SHOGUN  4.2.0
CKernelRidgeRegression Class Reference

## Detailed Description

Class KernelRidgeRegression implements Kernel Ridge Regression - a regularized least square method for classification and regression.

It is similar to support vector machines (cf. CSVM). However in contrast to SVMs a different objective is optimized that leads to a dense solution (thus not only a few support vectors are active in the end but all training examples). This makes it only applicable to rather few (a couple of thousand) training examples. In case a linear kernel is used RR is closely related to Fishers Linear Discriminant (cf. LDA).

Internally (for linear kernels) it is solved via minimizing the following system

$\frac{1}{2}\left(\sum_{i=1}^N(y_i-{\bf w}\cdot {\bf x}_i)^2 + \tau||{\bf w}||^2\right)$

which boils down to solving a linear system

${\bf w} = \left(\tau {\bf I}+ \sum_{i=1}^N{\bf x}_i{\bf x}_i^T\right)^{-1}\left(\sum_{i=1}^N y_i{\bf x}_i\right)$

and in the kernel case

${\bf \alpha}=\left({\bf K}+\tau{\bf I}\right)^{-1}{\bf y}$

where K is the kernel matrix and y the vector of labels. The expressed solution can again be written as a linear combination of kernels (cf. CKernelMachine) with bias $$b=0$$.

Definition at line 53 of file KernelRidgeRegression.h.

Inheritance diagram for CKernelRidgeRegression:
[legend]

## Public Member Functions

MACHINE_PROBLEM_TYPE (PT_REGRESSION)

CKernelRidgeRegression ()

CKernelRidgeRegression (float64_t tau, CKernel *k, CLabels *lab)

virtual ~CKernelRidgeRegression ()

virtual void set_tau (float64_t tau)

void set_epsilon (float64_t epsilon)

virtual bool load (FILE *srcfile)

virtual bool save (FILE *dstfile)

virtual EMachineType get_classifier_type ()

virtual const char * get_name () const

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 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 ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
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 ()

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)

bool has (const std::string &name) const

template<typename T >
bool has (const Tag< T > &tag) const

template<typename T , typename U = void>
bool has (const std::string &name) const

template<typename T >
void set (const Tag< T > &_tag, const T &value)

template<typename T , typename U = void>
void set (const std::string &name, const T &value)

template<typename T >
get (const Tag< T > &_tag) const

template<typename T , typename U = void>
get (const std::string &name) const

virtual void update_parameter_hash ()

virtual bool parameter_hash_changed ()

virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)

virtual CSGObjectclone ()

## Static Public Member Functions

static void * apply_helper (void *p)

## Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

Parameterm_gradient_parameters

uint32_t m_hash

## Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)

virtual bool solve_krr_system ()

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 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)

template<typename T >
void register_param (Tag< T > &_tag, const T &value)

template<typename T >
void register_param (const std::string &name, const T &value)

## Protected Attributes

float64_t m_tau

CKernelkernel

CCustomKernelm_custom_kernel

CKernelm_kernel_backup

bool use_batch_computation

bool use_linadd

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

 CKernelRidgeRegression ( )

default constructor

Definition at line 22 of file KernelRidgeRegression.cpp.

 CKernelRidgeRegression ( float64_t tau, CKernel * k, CLabels * lab )

constructor

Parameters
 tau regularization constant tau k kernel lab labels

Definition at line 28 of file KernelRidgeRegression.cpp.

 virtual ~CKernelRidgeRegression ( )
virtual

default destructor

Definition at line 71 of file KernelRidgeRegression.h.

## 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 152 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.

Reimplemented in CDomainAdaptationSVM.

Definition at line 248 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 254 of file KernelMachine.cpp.

 void * apply_helper ( void * p )
staticinherited

apply example helper, used in threads

Parameters
 p params of the thread
Returns
nothing really

Definition at line 424 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 232 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 187 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 518 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 531 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 266 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 252 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 524 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 259 of file Machine.cpp.

 CMulticlassLabels * apply_multiclass ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of multiclass classification problem

Definition at line 220 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 405 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 242 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 226 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 630 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 747 of file SGObject.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 194 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 623 of file KernelMachine.cpp.

 void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented from CMachine.

Definition at line 654 of file KernelMachine.cpp.

 CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 of file SGObject.cpp.

 bool equals ( CSGObject * other, float64_t accuracy = 0.0, bool tolerant = false )
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) tolerant allows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 651 of file SGObject.cpp.

 T get ( const Tag< T > & _tag ) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
 _tag name and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

 T get ( const std::string & name ) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
 name name of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

 float64_t get_alpha ( int32_t idx )
inherited

get alpha at given index

Parameters
 idx index of alpha
Returns
alpha

Definition at line 140 of file KernelMachine.cpp.

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

Definition at line 189 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 99 of file KernelMachine.cpp.

 float64_t get_bias ( )
inherited

get bias

Returns
bias

Definition at line 124 of file KernelMachine.cpp.

 bool get_bias_enabled ( )
inherited

get state of bias

Returns
state of bias

Definition at line 119 of file KernelMachine.cpp.

 virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type KernelRidgeRegression

Reimplemented from CMachine.

Definition at line 103 of file KernelRidgeRegression.h.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 of file SGObject.cpp.

 CKernel * get_kernel ( )
inherited

get kernel

Returns
kernel

Definition at line 88 of file KernelMachine.cpp.

 CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 76 of file Machine.cpp.

 bool get_linadd_enabled ( )
inherited

check if linadd is enabled

Returns
if linadd is enabled

Definition at line 109 of file KernelMachine.cpp.

 virtual EProblemType get_machine_problem_type ( ) const
virtualinherited

returns type of problem machine solves

Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.

Definition at line 299 of file Machine.h.

 float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 87 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 531 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 555 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 568 of file SGObject.cpp.

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

Reimplemented from CKernelMachine.

Reimplemented in CKRRNystrom.

Definition at line 109 of file KernelRidgeRegression.h.

 int32_t get_num_support_vectors ( )
inherited

get number of support vectors

Returns
number of support vectors

Definition at line 169 of file KernelMachine.cpp.

 ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 102 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 134 of file KernelMachine.cpp.

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

Definition at line 184 of file KernelMachine.cpp.

 bool has ( const std::string & name ) const
inherited

Checks if object has a class parameter identified by a name.

Parameters
 name name of the parameter
Returns
true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

 bool has ( const Tag< T > & tag ) const
inherited

Checks if object has a class parameter identified by a Tag.

Parameters
 tag tag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

 bool has ( const std::string & name ) const
inherited

Checks if a type exists for a class parameter identified by a name.

Parameters
 name name of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

 bool init_kernel_optimization ( )
inherited

initialise kernel optimisation

Returns
if operation was successful

Definition at line 211 of file KernelMachine.cpp.

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

Definition at line 296 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 329 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 CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression, and CBaseMulticlassMachine.

Definition at line 348 of file Machine.h.

 bool load ( FILE * srcfile )
virtual

load regression from file

Parameters
 srcfile file to load from
Returns
if loading was successful

Definition at line 103 of file KernelRidgeRegression.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "" )
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
Returns
TRUE if done, otherwise FALSE

Definition at line 402 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 occurs.

Definition at line 459 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 occurs.

Definition at line 454 of file SGObject.cpp.

 MACHINE_PROBLEM_TYPE ( PT_REGRESSION )

problem type

 bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 295 of file SGObject.cpp.

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

post lock

Definition at line 287 of file Machine.h.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 341 of file SGObject.cpp.

 void register_param ( Tag< T > & _tag, const T & value )
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
 _tag name and type information of parameter value value of the parameter

Definition at line 439 of file SGObject.h.

 void register_param ( const std::string & name, const T & value )
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
 name name of the parameter value value of the parameter along with type information

Definition at line 452 of file SGObject.h.

 bool save ( FILE * dstfile )
virtual

save regression to file

Parameters
 dstfile file to save to
Returns
if saving was successful

Definition at line 110 of file KernelRidgeRegression.cpp.

 bool save_serializable ( CSerializableFile * file, const char * prefix = "" )
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
Returns
TRUE if done, otherwise FALSE

Definition at line 347 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 occurs.

Reimplemented in CKernel.

Definition at line 469 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 occurs.

