SHOGUN  v3.0.0
CLibSVR Class Reference

## 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 $$c_i^*=-y_i+ \epsilon$$

This class also support the $$\nu$$-SVR regression version of the problem, where $$\nu$$ replaces the $$\epsilon$$ parameter and represents an upper bound on the fraction of margin errors and a lower bound on the fraction of support vectors. While it is easier to interpret, the resulting optimization problem usually takes longer to solve. Note that these different parameters do not result in different predictive power. For a given problem, the best SVR for each parametrization will lead to the same results. See the letter "Training \f$\nu\f$-Support Vector Regression: Theory and Algorithms" by Chih-Chung Chang and Chih-Jen Lin for the relation of $$\epsilon$$-SVR and $$\nu$$-SVR.

Definition at line 70 of file LibSVR.h.

Inheritance diagram for CLibSVR:
[legend]

## Public Member Functions

MACHINE_PROBLEM_TYPE (PT_REGRESSION)
CLibSVR ()
CLibSVR (float64_t C, float64_t svr_param, CKernel *k, CLabels *lab, LIBSVR_SOLVER_TYPE st=LIBSVR_EPSILON_SVR)
virtual ~CLibSVR ()
virtual EMachineType get_classifier_type ()
virtual const char * get_name () const
MACHINE_PROBLEM_TYPE (PT_BINARY)
void set_defaults (int32_t num_sv=0)
virtual SGVector< float64_tget_linear_term ()
virtual void set_linear_term (const SGVector< float64_t > linear_term)
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))
void set_kernel (CKernel *k)
CKernelget_kernel ()
void set_batch_computation_enabled (bool enable)
bool get_batch_computation_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 CRegressionLabelsapply_regression (CFeatures *data=NULL)
virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)
virtual float64_t apply_one (int32_t num)
virtual bool train_locked (SGVector< index_t > indices)
virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)
virtual CRegressionLabelsapply_locked_regression (SGVector< index_t > indices)
virtual SGVector< float64_tapply_locked_get_output (SGVector< index_t > indices)
virtual void data_lock (CLabels *labs, CFeatures *features=NULL)
virtual void data_unlock ()
virtual bool supports_locking () const
virtual bool train (CFeatures *data=NULL)
virtual CLabelsapply (CFeatures *data=NULL)
virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)
virtual CLatentLabelsapply_latent (CFeatures *data=NULL)
virtual void set_labels (CLabels *lab)
virtual CLabelsget_labels ()
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)
virtual CLabelsapply_locked (SGVector< index_t > indices)
virtual CMulticlassLabelsapply_locked_multiclass (SGVector< index_t > indices)
virtual CStructuredLabelsapply_locked_structured (SGVector< index_t > indices)
virtual CLatentLabelsapply_locked_latent (SGVector< index_t > indices)
virtual void post_lock (CLabels *labs, CFeatures *features)
bool is_data_locked () const
virtual EProblemType get_machine_problem_type () const
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
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="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
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 ()
SGStringList< char > get_modelsel_names ()
void print_modsel_params ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
virtual bool update_parameter_hash ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0)
virtual CSGObjectclone ()

## Static Public Member Functions

static void * apply_helper (void *p)

## Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
ParameterMapm_parameter_map
uint32_t m_hash

## Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)
virtual float64_tget_linear_term_array ()
SGVector< float64_tapply_get_outputs (CFeatures *data)
virtual void store_model_features ()
virtual bool is_label_valid (CLabels *lab) const
virtual bool train_require_labels () const
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
virtual void load_serializable_pre () throw (ShogunException)
virtual void load_serializable_post () throw (ShogunException)
virtual void save_serializable_pre () throw (ShogunException)
virtual void save_serializable_post () throw (ShogunException)

## Protected Attributes

svm_problem problem
svm_parameter param
struct svm_model * model
LIBSVR_SOLVER_TYPE solver_type
SGVector< float64_tm_linear_term
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)
CMKLmkl
CKernelkernel
CCustomKernelm_custom_kernel
CKernelm_kernel_backup
bool use_batch_computation
bool use_bias
float64_t m_bias
SGVector< float64_tm_alpha
SGVector< int32_t > m_svs
float64_t m_max_train_time
CLabelsm_labels
ESolverType m_solver_type
bool m_store_model_features
bool m_data_locked

## Constructor & Destructor Documentation

 CLibSVR ( )

default constructor, creates a EPISOLON-SVR

Definition at line 18 of file LibSVR.cpp.

