SHOGUN  v3.2.0
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Defines
Public Member Functions | Public Attributes | Protected Member Functions | Protected Attributes
CMultitaskClusteredLogisticRegression Class Reference

Detailed Description

class MultitaskClusteredLogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. Assumes task in group are related with a clustered structure.

See also:
CTaskGroup

Definition at line 23 of file MultitaskClusteredLogisticRegression.h.

Inheritance diagram for CMultitaskClusteredLogisticRegression:
Inheritance graph
[legend]

List of all members.

Public Member Functions

 CMultitaskClusteredLogisticRegression ()
 CMultitaskClusteredLogisticRegression (float64_t rho1, float64_t rho2, CDotFeatures *training_data, CBinaryLabels *training_labels, CTaskGroup *task_group, int32_t num_clusters)
virtual ~CMultitaskClusteredLogisticRegression ()
int32_t get_rho1 () const
void set_rho1 (float64_t rho1)
int32_t get_rho2 () const
void set_rho2 (float64_t rho2)
int32_t get_num_clusters () const
void set_num_clusters (int32_t num_clusters)
virtual const char * get_name () const
int32_t get_max_iter () const
float64_t get_q () const
int32_t get_regularization () const
int32_t get_termination () const
float64_t get_tolerance () const
float64_t get_z () const
void set_max_iter (int32_t max_iter)
void set_q (float64_t q)
void set_regularization (int32_t regularization)
void set_termination (int32_t termination)
void set_tolerance (float64_t tolerance)
void set_z (float64_t z)
virtual float64_t apply_one (int32_t i)
int32_t get_current_task () const
void set_current_task (int32_t task)
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 ()
CTaskRelationget_task_relation () const
void set_task_relation (CTaskRelation *task_relation)
virtual bool supports_locking () const
virtual void post_lock (CLabels *labels, CFeatures *features_)
virtual bool train_locked (SGVector< index_t > indices)
virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)
virtual void set_features (CDotFeatures *feat)
virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)
virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)
virtual CDotFeaturesget_features ()
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 ()
virtual EMachineType get_classifier_type ()
void set_solver_type (ESolverType st)
ESolverType get_solver_type ()
virtual void set_store_model_features (bool store_model)
virtual CLabelsapply_locked (SGVector< index_t > indices)
virtual 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 data_unlock ()
bool is_data_locked () const
virtual EProblemType get_machine_problem_type () const
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
SGStringList< char > get_modelsel_names ()
void print_modsel_params ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
virtual bool update_parameter_hash ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0)
virtual CSGObjectclone ()

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)
virtual bool train_locked_implementation (SGVector< index_t > *tasks)
virtual SGVector< float64_tapply_get_outputs (CFeatures *data=NULL)
SGVector< index_t > * get_subset_tasks_indices ()
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)

Protected Attributes

float64_t m_rho1
float64_t m_rho2
int32_t m_num_clusters
int32_t m_regularization
int32_t m_termination
int32_t m_max_iter
float64_t m_tolerance
float64_t m_q
float64_t m_z
int32_t m_current_task
CTaskRelationm_task_relation
SGMatrix< float64_tm_tasks_w
SGVector< float64_tm_tasks_c
vector< set< index_t > > m_tasks_indices
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

default constructor

Definition at line 18 of file MultitaskClusteredLogisticRegression.cpp.

CMultitaskClusteredLogisticRegression ( float64_t  rho1,
float64_t  rho2,
CDotFeatures training_data,
CBinaryLabels training_labels,
CTaskGroup task_group,
int32_t  num_clusters 
)

constructor

Parameters:
rho1rho1 regularization coefficient
rho2rho2 regularization coefficient
training_datatraining features
training_labelstraining labels
task_grouptask group
num_clustersnumber of task clusters

Definition at line 23 of file MultitaskClusteredLogisticRegression.cpp.

destructor

Definition at line 63 of file MultitaskClusteredLogisticRegression.cpp.


Member Function Documentation

CLabels * apply ( CFeatures data = NULL) [virtual, inherited]

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) [virtual, inherited]

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 57 of file LinearMachine.cpp.

SGVector< float64_t > apply_get_outputs ( CFeatures data = NULL) [protected, virtual, inherited]

apply get outputs

Reimplemented from CLinearMachine.

Definition at line 164 of file MultitaskLinearMachine.cpp.

CLatentLabels * apply_latent ( CFeatures data = NULL) [virtual, inherited]

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) [virtual, inherited]

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

Parameters:
indicesindex vector (of locked features) that is predicted

Definition at line 197 of file Machine.cpp.

CBinaryLabels * apply_locked_binary ( SGVector< index_t indices) [virtual, inherited]

applies on given indices

Reimplemented from CMachine.

Definition at line 138 of file MultitaskLinearMachine.cpp.

CLatentLabels * apply_locked_latent ( SGVector< index_t indices) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

apply machine to data in means of multiclass classification problem

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

Definition at line 230 of file Machine.cpp.

float64_t apply_one ( int32_t  i) [virtual, inherited]

applies to one vector

Reimplemented from CMultitaskLinearMachine.

Definition at line 163 of file MultitaskLogisticRegression.cpp.

CRegressionLabels * apply_regression ( CFeatures data = NULL) [virtual, inherited]

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) [virtual, inherited]

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 236 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 1156 of file SGObject.cpp.

CSGObject * clone ( ) [virtual, inherited]

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 1273 of file SGObject.cpp.

void data_lock ( CLabels labs,
CFeatures features 
) [virtual, inherited]

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

Only possible if supports_locking() returns true

Parameters:
labslabels used for locking
featuresfeatures used for locking

Reimplemented in CKernelMachine.

Definition at line 122 of file Machine.cpp.

void data_unlock ( ) [virtual, inherited]

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 153 of file Machine.cpp.

virtual CSGObject* deep_copy ( ) const [virtual, inherited]

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

Definition at line 126 of file SGObject.h.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0 
) [virtual, inherited]

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters:
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
Returns:
true if all parameters were equal, false if not

Definition at line 1177 of file SGObject.cpp.

float64_t get_bias ( ) [virtual, inherited]

get bias

Returns:
bias

Reimplemented from CLinearMachine.

Definition at line 205 of file MultitaskLinearMachine.cpp.

EMachineType get_classifier_type ( ) [virtual, inherited]
int32_t get_current_task ( ) const [inherited]

getter for current task

Returns:
current task index

Definition at line 50 of file MultitaskLinearMachine.cpp.

CDotFeatures * get_features ( ) [virtual, inherited]

get features

Returns:
features

Definition at line 112 of file LinearMachine.cpp.

SGIO * get_global_io ( ) [inherited]

get the io object

Returns:
io object

Definition at line 174 of file SGObject.cpp.

Parallel * get_global_parallel ( ) [inherited]

get the parallel object

Returns:
parallel object

Definition at line 209 of file SGObject.cpp.

Version * get_global_version ( ) [inherited]

get the version object

Returns:
version object

Definition at line 222 of file SGObject.cpp.

CLabels * get_labels ( ) [virtual, inherited]

get labels

Returns:
labels

Definition at line 86 of file Machine.cpp.

virtual EProblemType get_machine_problem_type ( ) const [virtual, inherited]

returns type of problem machine solves

Reimplemented in CBaseMulticlassMachine.

Definition at line 292 of file Machine.h.

int32_t get_max_iter ( ) const [inherited]

get max iter

Definition at line 171 of file MultitaskLogisticRegression.cpp.

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 1060 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 1084 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 1097 of file SGObject.cpp.

virtual const char* get_name ( ) const [virtual]

get name

Returns:
name of the object

Reimplemented from CMultitaskLogisticRegression.

Definition at line 84 of file MultitaskClusteredLogisticRegression.h.

int32_t get_num_clusters ( ) const

get number of clusters

Returns:
number of clusters

Definition at line 53 of file MultitaskClusteredLogisticRegression.cpp.

float64_t get_q ( ) const [inherited]

get q

Definition at line 191 of file MultitaskLogisticRegression.cpp.

int32_t get_regularization ( ) const [inherited]

get regularization

Definition at line 175 of file MultitaskLogisticRegression.cpp.

int32_t get_rho1 ( ) const

get rho1 regularization coefficient

Returns:
rho1 value

Definition at line 33 of file MultitaskClusteredLogisticRegression.cpp.

int32_t get_rho2 ( ) const

get rho1

Definition at line 38 of file MultitaskClusteredLogisticRegression.cpp.

ESolverType get_solver_type ( ) [inherited]

get solver type

Returns:
solver

Definition at line 112 of file Machine.cpp.

SGVector< index_t > * get_subset_tasks_indices ( ) [protected, inherited]

subset mapped task indices

Definition at line 210 of file MultitaskLinearMachine.cpp.

CTaskRelation * get_task_relation ( ) const [inherited]

getter for task relation

Returns:
task relation

Definition at line 62 of file MultitaskLinearMachine.cpp.

int32_t get_termination ( ) const [inherited]

get termination

Definition at line 179 of file MultitaskLogisticRegression.cpp.

float64_t get_tolerance ( ) const [inherited]

get tolerance

Definition at line 183 of file MultitaskLogisticRegression.cpp.

SGVector< float64_t > get_w ( ) const [virtual, inherited]

get w

Returns:
weight vector

Reimplemented from CLinearMachine.

Definition at line 186 of file MultitaskLinearMachine.cpp.

float64_t get_z ( ) const [inherited]

get z

Definition at line 187 of file MultitaskLogisticRegression.cpp.

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

Definition at line 289 of file Machine.h.

bool is_generic ( EPrimitiveType *  generic) const [virtual, inherited]

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 228 of file SGObject.cpp.

virtual bool is_label_valid ( CLabels lab) const [protected, virtual, inherited]

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 CGaussianProcessBinaryClassification, CGaussianProcessRegression, and CBaseMulticlassMachine.

Definition at line 341 of file Machine.h.

DynArray< TParameter * > * load_all_file_parameters ( int32_t  file_version,
int32_t  current_version,
CSerializableFile file,
const char *  prefix = "" 
) [inherited]

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters:
file_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns:
(sorted) array of created TParameter instances with file data

Definition at line 633 of file SGObject.cpp.

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

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

Parameters:
param_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns:
new array with TParameter instances with the attached data

Definition at line 474 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
) [virtual, inherited]

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

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

Definition at line 305 of file SGObject.cpp.

void load_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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

Exceptions:
ShogunExceptionWill be thrown if an error occurres.

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

Definition at line 989 of file SGObject.cpp.

void load_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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

Exceptions:
ShogunExceptionWill be thrown if an error occurres.

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

Definition at line 984 of file SGObject.cpp.

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

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

Parameters:
param_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 671 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
) [protected, virtual, inherited]

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

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

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

Definition at line 878 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 
) [protected, virtual, inherited]

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

Parameters:
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 818 of file SGObject.cpp.

void post_lock ( CLabels labels,
CFeatures features_ 
) [virtual, inherited]

post lock

Reimplemented from CMachine.

Definition at line 81 of file MultitaskLinearMachine.cpp.

void print_modsel_params ( ) [inherited]

prints all parameter registered for model selection and their type

Definition at line 1036 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "") [virtual, inherited]

prints registered parameters out

Parameters:
prefixprefix for members

Definition at line 240 of file SGObject.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
) [virtual, inherited]

Save this object to file.

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

Definition at line 246 of file SGObject.cpp.

void save_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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

Exceptions:
ShogunExceptionWill be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 999 of file SGObject.cpp.

void save_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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

Exceptions:
ShogunExceptionWill be thrown if an error occurres.

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

Definition at line 994 of file SGObject.cpp.

void set_bias ( float64_t  b) [virtual, inherited]

set bias

Parameters:
bnew bias

Reimplemented from CLinearMachine.

Definition at line 200 of file MultitaskLinearMachine.cpp.

void set_current_task ( int32_t  task) [inherited]

setter for current task

Parameters:
tasktask index

Definition at line 55 of file MultitaskLinearMachine.cpp.

void set_features ( CDotFeatures feat) [virtual, inherited]

set features

Parameters:
featfeatures to set

Reimplemented in CLDA, CLPBoost, and CLPM.

