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CMKLClassification Class Reference

Detailed Description

Multiple Kernel Learning for two-class-classification.

Learns an SVM classifier and its kernel weights. Makes only sense if multiple kernels are used.

See Also
CMKL

Definition at line 25 of file MKLClassification.h.

Inheritance diagram for CMKLClassification:
Inheritance graph
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Public Member Functions

 CMKLClassification (CSVM *s=NULL)
virtual ~CMKLClassification ()
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)
CSVMget_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_tget_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)
CKernelget_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_tget_alphas ()
bool create_new_model (int32_t num)
bool init_kernel_optimization ()
virtual CLabelsapply ()
virtual CLabelsapply (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 CLabelsget_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)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_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_tget_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
SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
- Protected Attributes inherited from CMKL
CSVMsvm
float64_t C_mkl
float64_t mkl_norm
float64_t ent_lambda
float64_t mkl_block_norm
float64_tbeta_local
int32_t mkl_iterations
float64_t mkl_epsilon
bool interleaved_optimization
float64_tW
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

CMKLClassification ( CSVM s = NULL)

Constructor

Parameters
sSVM to use as constraint generator in MKL SILP

Definition at line 7 of file MKLClassification.cpp.

~CMKLClassification ( )
virtual

Destructor

Definition at line 18 of file MKLClassification.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 21 of file MKLClassification.cpp.

virtual EClassifierType get_classifier_type ( )
protectedvirtual

get classifier type

Returns
classifier type MKL_CLASSIFICATION

Reimplemented from CMachine.

Definition at line 53 of file MKLClassification.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 31 of file MKLClassification.cpp.


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

SHOGUN Machine Learning Toolbox - Documentation