 CLibSVR ( float64_t C, float64_t svr_param, CKernel * k, CLabels * lab, LIBSVR_SOLVER_TYPE st = LIBSVR_EPSILON_SVR )

constructor

Parameters
 C constant C svr_param tube epsilon or SVR-NU depending on solver type k kernel lab labels st solver type to use, EPSILON-SVR or NU-SVR

Definition at line 25 of file LibSVR.cpp.

 ~CLibSVR ( )
virtual

Definition at line 51 of file LibSVR.cpp.

## Member Function Documentation

 CLabels * apply ( CFeatures * data = NULL )
virtualinherited

apply machine to data if data is not specified apply to the current features

Parameters
 data (test)data to be classified
Returns
classified labels

Definition at line 162 of file Machine.cpp.

 CBinaryLabels * apply_binary ( CFeatures * data = NULL )
virtualinherited

apply kernel machine to data for binary classification task

Parameters
 data (test)data to be classified
Returns
classified labels

Reimplemented from CMachine.

Definition at line 245 of file KernelMachine.cpp.

 SGVector< float64_t > apply_get_outputs ( CFeatures * data )
protectedinherited

apply get outputs

Parameters
 data features to compute outputs
Returns
outputs

Definition at line 251 of file KernelMachine.cpp.

 void * apply_helper ( void * p )
staticinherited

apply example helper, used in threads

Parameters
Returns
nothing really

Definition at line 421 of file KernelMachine.cpp.

 CLatentLabels * apply_latent ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 242 of file Machine.cpp.

 CLabels * apply_locked ( SGVector< index_t > indices )
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked

Parameters
 indices index vector (of locked features) that is predicted

Definition at line 197 of file Machine.cpp.

 CBinaryLabels * apply_locked_binary ( SGVector< index_t > indices )
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked. Binary case

Parameters
 indices index vector (of locked features) that is predicted
Returns
resulting labels

Reimplemented from CMachine.

Definition at line 515 of file KernelMachine.cpp.

 SGVector< float64_t > apply_locked_get_output ( SGVector< index_t > indices )
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked

Parameters
 indices index vector (of locked features) that is predicted
Returns
raw output of machine

Definition at line 528 of file KernelMachine.cpp.

 CLatentLabels * apply_locked_latent ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for latent problems

Definition at line 276 of file Machine.cpp.

 CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for multiclass problems

Definition at line 262 of file Machine.cpp.

 CRegressionLabels * apply_locked_regression ( SGVector< index_t > indices )
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked. Binary case

Parameters
 indices index vector (of locked features) that is predicted
Returns
resulting labels

Reimplemented from CMachine.

Definition at line 521 of file KernelMachine.cpp.

 CStructuredLabels * apply_locked_structured ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for structured problems

Definition at line 269 of file Machine.cpp.

 CMulticlassLabels * apply_multiclass ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of multiclass classification problem

Definition at line 230 of file Machine.cpp.

 float64_t apply_one ( int32_t num )
virtualinherited

apply kernel machine to one example

Parameters
 num which example to apply to
Returns
classified value

Reimplemented from CMachine.

Definition at line 402 of file KernelMachine.cpp.

 CRegressionLabels * apply_regression ( CFeatures * data = NULL )
virtualinherited

apply kernel machine to data for regression task

Parameters
 data (test)data to be classified
Returns
classified labels

Reimplemented from CMachine.

Definition at line 239 of file KernelMachine.cpp.

 CStructuredLabels * apply_structured ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 236 of file Machine.cpp.

 void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > * dict )
inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
 dict dictionary of parameters to be built.

Definition at line 1196 of file SGObject.cpp.

 CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 1313 of file SGObject.cpp.

 float64_t compute_svm_dual_objective ( )
inherited

compute svm dual objective

Returns
computed dual objective

Definition at line 242 of file SVM.cpp.

 float64_t compute_svm_primal_objective ( )
inherited

compute svm primal objective

Returns
computed svm primal objective

Definition at line 267 of file SVM.cpp.

 bool create_new_model ( int32_t num )
inherited

create new model

Parameters
 num number of alphas and support vectors in new model

Definition at line 191 of file KernelMachine.cpp.

 void data_lock ( CLabels * labs, CFeatures * features = NULL )
virtualinherited

Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called.

