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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 49 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 ()
 
BmrmStatistics get_result ()
 
ESolver get_solver ()
 
void set_solver (ESolver solver)
 
void set_w (SGVector< float64_t > W)
 
void set_store_train_info (bool store_train_info)
 
virtual EMachineType get_classifier_type ()
 
SGVector< float64_t > get_w () const
 
virtual CStructuredLabels * apply_structured (CFeatures *data=NULL)
 
virtual void store_model_features ()
 
 MACHINE_PROBLEM_TYPE (PT_STRUCTURED)
 
void set_model (CStructuredModel *model)
 
CStructuredModel * get_model () const
 
virtual void set_labels (CLabels *lab)
 
void set_features (CFeatures *f)
 
CFeatures * get_features () const
 
void set_surrogate_loss (CLossFunction *loss)
 
CLossFunction * get_surrogate_loss () const
 
virtual float64_t risk (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0, EStructRiskType rtype=N_SLACK_MARGIN_RESCALING)
 
CSOSVMHelper * get_helper () const
 
void set_verbose (bool verbose)
 
bool get_verbose () const
 
virtual bool train (CFeatures *data=NULL)
 
virtual CLabels * apply (CFeatures *data=NULL)
 
virtual CBinaryLabels * apply_binary (CFeatures *data=NULL)
 
virtual CRegressionLabels * apply_regression (CFeatures *data=NULL)
 
virtual CMulticlassLabels * apply_multiclass (CFeatures *data=NULL)
 
virtual CLatentLabels * apply_latent (CFeatures *data=NULL)
 
virtual CLabels * get_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 CLabels * apply_locked (SGVector< index_t > indices)
 
virtual CBinaryLabels * apply_locked_binary (SGVector< index_t > indices)
 
virtual CRegressionLabels * apply_locked_regression (SGVector< index_t > indices)
 
virtual CMulticlassLabels * apply_locked_multiclass (SGVector< index_t > indices)
 
virtual CStructuredLabels * apply_locked_structured (SGVector< index_t > indices)
 
virtual CLatentLabels * apply_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 CSGObject * shallow_copy () const
 
virtual CSGObject * deep_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)
 
SGIO * get_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallel * get_global_parallel ()
 
void set_global_version (Version *version)
 
Version * get_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 void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObject * clone ()
 

Public Attributes

SGIO * io
 
Parallel * parallel
 
Version * version
 
Parameter * m_parameters
 
Parameter * m_model_selection_parameters
 
Parameter * m_gradient_parameters
 
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 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_t > m_w
 
CStructuredModel * m_model
 
CLossFunction * m_surrogate_loss
 
CSOSVMHelper * m_helper
 
bool m_verbose
 
float64_t m_max_train_time
 
CLabels * m_labels
 
ESolverType m_solver_type
 
bool m_store_model_features
 
bool m_data_locked
 

Constructor & Destructor Documentation

CDualLibQPBMSOSVM ( )

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 152 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, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, and CBaggingMachine.

Definition at line 208 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 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
indicesindex 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 for binary problems

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 238 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 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 for regression problems

Reimplemented in CKernelMachine.

Definition at line 245 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 259 of file Machine.cpp.

CMulticlassLabels * apply_multiclass ( CFeatures *  data = NULL)
virtualinherited

apply machine to data in means of multiclass classification problem

Reimplemented in CNeuralNetwork, CCHAIDTree, CCARTree, CGaussianProcessClassification, CMulticlassMachine, CKNN, CC45ClassifierTree, CID3ClassifierTree, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CQDA, CRelaxedTree, and CBaggingMachine.

Definition at line 220 of file Machine.cpp.

virtual float64_t apply_one ( int32_t  i)
virtualinherited

applies to one vector

Reimplemented in CKernelMachine, CRelaxedTree, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CMultitaskLinearMachine, CMulticlassMachine, CKNN, CDistanceMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, CScatterSVM, CGaussianNaiveBayes, CPluginEstimate, and CFeatureBlockLogisticRegression.

Definition at line 247 of file Machine.h.

CRegressionLabels * apply_regression ( CFeatures *  data = NULL)
virtualinherited

apply machine to data in means of regression problem

Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CNeuralNetwork, CCHAIDTree, CStochasticGBMachine, CCARTree, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.

Definition at line 214 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 45 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 597 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 714 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 112 of file Machine.cpp.

void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 143 of file Machine.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 198 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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 618 of file SGObject.cpp.

uint32_t get_BufSize ( )

get size of cutting plane buffer

Returns
Size of the cutting plane buffer

Definition at line 121 of file DualLibQPBMSOSVM.h.

EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type CT_LIBQPSOSVM

Reimplemented from CMachine.

Definition at line 135 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 148 of file DualLibQPBMSOSVM.h.

bool get_cleanICP ( )

get ICP removal flag

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

Definition at line 134 of file DualLibQPBMSOSVM.h.

uint32_t get_cp_models ( )

get number of cutting plane models

Returns
Number of cutting plane models

Definition at line 184 of file DualLibQPBMSOSVM.h.

CFeatures * get_features ( ) const
inherited

get features

Returns
features

Definition at line 79 of file StructuredOutputMachine.cpp.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 235 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 277 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 290 of file SGObject.cpp.

CSOSVMHelper * get_helper ( ) const
inherited
Returns
training progress helper

Definition at line 186 of file StructuredOutputMachine.cpp.

float64_t get_K ( )

get K

Returns
K

Definition at line 160 of file DualLibQPBMSOSVM.h.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 76 of file Machine.cpp.

float64_t get_lambda ( )

get lambda

Returns
Regularization constant

Definition at line 84 of file DualLibQPBMSOSVM.h.

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.

CStructuredModel * get_model ( ) const
inherited

get structured model

Returns
structured model

Definition at line 50 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 498 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 522 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 535 of file SGObject.cpp.

virtual const char* get_name ( ) const
virtual
Returns
name of SGSerializable

Reimplemented from CLinearStructuredOutputMachine.

Definition at line 72 of file DualLibQPBMSOSVM.h.

BmrmStatistics get_result ( )

get bmrm result

Returns
Result returned from Bundle Method algorithm

Definition at line 190 of file DualLibQPBMSOSVM.h.

ESolver get_solver ( )

get training algorithm

Returns
Type of Bundle Method solver used for training

Definition at line 196 of file DualLibQPBMSOSVM.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 102 of file Machine.cpp.

CLossFunction * get_surrogate_loss ( ) const
inherited

get surrogate loss function

Returns
loss function

Definition at line 91 of file StructuredOutputMachine.cpp.

uint32_t get_Tmax ( )

get Tmax

Returns
Tmax

Definition at line 172 of file DualLibQPBMSOSVM.h.

float64_t get_TolAbs ( )

get absolute tolerance

Returns
Absolute tolerance

Definition at line 108 of file DualLibQPBMSOSVM.h.

float64_t get_TolRel ( )

get relative tolerance

Returns
Relative tolerance

Definition at line 96 of file DualLibQPBMSOSVM.h.

bool get_verbose ( ) const
inherited

get verbose

Returns
Status of verbose flag (enabled/disabled)

Definition at line 203 of file StructuredOutputMachine.cpp.

SGVector< float64_t > get_w ( ) const
inherited

get w

Returns
w

Definition at line 40 of file LinearStructuredOutputMachine.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
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

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

Definition at line 348 of file Machine.h.

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
filewhere to load from
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 369 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 occurs.

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

Definition at line 426 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 occurs.

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

Definition at line 421 of file SGObject.cpp.

MACHINE_PROBLEM_TYPE ( PT_STRUCTURED  )
inherited

problem type

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

Definition at line 262 of file SGObject.cpp.

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

post lock

Reimplemented in CMultitaskLinearMachine.

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 474 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 308 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 157 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 139 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 145 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 151 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 97 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 133 of file StructuredOutputMachine.cpp.

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

Definition at line 314 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 occurs.

Reimplemented in CKernel.

Definition at line 436 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 occurs.

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

Definition at line 431 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 115 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 141 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 128 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 178 of file DualLibQPBMSOSVM.h.

void set_features ( CFeatures *  f)
inherited

set features

Parameters
ffeatures

Definition at line 74 of file StructuredOutputMachine.cpp.

void set_generic ( )
inherited

Definition at line 41 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 46 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 51 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 56 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 61 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 66 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 71 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 76 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 81 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 86 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 91 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 96 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 101 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 106 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 111 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
ioio object to use

Definition at line 228 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 241 of file SGObject.cpp.

void set_global_version ( Version *  version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 283 of file SGObject.cpp.

void set_K ( float64_t  K)

set K

Parameters
KParameter K

Definition at line 154 of file DualLibQPBMSOSVM.h.

void set_labels ( CLabels *  lab)
virtualinherited

set labels

Parameters
lablabels

Reimplemented from CMachine.

Definition at line 67 of file StructuredOutputMachine.cpp.

void set_lambda ( float64_t  _lambda)

set lambda

Parameters
_lambdaRegularization constant

Definition at line 78 of file DualLibQPBMSOSVM.h.

void set_max_train_time ( float64_t  t)
inherited

set maximum training time

Parameters
tmaximimum training time

Definition at line 82 of file Machine.cpp.

void set_model ( CStructuredModel *  model)
inherited

set structured model

Parameters
modelstructured model to set

Definition at line 43 of file StructuredOutputMachine.cpp.

void set_solver ( ESolver  solver)

set training algorithm

Parameters
solverType of Bundle Method solver used for training

Definition at line 202 of file DualLibQPBMSOSVM.h.

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver 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_modelwhether model should be stored after training

Definition at line 107 of file Machine.cpp.

void set_store_train_info ( bool  store_train_info)

set enableing/disabling storing training information

Parameters
store_train_infoFlag enabling/disabling storing training information, Storing training information requires extra computational costs.

Definition at line 220 of file DualLibQPBMSOSVM.h.

void set_surrogate_loss ( CLossFunction *  loss)
inherited

set surrogate loss function

Parameters
lossloss function to set

Definition at line 84 of file StructuredOutputMachine.cpp.

void set_Tmax ( uint32_t  Tmax)

set Tmax

Parameters
TmaxParameter Tmax

Definition at line 166 of file DualLibQPBMSOSVM.h.

void set_TolAbs ( float64_t  TolAbs)

set absolute tolerance

Parameters
TolAbsAbsolute tolerance

Definition at line 102 of file DualLibQPBMSOSVM.h.

void set_TolRel ( float64_t  TolRel)

set relative tolerance

Parameters
TolRelRelative tolerance

Definition at line 90 of file DualLibQPBMSOSVM.h.

void set_verbose ( bool  verbose)
inherited

set verbose NOTE that track verbose information including primal objectives, training errors and duality gaps will make the training 2x or 3x slower.

Parameters
verboseflag enabling/disabling verbose information

Definition at line 198 of file StructuredOutputMachine.cpp.

void set_w ( SGVector< float64_t >  W)

set initial value of weight vector w

Parameters
Winitial weight vector

Definition at line 208 of file DualLibQPBMSOSVM.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 192 of file SGObject.cpp.

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 78 of file LinearStructuredOutputMachine.cpp.

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

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 293 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, CAutoencoder, CSGDQN, and COnlineSVMSGD.

Definition at line 39 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 239 of file Machine.h.

bool train_machine ( CFeatures *  data = NULL)
protectedvirtual

train dual SO-SVM

Reimplemented from CMachine.

Definition at line 86 of file DualLibQPBMSOSVM.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

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

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 303 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 369 of file SGObject.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 384 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

CSOSVMHelper* m_helper
protectedinherited

the helper that records primal objectives, duality gaps etc

Definition at line 223 of file StructuredOutputMachine.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.

CStructuredModel* m_model
protectedinherited

the model that contains the application dependent modules

Definition at line 214 of file StructuredOutputMachine.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 381 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 378 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.

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 220 of file StructuredOutputMachine.h.

bool m_verbose
protectedinherited

verbose outputs and statistics

Definition at line 226 of file StructuredOutputMachine.h.

SGVector< float64_t > m_w
protectedinherited

weight vector

Definition at line 82 of file LinearStructuredOutputMachine.h.

Parallel* parallel
inherited

parallel

Definition at line 372 of file SGObject.h.

Version* version
inherited

version

Definition at line 375 of file SGObject.h.

The documentation for this class was generated from the following files:
SHOGUN Machine Learning Toolbox - Documentation