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CDualLibQPBMSOSVM Class Reference

Detailed Description

Class DualLibQPBMSOSVM that uses Bundle Methods for Regularized Risk Minimization algorithms for structured output (SO) problems [1] presented in [2].

[1] Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y. Support Vector Machine Learning for Interdependent and Structured Ouput Spaces. http://www.cs.cornell.edu/People/tj/publications/tsochantaridis_etal_04a.pdf

[2] Teo, C.H., Vishwanathan, S.V.N, Smola, A. and Quoc, V.Le. Bundle Methods for Regularized Risk Minimization http://users.cecs.anu.edu.au/~chteo/pub/TeoVisSmoLe10.pdf

Definition at line 46 of file DualLibQPBMSOSVM.h.

Inheritance diagram for CDualLibQPBMSOSVM:
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Public Member Functions

 CDualLibQPBMSOSVM ()
 CDualLibQPBMSOSVM (CStructuredModel *model, CStructuredLabels *labs, float64_t _lambda, SGVector< float64_t > W=0)
virtual ~CDualLibQPBMSOSVM ()
virtual const char * get_name () const
void set_lambda (float64_t _lambda)
float64_t get_lambda ()
void set_TolRel (float64_t TolRel)
float64_t get_TolRel ()
void set_TolAbs (float64_t TolAbs)
float64_t get_TolAbs ()
void set_BufSize (uint32_t BufSize)
uint32_t get_BufSize ()
void set_cleanICP (bool cleanICP)
bool get_cleanICP ()
void set_cleanAfter (uint32_t cleanAfter)
uint32_t get_cleanAfter ()
void set_K (float64_t K)
float64_t get_K ()
void set_Tmax (uint32_t Tmax)
uint32_t get_Tmax ()
void set_cp_models (uint32_t cp_models)
uint32_t get_cp_models ()
void set_verbose (bool verbose)
bool get_verbose ()
BmrmStatistics get_result ()
ESolver get_solver ()
void set_solver (ESolver solver)
void set_w (SGVector< float64_t > W)
virtual EMachineType get_classifier_type ()
SGVector< float64_tget_w () const
virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)
virtual void store_model_features ()
 MACHINE_PROBLEM_TYPE (PT_STRUCTURED)
void set_model (CStructuredModel *model)
CStructuredModelget_model () const
virtual void set_labels (CLabels *lab)
void set_features (CFeatures *f)
CFeaturesget_features () const
void set_surrogate_loss (CLossFunction *loss)
CLossFunctionget_surrogate_loss () const
virtual float64_t risk (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0, EStructRiskType rtype=N_SLACK_MARGIN_RESCALING)
virtual bool train (CFeatures *data=NULL)
virtual CLabelsapply (CFeatures *data=NULL)
virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)
virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)
virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
virtual CLatentLabelsapply_latent (CFeatures *data=NULL)
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 bool train_locked (SGVector< index_t > indices)
virtual float64_t apply_one (int32_t i)
virtual CLabelsapply_locked (SGVector< index_t > indices)
virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)
virtual CRegressionLabelsapply_locked_regression (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 data_lock (CLabels *labs, CFeatures *features)
virtual void post_lock (CLabels *labs, CFeatures *features)
virtual void data_unlock ()
virtual bool supports_locking () const
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 ()

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

bool train_machine (CFeatures *data=NULL)
virtual float64_t risk_nslack_margin_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0)
virtual float64_t risk_nslack_slack_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0)
virtual float64_t risk_1slack_margin_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0)
virtual float64_t risk_1slack_slack_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0)
virtual float64_t risk_customized_formulation (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0)
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

SGVector< float64_tm_w
CStructuredModelm_model
CLossFunctionm_surrogate_loss
float64_t m_max_train_time
CLabelsm_labels
ESolverType m_solver_type
bool m_store_model_features
bool m_data_locked

Constructor & Destructor Documentation

default constructor

Definition at line 19 of file DualLibQPBMSOSVM.cpp.

CDualLibQPBMSOSVM ( CStructuredModel model,
CStructuredLabels labs,
float64_t  _lambda,
SGVector< float64_t W = 0 
)

constructor

Parameters
modelStructured Model
labsStructured labels
_lambdaRegularization constant
Winitial solution of weight vector

Definition at line 25 of file DualLibQPBMSOSVM.cpp.

~CDualLibQPBMSOSVM ( )
virtual

destructor

Definition at line 51 of file DualLibQPBMSOSVM.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 machine to data in means of binary classification problem

Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CLinearMachine, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, CGaussianProcessBinaryClassification, and CBaggingMachine.