Computes kernel matrix to speed up train/apply calls

Parameters
 labs labels used for locking features features used for locking

Reimplemented from CMachine.

Definition at line 620 of file KernelMachine.cpp.

 void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented from CMachine.

Definition at line 649 of file KernelMachine.cpp.

 virtual CSGObject* deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 160 of file SGObject.h.

 bool equals ( CSGObject * other, float64_t accuracy = 0.0 )
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
 other object to compare with accuracy accuracy to use for comparison (optional)
Returns
true if all parameters were equal, false if not

Definition at line 1217 of file SGObject.cpp.

 float64_t get_alpha ( int32_t idx )
inherited

get alpha at given index

Parameters
 idx index of alpha
Returns
alpha

Definition at line 137 of file KernelMachine.cpp.

 SGVector< float64_t > get_alphas ( )
inherited
Returns
vector of alphas

Definition at line 186 of file KernelMachine.cpp.

 bool get_batch_computation_enabled ( )
inherited

check if batch computation is enabled

Returns
if batch computation is enabled

Definition at line 96 of file KernelMachine.cpp.

 float64_t get_bias ( )
inherited

get bias

Returns
bias

Definition at line 121 of file KernelMachine.cpp.

 bool get_bias_enabled ( )
inherited

get state of bias

Returns
state of bias

Definition at line 116 of file KernelMachine.cpp.

 float64_t get_C1 ( )
inherited

get C1

Returns
C1

Definition at line 159 of file SVM.h.

 float64_t get_C2 ( )
inherited

get C2

Returns
C2

Definition at line 165 of file SVM.h.

 EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifie type LIBSVR

Reimplemented from CMachine.

Definition at line 56 of file LibSVR.cpp.

 float64_t get_epsilon ( )
inherited

get epsilon

Returns
epsilon

Definition at line 147 of file SVM.h.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 214 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 249 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 262 of file SGObject.cpp.

 CKernel * get_kernel ( )
inherited

get kernel

Returns
kernel

Definition at line 85 of file KernelMachine.cpp.

 CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 86 of file Machine.cpp.

inherited

Returns

Definition at line 106 of file KernelMachine.cpp.

 SGVector< float64_t > get_linear_term ( )
virtualinherited

get linear term

Returns
the linear term

Definition at line 332 of file SVM.cpp.

 float64_t * get_linear_term_array ( )
protectedvirtualinherited

get linear term copy as dynamic array

Returns
linear term copied to a dynamic array

Definition at line 302 of file SVM.cpp.

 virtual EProblemType get_machine_problem_type ( ) const
virtualinherited

returns type of problem machine solves

Reimplemented in CBaseMulticlassMachine.

Definition at line 291 of file Machine.h.

 float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 97 of file Machine.cpp.

 SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 1100 of file SGObject.cpp.

 char * get_modsel_param_descr ( const char * param_name )
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 1124 of file SGObject.cpp.

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

Parameters
 param_name name of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 1137 of file SGObject.cpp.

 virtual const char* get_name ( ) const
virtual
Returns
object name

Reimplemented from CSVM.

Definition at line 99 of file LibSVR.h.

 float64_t get_nu ( )
inherited

get nu

Returns
nu

Definition at line 153 of file SVM.h.

 int32_t get_num_support_vectors ( )
inherited

get number of support vectors

Returns
number of support vectors

Definition at line 166 of file KernelMachine.cpp.

 float64_t get_objective ( )
inherited

get objective

Returns
objective

Definition at line 216 of file SVM.h.

 int32_t get_qpsize ( )
inherited

get qpsize

Returns
qpsize

Definition at line 171 of file SVM.h.

 bool get_shrinking_enabled ( )
inherited

get state of shrinking

Returns
if shrinking is enabled

Definition at line 186 of file SVM.h.

 ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 112 of file Machine.cpp.

 int32_t get_support_vector ( int32_t idx )
inherited

get support vector at given index

Parameters
 idx index of support vector
Returns
support vector

Definition at line 131 of file KernelMachine.cpp.