Definition at line 105 of file LinearMachine.cpp.

void set_generic< complex128_t > ( ) [inherited]

set generic type to T

Definition at line 41 of file SGObject.cpp.

void set_global_io ( SGIO io) [inherited]

set the io object

Parameters:
ioio object to use

Definition at line 167 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel) [inherited]

set the parallel object

Parameters:
parallelparallel object to use

Definition at line 180 of file SGObject.cpp.

void set_global_version ( Version version) [inherited]

set the version object

Parameters:
versionversion object to use

Definition at line 215 of file SGObject.cpp.

void set_labels ( CLabels lab) [virtual, inherited]

set labels

Parameters:
lablabels

Reimplemented in CGaussianProcessMachine, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 75 of file Machine.cpp.

void set_max_iter ( int32_t  max_iter) [inherited]

set max iter

Definition at line 196 of file MultitaskLogisticRegression.cpp.

void set_max_train_time ( float64_t  t) [inherited]

set maximum training time

Parameters:
tmaximimum training time

Definition at line 92 of file Machine.cpp.

void set_num_clusters ( int32_t  num_clusters)

set number of clusters

Parameters:
num_clustersnumber of clusters

Definition at line 58 of file MultitaskClusteredLogisticRegression.cpp.

void set_q ( float64_t  q) [inherited]

set q

Definition at line 220 of file MultitaskLogisticRegression.cpp.

void set_regularization ( int32_t  regularization) [inherited]

set regularization

Definition at line 201 of file MultitaskLogisticRegression.cpp.

void set_rho1 ( float64_t  rho1)

set rho1

Parameters:
rho1value

Definition at line 43 of file MultitaskClusteredLogisticRegression.cpp.

void set_rho2 ( float64_t  rho2)

set rho1

Parameters:
rho2value

Definition at line 48 of file MultitaskClusteredLogisticRegression.cpp.

void set_solver_type ( ESolverType  st) [inherited]

set solver type

Parameters:
stsolver type

Definition at line 107 of file Machine.cpp.

void set_store_model_features ( bool  store_model) [virtual, inherited]

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

Parameters:
store_modelwhether model should be stored after training

Definition at line 117 of file Machine.cpp.

void set_task_relation ( CTaskRelation task_relation) [inherited]

setter for task relation

Parameters:
task_relationtask relation

Definition at line 68 of file MultitaskLinearMachine.cpp.

void set_termination ( int32_t  termination) [inherited]

set termination

Definition at line 206 of file MultitaskLogisticRegression.cpp.

void set_tolerance ( float64_t  tolerance) [inherited]

set tolerance

Definition at line 211 of file MultitaskLogisticRegression.cpp.

void set_w ( const SGVector< float64_t src_w) [virtual, inherited]

set w

Parameters:
src_wnew w

Reimplemented from CLinearMachine.

Definition at line 194 of file MultitaskLinearMachine.cpp.

void set_z ( float64_t  z) [inherited]

set z

Definition at line 216 of file MultitaskLogisticRegression.cpp.

virtual CSGObject* shallow_copy ( ) const [virtual, inherited]

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 117 of file SGObject.h.

void store_model_features ( ) [protected, virtual, inherited]

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 118 of file LinearMachine.cpp.

virtual bool supports_locking ( ) const [virtual, inherited]
Returns:
whether machine supports locking

Reimplemented from CMachine.

Definition at line 101 of file MultitaskLinearMachine.h.

bool train ( CFeatures data = NULL) [virtual, inherited]

train machine

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

Reimplemented in CRelaxedTree, CSGDQN, and COnlineSVMSGD.

Definition at line 49 of file Machine.cpp.

bool train_locked ( SGVector< index_t indices) [virtual, inherited]

train on given indices

Reimplemented from CMachine.

Definition at line 101 of file MultitaskLinearMachine.cpp.

bool train_locked_implementation ( SGVector< index_t > *  tasks) [protected, virtual]

train locked implementation

Parameters:
tasksarray of tasks indices

Reimplemented from CMultitaskLogisticRegression.

Definition at line 67 of file MultitaskClusteredLogisticRegression.cpp.

bool train_machine ( CFeatures data = NULL) [protected, virtual]

train machine

Parameters:
datafeatures to use for training

Reimplemented from CMultitaskLogisticRegression.

Definition at line 103 of file MultitaskClusteredLogisticRegression.cpp.

virtual bool train_require_labels ( ) const [protected, virtual, inherited]

returns whether machine require labels for training

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

Definition at line 347 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 235 of file SGObject.cpp.

bool update_parameter_hash ( ) [virtual, inherited]

Updates the hash of current parameter combination.

Returns:
bool if parameter combination has changed since last update.

Definition at line 187 of file SGObject.cpp.


Member Data Documentation

float64_t bias [protected, inherited]

bias

Definition at line 158 of file LinearMachine.h.

CDotFeatures* features [protected, inherited]

features

Definition at line 160 of file LinearMachine.h.

SGIO* io [inherited]

io

Definition at line 473 of file SGObject.h.

int32_t m_current_task [protected, inherited]

current task index

Definition at line 137 of file MultitaskLinearMachine.h.

bool m_data_locked [protected, inherited]

whether data is locked

Definition at line 363 of file Machine.h.

parameters wrt which we can compute gradients

Definition at line 488 of file SGObject.h.

uint32_t m_hash [inherited]

Hash of parameter values

Definition at line 494 of file SGObject.h.

CLabels* m_labels [protected, inherited]

labels

Definition at line 354 of file Machine.h.

int32_t m_max_iter [protected, inherited]

max iteration

Definition at line 119 of file MultitaskLogisticRegression.h.

float64_t m_max_train_time [protected, inherited]

maximum training time

Definition at line 351 of file Machine.h.

model selection parameters

Definition at line 485 of file SGObject.h.

int32_t m_num_clusters [protected]

number of clusters

Definition at line 112 of file MultitaskClusteredLogisticRegression.h.

map for different parameter versions

Definition at line 491 of file SGObject.h.

Parameter* m_parameters [inherited]

parameters

Definition at line 482 of file SGObject.h.

float64_t m_q [protected, inherited]

q of L1/Lq

Definition at line 125 of file MultitaskLogisticRegression.h.

int32_t m_regularization [protected, inherited]

regularization type

Definition at line 113 of file MultitaskLogisticRegression.h.

float64_t m_rho1 [protected]

rho1

Definition at line 106 of file MultitaskClusteredLogisticRegression.h.

float64_t m_rho2 [protected]

rho2

Definition at line 109 of file MultitaskClusteredLogisticRegression.h.

ESolverType m_solver_type [protected, inherited]

solver type

Definition at line 357 of file Machine.h.

bool m_store_model_features [protected, inherited]

whether model features should be stored after training

Definition at line 360 of file Machine.h.

CTaskRelation* m_task_relation [protected, inherited]

feature tree

Definition at line 140 of file MultitaskLinearMachine.h.

SGVector<float64_t> m_tasks_c [protected, inherited]

tasks interceptss

Definition at line 146 of file MultitaskLinearMachine.h.

vector< set<index_t> > m_tasks_indices [protected, inherited]

vector of sets of indices

Definition at line 149 of file MultitaskLinearMachine.h.

SGMatrix<float64_t> m_tasks_w [protected, inherited]

tasks w's

Definition at line 143 of file MultitaskLinearMachine.h.

int32_t m_termination [protected, inherited]

termination criteria

Definition at line 116 of file MultitaskLogisticRegression.h.

float64_t m_tolerance [protected, inherited]

tolerance

Definition at line 122 of file MultitaskLogisticRegression.h.

float64_t m_z [protected, inherited]

regularization coefficient

Definition at line 128 of file MultitaskLogisticRegression.h.

Parallel* parallel [inherited]

parallel

Definition at line 476 of file SGObject.h.

Version* version [inherited]

version

Definition at line 479 of file SGObject.h.

SGVector<float64_t> w [protected, inherited]

w

Definition at line 156 of file LinearMachine.h.


The documentation for this class was generated from the following files:
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Defines

SHOGUN Machine Learning Toolbox - Documentation