Definition at line 218 of file Machine.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
indicesindex 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 for binary problems

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 248 of file Machine.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 for regression problems

Reimplemented in CKernelMachine.

Definition at line 255 of file Machine.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

Reimplemented in CMulticlassMachine, CKNN, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CQDA, CRelaxedTree, and CBaggingMachine.

Definition at line 230 of file Machine.cpp.

virtual float64_t apply_one ( int32_t  i)
virtualinherited
CRegressionLabels * apply_regression ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of regression problem

Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.

Definition at line 224 of file Machine.cpp.

CStructuredLabels * apply_structured ( CFeatures data = NULL)
virtualinherited

apply structured machine to data for Structured Output (SO) problem

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

Reimplemented from CMachine.

Definition at line 44 of file LinearStructuredOutputMachine.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
dictdictionary 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.

void data_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

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

Only possible if supports_locking() returns true

Parameters
labslabels used for locking
featuresfeatures used for locking

Reimplemented in CKernelMachine.

Definition at line 122 of file Machine.cpp.

void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 153 of file Machine.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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
Returns
true if all parameters were equal, false if not

Definition at line 1217 of file SGObject.cpp.

uint32_t get_BufSize ( )

get size of cutting plane buffer

Returns
Size of the cutting plane buffer

Definition at line 118 of file DualLibQPBMSOSVM.h.

EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type CT_LIBQPSOSVM

Reimplemented from CMachine.

Definition at line 127 of file DualLibQPBMSOSVM.cpp.

uint32_t get_cleanAfter ( )

get number of iterations for cleaning ICP

Returns
Number of iterations that inactive cutting planes has to be inactive for to be removed

Definition at line 145 of file DualLibQPBMSOSVM.h.

bool get_cleanICP ( )

get ICP removal flag

Returns
Status of inactive cutting plane removal feature (enabled/disabled)

Definition at line 131 of file DualLibQPBMSOSVM.h.

uint32_t get_cp_models ( )

get number of cutting plane models

Returns
Number of cutting plane models

Definition at line 181 of file DualLibQPBMSOSVM.h.

CFeatures * get_features ( ) const
inherited

get features

Returns
features

Definition at line 69 of file StructuredOutputMachine.cpp.

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.

float64_t get_K ( )

get K

Returns
K

Definition at line 157 of file DualLibQPBMSOSVM.h.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 86 of file Machine.cpp.

float64_t get_lambda ( )

get lambda

Returns
Regularization constant

Definition at line 81 of file DualLibQPBMSOSVM.h.

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.

CStructuredModel * get_model ( ) const
inherited

get structured model

Returns
structured model

Definition at line 45 of file StructuredOutputMachine.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_namename 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_namename 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
name of SGSerializable

Reimplemented from CLinearStructuredOutputMachine.

Definition at line 69 of file DualLibQPBMSOSVM.h.

BmrmStatistics get_result ( )

get bmrm result

Returns
Result returned from Bundle Method algorithm

Definition at line 199 of file DualLibQPBMSOSVM.h.

ESolver get_solver ( )

get training algorithm

Returns
Type of Bundle Method solver used for training

Definition at line 205 of file DualLibQPBMSOSVM.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 112 of file Machine.cpp.

CLossFunction * get_surrogate_loss ( ) const
inherited

get surrogate loss function

Returns
loss function

Definition at line 81 of file StructuredOutputMachine.cpp.

uint32_t get_Tmax ( )

get Tmax

Returns
Tmax

Definition at line 169 of file DualLibQPBMSOSVM.h.

float64_t get_TolAbs ( )

get absolute tolerance

Returns
Absolute tolerance

Definition at line 105 of file DualLibQPBMSOSVM.h.

float64_t get_TolRel ( )

get relative tolerance

Returns
Relative tolerance

Definition at line 93 of file DualLibQPBMSOSVM.h.

bool get_verbose ( )

get verbose

Returns
Status of screen output (enabled/disabled)

Definition at line 193 of file DualLibQPBMSOSVM.h.

SGVector< float64_t > get_w ( ) const
inherited

get w

Returns
w

Definition at line 39 of file LinearStructuredOutputMachine.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
genericset 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
labthe labels being checked, guaranteed to be non-NULL

Reimplemented in CGaussianProcessRegression, and CBaseMulticlassMachine.

Definition at line 340 of file Machine.h.

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_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix 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_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix 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
filewhere to load from
prefixprefix 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
ShogunExceptionWill be thrown if an error occurres.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

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
ShogunExceptionWill be thrown if an error occurres.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.

Definition at line 1024 of file SGObject.cpp.