Definition at line 464 of file SGObject.cpp.

 void set ( const Tag< T > & _tag, const T & value )
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
 _tag name and type information of parameter value value of the parameter

Definition at line 328 of file SGObject.h.

 void set ( const std::string & name, const T & value )
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
 name name of the parameter value value of the parameter along with type information

Definition at line 354 of file SGObject.h.

 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 159 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 174 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 94 of file KernelMachine.cpp.

 void set_bias ( float64_t bias )
inherited

set bias to given value

Parameters
 bias new bias

Definition at line 129 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 114 of file KernelMachine.cpp.

 void set_epsilon ( float64_t epsilon )

set convergence precision for gauss seidel method

Parameters
 epsilon new epsilon

Definition at line 83 of file KernelRidgeRegression.h.

 void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 144 of file SGObject.cpp.

 void set_generic ( )
inherited

set generic type to T

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 261 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 274 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 316 of file SGObject.cpp.

 void set_kernel ( CKernel * k )
inherited

set kernel

Parameters
 k kernel

Definition at line 81 of file KernelMachine.cpp.

 void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab labels

Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 65 of file Machine.cpp.

 void set_linadd_enabled ( bool enable )
inherited

set linadd enabled

Parameters
 enable if linadd shall be enabled

Definition at line 104 of file KernelMachine.cpp.

 void set_max_train_time ( float64_t t )
inherited

set maximum training time

Parameters
 t maximimum training time

Definition at line 82 of file Machine.cpp.

 void set_solver_type ( ESolverType st )
inherited

set solver type

Parameters
 st solver type

Definition at line 97 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 107 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 149 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 179 of file KernelMachine.cpp.

 virtual void set_tau ( float64_t tau )
virtual

set regularization constant

Parameters
 tau new tau

Definition at line 77 of file KernelRidgeRegression.h.

 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 225 of file SGObject.cpp.

 bool solve_krr_system ( )
protectedvirtual

Train regression using Cholesky decomposition. Assumes that m_alpha is already allocated.

Returns
boolean to indicate success

Reimplemented in CKRRNystrom.

Definition at line 45 of file KernelRidgeRegression.cpp.

 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 453 of file KernelMachine.cpp.

 bool supports_locking ( ) const
virtualinherited
Returns
whether machine supports locking

Reimplemented from CMachine.

Definition at line 699 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, CAutoencoder, CLinearMachine, CSGDQN, and COnlineSVMSGD.

Definition at line 39 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 482 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 regressors are used and distance/kernels are initialized with train data)
Returns
whether training was successful

Reimplemented from CMachine.

Definition at line 69 of file KernelRidgeRegression.cpp.

 virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Definition at line 354 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 336 of file SGObject.cpp.

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

## Member Data Documentation

 SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

 CKernel* kernel
protectedinherited

kernel

Definition at line 311 of file KernelMachine.h.

 SGVector m_alpha
protectedinherited

coefficients alpha

Definition at line 332 of file KernelMachine.h.

 float64_t m_bias
protectedinherited

bias term b

Definition at line 329 of file KernelMachine.h.

 CCustomKernel* m_custom_kernel
protectedinherited

is filled with pre-computed custom kernel on data lock

Definition at line 314 of file KernelMachine.h.

 bool m_data_locked
protectedinherited

whether data is locked

Definition at line 370 of file Machine.h.

 Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

 CKernel* m_kernel_backup
protectedinherited

old kernel is stored here on data lock

Definition at line 317 of file KernelMachine.h.

 CLabels* m_labels
protectedinherited

labels

Definition at line 361 of file Machine.h.

 float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 358 of file Machine.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

 ESolverType m_solver_type
protectedinherited

solver type

Definition at line 364 of file Machine.h.

 bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 367 of file Machine.h.

 SGVector m_svs
protectedinherited

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

Definition at line 335 of file KernelMachine.h.

 float64_t m_tau
protected

regularization parameter tau

Definition at line 135 of file KernelRidgeRegression.h.

 Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

 bool use_batch_computation
protectedinherited

if batch computation is enabled

Definition at line 320 of file KernelMachine.h.

 bool use_bias
protectedinherited

if bias shall be used

Definition at line 326 of file KernelMachine.h.

 bool use_linadd
protectedinherited

if linadd is enabled

Definition at line 323 of file KernelMachine.h.

 Version* version
inherited

version

Definition at line 543 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation