Detailed Description

Class LibSVR, performs support vector regression using LibSVM.

The SVR solution can be expressed as

$f({\bf x})=\sum_{i=1}^{N} \alpha_i k({\bf x}, {\bf x_i})+b$

where $\alpha$ and $b$ are determined in training, i.e. using a pre-specified kernel, a given tube-epsilon for the epsilon insensitive loss, the follwoing quadratic problem is minimized (using sequential minimal decomposition (SMO))

$\begin{eqnarray*} \max_{{\bf \alpha},{\bf \alpha}^*} &-\frac{1}{2}\sum_{i,j=1}^N(\alpha_i-\alpha_i^*)(\alpha_j-\alpha_j^*){\bf x}_i^T {\bf x}_j -\sum_{i=1}^N(\alpha_i+\alpha_i^*)\epsilon - \sum_{i=1}^N(\alpha_i-\alpha_i^*)y_i\\ \mbox{wrt}:& {\bf \alpha},{\bf \alpha}^*\in{\bf R}^N\\ \mbox{s.t.}:& 0\leq \alpha_i,\alpha_i^*\leq C,\, \forall i=1\dots N\\ &\sum_{i=1}^N(\alpha_i-\alpha_i^*)y_i=0 \end{eqnarray*}$

Note that the SV regression problem is reduced to the standard SV classification problem by introducing artificial labels $-y_i$ which leads to the epsilon insensitive loss constraints *

$\begin{eqnarray*} {\bf w}^T{\bf x}_i+b-c_i-\xi_i\leq 0,&\, \forall i=1\dots N\\ -{\bf w}^T{\bf x}_i-b-c_i^*-\xi_i^*\leq 0,&\, \forall i=1\dots N \end{eqnarray*}$

with $c_i=y_i+ \epsilon$ and $c_i^*=-y_i+ \epsilon$

Definition at line 51 of file LibSVR.h.

Inheritance diagram for CLibSVR:

[legend]

Public Member Functions
	CLibSVR ()
	CLibSVR (float64_t C, float64_t epsilon, CKernel k, CLabels lab)
virtual	~CLibSVR ()
virtual EClassifierType	get_classifier_type ()
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 bool	train_machine (CFeatures *data=NULL)
Protected Member Functions inherited from CSVM
virtual float64_t *	get_linear_term_array ()
Protected Member Functions inherited from CKernelMachine
virtual void	store_model_features ()

Protected Attributes
svm_problem	problem
svm_parameter	param
struct svm_model *	model
Protected Attributes inherited from CSVM
SGVector< float64_t >	m_linear_term
bool	svm_loaded
float64_t	epsilon
float64_t	tube_epsilon
float64_t	nu
float64_t	C1
float64_t	C2
float64_t	objective
int32_t	qpsize
bool	use_shrinking
bool(*	callback )(CMKL mkl, const float64_t sumw, const float64_t suma)
CMKL *	mkl
Protected Attributes inherited from CKernelMachine
CKernel *	kernel
bool	use_batch_computation
bool	use_linadd
bool	use_bias
float64_t	m_bias
SGVector< float64_t >	m_alpha
SGVector< int32_t >	m_svs
Protected Attributes inherited from CMachine
float64_t	max_train_time
CLabels *	labels
ESolverType	solver_type
bool	m_store_model_features

Additional Inherited Members
Static Public Member Functions inherited from CKernelMachine
static void *	apply_helper (void *p)
Public Attributes inherited from CSGObject
SGIO *	io
Parallel *	parallel
Version *	version
Parameter *	m_parameters
Parameter *	m_model_selection_parameters

Constructor & Destructor Documentation

CLibSVR ( )

default constructor

Definition at line 16 of file LibSVR.cpp.

CLibSVR	(	float64_t	C,
		float64_t	epsilon,
		CKernel *	k,
		CLabels *	lab
	)

constructor

Parameters

C	constant C
epsilon	tube epsilon
k	kernel
lab	labels

Definition at line 22 of file LibSVR.cpp.

~CLibSVR ( )

virtual

Definition at line 33 of file LibSVR.cpp.

Member Function Documentation

EClassifierType get_classifier_type ( )

virtual

get classifier type

Returns: classifie type LIBSVR

Reimplemented from CMachine.

Definition at line 38 of file LibSVR.cpp.

virtual const char* get_name ( ) const

virtual

Returns: object name

Reimplemented from CSVM.

Definition at line 74 of file LibSVR.h.

bool train_machine ( CFeatures * data = NULL )

protectedvirtual

train regression

Parameters

data	training data (parameter can be avoided if distance or kernel-based regressor are used and distance/kernels are initialized with train data)

Returns: whether training was successful

Reimplemented from CMachine.

Definition at line 43 of file LibSVR.cpp.

Member Data Documentation

struct svm_model* model

protected

SVM model

Definition at line 93 of file LibSVR.h.

svm_parameter param

protected

SVM parameter

Definition at line 90 of file LibSVR.h.

svm_problem problem

protected

SVM problem

Definition at line 88 of file LibSVR.h.

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

Detailed Description

Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation