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CLibLinearMTL Class Reference

Detailed Description

class to implement LibLinear

Definition at line 91 of file LibLinearMTL.h.

Inheritance diagram for CLibLinearMTL:
[legend]

Public Member Functions

 CLibLinearMTL ()
 
 CLibLinearMTL (float64_t C, CDotFeatures *traindat, CLabels *trainlab)
 
virtual ~CLibLinearMTL ()
 
virtual EMachineType get_classifier_type ()
 
void set_C (float64_t c_neg, float64_t c_pos)
 
float64_t get_C1 ()
 
float64_t get_C2 ()
 
void set_epsilon (float64_t eps)
 
float64_t get_epsilon ()
 
void set_bias_enabled (bool enable_bias)
 
bool get_bias_enabled ()
 
virtual const char * get_name () const
 
int32_t get_max_iterations ()
 
void set_max_iterations (int32_t max_iter=1000)
 
void set_num_tasks (int32_t nt)
 
void set_linear_term (SGVector< float64_t > linear_term)
 
void set_task_indicator_lhs (SGVector< int32_t > ti)
 
void set_task_indicator_rhs (SGVector< int32_t > ti)
 
void set_task_similarity_matrix (SGSparseMatrix< float64_t > tsm)
 
void set_graph_laplacian (SGMatrix< float64_t > lap)
 
SGMatrix< float64_t > get_V ()
 
SGMatrix< float64_t > get_W ()
 
SGVector< float64_t > get_alphas ()
 
virtual float64_t compute_primal_obj ()
 
virtual float64_t compute_dual_obj ()
 
virtual float64_t compute_duality_gap ()
 
virtual bool train (CFeatures *data=NULL)
 
virtual SGVector< float64_t > get_w () const
 
virtual void set_w (const SGVector< float64_t > src_w)
 
virtual void set_bias (float64_t b)
 
virtual float64_t get_bias ()
 
virtual void set_compute_bias (bool compute_bias)
 
virtual bool get_compute_bias ()
 
virtual void set_features (CDotFeatures *feat)
 
virtual CBinaryLabels * apply_binary (CFeatures *data=NULL)
 
virtual CRegressionLabels * apply_regression (CFeatures *data=NULL)
 
virtual float64_t apply_one (int32_t vec_idx)
 
virtual CDotFeatures * get_features ()
 
virtual CLabels * apply (CFeatures *data=NULL)
 
virtual CMulticlassLabels * apply_multiclass (CFeatures *data=NULL)
 
virtual CStructuredLabels * apply_structured (CFeatures *data=NULL)
 
virtual CLatentLabels * apply_latent (CFeatures *data=NULL)
 
virtual void set_labels (CLabels *lab)
 
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 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)
 
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 >
T get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
T 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 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

virtual bool train_machine (CFeatures *data=NULL)
 
virtual SGVector< float64_t > apply_get_outputs (CFeatures *data)
 
virtual void store_model_features ()
 
void compute_bias (CFeatures *data)
 
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 C1
 
float64_t C2
 
bool use_bias
 
float64_t epsilon
 
int32_t max_iterations
 
SGVector< float64_t > m_linear_term
 
SGVector< float64_t > alphas
 
int32_t num_tasks
 
SGVector< int32_t > task_indicator_lhs
 
SGVector< int32_t > task_indicator_rhs
 
MappedSparseMatrix task_similarity_matrix
 
SGMatrix< float64_t > graph_laplacian
 
SGMatrix< float64_t > V
 
float64_t duality_gap
 
SGVector< float64_t > w
 
float64_t bias
 
CDotFeatures * features
 
bool m_compute_bias
 
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

CLibLinearMTL ( )

default constructor

Definition at line 29 of file LibLinearMTL.cpp.

CLibLinearMTL ( float64_t  C,
CDotFeatures *  traindat,
CLabels *  trainlab 
)

constructor (using L2R_L1LOSS_SVC_DUAL as default)

Parameters
Cconstant C
traindattraining features
trainlabtraining labels

Definition at line 35 of file LibLinearMTL.cpp.

~CLibLinearMTL ( )
virtual

destructor

Definition at line 68 of file LibLinearMTL.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 linear machine to data for binary classification problem

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

Reimplemented from CMachine.

Reimplemented in CDomainAdaptationSVMLinear.

Definition at line 70 of file LinearMachine.cpp.

SGVector< float64_t > apply_get_outputs ( CFeatures *  data)
protectedvirtualinherited

apply get outputs

Parameters
datafeatures to compute outputs
Returns
outputs

Definition at line 76 of file LinearMachine.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.

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, CKNN, CMulticlassMachine, CC45ClassifierTree, CID3ClassifierTree, CQDA, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CRelaxedTree, and CBaggingMachine.

Definition at line 220 of file Machine.cpp.

float64_t apply_one ( int32_t  vec_idx)
virtualinherited

applies to one vector

Reimplemented from CMachine.

Definition at line 59 of file LinearMachine.cpp.

CRegressionLabels * apply_regression ( CFeatures *  data = NULL)
virtualinherited

apply linear machine to data for regression problem

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

Reimplemented from CMachine.

Definition at line 64 of file LinearMachine.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
dictdictionary 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.

void compute_bias ( CFeatures *  data)
protectedinherited

Computes the added bias. The bias is computed as the mean error between the predictions and the true labels.

Definition at line 145 of file LinearMachine.cpp.

float64_t compute_dual_obj ( )
virtual

compute dual objective

Returns
dual objective

Definition at line 487 of file LibLinearMTL.cpp.

float64_t compute_duality_gap ( )
virtual

compute duality gap

Returns
duality gap

Definition at line 562 of file LibLinearMTL.cpp.

float64_t compute_primal_obj ( )
virtual

compute primal objective

Returns
primal objective

Definition at line 393 of file LibLinearMTL.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 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
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 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
_tagname 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
namename of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

SGVector<float64_t> get_alphas ( )

get alphas

Returns
matrix of example weights alphas

Definition at line 268 of file LibLinearMTL.h.

float64_t get_bias ( )
virtualinherited

get bias

Returns
bias

Definition at line 113 of file LinearMachine.cpp.

bool get_bias_enabled ( )

check if bias is enabled

Returns
if bias is enabled

Definition at line 159 of file LibLinearMTL.h.

float64_t get_C1 ( )

get C1

Returns
C1

Definition at line 129 of file LibLinearMTL.h.

float64_t get_C2 ( )

get C2

Returns
C2

Definition at line 135 of file LibLinearMTL.h.

virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
the classifier type

Reimplemented from CMachine.

Definition at line 116 of file LibLinearMTL.h.

bool get_compute_bias ( )
virtualinherited

Get compute bias

Returns
compute_bias

Definition at line 123 of file LinearMachine.cpp.

float64_t get_epsilon ( )

get epsilon

Returns
epsilon

Definition at line 147 of file LibLinearMTL.h.

CDotFeatures * get_features ( )
virtualinherited

get features

Returns
features

Definition at line 135 of file LinearMachine.cpp.

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.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 76 of file Machine.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.

int32_t get_max_iterations ( )

get the maximum number of iterations liblinear is allowed to do

Definition at line 165 of file LibLinearMTL.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_namename 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_namename 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 CLinearMachine.

Definition at line 162 of file LibLinearMTL.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 102 of file Machine.cpp.

SGMatrix<float64_t> get_V ( )

get V

Returns
matrix of weight vectors

Definition at line 228 of file LibLinearMTL.h.

SGVector< float64_t > get_w ( ) const
virtualinherited

get w

Returns
weight vector

Definition at line 98 of file LinearMachine.cpp.

SGMatrix<float64_t> get_W ( )

get W

Returns
matrix of weight vectors

Definition at line 237 of file LibLinearMTL.h.

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

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

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

Definition at line 312 of file SGObject.h.

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 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
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 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
ShogunExceptionwill be thrown if an error occurs.

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

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
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 454 of file SGObject.cpp.

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
prefixprefix 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
_tagname and type information of parameter
valuevalue 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
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 452 of file SGObject.h.

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 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
ShogunExceptionwill 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
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 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
_tagname and type information of parameter
valuevalue 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
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

void set_bias ( float64_t  b)
virtualinherited

set bias

Parameters
bnew bias

Definition at line 108 of file LinearMachine.cpp.

void set_bias_enabled ( bool  enable_bias)

set if bias shall be enabled

Parameters
enable_biasif bias shall be enabled

Definition at line 153 of file LibLinearMTL.h.

void set_C ( float64_t  c_neg,
float64_t  c_pos 
)

set C

Parameters
c_negC1
c_posC2

Definition at line 123 of file LibLinearMTL.h.

void set_compute_bias ( bool  compute_bias)
virtualinherited

Set m_compute_bias

Determines if bias compution is considered or not

Parameters
compute_biasnew m_compute_bias

Definition at line 118 of file LinearMachine.cpp.

void set_epsilon ( float64_t  eps)

set epsilon

Parameters
epsnew epsilon

Definition at line 141 of file LibLinearMTL.h.

void set_features ( CDotFeatures *  feat)
virtualinherited

set features

Parameters
featfeatures to set

Reimplemented in CLDA, CLPBoost, and CLPM.

Definition at line 128 of file LinearMachine.cpp.

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
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version *  version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

void set_graph_laplacian ( SGMatrix< float64_t >  lap)

set graph laplacian

Definition at line 219 of file LibLinearMTL.h.

void set_labels ( CLabels *  lab)
virtualinherited

set labels

Parameters
lablabels

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

Definition at line 65 of file Machine.cpp.

void set_linear_term ( SGVector< float64_t >  linear_term)

set the linear term for qp

Definition at line 183 of file LibLinearMTL.h.

void set_max_iterations ( int32_t  max_iter = 1000)

set the maximum number of iterations liblinear is allowed to do

Definition at line 171 of file LibLinearMTL.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_num_tasks ( int32_t  nt)

set number of tasks

Definition at line 177 of file LibLinearMTL.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_task_indicator_lhs ( SGVector< int32_t >  ti)

set task indicator for lhs

Definition at line 201 of file LibLinearMTL.h.

void set_task_indicator_rhs ( SGVector< int32_t >  ti)

set task indicator for rhs

Definition at line 207 of file LibLinearMTL.h.

void set_task_similarity_matrix ( SGSparseMatrix< float64_t >  tsm)

set task similarity matrix

Definition at line 213 of file LibLinearMTL.h.

void set_w ( const SGVector< float64_t >  src_w)
virtualinherited

set w

Parameters
src_wnew w

Definition at line 103 of file LinearMachine.cpp.

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.

void store_model_features ( )
protectedvirtualinherited

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 141 of file LinearMachine.cpp.

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

Reimplemented in CKernelMachine.

Definition at line 293 of file Machine.h.

bool train ( CFeatures *  data = NULL)
virtualinherited

Train machine

Returns
whether training was successful

Reimplemented from CMachine.

Reimplemented in CSGDQN.

Definition at line 169 of file LinearMachine.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.

Definition at line 239 of file Machine.h.

bool train_machine ( CFeatures *  data = NULL)
protectedvirtual

train linear SVM classifier

Parameters
datatraining data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data)
Returns
whether training was successful

Reimplemented from CMachine.

Definition at line 72 of file LibLinearMTL.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Reimplemented in COnlineLinearMachine, CKMeansBase, 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 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

SGVector<float64_t> alphas
protected

keep track of alphas

Definition at line 327 of file LibLinearMTL.h.

float64_t bias
protectedinherited

bias

Definition at line 192 of file LinearMachine.h.

float64_t C1
protected

C1

Definition at line 313 of file LibLinearMTL.h.

float64_t C2
protected

C2

Definition at line 315 of file LibLinearMTL.h.

float64_t duality_gap
protected

duality gap

Definition at line 350 of file LibLinearMTL.h.

float64_t epsilon
protected

epsilon

Definition at line 319 of file LibLinearMTL.h.

CDotFeatures* features
protectedinherited

features

Definition at line 194 of file LinearMachine.h.

SGMatrix<float64_t> graph_laplacian
protected

task similarity matrix

Definition at line 344 of file LibLinearMTL.h.

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

bool m_compute_bias
protectedinherited

If true, bias is computed in train method

Definition at line 196 of file LinearMachine.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.

CLabels* m_labels
protectedinherited

labels

Definition at line 361 of file Machine.h.

SGVector<float64_t> m_linear_term
protected

precomputed linear term

Definition at line 324 of file LibLinearMTL.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.

int32_t max_iterations
protected

maximum number of iterations

Definition at line 321 of file LibLinearMTL.h.

int32_t num_tasks
protected

set number of tasks

Definition at line 330 of file LibLinearMTL.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

SGVector<int32_t> task_indicator_lhs
protected

task indicator left hand side

Definition at line 333 of file LibLinearMTL.h.

SGVector<int32_t> task_indicator_rhs
protected

task indicator right hand side

Definition at line 336 of file LibLinearMTL.h.

MappedSparseMatrix task_similarity_matrix
protected

task similarity matrix

Definition at line 341 of file LibLinearMTL.h.

bool use_bias
protected

if bias shall be used

Definition at line 317 of file LibLinearMTL.h.

SGMatrix<float64_t> V
protected

parameter matrix n * d

Definition at line 347 of file LibLinearMTL.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.

SGVector<float64_t> w
protectedinherited

w

Definition at line 190 of file LinearMachine.h.

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