryLabels * get_submachine_outputs(int32_t i)
virtual CMultilabelLabels * apply_multilabel_output(CFeatures *data=NULL, int32_t n_outputs=5)
virtual bool init_machines_for_apply(CFeatures *data)=0
base rejection strategy class
virtual float64_t apply_one(int32_t vec_idx)
virtual void remove_machine_subset()=0
all of classes and functions are contained in the shogun namespace
CSGObject * get_element_safe(int32_t index) const
The class Features is the base class of all feature objects.
void set_rejection_strategy(CRejectionStrategy *rejection_strategy)
void set_prob_heuris(EProbHeuristicType prob_heuris)
Binary Labels for binary classification.
class MulticlassStrategy used to construct generic multiclass classifiers with ensembles of binary cl...
EProbHeuristicType get_prob_heuris()
virtual bool train_machine(CFeatures *data=NULL)
virtual const char * get_name() const
virtual int32_t get_num_rhs_vectors()=0
Multilabel Labels for multi-label classification.
void set_rejection_strategy(CRejectionStrategy *rejection_strategy)
bool set_machine(int32_t num, CMachine *machine)
virtual void set_labels(CLabels *lab)
EProbHeuristicType get_prob_heuris_type()