MACHINE_PROBLEM_TYPE ( PT_STRUCTURED  )
inherited

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_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset 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_baseset of TParameter instances to use for migration
targetparameter 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_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith 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

Reimplemented in CMultitaskLinearMachine.

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
prefixprefix for members

Definition at line 280 of file SGObject.cpp.

float64_t risk ( float64_t subgrad,
float64_t W,
TMultipleCPinfo info = 0,
EStructRiskType  rtype = N_SLACK_MARGIN_RESCALING 
)
virtualinherited

computes the value of the risk function and sub-gradient at given point

Parameters
subgradSubgradient computed at given point W
WGiven weight vector
infoHelper info for multiple cutting plane models algorithm
rtypeThe type of structured risk
Returns
Value of the computed risk at given point W

Definition at line 147 of file StructuredOutputMachine.cpp.

float64_t risk_1slack_margin_rescale ( float64_t subgrad,
float64_t W,
TMultipleCPinfo info = 0 
)
protectedvirtualinherited

1-slack formulation and margin rescaling

Parameters
subgradSubgradient computed at given point W
WGiven weight vector
infoHelper info for multiple cutting plane models algorithm
Returns
Value of the computed risk at given point W

Definition at line 129 of file StructuredOutputMachine.cpp.

float64_t risk_1slack_slack_rescale ( float64_t subgrad,
float64_t W,
TMultipleCPinfo info = 0 
)
protectedvirtualinherited

1-slack formulation and slack rescaling

Parameters
subgradSubgradient computed at given point W
WGiven weight vector
infoHelper info for multiple cutting plane models algorithm
Returns
Value of the computed risk at given point W

Definition at line 135 of file StructuredOutputMachine.cpp.

float64_t risk_customized_formulation ( float64_t subgrad,
float64_t W,
TMultipleCPinfo info = 0 
)
protectedvirtualinherited

customized risk type

Parameters
subgradSubgradient computed at given point W
WGiven weight vector
infoHelper info for multiple cutting plane models algorithm
Returns
Value of the computed risk at given point W

Definition at line 141 of file StructuredOutputMachine.cpp.

float64_t risk_nslack_margin_rescale ( float64_t subgrad,
float64_t W,
TMultipleCPinfo info = 0 
)
protectedvirtualinherited

n-slack formulation and margin rescaling

The value of the risk is evaluated as

\[ R({\bf w}) = \sum_{i=1}^{m} \max_{y \in \mathcal{Y}} \left[ \ell(y_i, y) + \langle {\bf w}, \Psi(x_i, y) - \Psi(x_i, y_i) \rangle \right] \]

The subgradient is by Danskin's theorem given as

\[ R'({\bf w}) = \sum_{i=1}^{m} \Psi(x_i, \hat{y}_i) - \Psi(x_i, y_i), \]

where \( \hat{y}_i \) is the most violated label, i.e.

\[ \hat{y}_i = \arg\max_{y \in \mathcal{Y}} \left[ \ell(y_i, y) + \langle {\bf w}, \Psi(x_i, y) \rangle \right] \]

Parameters
subgradSubgradient computed at given point W
WGiven weight vector
infoHelper info for multiple cutting plane models algorithm
Returns
Value of the computed risk at given point W

Definition at line 87 of file StructuredOutputMachine.cpp.

float64_t risk_nslack_slack_rescale ( float64_t subgrad,
float64_t W,
TMultipleCPinfo info = 0 
)
protectedvirtualinherited

n-slack formulation and slack rescaling

Parameters
subgradSubgradient computed at given point W
WGiven weight vector
infoHelper info for multiple cutting plane models algorithm
Returns
Value of the computed risk at given point W

Definition at line 123 of file StructuredOutputMachine.cpp.

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

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix 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
ShogunExceptionWill 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
ShogunExceptionWill be thrown if an error occurres.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.

Definition at line 1034 of file SGObject.cpp.

void set_BufSize ( uint32_t  BufSize)

set size of cutting plane buffer

Parameters
BufSizeSize of the cutting plane buffer (i.e. maximal number of iterations)

Definition at line 112 of file DualLibQPBMSOSVM.h.

void set_cleanAfter ( uint32_t  cleanAfter)

set number of iterations for cleaning ICP

Parameters
cleanAfterSpecifies number of iterations that inactive cutting planes has to be inactive for to be removed

Definition at line 138 of file DualLibQPBMSOSVM.h.

void set_cleanICP ( bool  cleanICP)

set ICP removal flag

Parameters
cleanICPFlag that enables/disables inactive cutting plane removal feature

Definition at line 125 of file DualLibQPBMSOSVM.h.

void set_cp_models ( uint32_t  cp_models)

set number of cutting plane models

Parameters
cp_modelsNumber of cutting plane models

Definition at line 175 of file DualLibQPBMSOSVM.h.

void set_features ( CFeatures f)
inherited

set features

Parameters
ffeatures

Definition at line 64 of file StructuredOutputMachine.cpp.