 SGVector< int32_t > get_support_vectors ( )
inherited
Returns
all support vectors

Definition at line 181 of file KernelMachine.cpp.

 float64_t get_tube_epsilon ( )
inherited

get tube epsilon

Returns
tube epsilon

Definition at line 135 of file SVM.h.

 bool init_kernel_optimization ( )
inherited

initialise kernel optimisation

Returns
if operation was successful

Definition at line 208 of file KernelMachine.cpp.

 bool is_data_locked ( ) const
inherited
Returns
whether this machine is locked

Definition at line 288 of file Machine.h.

 bool is_generic ( EPrimitiveType * generic ) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
 generic set to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 268 of file SGObject.cpp.

 virtual bool is_label_valid ( CLabels * lab ) const
protectedvirtualinherited

check whether the labels is valid.

Subclasses can override this to implement their check of label types.

Parameters
 lab the labels being checked, guaranteed to be non-NULL

Reimplemented in CGaussianProcessRegression, and CBaseMulticlassMachine.

Definition at line 340 of file Machine.h.

 bool load ( FILE * svm_file )
inherited

Parameters
 svm_file the file handle

Definition at line 90 of file SVM.cpp.

 DynArray< TParameter * > * load_all_file_parameters ( int32_t file_version, int32_t current_version, CSerializableFile * file, const char * prefix = "" )
inherited

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters
 file_version parameter version of the file current_version version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) file file to load from prefix prefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 673 of file SGObject.cpp.

 DynArray< TParameter * > * load_file_parameters ( const SGParamInfo * param_info, int32_t file_version, CSerializableFile * file, const char * prefix = "" )
inherited

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters
 param_info information of parameter file_version parameter version of the file, must be <= provided parameter version file file to load from prefix prefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 514 of file SGObject.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter() )
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
 file where to load from prefix prefix for members param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 345 of file SGObject.cpp.

 void load_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1029 of file SGObject.cpp.

 void load_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1024 of file SGObject.cpp.

 MACHINE_PROBLEM_TYPE ( PT_BINARY )
inherited

problem type

 MACHINE_PROBLEM_TYPE ( PT_REGRESSION )

problem type

 void map_parameters ( DynArray< TParameter * > * param_base, int32_t & base_version, DynArray< const SGParamInfo * > * target_param_infos )
inherited

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters
 param_base set of TParameter instances that are mapped to the provided target parameter infos base_version version of the parameter base target_param_infos set of SGParamInfo instances that specify the target parameter base

Definition at line 711 of file SGObject.cpp.

 TParameter * migrate ( DynArray< TParameter * > * param_base, const SGParamInfo * target )
protectedvirtualinherited

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters
 param_base set of TParameter instances to use for migration target parameter info for the resulting TParameter
Returns
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 918 of file SGObject.cpp.

 void one_to_one_migration_prepare ( DynArray< TParameter * > * param_base, const SGParamInfo * target, TParameter *& replacement, TParameter *& to_migrate, char * old_name = NULL )
protectedvirtualinherited

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters
 param_base set of TParameter instances to use for migration target parameter info for the resulting TParameter replacement (used as output) here the TParameter instance which is returned by migration is created into to_migrate the only source that is used for migration old_name with this parameter, a name change may be specified

Definition at line 858 of file SGObject.cpp.

 virtual void post_lock ( CLabels * labs, CFeatures * features )
virtualinherited

post lock

Definition at line 279 of file Machine.h.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1076 of file SGObject.cpp.

 void print_serializable ( const char * prefix = "" )
virtualinherited

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 280 of file SGObject.cpp.

 bool save ( FILE * svm_file )
inherited

write a SVM to a file

Parameters
 svm_file the file handle

Definition at line 206 of file SVM.cpp.

 bool save_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter() )
virtualinherited

Save this object to file.

Parameters
 file where to save the object; will be closed during returning if PREFIX is an empty string. prefix prefix for members param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 286 of file SGObject.cpp.

 void save_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1039 of file SGObject.cpp.

 void save_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1034 of file SGObject.cpp.

 bool set_alpha ( int32_t idx, float64_t val )
inherited

set alpha at given index to given value

Parameters
 idx index of alpha vector val new value of alpha vector
Returns
if operation was successful

Definition at line 156 of file KernelMachine.cpp.

 void set_alphas ( SGVector< float64_t > alphas )
inherited

set alphas to given values

Parameters
 alphas float vector with all alphas to set

Definition at line 171 of file KernelMachine.cpp.

 void set_batch_computation_enabled ( bool enable )
inherited

set batch computation enabled

Parameters
 enable if batch computation shall be enabled

Definition at line 91 of file KernelMachine.cpp.

