55 void CDualLibQPBMSOSVM::init()
60 SG_ADD(&m_lambda,
"m_lambda",
"Regularization constant lambda",
62 SG_ADD(&m_cleanICP,
"m_cleanICP",
"Inactive cutting plane removal flag",
66 "Number of inactive iterations after which ICP will be removed",
70 SG_ADD(&m_cp_models,
"m_cp_models",
"Number of cutting plane models",
106 m_lambda, m_BufSize, m_cleanICP, m_cleanAfter, m_K, m_Tmax,
111 m_lambda, m_BufSize, m_cleanICP, m_cleanAfter, m_K, m_Tmax,
116 m_lambda, m_BufSize, m_cleanICP, m_cleanAfter, m_K, m_Tmax,
121 m_lambda, m_BufSize, m_cleanICP, m_cleanAfter,
true ,
125 SG_ERROR(
"CDualLibQPBMSOSVM: m_solver=%d is not supported", m_solver);
Base class of the labels used in Structured Output (SO) problems.
void set_BufSize(uint32_t BufSize)
void set_w(SGVector< float64_t > W)
SGVector< float64_t > get_w() const
void set_cleanICP(bool cleanICP)
CStructuredModel * m_model
virtual int32_t get_dim() const =0
virtual void init_training()
void set_features(CFeatures *f)
BmrmStatistics svm_ppbm_solver(CDualLibQPBMSOSVM *machine, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose)
class CSOSVMHelper contains helper functions to compute primal objectives, dual objectives, average training losses, duality gaps etc. These values will be recorded to check convergence. This class is inspired by the matlab implementation of the block coordinate Frank-Wolfe SOSVM solver [1].
BmrmStatistics svm_bmrm_solver(CDualLibQPBMSOSVM *machine, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose)
void set_TolAbs(float64_t TolAbs)
void set_lambda(float64_t _lambda)
Class CStructuredModel that represents the application specific model and contains most of the applic...
all of classes and functions are contained in the shogun namespace
void set_Tmax(uint32_t Tmax)
The class Features is the base class of all feature objects.
BmrmStatistics svm_ncbm_solver(CDualLibQPBMSOSVM *machine, float64_t *w, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, bool is_convex, bool line_search, bool verbose)
void set_cp_models(uint32_t cp_models)
SGVector< float64_t > m_w
bool train_machine(CFeatures *data=NULL)
BmrmStatistics svm_p3bm_solver(CDualLibQPBMSOSVM *machine, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, uint32_t cp_models, bool verbose)
void set_cleanAfter(uint32_t cleanAfter)
void set_TolRel(float64_t TolRel)
void set_solver(ESolver solver)
virtual EMachineType get_classifier_type()
virtual ~CDualLibQPBMSOSVM()