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
ioio object to use

Definition at line 207 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 220 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 255 of file SGObject.cpp.

void set_K ( float64_t  K)

set K

Parameters
KParameter K

Definition at line 151 of file DualLibQPBMSOSVM.h.

void set_labels ( CLabels lab)
virtualinherited

set labels

Parameters
lablabels

Reimplemented from CMachine.

Definition at line 57 of file StructuredOutputMachine.cpp.

void set_lambda ( float64_t  _lambda)

set lambda

Parameters
_lambdaRegularization constant

Definition at line 75 of file DualLibQPBMSOSVM.h.

void set_max_train_time ( float64_t  t)
inherited

set maximum training time

Parameters
tmaximimum training time

Definition at line 92 of file Machine.cpp.

void set_model ( CStructuredModel model)
inherited

set structured model

Parameters
modelstructured model to set

Definition at line 38 of file StructuredOutputMachine.cpp.

void set_solver ( ESolver  solver)

set training algorithm

Parameters
solverType of Bundle Method solver used for training

Definition at line 211 of file DualLibQPBMSOSVM.h.

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver 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_modelwhether model should be stored after training

Definition at line 117 of file Machine.cpp.

void set_surrogate_loss ( CLossFunction loss)
inherited

set surrogate loss function

Parameters
lossloss function to set

Definition at line 74 of file StructuredOutputMachine.cpp.

void set_Tmax ( uint32_t  Tmax)

set Tmax

Parameters
TmaxParameter Tmax

Definition at line 163 of file DualLibQPBMSOSVM.h.

void set_TolAbs ( float64_t  TolAbs)

set absolute tolerance

Parameters
TolAbsAbsolute tolerance

Definition at line 99 of file DualLibQPBMSOSVM.h.

void set_TolRel ( float64_t  TolRel)

set relative tolerance

Parameters
TolRelRelative tolerance

Definition at line 87 of file DualLibQPBMSOSVM.h.

void set_verbose ( bool  verbose)

set verbose

Parameters
verboseFlag enabling/disabling screen output

Definition at line 187 of file DualLibQPBMSOSVM.h.

void set_w ( SGVector< float64_t W)

set initial value of weight vector w

Parameters
Winitial weight vector

Reimplemented from CLinearStructuredOutputMachine.

Definition at line 217 of file DualLibQPBMSOSVM.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 ( )
virtualinherited

Stores feature data of underlying model. Does nothing because Linear machines store the normal vector of the separating hyperplane and therefore the model anyway

Reimplemented from CMachine.

Definition at line 77 of file LinearStructuredOutputMachine.cpp.

virtual bool supports_locking ( ) const
virtualinherited
Returns
whether this machine supports locking

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 285 of file Machine.h.

bool train ( CFeatures data = NULL)
virtualinherited

train machine

Parameters
datatraining 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.

virtual bool train_locked ( SGVector< index_t indices)
virtualinherited

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

NOT IMPLEMENTED

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

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 231 of file Machine.h.

bool train_machine ( CFeatures data = NULL)
protectedvirtual

train dual SO-SVM

Reimplemented from CMachine.

Definition at line 87 of file DualLibQPBMSOSVM.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Reimplemented in COnlineLinearMachine, CKMeans, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.

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

SGIO* io
inherited

io

Definition at line 514 of file SGObject.h.

bool m_data_locked
protectedinherited

whether data is locked

Definition at line 362 of file Machine.h.

Parameter* m_gradient_parameters
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.

CLabels* m_labels
protectedinherited

labels

Definition at line 353 of file Machine.h.

float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 350 of file Machine.h.

CStructuredModel* m_model
protectedinherited

the model that contains the application dependent modules

Definition at line 191 of file StructuredOutputMachine.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.

CLossFunction* m_surrogate_loss
protectedinherited

the surrogate loss, for SOSVM, fixed to Hinge loss, other non-convex losses such as Ramp loss are also applicable, will be extended in the future

Definition at line 197 of file StructuredOutputMachine.h.

SGVector< float64_t > m_w
protectedinherited

weight vector

Definition at line 77 of file LinearStructuredOutputMachine.h.

Parallel* parallel
inherited

parallel

Definition at line 517 of file SGObject.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