 void set_bias ( float64_t bias )
inherited

set bias to given value

Parameters
 bias new bias

Definition at line 126 of file KernelMachine.cpp.

 void set_bias_enabled ( bool enable_bias )
inherited

set state of bias

Parameters
 enable_bias if bias shall be enabled

Definition at line 111 of file KernelMachine.cpp.

 void set_C ( float64_t c_neg, float64_t c_pos )
inherited

set C

Parameters
 c_neg new C constant for negatively labeled examples c_pos new C constant for positively labeled examples

Note that not all SVMs support this (however at least CLibSVM and CSVMLight do)

Definition at line 116 of file SVM.h.

 void set_callback_function ( CMKL * m, bool(*)(CMKL *mkl, const float64_t *sumw, const float64_t suma) cb )
inherited

set callback function svm optimizers may call when they have a new (small) set of alphas

Parameters
 m pointer to mkl object cb callback function

Definition at line 232 of file SVM.cpp.

 void set_defaults ( int32_t num_sv = 0 )
inherited

set default values for members a SVM object

Definition at line 48 of file SVM.cpp.

 void set_epsilon ( float64_t eps )
inherited

set epsilon

Parameters
 eps new epsilon

Definition at line 123 of file SVM.h.

 void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 41 of file SGObject.cpp.

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 207 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 220 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 255 of file SGObject.cpp.

 void set_kernel ( CKernel * k )
inherited

set kernel

Parameters
 k kernel

Definition at line 78 of file KernelMachine.cpp.

 void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab labels

Reimplemented in CGaussianProcessMachine, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 75 of file Machine.cpp.

 void set_linadd_enabled ( bool enable )
inherited

Parameters
 enable if linadd shall be enabled

Definition at line 101 of file KernelMachine.cpp.

 void set_linear_term ( const SGVector< float64_t > linear_term )
virtualinherited

set linear term of the QP

Parameters
 linear_term the linear term

Definition at line 314 of file SVM.cpp.

 void set_max_train_time ( float64_t t )
inherited

set maximum training time

Parameters
 t maximimum training time

Definition at line 92 of file Machine.cpp.

 void set_nu ( float64_t nue )
inherited

set nu

Parameters
 nue new nu

Definition at line 105 of file SVM.h.

 void set_objective ( float64_t v )
inherited

set objective

Parameters
 v objective

Definition at line 207 of file SVM.h.

 void set_qpsize ( int32_t qps )
inherited

set qpsize

Parameters
 qps new qpsize

Definition at line 141 of file SVM.h.

 void set_shrinking_enabled ( bool enable )
inherited

set state of shrinking

Parameters
 enable if shrinking will be enabled

Definition at line 177 of file SVM.h.

 void set_solver_type ( ESolverType st )
inherited

set solver type

Parameters
 st solver type

Definition at line 107 of file Machine.cpp.

 void set_store_model_features ( bool store_model )
virtualinherited

Setter for store-model-features-after-training flag

Parameters
 store_model whether model should be stored after training

Definition at line 117 of file Machine.cpp.

 bool set_support_vector ( int32_t idx, int32_t val )
inherited

set support vector at given index to given value

Parameters
 idx index of support vector val new value of support vector
Returns
if operation was successful

Definition at line 146 of file KernelMachine.cpp.

 void set_support_vectors ( SGVector< int32_t > svs )
inherited

set support vectors to given values

Parameters
 svs integer vector with all support vectors indexes to set

Definition at line 176 of file KernelMachine.cpp.

 void set_tube_epsilon ( float64_t eps )
inherited

set tube epsilon

Parameters
 eps new tube epsilon

Definition at line 129 of file SVM.h.

 virtual CSGObject* shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 151 of file SGObject.h.

 void store_model_features ( )
protectedvirtualinherited

Stores feature data of the SV indices and sets it to the lhs of the underlying kernel. Then, all SV indices are set to identity.

May be overwritten by subclasses in case the model should be stored differently.

Reimplemented from CMachine.

Definition at line 450 of file KernelMachine.cpp.

 bool supports_locking ( ) const
virtualinherited
Returns
whether machine supports locking

Reimplemented from CMachine.

Definition at line 707 of file KernelMachine.cpp.

 bool train ( CFeatures * data = NULL )
virtualinherited

train machine

Parameters
 data training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training.
Returns
whether training was successful

Reimplemented in CRelaxedTree, CSGDQN, and COnlineSVMSGD.

