CMKLOneClass Class Reference

Detailed Description

Multiple Kernel Learning for one-class-classification.

Learns a One-Class SVM classifier and its kernel weights. Makes only sense if multiple kernels are used.

See Also

CMKL

Definition at line 25 of file MKLOneClass.h.

Inheritance diagram for CMKLOneClass:

Public Member Functions
	CMKLOneClass (CSVM *s=NULL)
virtual	~CMKLOneClass ()
virtual float64_t	compute_sum_alpha ()
Public Member Functions inherited from CMKL
	CMKL (CSVM *s=NULL)
virtual	~CMKL ()
void	set_constraint_generator (CSVM *s)
void	set_svm (CSVM *s)
CSVM *	get_svm ()
void	set_C_mkl (float64_t C)
void	set_mkl_norm (float64_t norm)
void	set_elasticnet_lambda (float64_t elasticnet_lambda)
void	set_mkl_block_norm (float64_t q)
void	set_interleaved_optimization_enabled (bool enable)
bool	get_interleaved_optimization_enabled ()
float64_t	compute_mkl_primal_objective ()
virtual float64_t	compute_mkl_dual_objective ()
float64_t	compute_elasticnet_dual_objective ()
void	set_mkl_epsilon (float64_t eps)
float64_t	get_mkl_epsilon ()
int32_t	get_mkl_iterations ()
virtual bool	perform_mkl_step (const float64_t *sumw, float64_t suma)
virtual void	compute_sum_beta (float64_t *sumw)
virtual const char *	get_name () const
Public Member Functions inherited from CSVM
	CSVM (int32_t num_sv=0)
	CSVM (float64_t C, CKernel *k, CLabels *lab)
virtual	~CSVM ()
void	set_defaults (int32_t num_sv=0)
virtual SGVector< float64_t >	get_linear_term ()
virtual void	set_linear_term (SGVector< float64_t > linear_term)
bool	load (FILE *svm_file)
bool	save (FILE *svm_file)
void	set_nu (float64_t nue)
void	set_C (float64_t c_neg, float64_t c_pos)
void	set_epsilon (float64_t eps)
void	set_tube_epsilon (float64_t eps)
float64_t	get_tube_epsilon ()
void	set_qpsize (int32_t qps)
float64_t	get_epsilon ()
float64_t	get_nu ()
float64_t	get_C1 ()
float64_t	get_C2 ()
int32_t	get_qpsize ()
void	set_shrinking_enabled (bool enable)
bool	get_shrinking_enabled ()
float64_t	compute_svm_dual_objective ()
float64_t	compute_svm_primal_objective ()
void	set_objective (float64_t v)
float64_t	get_objective ()
void	set_callback_function (CMKL *m, bool(*cb)(CMKL *mkl, const float64_t *sumw, const float64_t suma))
Public Member Functions inherited from CKernelMachine
	CKernelMachine ()
virtual	~CKernelMachine ()
void	set_kernel (CKernel *k)
CKernel *	get_kernel ()
void	set_batch_computation_enabled (bool enable)
bool	get_batch_computation_enabled ()
void	set_linadd_enabled (bool enable)
bool	get_linadd_enabled ()
void	set_bias_enabled (bool enable_bias)
bool	get_bias_enabled ()
float64_t	get_bias ()
void	set_bias (float64_t bias)
int32_t	get_support_vector (int32_t idx)
float64_t	get_alpha (int32_t idx)
bool	set_support_vector (int32_t idx, int32_t val)
bool	set_alpha (int32_t idx, float64_t val)
int32_t	get_num_support_vectors ()
void	set_alphas (SGVector< float64_t > alphas)
void	set_support_vectors (SGVector< int32_t > svs)
SGVector< int32_t >	get_support_vectors ()
SGVector< float64_t >	get_alphas ()
bool	create_new_model (int32_t num)
bool	init_kernel_optimization ()
virtual CLabels *	apply ()
virtual CLabels *	apply (CFeatures *data)
virtual float64_t	apply (int32_t num)
Public Member Functions inherited from CMachine
	CMachine ()
virtual	~CMachine ()
virtual bool	train (CFeatures *data=NULL)
virtual void	set_labels (CLabels *lab)
virtual CLabels *	get_labels ()
virtual float64_t	get_label (int32_t i)
void	set_max_train_time (float64_t t)
float64_t	get_max_train_time ()
void	set_solver_type (ESolverType st)
ESolverType	get_solver_type ()
virtual void	set_store_model_features (bool store_model)
Public Member Functions inherited from CSGObject
	CSGObject ()
	CSGObject (const CSGObject &orig)
virtual	~CSGObject ()
virtual bool	is_generic (EPrimitiveType *generic) const
template<class T >
void	set_generic ()
void	unset_generic ()
virtual void	print_serializable (const char *prefix="")
virtual bool	save_serializable (CSerializableFile *file, const char *prefix="")
virtual bool	load_serializable (CSerializableFile *file, const char *prefix="")
void	set_global_io (SGIO *io)
SGIO *	get_global_io ()
void	set_global_parallel (Parallel *parallel)
Parallel *	get_global_parallel ()
void	set_global_version (Version *version)
Version *	get_global_version ()
SGVector< char * >	get_modelsel_names ()
char *	get_modsel_param_descr (const char *param_name)
index_t	get_modsel_param_index (const char *param_name)

Protected Member Functions
virtual void	init_training ()
virtual EClassifierType	get_classifier_type ()
Protected Member Functions inherited from CMKL
virtual bool	train_machine (CFeatures *data=NULL)
void	perform_mkl_step (float64_t *beta, float64_t *old_beta, int num_kernels, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *lin, float64_t *sumw, int32_t &inner_iters)
float64_t	compute_optimal_betas_via_cplex (float64_t *beta, const float64_t *old_beta, int32_t num_kernels, const float64_t *sumw, float64_t suma, int32_t &inner_iters)
float64_t	compute_optimal_betas_via_glpk (float64_t *beta, const float64_t *old_beta, int num_kernels, const float64_t *sumw, float64_t suma, int32_t &inner_iters)
float64_t	compute_optimal_betas_elasticnet (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective)
void	elasticnet_transform (float64_t *beta, float64_t lmd, int32_t len)
void	elasticnet_dual (float64_t *ff, float64_t *gg, float64_t *hh, const float64_t &del, const float64_t *nm, int32_t len, const float64_t &lambda)
float64_t	compute_optimal_betas_directly (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective)
float64_t	compute_optimal_betas_block_norm (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective)
float64_t	compute_optimal_betas_newton (float64_t *beta, const float64_t *old_beta, int32_t num_kernels, const float64_t *sumw, float64_t suma, float64_t mkl_objective)
virtual bool	converged ()
void	init_solver ()
bool	init_cplex ()
void	set_qnorm_constraints (float64_t *beta, int32_t num_kernels)
bool	cleanup_cplex ()
bool	init_glpk ()
bool	cleanup_glpk ()
bool	check_lpx_status (LPX *lp)
Protected Member Functions inherited from CSVM
virtual float64_t *	get_linear_term_array ()
Protected Member Functions inherited from CKernelMachine
virtual void	store_model_features ()

Additional Inherited Members
Static Public Member Functions inherited from CMKL
static bool	perform_mkl_step_helper (CMKL *mkl, const float64_t *sumw, const float64_t suma)
Public Attributes inherited from CSGObject
SGIO *	io
Parallel *	parallel
Version *	version
Parameter *	m_parameters
Parameter *	m_model_selection_parameters
Protected Attributes inherited from CMKL
CSVM *	svm
float64_t	C_mkl
float64_t	mkl_norm
float64_t	ent_lambda
float64_t	mkl_block_norm
float64_t *	beta_local
int32_t	mkl_iterations
float64_t	mkl_epsilon
bool	interleaved_optimization
float64_t *	W
float64_t	w_gap
float64_t	rho
CTime	training_time_clock
CPXENVptr	env
CPXLPptr	lp_cplex
LPX *	lp_glpk
bool	lp_initialized

Constructor & Destructor Documentation

CMKLOneClass

(

CSVM *

s = NULL

)

Constructor

Parameters

s	SVM to use as constraint generator in MKL SILP

Definition at line 6 of file MKLOneClass.cpp.

~CMKLOneClass ( )

virtual

Destructor

Definition at line 12 of file MKLOneClass.cpp.

Member Function Documentation

float64_t compute_sum_alpha ( )

virtual

compute beta independent term from objective, e.g., in 2-class MKL sum_i alpha_i etc

Implements CMKL.

Definition at line 16 of file MKLOneClass.cpp.

virtual EClassifierType get_classifier_type ( )

protectedvirtual

get classifier type

Returns

classifier type MKL ONECLASS

Reimplemented from CMachine.

Definition at line 53 of file MKLOneClass.h.

void init_training ( )

protectedvirtual

check run before starting training (to e.g. check if labeling is two-class labeling in classification case

Implements CMKL.

Definition at line 21 of file MKLOneClass.cpp.

---

The documentation for this class was generated from the following files:

• MKLOneClass.h
• MKLOneClass.cpp