SHOGUN  v2.0.0
CLeastAngleRegression Class Reference

## Detailed Description

Class for Least Angle Regression, can be used to solve LASSO.

LASSO is basically L1 regulairzed least square regression

where the L1 norm is defined as

Note: pre-processing of X and y are needed to ensure the correctness of this algorithm:

• X needs to be normalized: each feature should have zero-mean and unit-norm
• y needs to be centered: its mean should be zero

The above equation is equivalent to the following form

There is a correspondence between the regularization coefficient lambda and the hard constraint constant C. The latter form is easier to control by explicitly constraining the l1-norm of the estimator. In this implementation, we provide support for the latter form, moreover, we allow explicit control of the number of non-zero variables.

When no constraints is provided, the full path is generated.

Please see the following paper for more details.

@article{efron2004least,
title={Least angle regression},
author={Efron, B. and Hastie, T. and Johnstone, I. and Tibshirani, R.},
journal={The Annals of statistics},
volume={32},
number={2},
pages={407--499},
year={2004},
publisher={Institute of Mathematical Statistics}
}

Definition at line 72 of file LeastAngleRegression.h.

Inheritance diagram for CLeastAngleRegression:
[legend]

## Public Member Functions

MACHINE_PROBLEM_TYPE (PT_REGRESSION)
CLeastAngleRegression (bool lasso=true)
virtual ~CLeastAngleRegression ()
void set_max_non_zero (int32_t n)
int32_t get_max_non_zero () const
void set_max_l1_norm (float64_t norm)
float64_t get_max_l1_norm () const
void switch_w (int32_t num_variable)
int32_t get_path_size () const
SGVector< float64_tget_w (int32_t num_var)
virtual EMachineType get_classifier_type ()
virtual const char * get_name () const
virtual SGVector< float64_tget_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_features (CDotFeatures *feat)
virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)
virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)
virtual float64_t apply_one (int32_t vec_idx)
virtual CDotFeaturesget_features ()
virtual CMachineclone ()
virtual bool train (CFeatures *data=NULL)
virtual CLabelsapply (CFeatures *data=NULL)
virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)
virtual CLatentLabelsapply_latent (CFeatures *data=NULL)
virtual void set_labels (CLabels *lab)
virtual CLabelsget_labels ()
void set_max_train_time (float64_t t)
float64_t get_max_train_time ()
void set_solver_type (ESolverType st)
ESolverType get_solver_type ()
virtual void set_store_model_features (bool store_model)
virtual bool train_locked (SGVector< index_t > indices)
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_PARAMETER)
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=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_parameter_dictionary (CMap< TParameter *, CSGObject * > &dict)

## Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
ParameterMapm_parameter_map
uint32_t m_hash

## Protected Member Functions

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

## Protected Attributes

SGVector< float64_tw
float64_t bias
CDotFeaturesfeatures
float64_t m_max_train_time
CLabelsm_labels
ESolverType m_solver_type
bool m_store_model_features
bool m_data_locked

## Constructor & Destructor Documentation

 CLeastAngleRegression ( bool lasso = true )

default constructor

Parameters
 lasso - when true, it runs the LASSO, when false, it runs LARS

Definition at line 78 of file LeastAngleRegression.cpp.

 ~CLeastAngleRegression ( )
virtual

default destructor

Definition at line 86 of file LeastAngleRegression.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 linear machine to data for binary classification problem

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

Reimplemented from CMachine.

Definition at line 57 of file LinearMachine.cpp.

 SGVector< float64_t > apply_get_outputs ( CFeatures * data )
protectedvirtualinherited

apply get outputs

Parameters
 data features to compute outputs
Returns
outputs

Definition at line 63 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 242 of file Machine.cpp.

 CLabels * apply_locked ( SGVector< index_t > indices )
virtualinherited

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

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

Definition at line 197 of file Machine.cpp.

 CBinaryLabels * apply_locked_binary ( SGVector< index_t > indices )
virtualinherited

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

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

Definition at line 230 of file Machine.cpp.

 float64_t apply_one ( int32_t vec_idx )
virtualinherited

applies to one vector

Reimplemented from CMachine.

Definition at line 46 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 51 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 236 of file Machine.cpp.

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

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

Parameters
 dict dictionary of parameters to be built.

Definition at line 1204 of file SGObject.cpp.

 virtual CMachine* clone ( )
virtualinherited

clone

Reimplemented from CMachine.

Definition at line 153 of file LinearMachine.h.

 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
 labs labels used for locking features features 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 131 of file SGObject.h.

 virtual float64_t get_bias ( )
virtualinherited

get bias

Returns
bias

Definition at line 104 of file LinearMachine.h.

 virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type LinearRidgeRegression

Reimplemented from CMachine.

Definition at line 166 of file LeastAngleRegression.h.

 virtual CDotFeatures* get_features ( )
virtualinherited

get features

Returns
features

Definition at line 143 of file LinearMachine.h.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 224 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 259 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 272 of file SGObject.cpp.

 CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 86 of file Machine.cpp.

 virtual EProblemType get_machine_problem_type ( ) const
virtualinherited

returns type of problem machine solves

Reimplemented in CBaseMulticlassMachine.

Definition at line 287 of file Machine.h.

 float64_t get_max_l1_norm ( ) const

get max l1-norm of estimator for early stopping

Definition at line 114 of file LeastAngleRegression.h.

 int32_t get_max_non_zero ( ) const

get max number of non-zero variables for early stopping

Definition at line 98 of file LeastAngleRegression.h.

 float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 97 of file Machine.cpp.

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

Definition at line 1108 of file SGObject.cpp.

 char * get_modsel_param_descr ( const char * param_name )
inherited

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

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 1132 of file SGObject.cpp.

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

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

Definition at line 1145 of file SGObject.cpp.

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

Reimplemented from CLinearMachine.

Definition at line 172 of file LeastAngleRegression.h.

 int32_t get_path_size ( ) const

get path size

Returns
the size of variable selection path. Call get_w(i) to get the estimator of i-th entry on the path, where i can be in the range [0, path_size)
switch_w
get_w

Definition at line 143 of file LeastAngleRegression.h.

 ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 112 of file Machine.cpp.

 virtual SGVector get_w ( ) const
virtualinherited

get w

Returns
weight vector

Definition at line 77 of file LinearMachine.h.

 SGVector get_w ( int32_t num_var )

get w

Parameters
 num_var number of non-zero coefficients
Returns
the estimator with num_var non-zero coefficients. Note the returned memory references to some internal structures. The pointer will become invalid if train is called again. So make a copy if you want to call train multiple times.

Definition at line 157 of file LeastAngleRegression.h.

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

Definition at line 284 of file Machine.h.

 bool is_generic ( EPrimitiveType * generic ) const
virtualinherited

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

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

Definition at line 278 of file SGObject.cpp.

 virtual bool is_label_valid ( CLabels * lab ) const
protectedvirtualinherited

check whether the labels is valid.

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

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

Reimplemented in CBaseMulticlassMachine.

Definition at line 343 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_version parameter version of the file current_version version from which mapping begins (you want to use VERSION_PARAMETER for this in most cases) file file to load from prefix prefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 679 of file SGObject.cpp.

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

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

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

Definition at line 523 of file SGObject.cpp.

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

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

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

Reimplemented in CModelSelectionParameters.

Definition at line 354 of file SGObject.cpp.

 void load_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1033 of file SGObject.cpp.

 void load_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1028 of file SGObject.cpp.

 MACHINE_PROBLEM_TYPE ( PT_REGRESSION )

problem type

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

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

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

Definition at line 717 of file SGObject.cpp.

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

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

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

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

Definition at line 923 of file SGObject.cpp.

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

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

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

Definition at line 864 of file SGObject.cpp.

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

post lock

Definition at line 275 of file Machine.h.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1084 of file SGObject.cpp.

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 290 of file SGObject.cpp.

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

Save this object to file.

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

Reimplemented in CModelSelectionParameters.

Definition at line 296 of file SGObject.cpp.

 void save_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1043 of file SGObject.cpp.

 void save_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1038 of file SGObject.cpp.

 virtual void set_bias ( float64_t b )
virtualinherited

set bias

Parameters
 b new bias

Definition at line 95 of file LinearMachine.h.

 virtual void set_features ( CDotFeatures * feat )
virtualinherited

set features

Parameters
 feat features to set

Reimplemented in CLDA, CLPBoost, and CLPM.

Definition at line 113 of file LinearMachine.h.

 void set_generic< floatmax_t > ( )
inherited

set generic type to T

Definition at line 41 of file SGObject.cpp.

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 217 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 230 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 265 of file SGObject.cpp.

 void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab labels

Reimplemented in CRelaxedTree, and CMulticlassMachine.

Definition at line 75 of file Machine.cpp.

 void set_max_l1_norm ( float64_t norm )

set max l1-norm of estimator for early stopping

Parameters
 norm the max l1-norm for beta

Definition at line 107 of file LeastAngleRegression.h.

 void set_max_non_zero ( int32_t n )

set max number of non-zero variables for early stopping

Parameters
 n 0 means no constraint

Definition at line 91 of file LeastAngleRegression.h.

 void set_max_train_time ( float64_t t )
inherited

set maximum training time

Parameters
 t maximimum training time

Definition at line 92 of file Machine.cpp.

 void set_solver_type ( ESolverType st )
inherited

set solver type

Parameters
 st solver type

Definition at line 107 of file Machine.cpp.

 void set_store_model_features ( bool store_model )
virtualinherited

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

Parameters
 store_model whether model should be stored after training

Definition at line 117 of file Machine.cpp.

 virtual void set_w ( const SGVector< float64_t > src_w )
virtualinherited

set w

Parameters
 src_w new w

Definition at line 86 of file LinearMachine.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 122 of file SGObject.h.

 virtual 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 171 of file LinearMachine.h.

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

Definition at line 281 of file Machine.h.

 void switch_w ( int32_t num_variable )

switch estimator

Parameters
 num_variable number of non-zero coefficients

Definition at line 123 of file LeastAngleRegression.h.

 bool train ( CFeatures * data = NULL )
virtualinherited

train machine

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

Reimplemented in CRelaxedTree, CSGDQN, and COnlineSVMSGD.

Definition at line 49 of file Machine.cpp.

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

Definition at line 227 of file Machine.h.

 bool train_machine ( CFeatures * data = NULL )
protectedvirtual

train machine

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

NOT IMPLEMENTED!

Returns
whether training was successful

Reimplemented from CMachine.

Definition at line 91 of file LeastAngleRegression.cpp.

 virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Definition at line 349 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 285 of file SGObject.cpp.

 bool update_parameter_hash ( )
protectedvirtualinherited

Updates the hash of current parameter combination.

Returns
bool if parameter combination has changed since last update.

Definition at line 237 of file SGObject.cpp.

## Member Data Documentation

 float64_t bias
protectedinherited

bias

Definition at line 181 of file LinearMachine.h.

 CDotFeatures* features
protectedinherited

features

Definition at line 183 of file LinearMachine.h.

 SGIO* io
inherited

io

Definition at line 462 of file SGObject.h.

 bool m_data_locked
protectedinherited

whether data is locked

Definition at line 365 of file Machine.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 480 of file SGObject.h.

 CLabels* m_labels
protectedinherited

labels

Definition at line 356 of file Machine.h.

 float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 353 of file Machine.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 474 of file SGObject.h.

 ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 477 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 471 of file SGObject.h.

 ESolverType m_solver_type
protectedinherited

solver type

Definition at line 359 of file Machine.h.

 bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 362 of file Machine.h.

 Parallel* parallel
inherited

parallel

Definition at line 465 of file SGObject.h.

 Version* version
inherited

version

Definition at line 468 of file SGObject.h.

 protectedinherited

w

Definition at line 179 of file LinearMachine.h.

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

SHOGUN Machine Learning Toolbox - Documentation