Definition at line 49 of file Machine.cpp.

 bool train_locked ( SGVector< index_t > indices )
virtualinherited

Trains a locked machine on a set of indices. Error if machine is not locked

Parameters
 indices index vector (of locked features) that is used for training
Returns
whether training was successful

Reimplemented from CMachine.

Definition at line 479 of file KernelMachine.cpp.

 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 61 of file LibSVR.cpp.

 virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Definition at line 346 of file Machine.h.

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 275 of file SGObject.cpp.

 bool update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination.

Returns
bool if parameter combination has changed since last update.

Definition at line 227 of file SGObject.cpp.

## Member Data Documentation

 float64_t C1
protectedinherited

C1 regularization const

Definition at line 255 of file SVM.h.

 float64_t C2
protectedinherited

C2

Definition at line 257 of file SVM.h.

 bool(* callback)(CMKL *mkl, const float64_t *sumw, const float64_t suma)
protectedinherited

callback function svm optimizers may call when they have a new (small) set of alphas

Definition at line 267 of file SVM.h.

 float64_t epsilon
protectedinherited

epsilon

Definition at line 249 of file SVM.h.

 SGIO* io
inherited

io

Definition at line 514 of file SGObject.h.

 CKernel* kernel
protectedinherited

kernel

Definition at line 310 of file KernelMachine.h.

 SGVector m_alpha
protectedinherited

coefficients alpha

Definition at line 331 of file KernelMachine.h.

 float64_t m_bias
protectedinherited

bias term b

Definition at line 328 of file KernelMachine.h.

 CCustomKernel* m_custom_kernel
protectedinherited

is filled with pre-computed custom kernel on data lock

Definition at line 313 of file KernelMachine.h.

 bool m_data_locked
protectedinherited

whether data is locked

Definition at line 362 of file Machine.h.

inherited

parameters wrt which we can compute gradients

Definition at line 529 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 535 of file SGObject.h.

 CKernel* m_kernel_backup
protectedinherited

old kernel is stored here on data lock

Definition at line 316 of file KernelMachine.h.

 CLabels* m_labels
protectedinherited

labels

Definition at line 353 of file Machine.h.

 SGVector m_linear_term
protectedinherited

linear term in qp

Definition at line 244 of file SVM.h.

 float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 350 of file Machine.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 526 of file SGObject.h.

 ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 532 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 523 of file SGObject.h.

 ESolverType m_solver_type
protectedinherited

solver type

Definition at line 356 of file Machine.h.

 bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 359 of file Machine.h.

 SGVector m_svs
protectedinherited

array of support vectors'' (indices of feature objects)

Definition at line 334 of file KernelMachine.h.

 CMKL* mkl
protectedinherited

mkl object that svm optimizers need to pass when calling the callback function

Definition at line 270 of file SVM.h.

 struct svm_model* model
protected

SVM model

Definition at line 118 of file LibSVR.h.

 float64_t nu
protectedinherited

nu

Definition at line 253 of file SVM.h.

 float64_t objective
protectedinherited

objective

Definition at line 259 of file SVM.h.

 Parallel* parallel
inherited

parallel

Definition at line 517 of file SGObject.h.

 svm_parameter param
protected

SVM parameter

Definition at line 115 of file LibSVR.h.

 svm_problem problem
protected

SVM problem

Definition at line 113 of file LibSVR.h.

 int32_t qpsize
protectedinherited

qpsize

Definition at line 261 of file SVM.h.

 LIBSVR_SOLVER_TYPE solver_type
protected

solver type

Definition at line 121 of file LibSVR.h.

protectedinherited

Definition at line 247 of file SVM.h.

 float64_t tube_epsilon
protectedinherited

tube epsilon for support vector regression

Definition at line 251 of file SVM.h.

 bool use_batch_computation
protectedinherited

if batch computation is enabled

Definition at line 319 of file KernelMachine.h.

 bool use_bias
protectedinherited

if bias shall be used

Definition at line 325 of file KernelMachine.h.

protectedinherited

Definition at line 322 of file KernelMachine.h.

 bool use_shrinking
protectedinherited

if shrinking shall be used

Definition at line 263 of file SVM.h.

 Version* version
inherited

version

Definition at line 520 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation