68         return "MulticlassOneVsOneStrategy";
 
  114     void register_parameters();
 
virtual SGVector< int32_t > train_prepare_next()
int32_t m_train_pair_idx_2
2nd index of current submachine being trained 
void rescale_heuris_hamamura(SGVector< float64_t > outputs, const SGVector< int32_t > indx1, const SGVector< int32_t > indx2)
multiclass one vs one strategy used to train generic multiclass machines for K-class problems with bu...
virtual void train_start(CMulticlassLabels *orig_labels, CBinaryLabels *train_labels)
int32_t m_train_pair_idx_1
1st index of current submachine being trained 
SGVector< int32_t > m_num_samples
number of samples per machine 
CMulticlassOneVsOneStrategy()
int32_t m_num_classes
number of classes in this problem 
Multiclass Labels for multi-class classification. 
void set_num_classes(int32_t num_classes)
void rescale_heuris_hastie(SGVector< float64_t > outputs, const SGVector< int32_t > indx1, const SGVector< int32_t > indx2)
virtual const char * get_name() const 
void set_num_classes(int32_t num_classes)
virtual void rescale_outputs(SGVector< float64_t > outputs)
virtual ~CMulticlassOneVsOneStrategy()
virtual bool train_has_more()
void rescale_heuris_price(SGVector< float64_t > outputs, const SGVector< int32_t > indx1, const SGVector< int32_t > indx2)
all of classes and functions are contained in the shogun namespace 
int32_t m_num_machines
number of machines 
Binary Labels for binary classification. 
class MulticlassStrategy used to construct generic multiclass classifiers with ensembles of binary cl...
virtual int32_t get_num_machines()
virtual int32_t decide_label(SGVector< float64_t > outputs)